Detecting neural activity in the brain (or any other turbid medium) is useful for medical diagnostics, imaging, neuroengineering, brain-computer interfacing, and a variety of other diagnostic and consumer-related applications. For example, it may be desirable to detect neural activity in the brain of a user to determine if a particular region of the brain has been impacted by reduced blood irrigation, a hemorrhage, or any other type of damage. As another example, it may be desirable to detect neural activity in the brain of a user and computationally decode the detected neural activity into commands that can be used to control various types of consumer electronics (e.g., by controlling a cursor on a computer screen, changing channels on a television, turning lights on, etc.).
Neural activity and other attributes of the brain may be determined or inferred by measuring responses of tissue within the brain to light pulses. One technique to measure such responses is time-correlated single-photon counting (TCSPC). Time-correlated single-photon counting detects single photons and measures a time of arrival of the photons with respect to a reference signal (e.g., a light source). By repeating the light pulses, TCSPC may accumulate a sufficient number of photon events to statistically determine a histogram representing the distribution of detected photons. Based on the histogram of photon distribution, the response of tissue to light pulses may be determined in order to study the detected neural activity and/or other attributes of the brain.
The accompanying drawings illustrate various embodiments and are a part of the specification. The illustrated embodiments are merely examples and do not limit the scope of the disclosure. Throughout the drawings, identical or similar reference numbers designate identical or similar elements.
Optical measurement systems and methods are described herein. An exemplary optical measurement system includes a first light source, a second light source, a first detector, a second detector, and a processing unit. The first light source is configured to emit a first light pulse toward a target, and the second light source is configured to emit a second light pulse toward the target. The processing unit is configured to determine a plurality of temporal distributions of photons included in the first light pulse and the second light pulse and detected by the first detector and the second detector after the photons are scattered by the target. The processing unit is further configured to determine, based on the plurality of temporal distributions, a distance between the first light source and the second detector.
The systems and methods described herein provide various benefits. For example, the propagation of light through a turbid medium (e.g., the brain) may be modeled by a standard model of diffusion theory. In such a model, the scattering of light in the target depends on, among other things, the distance between the light source and the detector. When the distance between the source and the detector is known, the diffusion theory model can be used to infer spatial (e.g., depth localization) information about the detected signals (e.g., neural activity and/or other attributes of the brain). With the systems and methods described herein, the distance separating a light source and one or more detectors does not need to be known in advance. Rather, the separation distance can be determined analytically from the time-domain information detected by the detector. Moreover, the ability to analytically determine the source-detector distance eliminates any need for a fixed or static configuration of sources and detectors (or of wearable modules containing the sources and detectors). Instead, the optical measurement systems described herein may be embodied in a flexible, wearable device in which the light source-detector distance may vary, whether due the three-dimensional geometry of the user's body, targeting specific regions of the user, or any other reason. Thus, the optical measurement systems described herein may perform a calibration procedure, after the wearable device (e.g., headset) is placed on the user's head, to determine where the sources and detectors (or wearable modules) are located relative to one another during operation. These and other advantages and benefits of the present systems and methods are described more fully herein and/or will be made apparent in the description herein.
In some examples, optical measurement operations performed by optical measurement system 100 are associated with a time domain-based optical measurement technique. Example time domain-based optical measurement techniques include, but are not limited to, TCSPC, time domain near infrared spectroscopy (TD-NIRS), time domain diffusive correlation spectroscopy (TD-DCS), and time domain digital optical tomography (TD-DOT).
As shown, optical measurement system 100 includes a detector 104 that includes a plurality of individual photodetectors (e.g., photodetector 106), a processor 108 coupled to detector 104, a light source 110, a controller 112, and optical conduits 114 and 116 (e.g., light guides, as described more fully herein). However, one or more of these components may not, in certain embodiments, be considered to be a part of optical measurement system 100. For example, in implementations where optical measurement system 100 is wearable by a user, processor 108 and/or controller 112 may in some embodiments be separate from optical measurement system 100 and not configured to be worn by the user.
Detector 104 may include any number of photodetectors 106 as may serve a particular implementation, such as 2n photodetectors (e.g., 256, 512, . . . , 16384, etc.), where n is an integer greater than or equal to one (e.g., 4, 5, 8, 10, 11, 14, etc.). Photodetectors 106 may be arranged in any suitable manner.
Photodetectors 106 may each be implemented by any suitable circuit configured to detect individual photons of light incident upon photodetectors 106. For example, each photodetector 106 may be implemented by a single photon avalanche diode (SPAD) circuit and/or other circuitry as may serve a particular implementation.
Processor 108 may be implemented by one or more physical processing (e.g., computing) devices. In some examples, processor 108 may execute instructions (e.g., software) configured to perform one or more of the operations described herein.
Light source 110 may be implemented by any suitable component configured to generate and emit light. For example, light source 110 may be implemented by one or more laser diodes, distributed feedback (DFB) lasers, super luminescent diodes (SLDs), light emitting diodes (LEDs), diode-pumped solid-state (DPSS) lasers, super luminescent light emitting diode (sLEDs), vertical-cavity surface-emitting lasers (VCSELs), titanium sapphire lasers, a micro light emitting diodes (mLEDs), and/or any other suitable laser or light source configured to emit light in one or more discrete wavelengths or narrow wavelength bands. In some examples, the light emitted by light source 110 is high coherence light (e.g., light that has a coherence length of at least 5 centimeters) at a predetermined center wavelength. In some examples, the light emitted by light source 110 is emitted as a plurality of alternating light pulses of different wavelengths.
Light source 110 is controlled by controller 112, which may be implemented by any suitable computing device (e.g., processor 108), integrated circuit, and/or combination of hardware and/or software as may serve a particular implementation. In some examples, controller 112 is configured to control light source 110 by turning light source 110 on and off and/or setting an intensity of light generated by light source 110. Controller 112 may be manually operated by a user, or may be programmed to control light source 110 automatically.
Light emitted by light source 110 travels via an optical conduit 114 (e.g., a light pipe, a light guide, a waveguide, a single-mode optical fiber, and/or or a multi-mode optical fiber) to body 102 of a subject. Body 102 may include any suitable turbid medium. For example, in some implementations, body 102 is a head or any other body part of a human or other animal. Alternatively, body 102 may be a non-living object. For illustrative purposes, it will be assumed in the examples provided herein that body 102 is a human head.
As indicated by arrow 120, light emitted by light source 110 enters body 102 at a first location 122 on body 102. Accordingly, a distal end of optical conduit 114 may be positioned at (e.g., right above, in physical contact with, or physically attached to) first location 122 (e.g., to a scalp of the subject). In some examples, the light may emerge from optical conduit 114 and spread out to a certain spot size on body 102 to fall under a predetermined safety limit. At least a portion of light indicated by arrow 120 may be scattered within body 102.
As used herein, “distal” means nearer, along the optical path of the light emitted by light source 110 or the light received by detector 104, to the target (e.g., within body 102) than to light source 110 or detector 104. Thus, the distal end of optical conduit 114 is nearer to body 102 than to light source 110, and the distal end of optical conduit 116 is nearer to body 102 than to detector 104. Additionally, as used herein, “proximal” means nearer, along the optical path of the light emitted by light source 110 or the light received by detector 104, to light source 110 or detector 104 than to body 102. Thus, the proximal end of optical conduit 114 is nearer to light source 110 than to body 102, and the proximal end of optical conduit 116 is nearer to detector 104 than to body 102.
As shown, the distal end of optical conduit 116 (e.g., a light pipe, a light guide, a waveguide, a single-mode optical fiber, and/or a multi-mode optical fiber) is positioned at (e.g., right above, in physical contact with, or physically attached to) output location 126 on body 102. In this manner, optical conduit 116 may collect at least a portion of the scattered light (indicated as light 124) as it exits body 102 at location 126 and carry light 124 to detector 104. Light 124 may pass through one or more lenses and/or other optical elements (not shown) that direct light 124 onto each of the photodetectors 106 included in detector 104.
Photodetectors 106 may be connected in parallel in detector 104. An output of each of photodetectors 106 may be accumulated to generate an accumulated output of detector 104. Processor 108 may receive the accumulated output and determine, based on the accumulated output, a temporal distribution of photons detected by photodetectors 106. Processor 108 may then generate, based on the temporal distribution, a histogram representing a light pulse response of a target (e.g., tissue, blood flow, etc.) in body 102. Example embodiments of accumulated outputs are described herein.
In some examples, SPAD circuit 202 includes a SPAD and a fast gating circuit configured to operate together to detect a photon incident upon the SPAD. As described herein, SPAD circuit 202 may generate an output when SPAD circuit 202 detects a photon.
The fast gating circuit included in SPAD circuit 202 may be implemented in any suitable manner. For example, the fast gating circuit may include a capacitor that is pre-charged with a bias voltage before a command is provided to arm the SPAD. Gating the SPAD with a capacitor instead of with an active voltage source, such as is done in some conventional SPAD architectures, has a number of advantages and benefits. For example, a SPAD that is gated with a capacitor may be armed practically instantaneously compared to a SPAD that is gated with an active voltage source. This is because the capacitor is already charged with the bias voltage when a command is provided to arm the SPAD. This is described more fully in U.S. Pat. Nos. 10,158,038 and 10,424,683, which are incorporated herein by reference in their entireties.
In some alternative configurations, SPAD circuit 202 does not include a fast gating circuit. In these configurations, the SPAD included in SPAD circuit 202 may be gated in any suitable manner.
Control circuit 204 may be implemented by an application specific integrated circuit (ASIC) or any other suitable circuit configured to control an operation of various components within SPAD circuit 202. For example, control circuit 204 may output control logic that puts the SPAD included in SPAD circuit 202 in either an armed or a disarmed state.
In some examples, control circuit 204 may control a gate delay, which specifies a predetermined amount of time control circuit 204 is to wait after an occurrence of a light pulse (e.g., a laser pulse) to put the SPAD in the armed state. To this end, control circuit 204 may receive light pulse timing information, which indicates a time at which a light pulse occurs (e.g., a time at which the light pulse is applied to body 102). Control circuit 204 may also control a programmable gate width, which specifies how long the SPAD is kept in the armed state before being disarmed.
Control circuit 204 is further configured to control signal processing circuit 208. For example, control circuit 204 may provide histogram parameters (e.g., time bins, number of light pulses, type of histogram, etc.) to signal processing circuit 208. Signal processing circuit 208 may generate histogram data in accordance with the histogram parameters. In some examples, control circuit 204 is at least partially implemented by controller 112.
TDC 206 is configured to measure a time difference between an occurrence of an output pulse generated by SPAD circuit 202 and an occurrence of a light pulse. To this end, TDC 206 may also receive the same light pulse timing information that control circuit 204 receives. TDC 206 may be implemented by any suitable circuitry as may serve a particular implementation.
