The present disclosure relates generally to medical devices and, more particularly, to the use of light focusing continuous wave emission in photo-acoustic spectroscopy to analyze vascular network.
This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
In the field of medicine, doctors often desire to monitor certain physiological characteristics of their patients. Accordingly, a wide variety of devices have been developed for monitoring many such characteristics of a patient. Such devices provide doctors and other healthcare personnel with the information they need to provide the best possible healthcare for their patients. As a result, such monitoring devices have become an indispensable part of modern medicine.
Certain monitoring devices, for example, spectroscopy devices, are capable of measuring different physiological parameters, including oxygen saturation, hemoglobin, blood perfusion, and so forth. Spectroscopy devices typically irradiate a patient's tissue with a light. The irradiated region usually encompasses a wide array of blood vessels such as arterioles and capillaries. Absorbance data at known wavelengths of the irradiated light may then be analyzed to provide medical information representative of the physiological region of interest. However, spectroscopic devices may not able to evaluate precise regions of interest, such as individual blood vessels. Accordingly, it would be beneficial to develop systems and methods for monitoring very precise regions of interest, including individual blood vessels and other discrete vascular components.
Advantages of the disclosure may become apparent upon reading the following detailed description and upon reference to the drawings in which:
One or more specific embodiments of the present disclosure will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation are described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.
In certain medical contexts it may be desirable to ascertain various localized physiological parameters, such as parameters related to individual blood vessels or other discrete components of the vascular system. Examples of such parameters may include oxygen saturation, hemoglobin concentration, perfusion, and so forth for an individual blood vessel. One approach to measuring such localized parameters is referred to as photoacoustic (PA) spectroscopy.
PA spectroscopy involves a light source suitable for emitting light into a patient tissue such that the emitted light is absorbed by certain constituents of the tissue and/or the vascular system (e.g., blood). The absorbed light energy generates a proportionate increase in kinetic energy of the constituents in the tissue measurement site which in turn results in pressure fluctuations. The pressure fluctuations may be detected in the form of acoustic radiation (e.g., ultrasound) and the acoustic radiation may be used to determine the amount of light absorption, and thus the quantity of the constituents of interest, in the illuminated region. For example, the detected ultrasound energy may be proportional to the optical absorption coefficient of the blood or tissue constituent and the fluence of light at the wavelength of interest at the localized region being measured (e.g., a specific blood vessel). Thus, by emitting a light beam at a wavelength absorbed by constituents in the tissue and/or blood, PA spectroscopy may be used to estimate microcirculatory blood volume, as well as other parameters such as hemoglobin concentration and oxygen saturation (i.e., percentage of oxygen in the blood), at particular measurement sites. Further, it may be possible to create 2-dimensional (2D) as well as 3-dimensional (3D) images of tissue sites, as described in more detail below.
In certain embodiments, increased depth resolution measurements of the constituent may be achieved with the use of a frequency-domain (e.g., Fourier transform) PA spectroscopy system. In frequency-domain (FD) PA spectroscopy, an intensity modulated continuous wave light source may be used that is capable of employing linear frequency modulation (e.g., chirp modulation, sweep modulation) techniques. In linear frequency modulation (LFM), an optical waveform is created with a frequency that increases or decreases with time. Chirp modulation, sometimes referred to as sweep modulation, allows for the use of, for example, a sinusoidal LFM waveform. Techniques such as Fourier transforms may be used to efficiently process the sinusoidal LFM waveforms. Accordingly, the LFM waveform may be employed to irradiate patient tissue, and the ultrasound signals resulting from the irradiation may then be analyzed. A relationship between the time delay of acoustic response and the depth of constituents can be recovered using correlation processing and/or heterodyne signal processing.
One problem that may arise in PA spectroscopy may be attributed to the tendency of the emitted light to diffuse or scatter in the tissue of the patient. As a result, light emitted toward an internal structure or region, such as a blood vessel, may be diffused prior to reaching the region so that amount of light reaching the region is less than desired. Therefore, due to the diffusion of the light, less light may be available to be absorbed by the constituent of interest in the target region, thus reducing the ultrasonic waves generated at the target region of interest, such as a blood vessel. Therefore, the light-to-ultrasound conversion efficiency may be reduced due to the light diffusing properties of the intervening tissue between the surface of the skin and the internal structure or region of interest. In certain embodiments of the present disclosure, the emitted light may be focused on an internal region of interest by spatially modulating the illuminating light to reduce or eliminate the effects of light diffusion. Accordingly, a spatially modulated FD PA spectroscopy system may be capable of more precise measurements of a variety of vessel-specific physiological parameters, which may be desired for many applications.
With this in mind,
In one embodiment, the sensor 10 may include a light source 18 and an acoustic detector 20, such as an ultrasound transducer. The present discussion generally describes the use of continuous wave (CW) light sources to facilitate explanation. However, it should be appreciated that the photoacoustic sensor 10 may also be adapted for use with other types of light sources, such as pulsed light sources, in other embodiments. In certain embodiments, the light source 18 may be associated with one or more optical fibers for conveying light from one or more light generating components to the tissue site.
The photoacoustic spectroscopy sensor 8 may include a light source 18 and an acoustic detector 20 that may be of any suitable type. For example, in one embodiment the light source 18 may be one, two, or more light emitting components (such as light emitting diodes) adapted to transmit light at one or more specified wavelengths. In certain embodiments, the light source 18 may include a laser diode or a vertical cavity surface emitting laser (VCSEL). The laser diode may be a tunable laser, such that a single diode may be tuned to various wavelengths corresponding to a number of different absorbers of interest in the tissue and blood. That is, the light may be any suitable wavelength or wavelengths (such as a wavelength between about 500 nm to about 1100 nm or between about 600 nm to about 900 nm) that is absorbed by a constituent of interest in the blood or tissue. For example, wavelengths between about 500 nm to about 600 nm, corresponding with green visible light, may be absorbed by deoxyhemoglobin and oxyhemoglobin. In other embodiments, red wavelengths (e.g., about 600 nm to about 700 nm) and infrared or near infrared wavelengths (e.g., about 800 nm to about 1100 nm) may be used. In one embodiment, the selected wavelengths of light may penetrate between 1 mm to 3 cm into the tissue of the patient 24.
An acousto-optic modulator (AOM) 25 may modulate the intensity of the emitted light, for example, by using LFM techniques. The emitted light may be intensity modulated by the AOM 25 or by changes in the driving current of the LED emitting the light. The intensity modulation may result in any suitable frequency, such as from 1 MHz to 10 MHz or more. Accordingly, in one embodiment, the light source 18 may emit LFM chirps at a frequency sweep range approximately from 1 MHz to 5 MHz. In another embodiment, the frequency sweep range may be of approximately 0.5 MHz to 10 MHz. The frequency of the emitted light may be increasing with time during the duration of the chirp. In certain embodiments, the chirp may last approximately 1 second or less and have an associated energy of a 10 mJ or less, such as between 1 μJ to 2 mJ, 1-5 mJ, 1-10 mj. In such an embodiment, the limited duration of the light may prevent heating of the tissue while still emitting light of sufficient energy into the region of interest to generate the desired acoustic shock waves when absorbed by the constituent of interest.
