The present disclosure relates generally to magnetic resonance imaging, and more specifically to an apparatus and method of magnetic resonance imaging.
Clinical magnetic resonance imaging (MRI) is based on the MR signal that arises from the hydrogen nucleus, where the hydrogen is chemically bonded to oxygen in water or carbon in fat. Metabolic MRI uses signals that come from the nuclei of protons and other low atomic weight elements (e.g. sodium, phosphorus, oxygen, carbon, nitrogen, etc.) to generate images. Because these non-proton signals are much weaker, the resolution of these metabolic images is reduced for a given acquisition time. Signals from different nuclei are detected at different frequencies. The hardware for performing MR imaging requires an antenna, in which the sample is placed, that is tuned to resonate at the frequency of the MR signal to be detected.
One embodiment of the present disclosure entails a device having a plurality of antennas to couple to a Magnetic Resonance Imaging (MRI) system, wherein the MRI system measures a first set of signals from a first one of the antennas while the first antenna is positioned over an anatomical sample and engaged with the MRI system, and wherein the MRI system measures a second set of signals from a second one of the antennas engaged with the MRI system after the second antenna replaces the first antenna while maintaining image alignment with the anatomical sample.
Another embodiment of the present disclosure entails an MRI system comprising a Magnetic Resonance (MR) scanner to selectively couple to one among a plurality of antennas without compromising spatial alignment with an anatomical sample during signal acquisition by the MR scanner.
Yet another embodiment of the present disclosure entails a computer-readable storage medium having computer instructions for processing signals received from at least two among a plurality of antennas that selectively couple to an MR scanner without compromising spatial alignment with an anatomical sample during signal acquisition by the MR scanner.
Another embodiment of the present disclosure entails a method for selectively coupling one among a plurality of antennas to an MR scanner without compromising spatial alignment with an anatomical sample during signal acquisition by the MR scanner.
The guide 124 has two independent electromechanical connectors 108 and 114. The electromechanical connectors 108 and 114 have a male housing assembly with female electrical contacts 110 and 116 for engaging with electromechanical connectors of a cylindrical antenna. Each electrical contact of the electromechanical connectors in turn is connected to shielded coaxial cable pairs 112 and 118. Each coaxial cable pair provides shielded A and B radio frequency (RF) signals.
Each of the antennas 302 and 402 can utilize a common “bird-cage” antenna design as shown in
One purpose of the measurement of MR images at multiple frequencies is to produce accurate quantitative maps of metabolite concentrations and metabolic rates in imaging times that are acceptable to patients. This requires the highest signal intensity at the lowest noise level (highest signal-to-noise ratio, SNR) in the least time. The use of different antennae tuned to single frequencies ensures the most efficient antenna performance (highest SNR). The acquisition of spatially co-registered images at different frequencies by maintaining head position during changes of antennae as shown in
Unlike current proton clinical imaging that uses an arbitrary intensity scale, such metabolic MR imaging produces a quantitative bioscale. A quantitative bioscale in the present context can mean spatial distributions of metabolite concentrations that have direct biochemical interpretations of normal and diseased biological states. The interpretation of the metabolic map as a bioscale can be readily displayed using a color scale in which different thresholds of color represent biologically significant phases of a metabolic process. The metabolic process could be as severe as loss of tissue viability, or as benign as stages in normal tissue function.
Quantification requires correction of the imperfections of the imaging method caused by inhomogeneities in the main static magnetic field (B0) and the non-uniformity of the antennae sensitivity (B1) across the field of view. An embodiment for correcting B0 and B1 is described below.
Although images from different sources (e.g. Positron Emission Tomography-PET and Computed Tomography-CT) have been combined in other settings, image registration has been performed as a post-processing step to overlay one image over another. This approach works for qualitative images if the image distortions in the two images are not large. Image processing errors have lead to the new combined technology of CT and PET in the same device to acquire co-registered data. The present disclosure achieves this same goal of acquired co-registered data for different MR antennae.
The insertion of a sample such as human into a static magnetic field of an MR scanner distorts the homogeneity of the magnetic field. These inhomogeneities result in small, localized perturbations of the resonant frequency of the signal being detected. While current magnets are very homogeneous, the insertion of the human into the magnetic field results in considerable distortion of that field. Changes in position and orientation of the human alter these field distortions. If these sample-induced field imperfections are ignored by assuming a homogenous static magnet field, the concomitant frequency errors distort the images by altering the signal intensity and inaccurately placing that signal within the image. This error can be manifested in a number of ways including blurring, geometric distortions and, most importantly for quantification, signal loss. If left uncorrected, these artifacts produce an inaccurate metabolic map, both anatomically and quantitatively.