Signal processing circuit 208 is configured to perform one or more signal processing operations on data output by TDC 206. For example, signal processing circuit 208 may generate histogram data based on the data output by TDC 206 and in accordance with histogram parameters provided by control circuit 204. To illustrate, signal processing circuit 208 may generate, store, transmit, compress, analyze, decode, and/or otherwise process histograms based on the data output by TDC 206. In some examples, signal processing circuit 208 may provide processed data to control circuit 204, which may use the processed data in any suitable manner. In some examples, signal processing circuit 208 is at least partially implemented by processor 108.
In some examples, each photodetector 106 (e.g., SPAD circuit 202) may have a dedicated TDC 206 associated therewith. For example, for an array of N photodetectors 106, there may be a corresponding array of N TDCs 206. Alternatively, a single TDC 206 may be associated with multiple photodetectors 106. Likewise, a single control circuit 204 and a single signal processing circuit 208 may be provided for one or more SPAD circuits 202 and/or TDCs 206.
Timing diagram 300 shows a sequence of light pulses 302 (e.g., light pulses 302-1 and 302-2) that may be applied to the target (e.g., tissue within a brain of a user, blood flow, a fluorescent material used as a probe in a body of a user, etc.). Timing diagram 300 also shows a pulse wave 304 representing predetermined gated time windows (also referred as gated time periods) during which photodetectors 106 are gated ON to detect photons. As shown, light pulse 302-1 is applied at a time t0. At a time t1, a first instance of the predetermined gated time window begins. Photodetectors 106 may be armed at time t1, enabling photodetectors 106 to detect photons scattered by the target during the predetermined gated time window. In this example, time t1 is set to be at a certain time after time t0, which may minimize photons detected directly from the laser pulse, before the laser pulse reaches the target. However, in some alternative examples, time t1 is set to be equal to time t0.
At a time t2, the predetermined gated time window ends. In some examples, photodetectors 106 may be disarmed at time t2. In other examples, photodetectors 106 may be reset (e.g., disarmed and re-armed) at time t2 or at a time subsequent to time t2. During the predetermined gated time window, photodetectors 106 may detect photons scattered by the target. Photodetectors 106 may be configured to remain armed during the predetermined gated time window such that photodetectors 106 maintain an output upon detecting a photon during the predetermined gated time window. For example, a photodetector 106 may detect a photon at a time t3, which is during the predetermined gated time window between times t1 and t2. The photodetector 106 may be configured to provide an output indicating that the photodetector 106 has detected a photon. The photodetector 106 may be configured to continue providing the output until time t2, when the photodetector may be disarmed and/or reset. Optical measurement system 100 may generate an accumulated output from the plurality of photodetectors. Optical measurement system 100 may sample the accumulated output to determine times at which photons are detected by photodetectors 106 to generate a TPSF.
Optical measurement system 100 may be implemented by or included in any suitable device(s). For example, optical measurement system 100 may be included in a non-wearable device (e.g., a medical device and/or consumer device that is placed near the head or other body part of a user to perform one or more diagnostic, imaging, and/or consumer-related operations). Optical measurement system 100 may alternatively be included, in whole or in part, in a sub-assembly enclosure of a wearable invasive device (e.g., an implantable medical device for brain recording and imaging).
Alternatively, optical measurement system 100 may be included, in whole or in part, in a non-invasive wearable device that a user may wear to perform one or more diagnostic, imaging, analytical, and/or consumer-related operations. The non-invasive wearable device may be placed on a user's head or other part of the user to detect neural activity. In some examples, such neural activity may be used to make behavioral and mental state analysis, awareness and predictions for the user.
Mental state described herein refers to the measured neural activity related to physiological brain states and/or mental brain states, e.g., joy, excitement, relaxation, surprise, fear, stress, anxiety, sadness, anger, disgust, contempt, contentment, calmness, focus, attention, approval, creativity, positive or negative reflections/attitude on experiences or the use of objects, etc. Further details on the methods and systems related to a predicted brain state, behavior, preferences, or attitude of the user, and the creation, training, and use of neuromes can be found in U.S. Provisional Patent Application No. 63/047,991, filed Jul. 3, 2020. Exemplary measurement systems and methods using biofeedback for awareness and modulation of mental state are described in more detail in U.S. patent application Ser. No. 16/364,338, filed Mar. 26, 2019, published as US2020/0196932A1. Exemplary measurement systems and methods used for detecting and modulating the mental state of a user using entertainment selections, e.g., music, film/video, are described in more detail in U.S. patent application Ser. No. 16/835,972, filed Mar. 31, 2020, published as US2020/0315510A1. Exemplary measurement systems and methods used for detecting and modulating the mental state of a user using product formulation from, e.g., beverages, food, selective food/drink ingredients, fragrances, and assessment based on product-elicited brain state measurements are described in more detail in U.S. patent application Ser. No. 16/853,614, filed Apr. 20, 2020, published as US2020/0337624A1. Exemplary measurement systems and methods used for detecting and modulating the mental state of a user through awareness of priming effects are described in more detail in U.S. patent application Ser. No. 16/885,596, filed May 28, 2020, published as US2020/0390358A1. These applications and corresponding U.S. publications are incorporated herein by reference in their entirety.
Head-mountable component 502 includes a plurality of detectors 504, which may implement or be similar to detector 104, and a plurality of light sources 506, which may be implemented by or be similar to light source 110. It will be recognized that in some alternative embodiments, head-mountable component 502 may include a single detector 504 and/or a single light source 506.
Brain interface system 500 may be used for controlling an optical path to the brain and/or for transforming photodetector measurements into an intensity value that represents an optical property of a target within the brain. Brain interface system 500 allows optical detection of deep anatomical locations beyond skin and bone (e.g., skull) by extracting data from photons originating from light sources 506 and emitted to a target location within the user's brain, in contrast to conventional imaging systems and methods (e.g., optical coherence tomography (OCT), continuous wave near infrared spectroscopy (CW-NIRS)), which only image superficial tissue structures or through optically transparent structures.
Brain interface system 500 may further include a processor 508 configured to communicate with (e.g., control and/or receive signals from) detectors 504 and light sources 506 by way of a communication link 510. Communication link 510 may include any suitable wired and/or wireless communication link. Processor 508 may include any suitable housing and may be located on the user's scalp, neck, shoulders, chest, or arm, as may be desirable. In some variations, processor 508 may be integrated in the same assembly housing as detectors 504 and light sources 506. In some examples, processor 508 is implemented by or similar to processor 108 and/or controller 112.
As shown, brain interface system 500 may optionally include a remote processor 512 in communication with processor 508. For example, remote processor 512 may store measured data from detectors 504 and/or processor 508 from previous detection sessions and/or from multiple brain interface systems (not shown). In some examples, remote processor 512 is implemented by or similar to processor 108 and/or controller 112.
Power for detectors 504, light sources 506, and/or processor 508 may be provided via a wearable battery (not shown). In some examples, processor 508 and the battery may be enclosed in a single housing, and wires carrying power signals from processor 508 and the battery may extend to detectors 504 and light sources 506. Alternatively, power may be provided wirelessly (e.g., by induction).
In some alternative embodiments, head mountable component 502 does not include individual light sources. Instead, a light source configured to generate the light that is detected by detector 504 may be included elsewhere in brain interface system 500. For example, a light source may be included in processor 508 and/or in another wearable or non-wearable device and coupled to head mountable component 502 through an optical connection.
In some alternative embodiments, head mountable component 502 does not include individual detectors 504. Instead, one or more detectors configured to detect the scattered light from the target may be included elsewhere in brain interface system 500. For example, a detector may be included in processor 508 and/or in another wearable or non-wearable device and coupled to head mountable component 502 through an optical connection.
Light sources 604 are each configured to emit light (e.g., a sequence of light pulses) and may be implemented by any of the light sources described herein. Detectors 606 may each be configured to detect arrival times for photons of the light emitted by one or more light sources 604 after the light is scattered by the target. For example, a detector 606 may include a photodetector configured to generate a photodetector output pulse in response to detecting a photon of the light and a TDC configured to record a timestamp symbol in response to an occurrence of the photodetector output pulse, the timestamp symbol representative of an arrival time for the photon (i.e., when the photon is detected by the photodetector). Detectors 606 may be implemented by any of the detectors described herein.
Wearable assembly 602 may be implemented by any of the wearable devices, modular assemblies, and/or wearable units described herein. For example, wearable assembly 602 may be implemented by a wearable device (e.g., headgear) configured to be worn on a user's head. Wearable assembly 602 may additionally or alternatively be configured to be worn on any other part of a user's body.
Optical measurement system 600 may be modular in that one or more components of optical measurement system 600 may be removed, changed out, or otherwise modified as may serve a particular implementation. Additionally or alternatively, optical measurement system 600 may be modular such that one or more components of optical measurement system 600 may be housed in a separate housing (e.g., module) and/or may be movable relative to other components. Exemplary modular optical measurement systems are described in more detail in U.S. Provisional Patent Application No. 63/081,754, filed Sep. 22, 2020, U.S. Provisional Patent Application No. 63/038,459, filed Jun. 12, 2020, U.S. Provisional Patent Application No. 63/038,468, filed Jun. 12, 2020, U.S. Provisional Patent Application No. 63/038,481, filed Jun. 12, 2020, and U.S. Provisional Patent Application No. 63/064,688, filed Aug. 12, 2020, which applications are incorporated herein by reference in their respective entireties.
As shown, optical measurement system 700 includes a plurality of wearable modules 702 (e.g., modules 702-1 through 702-3). Module 702-1 can represent or include a first module housing, module 702-2 can represent or include a separate second module housing, module 703-3 can represent or include a separate third module housing, and so forth. While three modules 702 are shown to be included in optical measurement system 700, in alternative configurations, any number of modules 702 (e.g., a single module up to sixteen or more modules) may be included in optical measurement system 700.
Each module 702 includes a light source 704 (e.g., light source 704-1 of module 702-1, light source 704-2 of module 702-2, and light source 704-3 of module 702-3) and a plurality of detectors 706 (e.g., detectors 706-11 through 706-16 of module 702-1, detectors 706-21 through 706-26 of module 702-2, and detectors 706-31 through 706-36 of module 702-3). In the particular implementation shown in
Each light source 704 may be implemented by any light source described herein and may be configured to emit light directed at a target (e.g., the brain). Each light source 704 may also include any suitable optical components (e.g., an optical conduit) configured to guide and direct emitted light toward the target. Each light source 704 may be located at a center region of a surface 708 of the light source's corresponding module. For example, light source 704-1 is located at a center region of a surface 708 of module 702-1. In alternative implementations, a light source 704 of a module 702 may be located away from a center region of the module.
Each detector 706 may be implemented by any detector described herein and may include a plurality of photodetectors (e.g., SPADs) as well as other circuitry (e.g., TDCs). Each detector 706 may be configured to detect arrival times for photons of the light emitted by one or more light sources after the photons are scattered by the target. Each detector 706 may also include any suitable optical components (e.g., an optical conduit) configured to receive and guide photons scattered by the target toward the plurality of photodetectors included in the detector 706.
