Patent ID: 12186104

DESCRIPTION OF THE EMBODIMENTS

In the following description, numerous specific details are set forth such as examples of specific components, devices, methods, etc., in order to provide a thorough understanding of embodiments of the disclosure. It will be apparent, however, to one skilled in the art that these specific details need not be employed to practice embodiments of the disclosure. In other instances, well-known materials or methods have not been described in detail in order to avoid unnecessarily obscuring embodiments of the disclosure. While the disclosure is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that there is no intent to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the disclosure.

The systems and methods of the disclosure can provide high-resolution field/response map based intravoxel and/or voxel inhomogeneity correction for MR imaging, including chemical exchange saturation transfer (CEST) imaging, CEST spectroscopy, magnetization transfer (MT), MR image(s), MR spectroscopy (MRS) MR spectroscopic imaging (MRSI), or any combination thereof. In some examples, the systems and methods of the disclosure do not assume negligible intravoxel field inhomogeneity, such as routine B0field correction methods, and thus by generating an intravoxel correction coefficient, the systems and methods can identify and correct regions of non-negligible intravoxel field heterogeneity. Thus, the intravoxel inhomogeneity correction according to the disclosure can improve the CEST MRI and spectroscopy contrast and/or contrast to noise ratio (CNR), thereby improving the efficiency and accuracy of MR imaging, such as MM, MRS, and MRSI.

In some embodiments, the subvoxel inhomogeneity correction can be determined using a complete or a segment of Z-spectrum. In some embodiments, the voxel inhomogeneity correction can be performed without requiring the acquisition of the Z-spectral, thereby reducing the scan time. This can be beneficial to implementation in the emergency setting, where it is desired to minimize the imaging time for a timely intervention.

While some example of the disclosure may be specific to the brain, it will be understood that these example are nonlimiting and that the methods and systems may be performed for other parts of the body, including but not limited to the myocardium, muscle and kidney. Additionally, while some example of the disclosure may be specific to CEST imaging, it will be understood that these examples are also nonlimiting and that the methods and systems may also be applied to other types of MR imaging, including but not limited to MRS and MRSI. Further, while some example of the disclosure may be specific to correcting B0field map inhomogeneity, it will be understood that these examples are also nonlimiting and that the methods and systems may also be used to correct B1field map inhomogeneity.

FIG.1shows a method100of a high-resolution field map-based inhomogeneity correction (CIVIC) method for MR image(s)/spectrum (e.g., CEST, MT, MRS, MRSI, etc.) of a subject by determining and applying an intravoxel inhomogeneity correction coefficient according to embodiments.

In some embodiments, the method100may include a step110of acquiring an inhomogeneity field/response map (e.g., B0field inhomogeneity map) and a step120of acquiring MR image(s)/spectrum of one or more regions of interest (e.g., a brain) using one or more scans. In the steps110and120, a subject may be arranged in a magnetic resonance (MR) system capable of acquiring MR data/spectra and the data for field (e.g., B0field) inhomogeneity map. In some embodiments, the data may be acquired simultaneously. The MR system may include any available system and protocol, for example, capable of acquiring MRS/MRSI data, CEST data with and/or without Z-spectrum, field maps, among others, etc.

By way of example, the inhomogeneity field map may be a high-resolution B0field inhomogeneity map generated using routine B0field inhomogeneity map data and/or a high-resolution routine B0field inhomogeneity map data that directly maps the acquired B0field inhomogeneity map data. The B0field inhomogeneity map may be acquired and/or generated using any available methods.

The MR image(s)/spectrum may be CEST image(s)/spectrum with and/or without Z-spectrum, MT image(s)/spectrum with and/or without Z-spectrum, MRS/MRSI image(s)/spectrum, among others, or any combination thereof.

In some embodiments, the method100may include a step130of registering the MR image(s)/spectrum and B0inhomogeneity field map, for example, using any available method such as Statistical Parametric Mapping (SPM) and Advanced Normalization Tools (ANTs). Using the registered inhomogeneity field map, the method100may include a step140of determining an intravoxel inhomogeneity correction coefficient for each voxel of the region of interest. For example, the intravoxel inhomogeneity correction coefficient may be determined using a point-spread-function, a regression function, among others, or a combination thereof. The determination of the intravoxel inhomogeneity correction coefficient may be based on the type of data (e.g., CEST or MT with Z-spectrum, CEST or MT without Z-spectrum, MRS/MRSI data, etc.).FIGS.2A-12show examples of determining the intravoxel/voxel inhomogeneity correction coefficient for different types of MR data according to embodiments.