The light emitted by the light source 18 may be spatially modulated, such as via a modulator 22. For example, in one embodiment, the modulator 22 may be a spatial light modulator, such as a Holoeye® LC-R 2500 liquid crystal spatial light modulator. In one such embodiment, the spatial light modulator may have a resolution of 1024×768 pixels or any other suitable pixel resolution. During operation, the pixels of the modulator 22 may be divided into subgroups (such as square or rectangular subarrays or groupings of pixels) and the pixels within a subgroup may generally operate together. For example, the pixels of a modulator 22 may be generally divided into square arrays of 10×10, 20×20, 40×40, or 50×50 pixels. In one embodiment, each subgroup of pixels of the modulator 22 may be operated independently of the other subgroups. The pixels within a subgroup may be operated jointly (i.e., are on or off at the same time) though the subgroups themselves may be operated independently of one another. In this manner, each subgroup of pixels of the modulator 22 may be operated so as to introduce phase differences at different spatial locations within the emitted light. That is, the modulated light that has passed through one subgroup of pixels may be at one phase and that phase may be the same or different than the modulated light that has passed through other subgroups of pixels, i.e., some segments or portions of the modulated light wavefront may be ahead of or behind other portions of the wavefront. In one embodiment, the modulator 22 may be associated with additional optical components (e.g., lenses, reflectors, refraction gradients, polarizers, and so forth) through which the spatially modulated light passes before reaching the tissue of the patient 24.
In one example, the acoustic detector 20 may be one or more ultrasound transducers suitable for detecting ultrasound waves emanating from the tissue in response to the emitted light and for generating a respective optical or electrical signal in response to the ultrasound waves. For example, the acoustic detector 20 may be suitable for measuring the frequency and/or amplitude of the ultrasonic waves, the shape of the ultrasonic waves, and/or the time delay associated with the ultrasonic waves with respect to the light emission that generated the respective waves. In one embodiment an acoustic detector 20 may be an ultrasound transducer employing piezoelectric or capacitive elements to generate an electrical signal in response to acoustic energy emanating from the tissue of the patient 24, i.e., the transducer converts the acoustic energy into an electrical signal.
In one implementation, the acoustic detector 20 may be a low finesse Fabry-Perot interferometer mounted on an optical fiber. In such an embodiment, the incident acoustic waves emanating from the probed tissue modulate the thickness of a thin polymer film. This produces a corresponding intensity modulation of light reflected from the film. Accordingly, the acoustic waves are converted to optical information, which is transmitted through the optical fiber to an upstream optical detector, which may be any suitable detector. In some embodiments, a change in phase of the detected light may be detected via an appropriate interferometry device which generates an electrical signal that may be processed by the monitor 12. The use of a thin film as the acoustic detecting surface allows high sensitivity to be achieved, even for films of micrometer or tens of micrometers in thickness. In one embodiment, the thin film may be a 0.25 mm diameter disk of 50 micrometer thickness polyethylene terepthalate with an at least partially optically reflective (e.g., 40% reflective) aluminum coating on one side and a mirror reflective coating on the other (e.g., 100% reflective) that form the mirrors of the interferometer. The optical fiber may be any suitable fiber, such as a 50 micrometer core silica multimode fiber of numerical aperture 0.1 and an outer diameter of 0.25 mm.
The photoacoustic sensor 10 may include a memory or other data encoding component, depicted in
In one implementation, signals from the acoustic detector 20 (and decoded data from the encoder 26, if present) may be transmitted to the monitor 12. The monitor 12 may include data processing circuitry (such as one or more processors 30, application specific integrated circuits (ASICS), or so forth) coupled to an internal bus 32. Also connected to the bus 32 may be a RAM memory 34, a speaker 16 and/or a display 14. In one embodiment, a time processing unit (TPU) 40 may provide timing control signals to light drive circuitry 42, which controls operation of the light source 18, such as to control when, for how long, and/or how frequently the light source 18 is activated, and if multiple light sources are used, the multiplexed timing for the different light sources.
The TPU 40 may also control or contribute to operation of the acoustic detector 20 such that timing information for data acquired using the acoustic detector 20 may be obtained. Such timing information may be used in interpreting the shock wave data and/or in generating physiological information of interest from such acoustic data. For example, the timing of the acoustic data acquired using the acoustic detector 20 may be associated with the light emission profile of the light source 18 during data acquisition. Likewise, in one embodiment, data acquisition by the acoustic detector 20 may be gated, such as via a switching circuit 44, to account for differing aspects of light emission. For example, operation of the switching circuit 44 may allow for separate or discrete acquisition of data that corresponds to different respective wavelengths of light emitted at different times.
The received signal from the acoustic detector 20 may be amplified (such as via amplifier 46), may be filtered (such as via filter 48), and/or may be digitized if initially analog (such as via an analog-to-digital converter 50). The digital data may be provided directly to the processor 30, may be stored in the RAM 34, and/or may be stored in a queued serial module (QSM) 52 prior to being downloaded to RAM 34 as QSM 52 fills up. In one embodiment, there may be separate, parallel paths for separate amplifiers, filters, and/or A/D converters provided for different respective light wavelengths or spectra used to generate the acoustic data.
The data processing circuitry (such as processor 30) may derive one or more physiological characteristics based on data generated by the photoacoustic sensor 12. For example, based at least in part upon data received from the acoustic detector 20, the processor 30 may calculate the amount or concentration of a constituent of interest in a localized region of tissue or blood using various algorithms. In certain embodiments, these algorithms may use coefficients, which may be empirically determined, that relate the detected acoustic shock waves generated in response to emitted light waves at a particular wavelength or wavelengths to a given concentration or quantity of a constituent of interest within a localized region. Further, 2D and 3D images may be created by analyzing the ultrasound signals. Such analysis may incorporate techniques that can extract the image based on, for example, the observation that the magnitude of the ultrasonic signal is proportional to the energy deposited by the emitted light, and the further observation that different types of constituents absorb light at different wavelengths. In addition, in one embodiment the data processing circuitry (such as processor 30) may communicate with the TPU 40 and/or the light drive 42 to spatially modulate the wave front of light emitted by the light source 18 based on one or more algorithms, as discussed herein.