As shown in
In some examples, the spacing between a light source 704 (e.g., a distal end portion of a light emitting optical conduit) and each detector 706 (e.g., a distal end portion of a light receiving optical conduit) is maintained at the same fixed distance on each module 702 to ensure homogeneous coverage over specific areas and to facilitate processing of the detected signals. The fixed spacing also provides consistent spatial (lateral and depth) resolution across the target area of interest, e.g., brain tissue. Moreover, maintaining a known distance between the light source and each detector allows subsequent processing of the detected signals to infer spatial (e.g., depth localization, inverse modeling) information about the detected signals.
As shown in
Additionally or alternatively, one or more modules 702 included in optical measurement system 700 may be physically unconnected from one or more other modules 702 and thus is adjustable relative to the other module(s) 702. For example, optical measurement system 700 may include a plurality of physically unconnected modular assemblies similar to the modular assembly illustrated in
As shown in
In some configurations, one or more detectors 706 on a module 702 may be close enough to other light sources 704 on other modules 702 to detect photon arrival times for photons included in light pulses emitted by the other light sources 704. For example, detector 706-24 may detect photon arrival times for photons included in light pulses emitted by light source 704-1 (in addition to detecting photon arrival times for photons included in light pulses emitted by light source 704-2).
When optical measurement system 700 performs an optical measurement operation, a light source 704 (e.g., light source 704-1) directs a sequence of light pulses (e.g., laser pulses) toward the target. The light pulses may be short (e.g., 10-2000 picoseconds (ps)) and repeated at a high frequency (e.g., between 100,000 hertz (Hz) and 100 megahertz (MHz)). Photons in the emitted light pulses may be scattered by the target and detected by detectors 706. Optical measurement system 700 may measure a time relative to the light pulse for each detected photon. By counting the number of photons detected at each time relative to each light pulse repeated over a plurality of light pulses, optical measurement system 700 may generate a histogram that represents a light pulse response of the target (e.g., a temporal point spread function (TPSF)).
If the distance between the light source 704 and a detector 706 (the “source-detector distance”) is known, the detected signal (e.g., TPSF) can be used to infer spatial information (e.g., depth localization) about the detected signals. As used herein, the source-detector distance refers to the linear distance between the point where a light pulse emitted by a light source 704 exits module 702 (i.e., a distal end (light-emitting) surface of a light-emitting optical conduit) and the point where photons included in the light pulse and scattered by the target enter module 702 (i.e., a distal end (light-receiving) surface of the light-receiving optical conduit).
As mentioned above, the position of a light source 704 on a module 702 and the detectors 706 on the same module 702 may be fixed. Thus, the source-detector distance between the light source 704 and each detector 706 on the same module 702 may be known. However, the source-detector distance between a light source 704 (e.g., light source 704-1) on a first module (e.g., module 702-1) and a detector (e.g., detector 706-24) on a second module (e.g., module 702-2) may not be known and may vary, such as due to manufacturing variations in the modules 702, flexing/bending of the modular assembly to conform to a 3D surface, and/or adjustment of the pose of one or more modules 702 relative to other modules 702. However, the source-detector distance between the light source 704 on the first module and the detector 706 on the second module can be estimated by a calibration process based on the signals detected by detectors 706, as will now be described.
First detector 804-1 is separated from first light source 802-1 by a source-detector distance labeled d1, and second detector 804-2 is separated from second light source 802-2 by a source-detector distance labeled d2. First detector 804-1 and second detector 804-2 are separated by a distance labeled d3. Source-detector distance d1 and source-detector distance d2 may be known (e.g., from prior measurements, from module design and construction, etc.). In some examples, source-detector distance d1 and source-detector distance d2 are fixed, such as when the positions of light sources 802 and detectors 804 are fixed on their respective modules. On the other hand, distance d3 may be unknown, such as when a position of second light source 802-2 and second detector 804-2 are adjusted relative to a position of first light source 802-1 and first detector 804-1. Hence, a source-detector distance d4 between first light source 802-1 and second detector 804-2 (i.e., d1+d3) is unknown, as is a source-detector distance d5 between second light source 802-2 and first detector 804-1 (i.e., d2+d3). However, optical measurement system 800 is configured to determine source-detector distance d4 and source-detector distance d5 analytically as described herein.
The propagation of light through a turbid medium (e.g., the brain) may be modeled by a standard model of diffusion theory. In such models, the scattering of light in the target depends on, among other things, the source-detector distance and a parameter known as the reduced scattering coefficient u′s, which represents the scattering probability of light per unit length together with an anisotropy factor. If the source-detector distance is known, the reduced scattering coefficient u′s of the target may be determined analytically from a detected TPSF by fitting the TPSF with a standard model of diffusion theory. On the other hand, if the reduced scattering coefficient u′s of the target is known, the source-detector distance can be calculated from the acquired TPSF and the model of diffusion theory. Optical measurement system 800 is configured to use this relationship to determine source-detector distance d4 and source-detector distance d5 based on a plurality of temporal distributions (TPSFs) of the photons included in light pulses 810 and detected by detectors 804.
In a first step of the calibration process, optical measurement system 800 may obtain calibration data (e.g., TPSFs from detectors 804 and first and second distances d1 and d2). Optical measurement system 800 may obtain calibration data in any suitable way, such as by generating the calibration data and/or by accessing previously generated calibrated stored data from memory or some other archived location.
Optical measurement system 800 may determine a first temporal distribution (TPSF) of the first set of photons included in first light pulse 810-1 and detected by first detector 804-1. Optical measurement system 800 may also determine a second temporal distribution (TPSF) of the second set of photons included in first light pulse 810-1 and detected by second detector 804-2. Optical measurement system 800 may determine the first temporal distribution and the second temporal distribution in any suitable way, including in any way described herein.
Optical measurement system 800 may determine a third temporal distribution (TPSF) of the third set of photons included in second light pulse 810-2 and detected by second detector 804-2. Optical measurement system 800 may also determine a fourth temporal distribution (TPSF) of the fourth set of photons included in second light pulse 810-2 and detected by first detector 804-1. Optical measurement system 800 may determine the third temporal distribution and the fourth temporal distribution in any suitable way, including in any way described herein.
Optical measurement system 800 may also obtain values of source-detector distance d1 and source-detector distance d2. When d1 and d2 are known, optical measurement system 800 may use the first, second, third, and fourth temporal distributions (obtained as described above) to determine source-detector distance d4 and source-detector distance d5, as will be described below in more detail. Optical measurement system 800 may obtain source-detector distance d1 and source-detector distance d2 in any suitable way. As mentioned, d1 and d2 may be known. For example, first light source 802-1 and first detector 804-1 may be included in a first wearable module and positioned within the first wearable module at a fixed distance. Similarly, second light source 802-2 and second detector 804-2 may be included in a second wearable module and positioned within the second wearable module at a fixed distance. In some examples, the first wearable module and the second wearable module each includes memory that stores data (e.g., distance data, model information data, serial number data, etc.) that may be accessed by optical measurement system 800 and used to determine source-detector distance d1 and source-detector distance d2. In some examples, optical measurement system 800 may use the stored data (e.g., serial number data) to obtain source-detector distance d1 and source-detector distance d2 from a remote computing device (e.g., a remote server, etc.).
Additionally or alternatively, source-detector distance d1 and source-detector distance d2 may be provided to optical measurement system 800 by user input. For example, the position of one or more light sources 802 and/or detectors 804 may be adjustable, and the user may then measure or determine source-detector distance d1 and/or source-detector distance d2 and provide that information to optical measurement system 800.
In the second step of the calibration process, optical measurement system 800 may use the calibration data to determine reduced scattering coefficients u′s of target 808. For example, optical measurement system 800 may determine, based on the first temporal distribution and source-detector distance d1, a first reduced scattering coefficient u′s1 of target 808 within first region 812-1. Optical measurement system 800 may also determine, based on the second temporal distribution and source-detector distance d2, a second reduced scattering coefficient u′s2 of target 808 within second region 812-2. Optical measurement system 800 may determine first reduced scattering coefficient u′s1 and second reduced scattering coefficient u′s2 in any suitable way. For example, optical measurement system 800 may fit the first temporal distribution and the third temporal distribution to a standard model of diffusion theory and, based on the known values of source-detector distances d1 and d2, directly calculate first reduced scattering coefficient u′s1 and second reduced scattering coefficient u′s2.
In the third step of the calibration process, optical measurement system 800 may determine source-detector distance d4 and source-detector distance d5 based on the obtained calibration data and the calculated first reduced scattering coefficient u′s1 and second reduced scattering coefficient u′s2. The optical path of the second set of photons through target 808 has the previously calculated first reduced scattering coefficient u′s1 as well as the unknown third reduced scattering coefficient u′s3, and the optical path of the fourth set of photons through target 808 has the previously calculated second reduced scattering coefficient u′s2 as well as the unknown third reduced scattering coefficient u′s3. Thus, there exist two unknown values: third distance d3 and third reduced scattering coefficient u′s3 of target 808 in third region 812-3. These two unknown values may be resolved by fitting two different measurements—the second temporal distribution and the fourth temporal distribution—to the standard model of diffusion theory. Once third distance d3 is determined, source-detector distance d4 (i.e., d1+d3) and source-detector distance d5 (i.e., d2+d3) may be easily calculated.
Various modifications may be made to the embodiments described above. For example, where first reduced scattering coefficient μ′s1 and second reduced scattering coefficient μ′s2 are identical (or within a predetermined tolerance), optical measurement system 800 may assume that third reduced scattering coefficient μ′s3 is also the same. Accordingly, source-detector distance d4 may be determined directly from the second temporal distribution and the first reduced scattering coefficient μ′s1 (or third reduced scattering coefficient μ′s3). Similarly, source-detector distance d5 may be determined directly from the fourth temporal distribution and the second reduced scattering coefficient μ′s2 (or third reduced scattering coefficient μ′s3).
The calibration process described above may be performed with a single wavelength of light. That is, first light pulse 810-1 and second light pulse 810-2 have the same wavelength (e.g., 650 nm). In some examples, optical measurement system 800 may repeat the calibration process with one or more additional wavelengths (e.g., 810 nm). The reduced scattering coefficient u′s of target 808 is wavelength dependent, and thus target 808 will have different values of first, second, and third reduced scattering coefficients u′s1, u′s2, and u′s3 for each different wavelength. However, the source-detector distance d4 and source-detector distance d5 should be the same, so the results of the calibration process with the additional wavelengths can be used to validate, correct, or average the calibration with the first wavelength.
To illustrate, optical measurement system 800 may direct first light source 802-1 to also emit a third light pulse (not shown) toward target 808 and may direct second light source 802-2 to also emit a fourth light pulse (not shown) toward target 808. First light pulse 810-1 and second light pulse 810-2 may have a first wavelength (e.g., 650 nm), and the third and fourth light pulses may have a second wavelength (e.g., 810 nm) that is different from the first wavelength.
Optical measurement system 800 may determine a plurality of additional temporal distributions of additional photons detected by first detector 804-1 and second detector 804-2 and included in the third light pulse and the fourth light pulse. Optical measurement system 800 may determine, based on the plurality of additional temporal distributions of additional photons, source-detector distance d4 and source-detector distance d5. In some examples, optical measurement system 800 may confirm or validate the calculated source-detector distances d4 and d5 of the first calibration process if the result of the second calibration process matches or is within a predetermined tolerance (e.g., a predetermined amount or a predetermined percentage) of the result of the first calibration process.