Static field inhomogeneities can be corrected by measuring the magnetic field after the human has been placed in the magnetic field. This can be done by shimming the main magnetic field by applying small correction magnetic gradients (room temperature shims) and by measuring the resultant B0 field. This B0 field correction map can be incorporated into the image reconstruction process of other images to remove the effect of the inhomogeneities of the static magnetic field caused by the sample.
For example, the shimming correction and mapping of B0 is most efficiently performed using the proton frequency that has the highest MR sensitivity in humans. Other signals such as from sodium can be used but are more time consuming. The resultant corrections can then be applied to a second frequency, but only if the position of the human does not change in the magnetic field. Any change in location or orientation of the human alters the B0 distortions and invalidates the correction. For this reason, a sample is maintained in a fixed position while a change of antenna takes place.
The B0 mapping over the human must also be performed for the calibration phantom in the same way as done for the human. The homogeneity of the actual static magnetic field with the human or phantom present is computed from the phase difference between two or more complex MR images collected with different echo-times. The images used to compute the static field measurement can be from the same nucleus as the metabolic data to be corrected (e.g., sodium data used to compute a B0 field map to correct sodium images) or from a different nucleus as the data to be corrected (e.g., proton data used to compute a field map to correct sodium data). If the static magnetic field correction is applied to a different nucleus from that used to determine it, a correction factor of the ratio of the two gyromagnetic ratios is applied. The essential requirement is that the sample location and orientation within the B0 magnetic field does not change between the measurements from the different antennae.
The arbitrarily scaled MR image voxel intensities are converted into biologically meaningful metabolic concentrations. This can be done in two ways. Either external phantoms of known concentration can be placed in the same field of view as the human or separate acquisitions can be performed using the same antenna with equal electrical loading for the human and calibration phantom. Both methods require that the images acquired from the human and the calibration phantoms have the B1 sensitivity correction determined with the same antenna. The B0 correction can be determined from any antenna and is applied prior to the B1 correction.
An external calibration phantom can consist of two or more (e.g. three) vials as shown in
Another method of quantification that avoids the use of separate acquisitions for the calibration phantom and human is to use separate calibration phantoms in the same field of view as the human imaging. These phantoms must be placed around the human in the limited space available as shown in
As the antenna sensitivity is usually inhomogeneous across biological samples, there are spatially varying quantification errors. Antenna sensitivity includes both the transmit sensitivity (B1+) and the receive sensitivity (B1−). According to the principle of reciprocity, one can reasonably assume transmit and receive sensitivities are the same at low frequencies. As metabolic images are acquired under fully relaxed conditions, the three-dimensional transit sensitivity can be estimated using the double flip angle approach by varying the transmit gain:
S
1
=s
rρ sin(2θ),S2=Srρ sin(θ),θ=cos−1(S1S2/2)
where S1 and S2 are the corresponding image voxel intensities with 1x and 2x transmit power, and consequently excitation angles of θ and 2θ, and where sr is the image receive sensitivity and is proportional to θ based on the reciprocity assumption. ρ is a measure of image spin density that is of interest. cos−1(y) is the inverse cosine of y. The transmit power can be adjusted so that the maximum flip angles across the field of view are less than 180 degrees. The antenna sensitivity corrected images are then: {circumflex over (ρ)}=S1/(θsin2θ)·{circumflex over (ρ)} differs from ρ by a constant factor due to the scale of receive sensitivity.
The images can be collected with a nominal flip angle pair of (90°, 45°) or one can use a flip angle pair of (108°, 54°) to maximize the combined SNR when images from both flip angles are averaged after antenna sensitivity correction. The individual images can be low pass filtered to improve B1 mapping. For the use of a separate calibration phantom method, both the phantom and human images can be corrected for antenna sensitivity and then normalized by the corresponding signal intensity of the normalization sphere in both set of images. A linear calibration curve (in the form of S=ax+b, where S is the unknown metabolic concentration to be determined, a and b are constants, and x is the normalized and antenna-sensitivity corrected voxel signal intensity) is then derived from the normalized signal intensities in the calibration phantom. Non-linear functions can also be used that can be more appropriate to the SNR of the data.
In practice, obtaining the B1 map accurately from the normalization sphere can be limited by low SNR. An alternative method to the normalization sphere is to use a common central region of interest within the human and phantom images for normalization. Assuming that the antenna sensitivity varies very slowly over a region of interest at the isocenter of the field of view (or other locations), the average B1 sensitivity can be calculated for that region in the calibration phantom and human images.
For the phantom, this region of interest can contain the two or more (e.g. three) calibration vials, the signal intensities of which are then antenna sensitivity corrected, and used to generate the calibration curve. The average B1 sensitivity from the same Region of Interest (ROI) in the human images is also calculated and used for normalization with the phantom images. The B1 map over the whole brain is generated to correct for the antenna sensitivity over the entire human image and then normalized by the B1 sensitivity in the region of interest so that the calibration curve can be applied to obtain the metabolite concentration map. The single acquisition method requires no normalization as the images of the human and calibration phantoms are acquired simultaneously with identical electrical loading of the antenna.