In one embodiment, processor 30 may access and execute coded instructions, such as for implementing the algorithms discussed herein, from one or more storage components of the monitor 12, such as the RAM 34, a ROM 60, and/or the mass storage 62. Additionally, the RAM 34, ROM 60, and/or the mass storage 62 may serve as data repositories for information such as templates for LFM reference chirps, coefficient curves, and so forth. For example, code encoding executable algorithms may be stored in the ROM 60 or mass storage device 62 (such as a magnetic or solid state hard drive or memory or an optical disk or memory) and accessed and operated according to processor 30 instructions using stored data. Such algorithms, when executed and provided with data from the sensor 10, may calculate a physiological characteristic as discussed herein (such as the type, concentration, and/or amount of a constituent of interest). Once calculated, the physiological characteristic may be displayed on the display 14 for a caregiver to monitor or review.
With the foregoing system discussion in mind, light emitted by the light source 18 of the photoacoustic sensor 10 may be used to generate acoustic signals in proportion the amount of an absorber (e.g., a constituent of interest) in a targeted localized region. However, as noted above, the emitted light may be scattered upon entering the tissue, with the amount of scatter or dispersion increasing as the light penetrates deeper into the tissue. Thus, for localized regions or structures of interest, such as blood vessels, the greater the depth of such vessels beneath the tissue surface, the greater the dispersion of the emitted light before reaching the localized region or structure. For example, referring to
Turning to
The CW light 70 may be intensity modulated by the AOM 22, for example, by using LFM techniques. The CW light 70 may then be focused on one or more concurrent focal points by spatially modulating the CW light 70 to yield an inverse wave diffusion effect upon entering the scattering medium, i.e., the patient tissue. In effect, multi-path interference may be employed so that the scattering process itself focuses the emitted light onto the desired focal point or points. In particular, to the extent that at any given time the disorder in a medium is fixed or determinable, light scattering in the medium is deterministic and this knowledge may be utilized to modulate the emitted light such that the resulting scatter in the medium results in the light being concentrated or focused on a desired region of interest.
The CW light 70 may be further spatially modulated using a liquid crystal phase modulator or other suitable modulator 22. For example, to the extent that a continuous light wave may have a planar wavefront, a spatially modulated light wave, as discussed herein, may have a wavefront that is not planar and instead may be shaped by breaking the wavefront up into numerous sub-planes (e.g., square or rectangular segments) that are not all at the same phase, such that different portions of the wavefront reach the tissue surface at different times. The operation of the modulator 22 may be updated or iterated based upon feedback from the acoustic detector 20. For example, in one embodiment the signals generated by the acoustic detector 20 may be processed by a processor 30 which may in turn evaluate the processed signal in accordance with one or more algorithms or thresholds (such as a signal-to-noise threshold) and adjust operation of the modulator 22 accordingly. In one embodiment adaptive learning algorithms or other suitable analysis algorithms (e.g., neural networks, genetic algorithms, and so forth) may be employed to evaluate the processed signal and to make adjustments to the modulation.
In one example, an algorithm may be stored in the memory 34 and executed by the processor 30 to generate the inverse diffusion wavefront. One such algorithm may utilize the linearity of the scattering process in the tissue to generate the diffusion wavefront. For example, in one embodiment, the inverse diffusion wavefront may be generated in accordance with the equation:
where Em is the linear combination of the fields coming from N different wavefront segments generated by the modulator 22, An is the amplitude of the light reflected from segment n, φn is the phase of the light reflected from segment n, and tmn is the scattering in the sample and propagation through the optical system. In accordance with such an equation, the magnitude of Em may be maximized when all terms are in phase. The optimal phase for a segment, n, of the light wavefront at a given time may be determined by cycling its phase from 0 to 2π while the phase of other segments is held constant. This process may then be repeated for each segment. The optimal phase for each segment for which the target intensity is highest may then be stored. Once the optimized phase is known for each segment of the wavefront, the modulator 22 may be programmed based on the stored values such that differential activation of the pixels or subgroups of pixels defined for the modulator 22 (such as for a liquid crystal phase modulator) spatially modulates the light incident upon the modulator 22. That is, differential adjustment of the opacity of elements defined by the modulator 22 (such as square or rectangular groupings of pixels of a liquid crystal element) may yield a light with a wavefront in which different segments or portions of the wavefront are out of phase, i.e., staggered with respect to one another. When the resulting spatially modulated light is transmitted through the tissue, the contributions attributable to each modulated portion of the wavefront of the light may constructively interfere with one another to yield the desired light intensity at the localized region of interest, as depicted in
While the preceding describes one implementation for generating a spatially modulated wavefront, such a wavefront may also be generated by an algorithm stored in the memory 34 and executed by the processor 30 that models the optical field E at a point rb within a medium in accordance with:
E(rb)=∫g(rb,ra)φ(ra)d3ra (2)
in which g is Green's function describing propagation from φ(ra) to point rb. In an embodiment, each segment of the phase modulator is treated as a planar source having amplitude A and phase φ. If the phase modulator is assumed to be illuminated uniformly, the amplitudes A at each segment may be assumed to be equal. By integrating the surface area S of each of the N segments, Equation (2) may be represented as:
which in turn yields
Changing the phase of a segment a of the phase modulator 22 while holding the phase of other segments unchanged causes the intensity I at point rb to respond in accordance with:
I(rb)≡|E(rb)|2=I0b+2ARe(E*bāgbaeiφa) (5)
in which:
Where the number of segments N is large Ebā≈E(rb) and is therefore essentially the same across all segments. By analyzing each segment a in this manner, the coefficients gba may be measured up to an unknown common prefactor E(rb). By determining the coefficients gba, the optical field at point rb (e.g., E(rb)) may be maximized by setting φa equal to −arg(gba) for each of the segments. This combination of segment phases thus can yield an aggregate light intensity maximum at the region of interest:
in which the different light channels associated with each channel will undergo constructive interference to reach the region of interest.
The amount of intensity enhancement observed at the localized region 74 may be related to the numbers of segments or regions into which the wavefront of the CW light 70 is broken. To the extent that the constants tmn are statistically independent and obey a circular Gaussian distribution, the expected enhancement, η, may be represented as:
where η is the ratio between the enhanced light intensity at the region of interest and the average light intensity at the region of interest prior to enhancement.
In one example, correlation processing (e.g., matched filter compression) may be used to process the LFM responses received by the acoustic detector 20. Match filter detection allows the matching of the peaks and valleys of the reference signal (e.g., LFM reference chirp) with the corresponding detected acoustic signal so as to reduce or eliminate noise. Matched filter detection of a signal s(t) is based on the observation that the highest signal-to-noise ratio is achieved at the time t=t0 if the filter frequency response H(w) is equal to the complex subjugate of the signal spectrum:
H(w)=S*(w)e−twt
where the signal output of the filter with spectral response (10) is given by:
where Bs (t−t0) is the correlation function of the signal s(t). Accordingly, the received acoustic response can be correlated with the reference LFM signal (e.g., chirp reference) to compute Bs(t−t0). In certain embodiments, fast Fourier transform (FFT) techniques may be used to compute the correlation function in the frequency domain and transform back to the time domain using the inverse FFT. The depth of various photoacoustic sources (e.g., discrete vascular components) may be determined based on the speed of propagation and the time of the observed acoustic responses to the emitted light. By varying the wavelengths used for observation, various different types of constituents may be derived based on the observation that different types of constituents absorb light at different wavelengths. Accordingly, a 2D image of the observed area may be constructed by using the derived depths, the constituent types, the constituent amount, and or the constituent concentration found at each depth. A 3D image may be constructed by layering a set of 2D images, each layer corresponding to a different tissue depth.