In the examples described above, optical measurement system 800 may determine the source-detector distance between a light source (e.g., light source 704-1) on a first wearable module (e.g., wearable module 702-1) and a detector (e.g., detector 706-24) on a second wearable module (e.g., wearable module 704-2). In some examples, one or more additional detectors (e.g., detector 706-33) on the second or other wearable modules (e.g., wearable module 704-3) separate from the first wearable module may also be close enough to the light source to detect photons from light pulses emitted by the light source and scattered by the target. Accordingly, optical measurement system 800 may be configured to perform the calibration process described above to determine the source-detector distance between the light source (e.g., light source 704-1) on the first wearable module (e.g., wearable module 702-1) and a plurality of detectors (e.g., detectors 706-24 and 706-34) on one or more different wearable modules (e.g., wearable modules 702-2 and 702-3). Additionally, optical measurement system 800 may be configured to perform this calibration process for each of a plurality of wearable modules and/or light sources. To perform such calibration on a plurality of wearable modules and/or light sources, optical measurement system 800 may time-multiplex the calibration process (e.g., the emission of light pulses from light sources) so that each detector detects photons from only one light source at a time.
In some examples, first detector 804-1 and second detector 804-2 are positioned substantially inline with first light source 802-1 and second light source 802-2. With such configuration, the third set of photons and the fourth set of photons both pass through the same third region 812-3. This configuration is illustrated with reference to
In alternative examples, a detector 706 is not positioned inline with a first light source and a second light source. For example, as shown in
In some examples, the reduced scattering coefficient μ′sD25 may be estimated based on the known reduced scattering coefficient μ′s in adjacent or nearby regions of the target.
In the examples described above, the source-detector distance between a light source and a detector may be determined based on a plurality of temporal distributions obtained from the detection, by two different detectors, of light pulses emitted from two different light sources but having the same wavelength. In alternative embodiments, as will be illustrated with reference to
Optical measurement system 800 may determine a first temporal distribution (TPSF) of the first set of photons included in first light pulse 1002-1 and detected by first detector 804-1. Optical measurement system 800 may also determine a second temporal distribution (TPSF) of the second set of photons included in first light pulse 1002-1 and detected by second detector 804-2. Optical measurement system 800 may determine the first temporal distribution and the second temporal distribution in any suitable way, including in any way described herein.
Optical measurement system 800 may determine a third temporal distribution (TPSF) of the third set of photons included in second light pulse 1002-2 and detected by first detector 804-1. Optical measurement system 800 may also determine a fourth temporal distribution (TPSF) of the fourth set of photons included in second light pulse 1002-2 and detected by second detector 804-2. Optical measurement system 800 may determine the third temporal distribution and the fourth temporal distribution in any suitable way, including in any way described herein.
Target 808 (e.g., biological tissue) scatters different wavelengths of light differently, and the distance between light source 802-1 and detectors 804 affects the intensity and time-of-flight of the photons included in first light pulse 1002-1 and second light pulse 1002-2 and detected by detectors 804. For example,
Similarly, as shown in
Based on the wavelength-dependence of peak photon arrival times at a detector, a model can be generated and used to determine, based on the detection signal of second detector 804-2, the source-detector distance d4.
In some examples, the source-detector distance (e.g., the distance between peaks of TPSFs of different wavelengths) may also be related to one or more other parameters, such as the absolute distance of a TPSF peak from the light pulse 1002 (e.g., the difference between time t1 and time t0 or the difference between time t2 and time t0), the wavelengths used, TPSF peak intensity, difference in peak intensity, light pulse intensity, tissue type, tissue properties (e.g., tissue oxygenation, saturation, etc.), etc. Such additional parameters may also be included in the source-detector distance estimation model.
In some examples, the source-detector distance estimation model may be generated in real-time (e.g., in operation while worn by the user) during the calibration process. For example, ΔtD for various source-detector distances for inline detectors 706 may be measured in accordance with the calibration process described above, and this data may then be used to generate a source-detector distance estimation model that may be used to determine source-detector distances for detectors that are not inline with multiple light sources. Additionally or alternatively, the source-detector distance estimation model may be generated prior to performing the calibration process and/or prior to the user using the optical measurement system. Data representative of the source-detector distance estimation model may be stored by optical measurement system 1000, or may otherwise be stored remotely from optical measurement system 1000 and accessible by optical measurement system 1000 during the calibration process.
Returning again to
In some examples, the optical measurement systems described herein (e.g., optical measurement system 100, 700, or 800) may further include a processing unit configured to perform one or more operations based on photon arrival times detected by the detectors described herein. For example,
Optical measurement system 1302 may be an implementation of optical measurement system 100, 700, or 800 and, as shown, includes a wearable assembly 1304, which includes N light sources 1306 (e.g., light sources 1306-1 through 1306-N) and M detectors 1308 (e.g., detectors 1308-1 through 1308-M). Optical measurement system 1302 may include any of other components as may serve a particular implementation.
Wearable assembly 1304 may be implemented by any of the wearable devices, wearable modules, and/or wearable units described herein (e.g., wearable assembly 602). For example, wearable assembly 1304 may be implemented by a wearable device configured to be worn on a user's head. Wearable assembly 1304 may additionally or alternatively be configured to be worn on any other part of a user's body. In some examples, optical measurement system 1302 may include a plurality of wearable assemblies 1304.
Light sources 1306 are each configured to emit light and may be implemented by any of the light sources described herein. Detectors 1308 may each be configured to detect arrival times for photons of the light emitted by one or more light sources 1306 after the light is scattered by the target or diverted without being scattered by the target. For example, a detector 1308 may include a photodetector configured to generate a photodetector output pulse in response to detecting a photon of the light and a TDC configured to record a timestamp symbol in response to an occurrence of the photodetector output pulse, the timestamp symbol representative of an arrival time for the photon. Detectors 1308 may be implemented by any of the detectors described herein.
In configuration 1300-1, a processing unit 1310 is also included in wearable assembly 1304. In configuration 1300-2, processing unit 1310 is not included in wearable assembly 1304 (i.e., processing unit 1310 is located external to wearable assembly 1304). Either configuration 1300-1 or 1300-2 may be used in accordance with the systems, circuits, and methods described herein.
Detectors 1308 on wearable assembly 1304 may output signals representative of photon arrivals, as described herein. Processing unit 1310 is configured to receive the output signals and perform one or more operations based on the signals. For example, processing unit 1310 may generate measurement data (e.g., one or more histograms) based on the signals, as described herein.
As mentioned, in configuration 1300-2, processing unit 1310 is not included in wearable assembly 1304. For example, processing unit 1310 may be included in a wearable device separate from wearable assembly 1304. To illustrate, processing unit 1310 may be included in a wearable device configured to be worn off the head (e.g., on a belt) while wearable assembly 1304 is worn on the head. In these examples, one or more communication interfaces (e.g., cables, wireless interfaces, etc.) may be used to facilitate communication between wearable assembly 1304 and the separate wearable device.
Additionally or alternatively, in configuration 1300-2, processing unit 1310 may be remote from the user (i.e., not worn by the user). For example, processing unit 1310 may be implemented by a stand-alone computing device communicatively coupled to wearable assembly 1304 by way of one or more communication interfaces (e.g., cables, wireless interfaces, etc.).
In some examples, processing unit 1310 may be distributed between multiple devices and/or multiple locations as may serve a particular implementation. Processing unit 1310 may be implemented by processor 108, controller 112, control circuit 204, and/or any other suitable processing and/or computing device or circuit.
For example,
Memory 1402 may be implemented by any suitable non-transitory computer-readable medium and/or non-transitory processor-readable medium, such as any combination of non-volatile storage media and/or volatile storage media. Exemplary non-volatile storage media include, but are not limited to, read-only memory, flash memory, a solid-state drive, a magnetic storage device (e.g., a hard drive), ferroelectric random-access memory (“RAM”), and an optical disc. Exemplary volatile storage media include, but are not limited to, RAM (e.g., dynamic RAM).
Memory 1402 may maintain (e.g., store) executable data used by processor 1404 to perform one or more of the operations described herein. For example, memory 1402 may store instructions 1406 that may be executed by processor 1404 to perform any of the operations described herein. Instructions 1406 may be implemented by any suitable application, program (e.g., sound processing program), software, code, and/or other executable data instance. Memory 1402 may also maintain any data received, generated, managed, used, and/or transmitted by processor 1404.
Processor 1404 may be configured to perform (e.g., execute instructions 1406 stored in memory 1402 to perform) various operations described herein. For example, processor 1404 may be configured to perform any of the operations described herein as being performed by processing unit 1310.
In operation 2102, a first light source is directed to emit a first light pulse toward a target. Operation 2102 may be performed in any of the ways described herein.
In operation 2104, a plurality of temporal distributions of photons are determined. The photons are included in the first light pulse and the second light pulse and detected by a first detector and a second detector after the photons are scattered by the target. Operation 2104 may be performed in any of the ways described herein.
In operation 2106, the optical member directs the first light pulse and the second light pulse to a proximal end of a light guide included in the wearable module. Operation 2106 may be performed in any of the ways described herein.
In operation 2108, a distance between the first light source and the second detector is determined based on the plurality of temporal distributions. Operation 2108 may be performed in any of the ways described herein.
In operation 2202, a light source is directed to emit a first light pulse toward a target, the first light pulse having a first wavelength. Operation 2202 may be performed in any of the ways described herein.
In operation 2204, the light source is directed to emit a second light pulse toward the target, the second light pulse having a second wavelength that is different from the first wavelength. Operation 2204 may be performed in any of the ways described herein.
In operation 2206, a plurality of temporal distributions of photons are determined, the photons being included in the first light pulse and the second light pulse and detected by a detector after the photons are scattered by the target. Operation 2206 may be performed in any of the ways described herein.
In operation 2208, a distance between the light source and the detector is determined based on the plurality of temporal distributions and a source-detector distance estimation model. Operation 2208 may be performed in any of the ways described herein.
In some examples, a non-transitory computer-readable medium storing computer-readable instructions may be provided in accordance with the principles described herein. The instructions, when executed by a processor of a computing device, may direct the processor and/or computing device to perform one or more operations, including one or more of the operations described herein. Such instructions may be stored and/or transmitted using any of a variety of known computer-readable media.
A non-transitory computer-readable medium as referred to herein may include any non-transitory storage medium that participates in providing data (e.g., instructions) that may be read and/or executed by a computing device (e.g., by a processor of a computing device). For example, a non-transitory computer-readable medium may include, but is not limited to, any combination of non-volatile storage media and/or volatile storage media. Exemplary non-volatile storage media include, but are not limited to, read-only memory, flash memory, a solid-state drive, a magnetic storage device (e.g. a hard disk, a floppy disk, magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and an optical disc (e.g., a compact disc, a digital video disc, a Blu-ray disc, etc.). Exemplary volatile storage media include, but are not limited to, RAM (e.g., dynamic RAM).