From the foregoing descriptions, it would be evident to an artisan with ordinary skill in the art that the aforementioned embodiments can be modified, reduced, or enhanced without departing from the scope and spirit of the claims described below. For example, the present illustration shows two antennas 302 and 402. The apparatus 100 can be designed for three or more antennas. Additionally, the apparatus 100 can be modified so that antenna engagement mechanism is performed by other common mechanical means other than a slideable assembly as presented by the disclosure.
Other suitable modifications can be applied to the present disclosure. Accordingly, the reader is directed to the claims for a fuller understanding of the breadth and scope of the present disclosure.
The machine may comprise a server computer, a client user computer, a personal computer (PC), a tablet PC, a laptop computer, a desktop computer, a control system, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. It will be understood that a device of the present disclosure includes broadly any electronic device that provides voice, video or data communication. Further, while a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.
The computer system 1900 may include a processor 1902 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory 1904 and a static memory 1906, which communicate with each other via a bus 1908. The computer system 1900 may further include a video display unit 1910 (e.g., a liquid crystal display (LCD), a flat panel, a solid state display, or a cathode ray tube (CRT)). The computer system 1900 may include an input device 1912 (e.g., a keyboard), a cursor control device 1914 (e.g., a mouse), a disk drive unit 1916, a signal generation device 1918 (e.g., a speaker or remote control) and a network interface device 1920.
The disk drive unit 1916 may include a machine-readable medium 1922 on which is stored one or more sets of instructions (e.g., software 1924) embodying any one or more of the methodologies or functions described herein, including those methods illustrated above. The instructions 1924 may also reside, completely or at least partially, within the main memory 1904, the static memory 1906, and/or within the processor 1902 during execution thereof by the computer system 1900. The main memory 1904 and the processor 1902 also may constitute machine-readable media.
Dedicated hardware implementations including, but not limited to, application specific integrated circuits, programmable logic arrays and other hardware devices can likewise be constructed to implement the methods described herein. Applications that may include the apparatus and systems of various embodiments broadly include a variety of electronic and computer systems. Some embodiments implement functions in two or more specific interconnected hardware modules or devices with related control and data signals communicated between and through the modules, or as portions of an application-specific integrated circuit. Thus, the example system is applicable to software, firmware, and hardware implementations.
In accordance with various embodiments of the present disclosure, the methods described herein are intended for operation as software programs running on a computer processor. Furthermore, software implementations can include, but not limited to, distributed processing or component/object distributed processing, parallel processing, or virtual machine processing can also be constructed to implement the methods described herein.
The present disclosure contemplates a machine readable medium containing instructions 1924, or that which receives and executes instructions 1924 from a propagated signal so that a device connected to a network environment 1926 can send or receive voice, video or data, and to communicate over the network 1926 using the instructions 1924. The instructions 1924 may further be transmitted or received over a network 1926 via the network interface device 1920.
While the machine-readable medium 1922 is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure.
The term “machine-readable medium” shall accordingly be taken to include, but not be limited to: solid-state memories such as a memory card or other package that houses one or more read-only (non-volatile) memories, random access memories, or other re-writable (volatile) memories; magneto-optical or optical medium such as a disk or tape; and/or a digital file attachment to e-mail or other self-contained information archive or set of archives is considered a distribution medium equivalent to a tangible storage medium. Accordingly, the disclosure is considered to include any one or more of a machine-readable medium or a distribution medium, as listed herein and including art-recognized equivalents and successor media, in which the software implementations herein are stored.
Although the present specification describes components and functions implemented in the embodiments with reference to particular standards and protocols, the disclosure is not limited to such standards and protocols. Each of the standards for Internet and other packet switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP) represent examples of the state of the art. Such standards are periodically superseded by faster or more efficient equivalents having essentially the same functions. Accordingly, replacement standards and protocols having the same functions are considered equivalents.
The illustrations of embodiments described herein are intended to provide a general understanding of the structure of various embodiments, and they are not intended to serve as a complete description of all the elements and features of apparatus and systems that might make use of the structures described herein. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Figures are also merely representational and may not be drawn to scale. Certain proportions thereof may be exaggerated, while others may be minimized. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.
Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description.
The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b), requiring an abstract that will allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.
The present application claims the priority of U.S. provisional patent application No. 61/028,003 filed Feb. 12, 2008, entitled Magnetic Resonance Imaging, Attorney Docket no. 7940-21 (DA081). All sections of the aforementioned application are incorporated herein by reference.
Filing Document | Filing Date | Country | Kind | 371c Date |
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PCT/US2009/033960 | 2/12/2009 | WO | 00 | 8/9/2010 |
Number | Date | Country | |
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61028003 | Feb 2008 | US |