In another embodiment, heterodyne mixing with coherent detection may be used to decode the LFM responses received by the acoustic detector 20. This technique is based on heterodyne mixing of LFM waveforms and coherent detection of the down-shifted signal at the single frequency specified by the internally generated LFM reference signal. The signal detected by the acoustic detector 20 contains the chirp f(t)=f0+β(t−t0) delayed by the time t0=z/ca where β is the frequency f sweep rate, z is the tissue depth, and ca is the speed of sound in tissue. The delayed response signal s(t) is given by:
where As is the complex amplitude and is assumed to be a constant within the chirp bandwidth. Computing the product s(t)·r(t) where r(t) is the chirp reference signal, and removing the sum frequency components using, for example, a low-pass filter, gives the down-shifted signal V(t):
Equation 13 shows that for the specific depth z, the signal V(t) contains the frequency component fz=βz/ca. Therefore, heterodyne mixing provides a direct relationship between the spectrum of the down-shifted signal and the depth of the photoacoustic sources. Any suitable coherent lock-in algorithm may be used to suppress all signals at the frequencies f≠fr. Setting the reference frequency fr is equivalent to selecting a specific depth for observation. As mentioned above, a 2D image of the observed area may be constructed by selecting a specific depth for observation and deriving the constituent types found at each depth. Similarly, a 3D image may be constructed by layering a set of 2D images with each layer corresponding to a different tissue depth.
Thus, in accordance with the present disclosure, emitted light may be intensity and spatially modulated so as to converge on a region of interest within an otherwise scattering medium (e.g., tissue). In the context of photoacoustic spectroscopy, such convergence may be used to increase the fluence of light at the internal region of interest (e.g., light absorber) and to, thereby, improve the signal-to-noise ratio of the generated acoustic signal. That is, focusing the emitted light on the internal region (such as by spatial modulation of the respective CW light wavefronts) generates a stronger acoustic signal, thereby improving the measurement process. Such techniques allow for precise measurements in individual vasocirculatory structures. For example hemoglobin concentration and oxygen saturation (i.e., percentage of oxygen in the blood) measurements may now be derived in localized regions of interest. The optical absorption spectra of oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (Hb) may be used to determine precise quantities of these two chromophores in the area being observed by irradiating the area with light near certain wavelengths such as 660 nm and 900 nm. The chromophores preferentially absorb light at certain wavelengths resulting in enhanced or reduced ultrasonic responses based on which wavelength is currently used to irradiate the tissue. The resulting ultrasonic responses may be analyzed to measure hemoglobin concentration as well as oxygen saturation in arterial and venous conduits. Such measurements allow for determination of conditions such as anemia, iron deficiency, low or high blood oxygenation, and so forth.
Imaging modalities may also be employed that allow for enhanced detail and image resolution of the tissue site under observation. Indeed, detailed in vivo 2D and 3D imaging may be created by deriving an image based on the type, amount concentration, and/or the location of the various tissue constituents observed by the photoacoustic spectroscopy system 8. The signals resulting from such observations may be processed by the techniques disclosed above, such as the algorithmic techniques, to derive an image corresponding to the image of the area under observation. Such imaging may be useful for capillary mapping, skin melanoma detection, and so forth. It is thus possible to observe the micro circulation of blood among individual arterioles and venules, thus enabling the characterization of blood flow and tissue perfusion (e.g., hydrostatic pressure measurements, osmotic pressure measurements) at a capillary level. Additionally, soft brain tissues having different optical absorption properties may be observed by the techniques disclosed herein. For example, an absorption contrast and resulting ultrasonic response between a lesion area and a healthy area may be significantly different. Accordingly, a lesion area may be identified and imaged during in vivo examination of brain tissue using the photoacoustic spectroscopy system 8.
Turning to
The light incident upon the tissue sample may encounter a light absorber and experience kinetic energy activity that results in ultrasonic shock waves. The resultant ultrasonic shockwaves will generate acoustic waves 92 that can be detected, for example, by the acoustic detector 20 (block 94). The acoustic detector 20 is capable of converting the detected acoustic waves into electric signals (block 96). In certain embodiments, the electronic signals are processed by a variety of algorithms as described above so as to determine a concentration or quantity measure of light absorbers within a localized region of the tissue (block 98). As mentioned previously, the algorithms are capable of using a variety of spatial modulation intensity enhancement techniques to observe the localized region. Similarly, LFM processing techniques may be employed to process the LFM components of the signal. The processed signal may be used to determine localized measurements of certain physiologic parameters such as hemoglobin concentration and oxygen saturation. Other measurements may be obtained based on microcirculatory observations, such as hydrostatic pressure measurements and osmotic pressure measurements. Further, imaging modalities may be employed to produce in vivo images such as capillary maps, tissue maps, brain lesion images, and so forth, based on, for example, differing absorption contrasts among tissue regions. Indeed, the techniques disclosed herein allow for very precise imaging of tissue as well as for obtaining measurements of highly localized regions of interest. The logic 82 may then iteratively modulate the emitted light and process the resulting signal so as to continuously observe the region of interest, as illustrated.
While the disclosure may be susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and have been described in detail herein. However, it should be understood that the embodiments provided herein are not intended to be limited to the particular forms disclosed. Indeed, the disclosed embodiments may be applied to various types of medical devices and monitors, as well as to electronic device in general. Rather, the various embodiments may cover all modifications, equivalents, and alternatives falling within the spirit and scope of the disclosure as defined by the following appended claims.