As shown in
Communication interface 2302 may be configured to communicate with one or more computing devices. Examples of communication interface 2302 include, without limitation, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, an audio/video connection, and any other suitable interface.
Processor 2304 generally represents any type or form of processing unit capable of processing data and/or interpreting, executing, and/or directing execution of one or more of the instructions, processes, and/or operations described herein. Processor 2304 may perform operations by executing computer-executable instructions 2312 (e.g., an application, software, code, and/or other executable data instance) stored in storage device 2306.
Storage device 2306 may include one or more data storage media, devices, or configurations and may employ any type, form, and combination of data storage media and/or device. For example, storage device 2306 may include, but is not limited to, any combination of the non-volatile media and/or volatile media described herein. Electronic data, including data described herein, may be temporarily and/or permanently stored in storage device 2306. For example, data representative of computer-executable instructions 2312 configured to direct processor 2304 to perform any of the operations described herein may be stored within storage device 2306. In some examples, data may be arranged in one or more databases residing within storage device 2306.
I/O module 2308 may include one or more I/O modules configured to receive user input and provide user output. I/O module 2308 may include any hardware, firmware, software, or combination thereof supportive of input and output capabilities. For example, I/O module 2308 may include hardware and/or software for capturing user input, including, but not limited to, a keyboard or keypad, a touchscreen component (e.g., touchscreen display), a receiver (e.g., an RF or infrared receiver), motion sensors, and/or one or more input buttons.
I/O module 2308 may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers. In certain embodiments, I/O module 2308 is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces and/or any other graphical content as may serve a particular implementation.
In the preceding description, various exemplary embodiments have been described with reference to the accompanying drawings. It will, however, be evident that various modifications and changes may be made thereto, and additional embodiments may be implemented, without departing from the scope of the invention as set forth in the claims that follow. For example, certain features of one embodiment described herein may be combined with or substituted for features of another embodiment described herein. The description and drawings are accordingly to be regarded in an illustrative rather than a restrictive sense.
The present application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 63/071,473, filed on Aug. 28, 2020, and to U.S. Provisional Patent Application No. 62/992,543, filed on Mar. 20, 2020, and to U.S. Provisional Patent Application No. 62/979,866, filed on Feb. 21, 2020. These applications are incorporated herein by reference in their respective entireties.
Number | Name | Date | Kind |
---|---|---|---|
4018534 | Thorn et al. | Apr 1977 | A |
4207892 | Binder | Jun 1980 | A |
4281645 | Jobsis | Aug 1981 | A |
4321930 | Jobsis | Mar 1982 | A |
4515165 | Carroll | May 1985 | A |
4655225 | Dahne et al. | Apr 1987 | A |
4928248 | Takahashi et al. | May 1990 | A |
4963727 | Cova | Oct 1990 | A |
4995044 | Blazo | Feb 1991 | A |
5088493 | Giannini | Feb 1992 | A |
5090415 | Yamashita | Feb 1992 | A |
5218962 | Mannheimer et al. | Jun 1993 | A |
5309458 | Carl | May 1994 | A |
5386827 | Chance et al. | Feb 1995 | A |
5528365 | Gonatas et al. | Jun 1996 | A |
5625458 | Alfano et al. | Apr 1997 | A |
5761230 | Oono et al. | Jun 1998 | A |
5853370 | Chance et al. | Dec 1998 | A |
5895984 | Renz | Apr 1999 | A |
5929982 | Anderson | Jul 1999 | A |
5983120 | Groner et al. | Nov 1999 | A |
5987045 | Albares et al. | Nov 1999 | A |
6163715 | Larsen et al. | Dec 2000 | A |
6240309 | Yamashita et al. | May 2001 | B1 |
6291824 | Battarbee | Sep 2001 | B1 |
6384663 | Cova et al. | May 2002 | B2 |
6541752 | Zappa et al. | Apr 2003 | B2 |
6542763 | Yamashita et al. | Apr 2003 | B1 |
6618614 | Chance | Sep 2003 | B1 |
6640133 | Yamashita | Oct 2003 | B2 |
6683294 | Herbert et al. | Jan 2004 | B1 |
6748254 | O'Neil | Jun 2004 | B2 |
6992772 | Block | Jan 2006 | B2 |
7095491 | Forstner et al. | Aug 2006 | B2 |
7356365 | Schurman | Apr 2008 | B2 |
7507596 | Yaung et al. | Mar 2009 | B2 |
7547872 | Niclass et al. | Jun 2009 | B2 |
7613504 | Rowe | Nov 2009 | B2 |
7667400 | Goushcha | Feb 2010 | B1 |
7705284 | Inoue et al. | Apr 2010 | B2 |
7714292 | Agarwal et al. | May 2010 | B2 |
7774047 | Yamashita et al. | Aug 2010 | B2 |
7899506 | Xu et al. | Mar 2011 | B2 |
8026471 | Itzler | Sep 2011 | B2 |
8078250 | Chen et al. | Dec 2011 | B2 |
8082015 | Yodh et al. | Dec 2011 | B2 |
8115170 | Stellari et al. | Feb 2012 | B2 |
8168934 | Niclass et al. | May 2012 | B2 |
8352012 | Besio | Jan 2013 | B2 |
8633431 | Kim | Jan 2014 | B2 |
8637875 | Finkelstein et al. | Jan 2014 | B2 |
8754378 | Prescher et al. | Jun 2014 | B2 |
8817257 | Herve | Aug 2014 | B2 |
8937509 | Xu et al. | Jan 2015 | B2 |
8986207 | Li | Mar 2015 | B2 |
9012860 | Nyman et al. | Apr 2015 | B2 |
9041136 | Chia | May 2015 | B2 |
9058081 | Baxter | Jun 2015 | B2 |
9076707 | Harmon | Jul 2015 | B2 |
9101279 | Ritchey et al. | Aug 2015 | B2 |
9131861 | Ince et al. | Sep 2015 | B2 |
9157858 | Claps | Oct 2015 | B2 |
9160949 | Zhang et al. | Oct 2015 | B2 |
9176241 | Frach | Nov 2015 | B2 |
9178100 | Webster et al. | Nov 2015 | B2 |
9190552 | Brunel et al. | Nov 2015 | B2 |
9201138 | Eisele et al. | Dec 2015 | B2 |
9209320 | Webster | Dec 2015 | B1 |
9257523 | Schneider et al. | Feb 2016 | B2 |
9257589 | Niclass et al. | Feb 2016 | B2 |
9299732 | Webster et al. | Mar 2016 | B2 |
9299873 | Mazzillo et al. | Mar 2016 | B2 |
9312401 | Webster | Apr 2016 | B2 |
9316735 | Baxter | Apr 2016 | B2 |
9331116 | Webster | May 2016 | B2 |
9368487 | Su et al. | Jun 2016 | B1 |
9401448 | Bienfang et al. | Jul 2016 | B2 |
9407796 | Dinten et al. | Aug 2016 | B2 |
9419635 | Kumar et al. | Aug 2016 | B2 |
9431439 | Soga et al. | Aug 2016 | B2 |
9442201 | Schmand et al. | Sep 2016 | B2 |
9449377 | Sarkar et al. | Sep 2016 | B2 |
9450007 | Motta et al. | Sep 2016 | B1 |
9466631 | Fallica et al. | Oct 2016 | B2 |
9476979 | Drader et al. | Oct 2016 | B2 |
9478579 | Dai et al. | Oct 2016 | B2 |
9529079 | Droz | Dec 2016 | B1 |
9535157 | Caley et al. | Jan 2017 | B2 |
9574936 | Heinonen | Feb 2017 | B2 |
9625580 | Kotelnikov et al. | Apr 2017 | B2 |
9627569 | Harmon | Apr 2017 | B2 |
9634826 | Park | Apr 2017 | B1 |
9639063 | Dutton et al. | May 2017 | B2 |
9640704 | Frey et al. | May 2017 | B2 |
9658158 | Renna et al. | May 2017 | B2 |
9659980 | McGarvey et al. | May 2017 | B2 |
9671284 | Dandin | Jun 2017 | B1 |
9681844 | Xu et al. | Jun 2017 | B2 |
9685576 | Webster | Jun 2017 | B2 |
9702758 | Nouri | Jul 2017 | B2 |
9728659 | Hirigoyen et al. | Aug 2017 | B2 |
9741879 | Frey et al. | Aug 2017 | B2 |
9753351 | Eldada | Sep 2017 | B2 |
9767246 | Dolinsky et al. | Sep 2017 | B2 |
9768211 | Harmon | Sep 2017 | B2 |
9773930 | Motta et al. | Sep 2017 | B2 |
9804092 | Zeng et al. | Oct 2017 | B2 |
9812438 | Schneider et al. | Nov 2017 | B2 |
9831283 | Shepard et al. | Nov 2017 | B2 |
9851302 | Mattioli Della Rocca et al. | Dec 2017 | B2 |
9867250 | Powers et al. | Jan 2018 | B1 |
9869753 | Eldada | Jan 2018 | B2 |
9881963 | Chen et al. | Jan 2018 | B1 |
9882003 | Aharoni | Jan 2018 | B1 |
9886095 | Pothier | Feb 2018 | B2 |
9899544 | Mazzillo et al. | Feb 2018 | B1 |
9899557 | Muscara'et al. | Feb 2018 | B2 |
9939316 | Scott et al. | Apr 2018 | B2 |
9939536 | O'Neill et al. | Apr 2018 | B2 |
9946344 | Ayaz et al. | Apr 2018 | B2 |
D817553 | Aaskov et al. | May 2018 | S |
9983670 | Coleman | May 2018 | B2 |
9997551 | Mandai et al. | Jun 2018 | B2 |
10016137 | Yang et al. | Jul 2018 | B1 |
D825112 | Saez | Aug 2018 | S |
10056415 | Na et al. | Aug 2018 | B2 |
10103513 | Zhang et al. | Oct 2018 | B1 |
10141458 | Zhang et al. | Nov 2018 | B2 |
10154815 | Al-Ali et al. | Dec 2018 | B2 |
10157954 | Na et al. | Dec 2018 | B2 |
10158038 | Do Valle | Dec 2018 | B1 |
10219700 | Yang et al. | Mar 2019 | B1 |
10256264 | Na et al. | Apr 2019 | B2 |
10340408 | Katnani | Jul 2019 | B1 |
10424683 | Do Valle | Sep 2019 | B1 |
10483125 | Inoue | Nov 2019 | B2 |
10515993 | Field et al. | Dec 2019 | B2 |