By way of example,FIG.2Aillustrates an example of a voxel200of a MR image andFIG.2Bshows the corresponding B0inhomogeneity field map300for which the intravoxel inhomogeneity has been quantified (i.e., corresponding intravoxel inhomogeneity coefficient for the voxel200). As shown in these figures, each voxel of the MR image corresponds to multiple subvoxels of the B0inhomogeneity map. In this example, the B0inhomogeneity field map300shown inFIG.2Bhas 2×2×n subvoxels for each MR (e.g., CEST or MRS/MRSI) image voxel. It will be understood that the intravoxel inhomogeneity coefficient can be determined for more or fewer subvoxels for each voxel. This subvoxel information can be used to generate the intravoxel inhomogeneity correction coefficient for each voxel.

After the intravoxel inhomogeneity correction coefficient is determined for each voxel of the region of interest, the method100may include a step150of correcting the MR image(s), MR spectroscopy (MRS) and/or MR spectroscopy imaging using the intravoxel inhomogeneity correction coefficient for each voxel. For example, the CEST MR image(s), magnetization transfer (MT) MR image(s), CEST MR spectroscopy, CEST MR spectroscopy imaging, MR spectroscopy (MRS) and/or MR spectroscopy imaging may be reconstructed using the intravoxel inhomogeneity correction coefficient for each voxel.

In some embodiments, the method100may include an optional step160of standardizing the corrected MR image(s)/spectrum from the step150. For example, the corrected MR image(s) spectrum may be standardized by correcting the original Z-spectrum offset by offset, such as saturation or frequency offset. By way of example, the step160may include determining a quasi-steady state signal (I/I0) for each voxel/component for each saturation offset. This way, image(s) and related quantitative information may be compared for example, between centers that use different system scan parameters (e.g., saturation duration and relaxation delay) used to acquire the MR images.

For example, the standardizing step160may be performed on CEST/MT Z-spectral images corrected according to embodiments, for example, a method300(seeFIG.3) and/or may be performed on CEST/MT (non Z-spectrum) image(s) corrected according to embodiments, for example, according to a method1000(seeFIG.10). The standardizing step160may also be performed on CEST Z-spectral and/or CEST (non Z-spectrum) images and/or MT Z-spectral images and/or MT (non Z-spectrum) image(s) that have been corrected in steps140and150using other known or available methods.

In some embodiments, in the step160, each voxel or a component of the corrected images/spectra for each offset may be standardized using a quasi-steady state CEST effect (e.g., the quasi-steady state signal (I/I0). In some embodiments, the quasi-steady state signal (I/I0) for each voxel/component of the image/spectrum may be determined using a steady state spinlock relaxation rate (R1ρ) calculated using a tilt angle (θ) calculated from the corrected image/spectrum, scan parameters, parameters determined from the T1 map, B1 map, and scan parameters.FIGS.13-16Dshow examples of determining the quasi-steady state CEST effect and one or more measurements according to embodiments.

Using this effect, the images may then be standardized so that the images/spectrum may be compared between different machines, scanning parameters, among others, or any combination thereof. For example, this can facilitate the use of CEST Z-spectral and/or CEST (non-spectrum) images in multi-center studies. This can offer flexibility to choose short saturation duration and relaxation delay experimentally when the scan time needs to be minimized, and the steady-state effect can be generated afterwards.

After the image(s) is corrected in step150and/or (optionally) standardized in step160, the method100may include a step170of outputting the corrected and/or standardized, corrected and/or standardized images, associated quantitative information, among others, or any combination thereof.

In some embodiments, the step170may include determining or generating quantitative information. The quantitative information may include but is not limited to one or more measurements; one or more quantitative images (of the region of interest based on the corrected and/or standardized data (e.g., image(s)/spectrum), the one or more measurements, among others, or a combination thereof; other information; or any combination thereof. In some embodiments, the one or more measurements may include soft-tissue measurements, morphological studies, chemical-shift measurements, magnetization-transfer measurements, MRS, measurements of one or more types of nuclear Overhauser effect measurements, and/or functional imaging. By way of specific examples, the one or more measurements may include tissue pH, temperature, creatine level, phosphocreatine level, glycogen level, glucose level, total amide proton level, among others, or any combination thereof.