Number | Name | Date | Kind |
---|---|---|---|
3638640 | Shaw | Feb 1972 | A |
4714341 | Hamaguri et al. | Dec 1987 | A |
4805623 | Jöbsis | Feb 1989 | A |
4807631 | Hersh et al. | Feb 1989 | A |
4817623 | Stoddart et al. | Apr 1989 | A |
4911167 | Corenman et al. | Mar 1990 | A |
4913150 | Cheung et al. | Apr 1990 | A |
4936679 | Mersch | Jun 1990 | A |
4938218 | Goodman et al. | Jul 1990 | A |
4971062 | Hasebe et al. | Nov 1990 | A |
4972331 | Chance | Nov 1990 | A |
4974591 | Awazu et al. | Dec 1990 | A |
5028787 | Rosenthal et al. | Jul 1991 | A |
5065749 | Hasebe et al. | Nov 1991 | A |
5084327 | Stengel | Jan 1992 | A |
5119815 | Chance | Jun 1992 | A |
5122974 | Chance | Jun 1992 | A |
5140989 | Lewis et al. | Aug 1992 | A |
5167230 | Chance | Dec 1992 | A |
5190038 | Polson et al. | Mar 1993 | A |
5246003 | DeLonzor | Sep 1993 | A |
5247931 | Norwood | Sep 1993 | A |
5263244 | Centa et al. | Nov 1993 | A |
5275159 | Griebel | Jan 1994 | A |
5279295 | Martens et al. | Jan 1994 | A |
5297548 | Pologe | Mar 1994 | A |
5349961 | Stoddart et al. | Sep 1994 | A |
5355880 | Thomas et al. | Oct 1994 | A |
5372136 | Steuer et al. | Dec 1994 | A |
5385143 | Aoyagi | Jan 1995 | A |
5390670 | Centa et al. | Feb 1995 | A |
5413099 | Schmidt et al. | May 1995 | A |
5469845 | DeLonzor et al. | Nov 1995 | A |
5482036 | Diab et al. | Jan 1996 | A |
5483646 | Uchikoga | Jan 1996 | A |
5553614 | Chance | Sep 1996 | A |
5564417 | Chance | Oct 1996 | A |
5575285 | Takanashi et al. | Nov 1996 | A |
5611337 | Bukta | Mar 1997 | A |
5630413 | Thomas et al. | May 1997 | A |
5645059 | Fein et al. | Jul 1997 | A |
5645060 | Yorkey | Jul 1997 | A |
5680857 | Pelikan et al. | Oct 1997 | A |
5692503 | Keunstner | Dec 1997 | A |
5730124 | Yamauchi | Mar 1998 | A |
5758644 | Diab et al. | Jun 1998 | A |
5779631 | Chance | Jul 1998 | A |
5782757 | Diab et al. | Jul 1998 | A |
5786592 | Hök | Jul 1998 | A |
5830136 | DeLonzor et al. | Nov 1998 | A |
5830139 | Abreu | Nov 1998 | A |
5831598 | Kauffert et al. | Nov 1998 | A |
5842981 | Larsen et al. | Dec 1998 | A |
5871442 | Madarasz et al. | Feb 1999 | A |
5873821 | Chance et al. | Feb 1999 | A |
5920263 | Huttenhoff et al. | Jul 1999 | A |
5995855 | Kiani et al. | Nov 1999 | A |
5995856 | Mannheimer et al. | Nov 1999 | A |
5995859 | Takahashi | Nov 1999 | A |
6011986 | Diab et al. | Jan 2000 | A |
6064898 | Aldrich | May 2000 | A |
6081742 | Amano et al. | Jun 2000 | A |
6088607 | Diab et al. | Jul 2000 | A |
6120460 | Abreu | Sep 2000 | A |
6134460 | Chance | Oct 2000 | A |
6150951 | Olejniczak | Nov 2000 | A |
6154667 | Miura et al. | Nov 2000 | A |
6163715 | Larsen et al. | Dec 2000 | A |
6181958 | Steuer et al. | Jan 2001 | B1 |
6181959 | Schöllermann et al. | Jan 2001 | B1 |
6230035 | Aoyagi et al. | May 2001 | B1 |
6266546 | Steuer et al. | Jul 2001 | B1 |
6285895 | Ristolainen et al. | Sep 2001 | B1 |
6309352 | Oraevsky et al. | Oct 2001 | B1 |
6312393 | Abreu | Nov 2001 | B1 |
6353750 | Kimura et al. | Mar 2002 | B1 |
6397091 | Diab et al. | May 2002 | B2 |
6405069 | Oraevsky et al. | Jun 2002 | B1 |
6415236 | Kobayashi et al. | Jul 2002 | B2 |
6419671 | Lemberg | Jul 2002 | B1 |
6438399 | Kurth | Aug 2002 | B1 |
6461305 | Schnall | Oct 2002 | B1 |
6466809 | Riley | Oct 2002 | B1 |
6487439 | Skladnev et al. | Nov 2002 | B1 |
6501974 | Huiku | Dec 2002 | B2 |
6501975 | Diab et al. | Dec 2002 | B2 |
6519376 | Biagi et al. | Feb 2003 | B2 |
6526301 | Larsen et al. | Feb 2003 | B2 |
6544193 | Abreu | Apr 2003 | B2 |
6546267 | Sugiura et al. | Apr 2003 | B1 |
6549795 | Chance | Apr 2003 | B1 |
6580086 | Schulz et al. | Jun 2003 | B1 |
6591122 | Schmitt | Jul 2003 | B2 |
6594513 | Jobsis et al. | Jul 2003 | B1 |
6606509 | Schmitt | Aug 2003 | B2 |
6606511 | Ali et al. | Aug 2003 | B1 |
6615064 | Aldrich | Sep 2003 | B1 |
6618042 | Powell | Sep 2003 | B1 |
6622095 | Kobayashi et al. | Sep 2003 | B2 |
6654621 | Palatnik et al. | Nov 2003 | B2 |
6654624 | Diab et al. | Nov 2003 | B2 |
6658276 | Kianl et al. | Dec 2003 | B2 |
6658277 | Wasserman | Dec 2003 | B2 |
6662030 | Khalil et al. | Dec 2003 | B2 |
6668183 | Hicks et al. | Dec 2003 | B2 |
6671526 | Aoyagi et al. | Dec 2003 | B1 |
6671528 | Steuer et al. | Dec 2003 | B2 |
6678543 | Diab et al. | Jan 2004 | B2 |
6684090 | Ali et al. | Jan 2004 | B2 |
6690958 | Walker et al. | Feb 2004 | B1 |
6697658 | Al-Ali | Feb 2004 | B2 |
6708048 | Chance | Mar 2004 | B1 |
6711424 | Fine et al. | Mar 2004 | B1 |
6711425 | Reuss | Mar 2004 | B1 |
6714245 | Ono | Mar 2004 | B1 |
6731274 | Powell | May 2004 | B2 |
6785568 | Chance | Aug 2004 | B2 |
6793654 | Lemberg | Sep 2004 | B2 |
6801797 | Mannheimer et al. | Oct 2004 | B2 |
6801798 | Geddes et al. | Oct 2004 | B2 |
6801799 | Mendelson | Oct 2004 | B2 |
6829496 | Nagai et al. | Dec 2004 | B2 |
6839496 | Mills et al. | Jan 2005 | B1 |
6850053 | Daalmans et al. | Feb 2005 | B2 |
6863652 | Huang et al. | Mar 2005 | B2 |
6873865 | Steuer et al. | Mar 2005 | B2 |
6889153 | Dietiker | May 2005 | B2 |
6898451 | Wuori | May 2005 | B2 |
6939307 | Dunlop | Sep 2005 | B1 |
6947780 | Scharf | Sep 2005 | B2 |