10533893 | Leonardo | Jan 2020 | B2 |
10541660 | McKisson | Jan 2020 | B2 |
10558171 | Kondo | Feb 2020 | B2 |
10594306 | Dandin | Mar 2020 | B2 |
10627460 | Alford et al. | Apr 2020 | B2 |
10695167 | Van Heugten et al. | Jun 2020 | B2 |
10697829 | Delic | Jun 2020 | B2 |
10772561 | Donaldson | Sep 2020 | B2 |
10809796 | Armstrong-Muntner | Oct 2020 | B2 |
10825847 | Furukawa | Nov 2020 | B2 |
10912504 | Nakaji | Feb 2021 | B2 |
10976386 | Alford | Apr 2021 | B2 |
10983177 | Jiménez-Martínez | Apr 2021 | B2 |
10996293 | Mohseni | May 2021 | B2 |
11006876 | Johnson | May 2021 | B2 |
11006878 | Johnson | May 2021 | B2 |
11137283 | Balamurugan et al. | Oct 2021 | B2 |
11630310 | Seidman et al. | Apr 2023 | B2 |
20020033454 | Cheng | Mar 2002 | A1 |
20020195545 | Nishimura | Dec 2002 | A1 |
20040057478 | Saito | Mar 2004 | A1 |
20040064052 | Chance et al. | Apr 2004 | A1 |
20040078216 | Toto | Apr 2004 | A1 |
20040160996 | Giorgi et al. | Aug 2004 | A1 |
20050038344 | Chance | Feb 2005 | A1 |
20050061986 | Kardynal et al. | Mar 2005 | A1 |
20050124863 | Cook | Jun 2005 | A1 |
20050228291 | Chance | Oct 2005 | A1 |
20060171845 | Martin | Aug 2006 | A1 |
20060197452 | Zhang | Sep 2006 | A1 |
20060264722 | Hannula et al. | Nov 2006 | A1 |
20070038116 | Yamanaka | Feb 2007 | A1 |
20070083097 | Fujiwara | Apr 2007 | A1 |
20080021341 | Harris et al. | Jan 2008 | A1 |
20090012402 | Mintz | Jan 2009 | A1 |
20090054789 | Kiguchi et al. | Feb 2009 | A1 |
20090163775 | Barrett | Jun 2009 | A1 |
20090313048 | Kahn et al. | Dec 2009 | A1 |
20100188649 | Prahl | Jul 2010 | A1 |
20100210952 | Taira et al. | Aug 2010 | A1 |
20100249557 | Besko et al. | Sep 2010 | A1 |
20100301194 | Patel | Dec 2010 | A1 |
20110208675 | Shoureshi et al. | Aug 2011 | A1 |
20110248175 | Frach | Oct 2011 | A1 |
20120016635 | Brodsky et al. | Jan 2012 | A1 |
20120029304 | Medina et al. | Feb 2012 | A1 |
20120083673 | Al-Ali et al. | Apr 2012 | A1 |
20120101838 | Lingard et al. | Apr 2012 | A1 |
20130015331 | Birk | Jan 2013 | A1 |
20130030267 | Lisogurski | Jan 2013 | A1 |
20130030270 | Chiou et al. | Jan 2013 | A1 |
20130032713 | Barbi et al. | Feb 2013 | A1 |
20130090541 | MacFarlane et al. | Apr 2013 | A1 |
20130144644 | Simpson | Jun 2013 | A1 |
20130153754 | Drader et al. | Jun 2013 | A1 |
20130221221 | Bouzid et al. | Aug 2013 | A1 |
20130225953 | Oliviero et al. | Aug 2013 | A1 |
20130342835 | Blacksberg | Dec 2013 | A1 |
20140027607 | Mordarski et al. | Jan 2014 | A1 |
20140028211 | Imam | Jan 2014 | A1 |
20140055181 | Chavpas | Feb 2014 | A1 |
20140066783 | Kiani | Mar 2014 | A1 |
20140171757 | Kawato et al. | Jun 2014 | A1 |
20140185643 | McComb et al. | Jul 2014 | A1 |
20140191115 | Webster et al. | Jul 2014 | A1 |
20140211194 | Pacala et al. | Jul 2014 | A1 |
20140217264 | Shepard | Aug 2014 | A1 |
20140275891 | Muehlemann et al. | Sep 2014 | A1 |
20140289001 | Shelton | Sep 2014 | A1 |
20140291481 | Zhang et al. | Oct 2014 | A1 |
20150011848 | Ruchti | Jan 2015 | A1 |
20150038811 | Asaka | Feb 2015 | A1 |
20150038812 | Ayaz et al. | Feb 2015 | A1 |
20150041625 | Dutton | Feb 2015 | A1 |
20150041627 | Webster | Feb 2015 | A1 |
20150054111 | Niclass et al. | Feb 2015 | A1 |
20150057511 | Basu | Feb 2015 | A1 |
20150077279 | Song | Mar 2015 | A1 |
20150094552 | Golda | Apr 2015 | A1 |
20150150505 | Kaskoun et al. | Jun 2015 | A1 |
20150157262 | Schuessler | Jun 2015 | A1 |
20150157435 | Chasins et al. | Jun 2015 | A1 |
20150182136 | Durduran et al. | Jul 2015 | A1 |
20150192677 | Yu et al. | Jul 2015 | A1 |
20150200222 | Webster | Jul 2015 | A1 |
20150201841 | Ishikawa et al. | Jul 2015 | A1 |
20150293224 | Eldada et al. | Oct 2015 | A1 |
20150327777 | Kostic et al. | Nov 2015 | A1 |
20150333095 | Fallica et al. | Nov 2015 | A1 |
20150355019 | Nouri et al. | Dec 2015 | A1 |
20150364635 | Bodlovic et al. | Dec 2015 | A1 |
20160049765 | Eldada | Feb 2016 | A1 |
20160099371 | Webster | Apr 2016 | A1 |
20160119983 | Moore | Apr 2016 | A1 |
20160150963 | Roukes et al. | Jun 2016 | A1 |
20160161600 | Eldada et al. | Jun 2016 | A1 |
20160181302 | McGarvey et al. | Jun 2016 | A1 |
20160182902 | Guo | Jun 2016 | A1 |
20160218236 | Dhulla et al. | Jul 2016 | A1 |
20160247301 | Fang | Aug 2016 | A1 |
20160278715 | Yu et al. | Sep 2016 | A1 |
20160287107 | Szabados | Oct 2016 | A1 |
20160296168 | Abreu | Oct 2016 | A1 |
20160341656 | Liu et al. | Nov 2016 | A1 |
20160345880 | Nakaji et al. | Dec 2016 | A1 |
20160356718 | Yoon et al. | Dec 2016 | A1 |
20160357260 | Raynor et al. | Dec 2016 | A1 |
20170030769 | Clemens et al. | Feb 2017 | A1 |
20170047372 | McGarvey et al. | Feb 2017 | A1 |
20170052065 | Sharma et al. | Feb 2017 | A1 |
20170085547 | De Aguiar et al. | Mar 2017 | A1 |
20170118423 | Zhou et al. | Apr 2017 | A1 |
20170124713 | Jurgenson et al. | May 2017 | A1 |
20170131143 | Andreou et al. | May 2017 | A1 |
20170139041 | Drader et al. | May 2017 | A1 |
20170141100 | Tseng et al. | May 2017 | A1 |
20170164857 | Soulet De Brugere | Jun 2017 | A1 |
20170172447 | Mitra et al. | Jun 2017 | A1 |
20170176579 | Niclass et al. | Jun 2017 | A1 |
20170176596 | Shpunt et al. | Jun 2017 | A1 |
20170179173 | Mandai et al. | Jun 2017 | A1 |
20170186798 | Yang et al. | Jun 2017 | A1 |
20170202518 | Furman et al. | Jul 2017 | A1 |
20170265822 | Du | Sep 2017 | A1 |
20170276545 | Henriksson | Sep 2017 | A1 |
20170281086 | Donaldson | Oct 2017 | A1 |
20170299700 | Pacala et al. | Oct 2017 | A1 |
20170303789 | Tichauer et al. | Oct 2017 | A1 |
20170314989 | Mazzillo et al. | Nov 2017 | A1 |
20170338969 | Paul et al. | Nov 2017 | A1 |
20170363467 | Clemens et al. | Dec 2017 | A1 |
20170367650 | Wallois | Dec 2017 | A1 |
20180003821 | Imai | Jan 2018 | A1 |
20180014741 | Chou | Jan 2018 | A1 |
20180019268 | Zhang et al. | Jan 2018 | A1 |
20180020960 | Sarussi | Jan 2018 | A1 |
20180026147 | Zhang et al. | Jan 2018 | A1 |
20180027196 | Yang et al. | Jan 2018 | A1 |
20180033895 | Mazzillo et al. | Feb 2018 | A1 |
20180039053 | Kremer et al. | Feb 2018 | A1 |
20180045816 | Jarosinski et al. | Feb 2018 | A1 |
20180062345 | Bills et al. | Mar 2018 | A1 |
20180066986 | Kasai et al. | Mar 2018 | A1 |
20180069043 | Pan et al. | Mar 2018 | A1 |
20180070830 | Sutin et al. | Mar 2018 | A1 |
20180070831 | Sutin et al. | Mar 2018 | A1 |
20180081061 | Mandai et al. | Mar 2018 | A1 |
20180089531 | Geva et al. | Mar 2018 | A1 |
20180089848 | Yang et al. | Mar 2018 | A1 |
20180090526 | Mandal et al. | Mar 2018 | A1 |
20180090536 | Mandai et al. | Mar 2018 | A1 |
20180102442 | Wang et al. | Apr 2018 | A1 |
20180103528 | Moore | Apr 2018 | A1 |
20180103861 | Sutin et al. | Apr 2018 | A1 |
20180117331 | Kuzniecky | May 2018 | A1 |
20180120152 | Leonardo | May 2018 | A1 |
20180122560 | Okuda | May 2018 | A1 |
20180156660 | Turgeon | Jun 2018 | A1 |
20180167606 | Cazaux et al. | Jun 2018 | A1 |
20180175230 | Droz et al. | Jun 2018 | A1 |
20180180473 | Clemens et al. | Jun 2018 | A1 |
20180185667 | Huang | Jul 2018 | A1 |
20180217261 | Wang | Aug 2018 | A1 |
20180296094 | Nakamura | Oct 2018 | A1 |
20180366342 | Inoue et al. | Dec 2018 | A1 |
20190006399 | Otake et al. | Jan 2019 | A1 |
20190025406 | Krelboim et al. | Jan 2019 | A1 |
20190026849 | Demeyer | Jan 2019 | A1 |
20190088697 | Furukawa et al. | Mar 2019 | A1 |
20190091483 | Deckert | Mar 2019 | A1 |
20190113385 | Fukuchi | Apr 2019 | A1 |
20190120975 | Ouvrier-Buffet | Apr 2019 | A1 |
20190167211 | Everman et al. | Jun 2019 | A1 |
20190175068 | Everdell | Jun 2019 | A1 |
20190192031 | Laszlo et al. | Jun 2019 | A1 |
20190200888 | Poltorak | Jul 2019 | A1 |
20190209012 | Yoshimoto et al. | Jul 2019 | A1 |
20190239753 | Wentz | Aug 2019 | A1 |
20190261869 | Franceschini | Aug 2019 | A1 |
20190298158 | Dhaliwal | Oct 2019 | A1 |
20190343395 | Cussac | Nov 2019 | A1 |
20190355773 | Field et al. | Nov 2019 | A1 |
20190355861 | Katnani | Nov 2019 | A1 |
20190363210 | Do Valle | Nov 2019 | A1 |
20190378869 | Field et al. | Dec 2019 | A1 |
20190388018 | Horstmeyer | Dec 2019 | A1 |
20190391213 | Alford | Dec 2019 | A1 |
20200022581 | Vanegas | Jan 2020 | A1 |
20200041727 | Yamamoto | Feb 2020 | A1 |
20200044098 | Azuma | Feb 2020 | A1 |
20200056263 | Bhattacharyya | Feb 2020 | A1 |
20200057115 | Jiménez-Martínez | Feb 2020 | A1 |
20200057116 | Zorzos et al. | Feb 2020 | A1 |
20200057146 | Steinkogler et al. | Feb 2020 | A1 |
20200060542 | Alford | Feb 2020 | A1 |
20200088811 | Mohseni | Mar 2020 | A1 |
20200109481 | Sobek | Apr 2020 | A1 |
20200123416 | Bhattacharyya | Apr 2020 | A1 |
20200136632 | Lin | Apr 2020 | A1 |
20200182692 | Lilic | Jun 2020 | A1 |
20200188030 | Kopper et al. | Jun 2020 | A1 |
20200191883 | Bhattacharyya | Jun 2020 | A1 |
20200196932 | Johnson et al. | Jun 2020 | A1 |
20200241094 | Alford | Jul 2020 | A1 |
20200253479 | Nurmikko | Aug 2020 | A1 |
20200256929 | Ledbetter et al. | Aug 2020 | A1 |
20200309873 | Ledbetter et al. | Oct 2020 | A1 |
20200315510 | Johnson | Oct 2020 | A1 |
20200334559 | Anderson | Oct 2020 | A1 |
20200337624 | Johnson | Oct 2020 | A1 |
20200341081 | Mohseni et al. | Oct 2020 | A1 |
20200348368 | Garber et al. | Nov 2020 | A1 |
20200381128 | Pratt | Dec 2020 | A1 |
20200390358 | Johnson | Dec 2020 | A1 |
20200393902 | Mann et al. | Dec 2020 | A1 |
20200400763 | Pratt | Dec 2020 | A1 |
20210015385 | Katnani | Jan 2021 | A1 |
20210011094 | Bednarke | Feb 2021 | A1 |
20210041512 | Pratt | Feb 2021 | A1 |
20210063510 | Ledbetter | Mar 2021 | A1 |
20210013974 | Seidman | May 2021 | A1 |
20210139742 | Seidman | May 2021 | A1 |
20210223098 | Ledvina et al. | Jul 2021 | A1 |
20210265512 | Ayel | Aug 2021 | A1 |
20210290064 | Do Valle | Sep 2021 | A1 |
20210294996 | Field | Sep 2021 | A1 |
Number | Date | Country |
---|---|---|
200950235 | Sep 2007 | CN |
107865635 | Apr 2018 | CN |
0656536 | Apr 2004 | EP |
2294973 | Mar 2011 | EP |
3419168 | Dec 2018 | EP |
3487072 | May 2019 | EP |
3011932 | Apr 2015 | FR |
2012125370 | Jul 2012 | JP |
20170087639 | Jul 2017 | KR |
8804034 | Jun 1988 | WO |
1999053577 | Oct 1999 | WO |
2008144831 | Dec 2008 | WO |
2011083563 | Jul 2011 | WO |
2012135068 | Oct 2012 | WO |
2013034770 | Mar 2013 | WO |
2013066959 | May 2013 | WO |
2015052523 | Apr 2015 | WO |
2015109005 | Jul 2015 | WO |
2016166002 | Oct 2016 | WO |
2017004663 | Jan 2017 | WO |
2017083826 | May 2017 | WO |
2017130682 | Aug 2017 | WO |
2017150146 | Sep 2017 | WO |
2017203936 | Nov 2017 | WO |
2018007829 | Jan 2018 | WO |
2018033751 | Feb 2018 | WO |
2018122560 | Jul 2018 | WO |
2019221784 | Nov 2019 | WO |
Entry |
---|
Alayed, et al.,“Characterization of a Time-Resolved Diffuse Optical Spectroscopy Prototype Using Low-Cost, Compact Single Photon Avalanche Detectors for Tissue Optics Applications,” Sensors 2018, 18, 3680; doi:10.3390/s18113680. |
Bellis, Stephen et al.,“Photon counting imaging: the DigitalAPD,” Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Feb. 2006, vol. 6068, pp. 111-120. |
Blutman, et al.,“A 0.1 pJ Freeze Vernier Time-to-Digital Converter in 65nm CMOS,” 2014 International Symposium on Circuits and Systems (ISCAS), Melbourne, Australia. |
Cambie, Dario et al.,“Every photon counts: understanding and optimizing photon paths in luminescent solar concentrator-based photomicroreactors (LSC-PMs),” React. Chem. Eng., 2017, 2, 561-566. |
Contini, et al.,“Photon migration through a turbid slab described by a model based on diffusion approximation. I. Theory,” Appl. Opt. 36(19), 4587 (1997). |
Dalla Mora, et al.,“Fast-Gated Single-Photon Avalanche Diode for Wide Dynamic Range Near Infrared Spectroscopy,” IEEE Journal of Selected Topics in Quantum Electronics, vol. 16, No. 4, Jul./Aug. 2010 ,2010 , 1023-1030. |
Dalla Mora, et al.,“Memory effect in silicon time-gated single-photon avalanche diodes,” http://dx.doi.org/10.1063/1.4915332, Journal of Applied Physics 117, 114501, 2015 ,2015 ,1-7. |
De Heyn, et al.,“A fast start-up 3GHz-10GHz digitally controlled oscillator for UWB impulse radio in 90nm CMOS,” 2007 European Solid-State Circuits Conference—(ESSCIRC), Munich, Germany, pp. 484-487. |
Di Sieno, et al.,“Probe-hosted large area silicon photomultiplier and high-throughput timing electronics for enhanced performance time-domain functional near-infrared spectroscopy,” Biomed. Opt. Express 11(11), 6389 (2020). |
Dutton, et al.,“A Time-Correlated Single-Photon-Counting Sensor with 14GS/s Histogramming Time-to-Digital Converter,” 2015 IEEE International Solid-State Circuits Conference ISSCC 2015 / Session 11 / Sensors and Imagers for Life Sciences / 11.5. |
Fishburn, et al.,“Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS,” Neuroimage. Jan. 1, 2019; 184: 171-179. doi:10.1016/j.neuroimage.2018.09.025. |
Fisher, et al.,“A Reconfigurable Single-Photon-Counting Integrating Receiver for Optical Communications,” IEEE Journal of Solid-State Circuits, vol. 48, No. 7, Jul. 2013, https://www.researchgate.net/publication/260626902. |
Gallivanoni, et al.,“Progress in Quenching Circuits for Single Photon Avalanche Diodes,” IEEE Transactions on Nuclear Science, vol. 57, No. 6, Dec. 2010. |
Gnecchi, et al.,“A 1×16 SIPM Array for Automotive 3D Imaging LiDAR Systems.” |
Harmon, Eric S. et al.,“Compound Semiconductor SPAD Arrays, LightSpin Technologies,” http://www.lightspintech.com/publications.html. |
Henderson, et al.,“A 192 x 128 Time Correlated SPAD Image Sensor in 40-nm CMOS Technology,” IEEE Journal of Solid-State Circuits, IEEE Journal of Solid-State Circuits, 2019. |
Henderson, et al.,“A 256×256 40nm/90nm CMOS 3D-Stacked 120dB Dynamic-Range Reconfigurable Time-Resolved SPAD Imager,” 2019 IEEE International Solid-State Circuits Conference—(ISSCC), San Francisco, CA, USA, 2019, pp. 106-108. doi: 10.1109/ISSCC.2019.8662355. |
Huppert, et al.,“HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain,” Appl. Opt. 48(10), D280 (2009). |
Kienle, et al.,“Improved solutions of the steady-state and the time-resolved diffusion equations for reflectance from a semi-infinite turbid medium,” J. Opt. Soc. Am. A 14(1), 246 (1997). |
Konugolu, et al.,“Broadband (600-1350 nm) Time-Resolved Diffuse Optical Spectrometer for Clinical Use,” IEEE Journal of Selected Topics in Quantum Electronics, vol. 22, No. 3, May/Jun. 2016. |
Lacerenza, et al.,“Wearable and wireless time-domain near-infrared spectroscopy system for brain and muscle hemodynamic monitoring,” Biomed. Opt. Express 11(10), 5934 (2020). |
Lange, et al.,“Clinical Brain Monitoring with Time Domain NIRS: A Review and Future Perspectives,” Applied Sciences 9(8), 1612 (2019). |
Lange, et al.,“MAESTROS: A Multiwavelength Time-Domain NIRS System to Monitor Changes in Oxygenation and Oxidation State of Cytochrome-C-Oxidase,” IEEE J. Select. Topics Quantum Electron. 25(1), 1-12 (2019). |
Lee, et al.,“High-Performance Back-Illuminated Three-Dimensional Stacked Single-Photon Avalanche Diode Implemented in 45-nm CMOS Technology,” IEEE Journal of Selected Topics in Quantum Electronics 6, 1-9 (2018). |
Mandai, et al.,“A 4 X 4 X 416 digital SIPM array with 192 TDCs for multiple high-resolution timestamp acquisition,” 2013 JINST 8 PO5024. |
Martelli, et al.,“Optimal estimation reconstruction of the optical properties of a two-layered tissue phantom from time-resolved single-distance measurements,” Journal of Biomedical Optics 20(11), 115001 (Nov. 2015). |
Maruyama, et al.,“A 1024 x 8, 700-ps Time-Gated SPAD Line Sensor for Planetary Surface Exploration With Laser Raman Spectroscopy and LIBS,” IEEE Journal of Solid-State Circuits, vol. 49, No. 1, Jan. 2014 ,2014 , 179-189. |
Mita, et al.,“High-Speed and Compact Quenching Circuit for Single-Photon Avalanche Diodes,” IEEE Transactions on Instrumentation and Measurement, vol. 57, No. 3, Mar. 2008. pp. 543-547. |
Mora, et al.,“Fast silicon photomultiplier improves signal harvesting and reduces complexity in time-domain diffuse optics,” Opt. Express 23(11), 13937 (2015). |
Mora, Alberto D. et al.,“Fast-Gated Single-Photon Avalanche Diode for Wide Dynamic Range Near Infrared Spectroscopy,” IEEE Journal of Selected Topics in Quantum Electronics, vol. 16, No. 4, pp. 1023-1030, Jul./Aug. 2010. |
Parmesan, et al.,“A 256 x 256 SPAD array with in-pixel Time to Amplitude Conversion for Fluorescence Lifetime Imaging Microscopy,” 2015. |
Pifferi, et al.,“Performance assessment of photon migration instruments: the MEDPHOT protocol,” Applied Optics, 44(11), 2104-2114. |
Prahl, et al.,“Optical Absorption of Hemoglobin,” http://omic.ogi.edu/spectra/hemoglobin/index.html. |
Puszka, et al.,“Time-resolved diffuse optical tomography using fast-gated single-photon avalanche diodes,” Biomedical optics express, 2013, vol. 4, No. 8, pp. 1351-1365 (Year: 2013). |
Re, et al.,“Multi-channel medical device for time domain functional near infrared spectroscopy based on wavelength space multiplexing,” Biomed. Opt. Express 4(10), 2231 (2013). |
Renna, et al.,“Eight-Wavelength, Dual Detection Channel Instrument for Near-Infrared Time-Resolved Diffuse Optical Spectroscopy,” IEEE J. Select. Topics Quantum Electron. 25(1), 1-11 (2019). |
Richardson, et al.,“A 32x32 50ps resolution 10 bit time to digital converter array in 130nm CMOS for time correlated imaging,” CICC 2009 Proceedings of the IEEE 2009 Custom Integrated Circuits Conference. IEEE Society, San Jose, U.S.A., pp. 77-80, CICC 2009, San Jose, U.S.A., Sep. 13, 2009. https://doi.org/doi:10.1109/CICC.2009.5280890. |