6949081 | Chance | Sep 2005 | B1 |
6961598 | Diab | Nov 2005 | B2 |
6983178 | Fine et al. | Jan 2006 | B2 |
6993371 | Kiani et al. | Jan 2006 | B2 |
6996427 | Ali et al. | Feb 2006 | B2 |
7020506 | Fine et al. | Mar 2006 | B2 |
7024235 | Melker et al. | Apr 2006 | B2 |
7027849 | Al-Ali | Apr 2006 | B2 |
7030749 | Al-Ali | Apr 2006 | B2 |
7035697 | Brown | Apr 2006 | B1 |
7047056 | Hannula et al. | May 2006 | B2 |
7127278 | Melker et al. | Oct 2006 | B2 |
7162306 | Caby et al. | Jan 2007 | B2 |
7184148 | Alphonse | Feb 2007 | B2 |
7209775 | Bae et al. | Apr 2007 | B2 |
7236811 | Schmitt | Jun 2007 | B2 |
7254432 | Fine | Aug 2007 | B2 |
7263395 | Chan et al. | Aug 2007 | B2 |
7272426 | Schmid | Sep 2007 | B2 |
7327463 | Alphonse | Feb 2008 | B2 |
7373193 | Al-Ali et al. | May 2008 | B2 |
7447388 | Bates et al. | Nov 2008 | B2 |
7483731 | Hoarau et al. | Jan 2009 | B2 |
7650177 | Hoarau et al. | Jan 2010 | B2 |
7680522 | Andershn et al. | Mar 2010 | B2 |
7869850 | Hoarau et al. | Jan 2011 | B2 |
7899510 | Hoarau | Mar 2011 | B2 |
8060171 | Hoarau | Nov 2011 | B2 |
8109272 | Baker, Jr. | Feb 2012 | B2 |
8123695 | Baker, Jr. | Feb 2012 | B2 |
8145288 | Baker, Jr. | Mar 2012 | B2 |
8175671 | Hoarau | May 2012 | B2 |
8190224 | Hoarau | May 2012 | B2 |
8190225 | Hoarau | May 2012 | B2 |
8195264 | Hoarau | Jun 2012 | B2 |
8233954 | Klingt et al. | Jul 2012 | B2 |
8311601 | Besko | Nov 2012 | B2 |
8364220 | Sandmore | Jan 2013 | B2 |
8396527 | Hoarau | Mar 2013 | B2 |
8417309 | Price | Apr 2013 | B2 |
20010005773 | Larsen et al. | Jun 2001 | A1 |
20010020122 | Steuer et al. | Sep 2001 | A1 |
20010039376 | Steuer et al. | Nov 2001 | A1 |
20010044700 | Kobayashi et al. | Nov 2001 | A1 |
20020026106 | Khalil et al. | Feb 2002 | A1 |
20020035318 | Mannheimer et al. | Mar 2002 | A1 |
20020038079 | Steuer et al. | Mar 2002 | A1 |
20020042558 | Mendelson | Apr 2002 | A1 |
20020049389 | Abreu | Apr 2002 | A1 |
20020062071 | Diab et al. | May 2002 | A1 |
20020111748 | Kobayashi et al. | Aug 2002 | A1 |
20020133068 | Huiku | Sep 2002 | A1 |
20020156354 | Larson | Oct 2002 | A1 |
20020161287 | Schmitt | Oct 2002 | A1 |
20020161290 | Chance | Oct 2002 | A1 |
20020165439 | Schmitt | Nov 2002 | A1 |
20020198443 | Ting | Dec 2002 | A1 |
20030023140 | Chance | Jan 2003 | A1 |
20030055324 | Wasserman | Mar 2003 | A1 |
20030060693 | Monfre et al. | Mar 2003 | A1 |
20030139687 | Abreu | Jul 2003 | A1 |
20030144584 | Mendelson | Jul 2003 | A1 |
20030220548 | Schmitt | Nov 2003 | A1 |
20030220576 | Diab | Nov 2003 | A1 |
20040010188 | Wasserman | Jan 2004 | A1 |
20040054270 | Pewzner et al. | Mar 2004 | A1 |
20040087846 | Wasserman | May 2004 | A1 |
20040107065 | Al-Ali | Jun 2004 | A1 |
20040127779 | Steuer et al. | Jul 2004 | A1 |
20040171920 | Mannheimer et al. | Sep 2004 | A1 |
20040176670 | Takamura et al. | Sep 2004 | A1 |
20040176671 | Fine et al. | Sep 2004 | A1 |
20040230106 | Schmitt et al. | Nov 2004 | A1 |
20050054907 | Page et al. | Mar 2005 | A1 |
20050080323 | Kato | Apr 2005 | A1 |
20050101850 | Parker | May 2005 | A1 |
20050113651 | Wood et al. | May 2005 | A1 |
20050113656 | Chance | May 2005 | A1 |
20050168722 | Forstner et al. | Aug 2005 | A1 |
20050175540 | Oraevsky et al. | Aug 2005 | A1 |
20050177034 | Beaumont | Aug 2005 | A1 |
20050192488 | Bryenton et al. | Sep 2005 | A1 |
20050203357 | Debreczeny et al. | Sep 2005 | A1 |
20050228248 | Dietiker | Oct 2005 | A1 |
20050267346 | Faber et al. | Dec 2005 | A1 |
20050283059 | Iyer et al. | Dec 2005 | A1 |
20060009688 | Lamego et al. | Jan 2006 | A1 |
20060015021 | Cheng | Jan 2006 | A1 |
20060020181 | Schmitt | Jan 2006 | A1 |
20060030763 | Mannheimer et al. | Feb 2006 | A1 |
20060052680 | Diab | Mar 2006 | A1 |
20060058683 | Chance | Mar 2006 | A1 |
20060063992 | Yu et al. | Mar 2006 | A1 |
20060063993 | Yu et al. | Mar 2006 | A1 |
20060064024 | Schnall | Mar 2006 | A1 |
20060195028 | Hannula et al. | Aug 2006 | A1 |
20060224053 | Black et al. | Oct 2006 | A1 |
20060224058 | Mannheimer | Oct 2006 | A1 |
20060247501 | Ali | Nov 2006 | A1 |
20060253007 | Cheng et al. | Nov 2006 | A1 |
20060258921 | Addison et al. | Nov 2006 | A1 |
20070015992 | Filkins et al. | Jan 2007 | A1 |
20070093702 | Yu et al. | Apr 2007 | A1 |
20070197886 | Naganuma et al. | Aug 2007 | A1 |
20080200781 | Van Herpen et al. | Aug 2008 | A1 |
20080296514 | Metzger et al. | Dec 2008 | A1 |
20110083509 | Li et al. | Apr 2011 | A1 |
Number | Date | Country |
---|---|---|
732799 | May 2001 | AU |
19640807 | Sep 1997 | DE |
0630203 | Dec 1994 | EP |
0919180 | Jun 1999 | EP |
1743576 | Jan 2007 | EP |
2311858 | Oct 1997 | GB |
3170866 | Jul 1991 | JP |
3238813 | Oct 1991 | JP |
4191642 | Jul 1992 | JP |
4332536 | Nov 1992 | JP |
7124138 | May 1995 | JP |
7136150 | May 1995 | JP |
10216115 | Aug 1998 | JP |
2003194714 | Jul 2003 | JP |
2003210438 | Jul 2003 | JP |
2003275192 | Sep 2003 | JP |
2003339678 | Dec 2003 | JP |
2004008572 | Jan 2004 | JP |
2004113353 | Apr 2004 | JP |
2004135854 | May 2004 | JP |
2004194908 | Jul 2004 | JP |
2004202190 | Jul 2004 | JP |
2004248819 | Sep 2004 | JP |
2004290545 | Oct 2004 | JP |
WO9101678 | Feb 1991 | WO |
WO9221281 | Dec 1992 | WO |
WO9309711 | May 1993 | WO |
WO9403102 | Feb 1994 | WO |
WO9512349 | May 1995 | WO |
WO9749330 | Dec 1997 | WO |
WO9842249 | Oct 1998 | WO |
WO9842251 | Oct 1998 | WO |
WO9843071 | Oct 1998 | WO |
WO9932030 | Jul 1999 | WO |
WO0021438 | Apr 2000 | WO |
WO0140776 | Jun 2001 | WO |
WO2005009221 | Feb 2005 | WO |
2007003952 | Jan 2007 | WO |
WO2007051066 | May 2007 | WO |
WO2008149342 | Dec 2008 | WO |
Entry |
---|
Meyer et al., Tailoring Ultrasonic Beams with Optoacoustic Holography, 2003, Proceedings of SPIE, vol. 4969, pp. 105-114. |
Kihm et al., Interferometry-Based Optoacoustic Tomography, 2009, Taylor & Francis Group, LLC, pp. 239-250. |
Telenkov et al., Photothermoacoustic Imaging of Biological Tissues: Maximum Depth Characterization Comparison of Time and Frequency-Domain Measurements, Jul./Aug. 2009, Journal of Biomedical Optics, vol. 14(4), 12 pp. |
Aoyagi, T., et al.; “Analysis of Motion Artifacts in Pulse Oximetry,” Japanese Society ME, vol. 42, p. 20 (1993) (Article in Japanese—contains English summary of article). |
Barreto, A.B., et al.; “Adaptive Cancelation of Motion artifact in Photoplethysmographic Blood Volume Pulse Measurements for Exercise Evaluation,” IEEE-EMBC and CMBEC—Theme 4: Signal Processing, pp. 983-984 (1995). |
Vincente, L.M., et al.; “Adaptive Pre-Processing of Photoplethysmographic Blood Volume Pulse Measurements,” pp. 114-117 (1996). |
Plummer, John L., et al.; “Identification of Movement Artifact by the Nellcor N-200 and N-3000 Pulse Oximeters,” Journal of clinical Monitoring, vol. 13, pp. 109-113 (1997). |
Barnum, P.T., et al.; “Novel Pulse Oximetry Technology Capable of Reliable Bradycardia Monitoring in the Neonate,” Respiratory Care, vol. 42, No. 1, p. 1072 (Nov. 1997). |
Poets, C. F., et al.; “Detection of movement artifact in recorded pulse oximeter saturation,” Eur. J. Pediatr.; vol. 156, pp. 808-811 (1997). |
Masin, Donald I., et al.; “Fetal Transmission Pulse Oximetry,” Proceedings 19th International Conference IEEE/EMBS, Oct. 30-Nov. 2, 1997; pp. 2326-2329. |
Leahy, Martin J., et al.; “Sensor Validation in Biomedical Applications,” IFAC Modelling and Control in Biomedical Systems, Warwick, UK; pp. 221-226 (1997). |
Barreto, Armando B., et al.; “Adaptive LMS Delay Measurement in dual Blood Volume Pulse Signals for Non-Invasive Monitoring,” IEEE, pp. 117-120 (1997). |
East, Christine E., et al.; “Fetal Oxygen Saturation and Uterine Contractions During Labor,” American Journal of Perinatology, vol. 15, No. 6, pp. 345-349 (Jun. 1998). |
Hayes, Matthew J., et al.; “Quantitative evaluation of photoplethysmographic artifact reduction for pulse oximetry,” SPIE, vol. 3570, pp. 138-147 (Sep. 1998). |
Edrich, Thomas, et al.; “Can the Blood Content of the Tissues be Determined Optically During Pulse Oximetry Without Knowledge of the Oxygen Saturation?—An In-Vitro Investigation,” Proceedings of the 20th Annual International conference of the IEEE Engie in Medicine and Biology Society, vol. 20, No. 6, p. 3072-3075, 1998. |
Hayes, Matthew J., et al.; “Artifact reduction in photoplethysmography,” Applied Optics, vol. 37, No. 31, pp. 7437-7446 (Nov. 1998). |
Such, Hans Olaf; “Optoelectronic Non-invasive Vascular Diagnostics Using multiple Wavelength and Imaging Approach,” Dissertation, (1998). |
Todd, Bryan, et al.; “The Identification of Peaks in Physiological Signals,” Computers and Biomedical Research, vol. 32, pp. 322-335 (1999). |
Rhee, Sokwoo, et al.; “Design of a Artifact-Free Wearable Plethysmographic Sensor,” Proceedings of the First joint BMES/EMBS Conference, Oct. 13-16, 1999, Altanta, Georgia, p. 786. |
Rheineck-Leyssius, Aart t., et al.; “Advanced Pulse Oximeter Signal Processing Technology Compared to Simple Averaging: I. Effect on Frequency of Alarms in the Operating Room,” Journal of clinical Anestesia, vol. 11, pp. 192-195 (1999). |
Kaestle, S.; “An Algorithm for Reliable Processing of Pulse Oximetry Signals Under strong Noise Conditions,” Dissertation Book, Lubeck University, Germany (1999). |
Goldman, Julian M.; “Masimo Signal Extraction Pulse Oximetry,” Journal of Clinical Monitoring and Computing, vol. 16, pp. 475-483 (2000). |
Coetzee, Frans M.; “Noise-Resistant Pulse Oximetry Using a Synthetic Reference Signal,” IEEE Transactions on Biomedical Engineering, vol. 47, No. 8, Aug. 2000, pp. 1018-1026. |
Kaestle, S.; “Determining Artefact Sensitivity of New Pulse Oximeters in Laboratory Using Signals Obtained from Patient,” Biomedizinische Technik, vol. 45 (2000). |
Tremper, K.K.; “A Second Generation Technique for Evaluating Accuracy and Reliability of Second Generation Pulse Oximeters,” Journal of Clinical Monitoring and Computing, vol. 16, pp. 473-474 (2000). |
Belal, Suliman Yousef, et al.; “A fuzzy system for detecting distorted plethysmogram pulses in neonates and paediatric patients,” Physiol. Meas., vol. 22, pp. 397-412 (2001). |
Hayes, Matthew J., et al.; “A New Method for Pulse Oximetry Possessing Inherent Insensitivity to Artifact,” IEEE Transactions on Biomedical Engineering, vol. 48, No. 4, pp. 452-461 (Apr. 2001). |