Takai, et al.,“Single-Photon Avalanche Diode with Enhanced NIR-Sensitivity for Automotive LIDAR Systems,” Sensors, 2016, 16(4): 459, pp. 1-9 (Year: 2016). |
Torricelli, et al.,“Time domain functional NIRS imaging for human brain mapping,” NeuroImage 85, 28-50 (2014). |
Wabnitz, et al.,“Depth-selective data analysis for time-domain fNIRS: moments vs. time windows,” Biomed. Opt. Express 11(8), 4224 (2020). |
Wabnitz, et al.,“Performance assessment of time-domain optical brain imagers, part 1: basic instrumental performance protocol,” Journal of Biomedical Optics 19(8), 086010 (Aug. 2014). |
Wabnitz, et al.,“Performance assessment of time-domain optical brain imagers, part 2: nEUROPt protocol,” Journal of Biomedical Optics 19(8), 086012 (Aug. 2014). |
Wojtkiewicz, et al.,“Self-calibrating time-resolved near infrared spectroscopy,” Biomed. Opt. Express 10(5), 2657 (2019). |
Zhang, et al.,“A CMOS SPAD Imager with Collision Detection and 128 Dynamically Reallocating TDCs for Single-Photon Counting and 3D Time-of-Flight Imaging,” Sensors (Basel, Switzerland), 18(11), 4016. doi:10.3390/s18114016. |
Zucchelli, et al.,“Method for the discrimination of superficial and deep absorption variations by time domain fNIRS,” 2013 OSA Dec. 1, 2013 | vol. 4, No. 12 | DOI:10.1364/BOE.4.002893 | Biomedical Optics Express 2893. |
Alayed, et al.,“Characterization of a Time-Resolved Diffuse Optical Spectroscopy Prototype Using Low-Cost, Compact Single Photon Avalanche Detectors for Tissue Optics Applications,” Sensors 2018, 18, 3680; doi:10.3390/s18113680, Oct. 29, 2018. |
Bellis, et al.,“Photon counting imaging: the DigitalAPD,” Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Feb. 2006, vol. 6068, pp. 111-120. |
Blutman, et al.,“A 0.1 pJ Freeze Vernier Time-to-Digital Converter in 65nm CMOS,” 2014 International Symposium on Circuits and Systems (ISCAS), Melbourne, Australia, Jun. 1-5, 2014. |
Cambie, et al.,“Every photon counts: understanding and optimizing photon paths in luminescent solar concentrator-based photomicroreactors (LSC-PMs),” React. Chem. Eng., 2017, 2, 561-566. |
Dalla Mora, et al.,“Fast-Gated Single-Photon Avalanche Diode for Wide Dynamic Range Near Infrared Spectroscopy,” IEEE Journal of Selected Topics in Quantum Electronics, vol. 16, No. 4, Jul./Aug. 2010. |
Dalla Mora, et al.,“Memory effect in silicon time-gated single-photon avalanche diodes,” http://dx.doi.org/10.1063/1.4915332, Journal of Applied Physics 117, 114501, 2015. |
De Heyn, et al.,“A fast start-up 3GHz-10GHz digitally controlled oscillator for UWB impulse radio in 90nm CMOS,” 2007 European Solid-State Circuits Conference—(ESSCIRC), Munich, Germany, pp. 484-487, Sep. 11-13, 2007. |
Dutton, et al.,“A Time-Correlated Single-Photon-Counting Sensor with 14GS/s Histogramming Time-to-Digital Converter,” 2015 IEEE International Solid-State Circuits Conference ISSCC 2015 / Session 11 / Sensors and Imagers for Life Sciences / 11.5, Feb. 22-26, 2015. |
Gnecchi, et al.,“A 1×16 SIPM Array for Automotive 3D Imaging LiDAR Systems.”, Proceedings of the 2017 International Image Sensor Workshop (IISW), Hiroshima, Japan (2017). |
Harmon, et al.,“Compound Semiconductor SPAD Arrays,” LightSpin Technologies, http://www.lightspintech.com/publications.html (2013). |
Henderson, et al.,“A 192 x 128 Time Correlated SPAD Image Sensor in 40-nm CMOS Technology,” IEEE Journal of Solid-State Circuits, 2019. |
Lange, et al.,“MAESTROS: A Multiwavelength Time-Domain NIRS Systern to Monitor Changes in Oxygenation and Oxidation State of Cytochrome-C-Oxidase,” IEEE J. Select. Topics Quantum Electron. 25(1), 1-12 (2019). |
Mandai, et al.,“A 4 X 4 X 416 digital SIPM array with 192 TDCs for multiple high-resolution timestamp acquisition,” 2013 JINST 8 PO5024, May 31, 2013. |
Maruyama, et al.,“A 1024 x 8, 700-ps Time-Gated SPAD Line Sensor for Planetary Surface Exploration With Laser Raman Spectroscopy and LIBS,” IEEE Journal of Solid-State Circuits, vol. 49, No. 1, Jan. 2014. |
Mora, et al.,“Fast-Gated Single-Photon Avalanche Diode for Wide Dynamic Range Near Infrared Spectroscopy,” IEEE Joumal of Selected Topics in Quantum Electronics, vol. 16, No. 4, pp. 1023-1030, Jul./Aug. 2010. |
Parmesan, et al.,“A 256 x 256 SPAD array with in-pixel Time to Amplitude Conversion for Fluorescence Lifetime Imaging Microscopy,”, Memory 900.M4, 2015. |
Pifferi, et al.,“Performance assessment of photon migration instruments: the MEDPHOT protocol,” Applied Optics, 44(11), 2104-2114 (2005). |
Prahl, et al.,“Optical Absorption of Hemoglobin,” http:/omic.ogi.edu/spectra/hemoglobin/index.html (1999). |
Zhang, et al.,“A CMOS SPAD Imager with Collision Detection and 128 Dynamically Reallocating TDCs for Single-Photon Counting and 3D Time-of-Flight Imaging,” Sensors (Basel, Switzerland), 18(11), 4016. doi:10.3390/s18114016 Nov. 17, 2018. |
International Search Report and Written Opinion received in International Application No. PCT/2020/027537, dated Sep. 7, 2020. |
International Search Report and Written Opinion received in International Application No. PCT/2020/028820, dated Aug. 26, 2020. |
International Search Report and Written Opinion received in International Application No. PCT/US20/34062, dated Aug. 26, 2020. |
International Search Report and Written Opinion received in International Application No. PCT/US2018/058580, dated Feb. 12, 2019. |
International Search Report and Written Opinion received in International Application No. PCT/US2018/062777, dated Feb. 13, 2019. |
International Search Report and Written Opinion received in International Application No. PCT/US2019/019317, dated May 28, 2019. |
Non-Final Office Action received in U.S. Appl. No. 16/177,351, dated Apr. 1, 2019. |
Non-Final Office Action received in U.S. Appl. No. 16/283,730, dated May 16, 2019. |
Non-Final Office Action received in U.S. Appl. No. 16/370,991, dated Feb. 10, 2020. |
Non-Final Office Action received in U.S. Appl. No. 16/537,360, dated Feb. 25, 2020. |
Non-Final Office Action received in U.S. Appl. No. 16/544,850, dated Jun. 25, 2020. |
Non-Final Office Action received in U.S. Appl. No. 16/856,524, dated Dec. 1, 2020. |
Partial Search Report received in International Application No. PCT/2020/028820, dated Jul. 1, 2020. |
Partial Search Report received in International Application No. PCT/US2020/027537, dated Jul. 17, 2020. |
Chen, et al., “A PVT Insensitive Field Programmable Gate Array Time-to-digital Converter”, 2013 IEEE Nordic-Mediterranean Workshop on Time-To-Digital Converters. Oct. 3, 2013. |
Field, et al., “A 100-fps, Time-Correlated Single-PhotonCounting-Based Fluorescence-Lifetime Imager in 130-nm CMOS”, IEEE Journal of Solid-State Circuits, vol. 49, No. 4, Apr. 2014. |
Lebid, et al., “Multi-Timescale Measurements of Brain Responses in Visual Cortex During Functional Stimulation Using Time-Resolved Spectroscopy”, SPIE vol. 5826. Dec. 31, 2005. p. 609, last paragraph—p. 610, paragraph 1. |
Zheng, et al., “An Integrated Bias Voltage Control Method for SPAD Arrays”, Oct. 1, 2018, IEEE Service Center. |
“emojipedia.org”, https://emojipedia.org (accessed May 27, 2021). |
“International Search Report and Written Opinion received in International Application No. PCT/2021/018188”. |
“International Search Report and Written Opinion received in International Application No. PCT/US2021/018155”. |
“International Search Report and Written Opinion received in International Application No. PCT/US2021/018187”. |
“International Search Report and Written Opinion received in International Application No. PCT/US2021/018190”. |
“scienceofpeople.com/emojis”, https://www.scienceofpeople.com/emojis/ (accessed May 27, 2021). |
Hebert, et al.,“Spatiotemporal image correlation spectroscopy (STICS) theory, verification, and application to protein velocity mapping in living CHO cells”, Biophysical journal 88, No. 5 (2005): 3601-3614. |
Kheng, et al.,“Image Processing”, https://www.comp.nus.edu.sg/˜cs4243/lecture/imageproc.pdf, Mar. 9, 2014. |
Sneha, et al.,“Understanding Correlation”, https://www.allaboutcircuits.com/technical-articles/understanding-correlation/, Jan. 4, 2017. |
Xu, et al.,“A 655 μW Silicon Photomultiplier-Based NIRS/EEG/EIT Monitoring ASIC for Wearable Functional Brain Imaging”, IEEE Transactions on Biomedical Circuits and Systems, IEEE, US, vol. 12, No. 6, Dec. 1, 2018. |
Zucconi, et al., “The Autocorrelation Function”, https://www.alanzucconi.com/2016/06/06/autocorrelation-function/, Jun. 6, 2016. |
Number | Date | Country | |
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20210259554 A1 | Aug 2021 | US |
Number | Date | Country | |
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63071473 | Aug 2020 | US | |
62992543 | Mar 2020 | US | |
62979866 | Feb 2020 | US |