Maletras, Francois-Xavier, et al.; “Construction and calibration of a new design of Fiber Optic Respiratory Plethysmograph (FORP),” Optomechanical Design and Engineering, Proceedings of SPIE, vol. 4444, pp. 285-293 (2001). |
Relente, A.R., et al.; “Characterization and Adaptive Filtering of Motion Artifacts in Pulse Oximetry using Accelerometers,” Proceedings of the Second joint EMBS/BMES Conference, Houston, Texas, Oct. 23-26, 2002; pp. 1769-1770. |
Yao, Jianchu, et al.; “Design of a Plug-and-Play Pulse Oximeter,” Proceedings of the Second Joint EMBS/BMES Conference, Houston, Texas, Oct. 23-26, 2002; pp. 1752-1753. |
Jopling, Michae W., et al.; “Issues in the Laboratory Evaluation of Pulse Oximeter Performance,” Anesth Analg, vol. 94, pp. S62-S68 (2002). |
Gostt, R., et al.; “Pulse Oximetry Artifact Recognition Algorithm for Computerized Anaesthetic Records,” Journal of Clinical Monitoring and Computing Abstracts, p. 471 (2002). |
Chan, K.W., et al.; “17.3: Adaptive Reduction of Motion Artifact from Photoplethysmographic Recordings using a Variable Step-Size LMS Filter,” IEEE, pp. 1343-1346 (2002). |
Yamaya, Yoshiki, et al.; “Validity of pulse oximetry during maximal exercise in normoxia, hypoxia, and hyperoxia,” J. Appl. Physiol., vol. 92, pp. 162-168 (2002). |
Cyrill, D., et al.; “Adaptive Comb Filter for Quasi-Periodic Physiologic Signals,” Proceedings of the 25th Annual International Conference of the IEEE EMBS, Cancun, Mexico, Sep. 17-21, 2003; pp. 2439-2442. |
Stetson, Paul F.; “Determining Heart Rate from Noisey Pulse Oximeter Signals Using Fuzzy Logic,” The IEEE International Conference on Fuzzy Systems, St. Louis, Missouri, May 25-28, 2003; pp. 1053-1058. |
Aoyagi, Takuo; “Pulse oximetry: its invention, theory, and future,” Journal of Anesthesia, vol. 17, pp. 259-266 (2003). |
Lee, C.M., et al.; “Reduction of motion artifacts from photoplethysinographic recordings using wavelet denoising approach,” IEEE EMBS Asian-Pacific Conference on Biomedical Engineering, Oct. 20-22, 2003; pp. 194-195. |
Johansson, A.; “Neural network for photoplethysmographic respiratory rate monitoring,” Medical & Biological Engineering & Computing, vol. 41, pp. 242-248 (2003). |
Addison, Paul S., et al.; “A novel time-frequency-based 3D Lissajous figure method and its application to the determination of oxygen saturation from the photoplethysmogram,” Institute of Physic Publishing, Meas. Sci. Technol., vol. 15, pp. L15-L18 (2004). |
Yao, Jianchu, et al.; “A Novel Algorithm to Separate Motion Artifacts from Photoplethysmographic Signals Obtained With a Reflectance Pulse Oximeter,” Proceedings of the 26th Annual International conference of the IEEE EMBS, San Francisco, California, Sep. 2004, pp. 2153-2156. |
Matsuzawa, Y., et al.; “Pulse Oximeter,” Home Care Medicine, pp. 42-45 (Jul. 2004); (Article in Japanese—contains English summary of article). |
Yan, Yong-sheng, et al.; “Reduction of motion artifact in pulse oximetry by smoothed pseudo Wigner-Ville distribution,” Journal of NeuroEngineering and Rehabilitation, vol. 2, No. 3 (9 pages) (Mar. 2005). |
Telenkov, Sergey A., et al.; “Fourier-domain biophotoacoustic subsurface depth selective amplitude and phase imaging of turbid phantoms and biological tissue,” Journal of Biomedical Optics, 11(4), 044006, Jul./Aug. 2006. |
Vellekoop, I.M., et al.; “Focusing coherent light through opaque strongly scattering media,” Optics Letters, vol. 32, No. 16, Aug. 15, 2007. |
Maslov, Konstantin, et al.; “Photoacoustic imaging of biological tissue with intensity-modulated continuous-wave laser,” Journal of Biomedical Optics, 13(2), 024006, Mar./Apr. 2008. |
Vellekoop, I.M., et al.; “Focusing Coherent Light Through Opaque Strongly Scattering Media,” Aug. 15, 2007 Optical Society of America, vol. 32, No. 16, Optics Letters, pp. 2309-2311. |
Vellekoop, I.M., et al., “Demixing Light Paths Inside Disordered Metamaterials,” Jan. 7, 2008, vol. 16, No. 1, Optics Express, pp. 67-80. |
U.S. Appl. No. 61/245,580, filed Sep. 24, 2009, McKenna. |
U.S. Appl. No. 12/576,377, filed Oct. 9, 2009, Youzhi Li et al. |
International Search Report and Written Opinion for PCT Application No. PCT/US2011/043029 dated Oct. 19, 2011; 13 pgs. |
Huang, J., et al.; “Low Power Motion Tolerant Pulse Oximetry,” Abstracts, A7, p. S103; 2002. |
Lang, P., et al.; “Signal Identification and Quality Indicator™ for Motion Resistant Pulse Oximetry,” Abstracts, A10, p. S105; 2002. |
Hamilton, Patrick S., et al.; “Effect of Adaptive Motion-Artifact Reduction on QRS Detection,” Biomedical Instrumentation & Technology, pp. 197-202; 2000. |
Kim, J.M., et al.; “Signal Processing Using Fourier & Wavelet Transform,” pp. II-310-II-311; 2001. |
Odagiri, Y.; “Pulse Wave Measuring Device,” Micromechatronics, vol. 42, No. 3, pp. 6-11 (Article in Japanese—contains English summary of article) 1998. |
Neumann, R., et al.; “Fourier Artifact suppression Technology Provides Reliable SpO2,,” Abstracts, A11, p. S105; 2002. |
Number | Date | Country | |
---|---|---|---|
20120029829 A1 | Feb 2012 | US |