In some embodiments, the step170may include generating a report using the quantitative information, images, among others, or any combination thereof.

FIG.3shows an example300of a high-resolution field map-based CEST intravoxel inhomogeneity correction (CIVIC) method for CEST Z-spectral imaging or MT Z-spectral imaging according to embodiments. As shown inFIG.3, the method300may include a step310of acquiring a B0inhomogeneity field map and a step320of acquiring CEST/MT Z-spectrum image(s).

In some embodiments, the B0inhomogeneity field map may be a high resolution. The high resolution (“HR”) B0inhomogeneity field map may be acquired or constructed using any available methods. For example, the B0inhomogeneity field map may be acquired using available methods. By way of example, the B0inhomogeneity field map can be constructed by interpolation of a Water saturation shift referencing (“WASSR”) map, non-high resolution (or routine) B0inhomogeneity field map resolution field map, directly from an acquired high resolution B0inhomogeneity field map, among others, or a combination thereof. For example, B0inhomogeneity field map may be determined using the CEST Z-spectrum image(s). By way of example, the lowest point of the WAS SR Z-spectrum can be determined either by symmetry analysis or fitting, which is taken as the bulk water resonance frequency, and the difference between the WASSR resonance frequency and that from the water resonance may be considered as the B0field inhomogeneity.

In some embodiments, the CEST/MT Z-spectrum image(s) may be acquired using any available methods. In some embodiments, the CEST/MT Z-spectrum image(s) may be non-high resolution CEST/MT MRI data.

In some embodiments, the method300may include a step330of registering the (reconstructed, high resolution) B0inhomogeneity field map to the CEST/MT Z-spectrum image(s) using any available methods.

The method300may further include a step340of determining an intravoxel inhomogeneity correction coefficient for each voxel using the constructed data. For example, the step340may include constructing a point-spread-function (PSF) from the HR subvoxel field inhomogeneity map, per voxel of CEST images for the region of interest.

By way of example, each voxel of CEST/MT image corresponds to multiple subvoxels of the high-resolution B0inhomogeneity map, for example, as shown inFIG.2B. Such subvoxel information can be used to generate the B0point spread function (PSF). For example, the histogram (e.g. the frequency of B0inhomogeneity and whose width is equal to a given interval), may be determined based on the subvoxels B0for each voxel. Because the B0inhomogeneity PSF may have very different frequency characteristics from that of Z-spectrum, the histogram can be built using a reasonably given interval depending on the typical field inhomogeneity, for example, with intervals of 1 Hz. This subvoxel discrete B0field response for each voxel may be represented by the intravoxel inhomogeneity correction coefficient for that voxel. In some embodiments, the intravoxel inhomogeneity correction coefficient may also be determined using other available methods.

In some embodiments, the method300may include a step350of constructing voxel discrete Z-spectrum. In some embodiments, the step350may include constructing CEST spectrum/spectra from non-HR CEST images, per voxel of CEST images, or for each region of interest (ROI). In some embodiments, for the step350, the CEST Z-spectrum can be constructed using the same interval of B0PSF (e.g., 1 Hz), per voxel.

In some embodiments, the method300may include a step360of correcting the CEST/MT Z-spectrum using the intravoxel inhomogeneity correction coefficient. For example, the step360may include deconvolving the intravoxel inhomogeneity correction coefficient (e.g., subvoxel field inhomogeneity PSF) from the CEST/MT Z-spectral images, per voxel/ROI.

In some embodiments, after the construction of B0PSF and Z-spectrum, the B0PSF can be deconvolved from the Z-spectrum. For example, ZappZorig⊙ΔB0, where Zappis the apparent Z spectrum obtained with B0heterogeneity, and Zorigis the desired Z spectrum in the absence of B0heterogeneity, in which ΔB0is subvoxel magnetic field function. The step360is not limited to the deconvolution method described and may use additional and/or alternative methods, such as numerical fitting.

In some embodiments, the method300may also include a step370of resampling the CEST/MT Z-spectrum/images using the intravoxel inhomogeneity correction coefficient for each voxel of the region of interest (i.e., the deconvolution-reconstructed signal for CEST Z-spectrum/images, per voxel/ROI). By way of example, the restored Zorighas the same interval as the intravoxel B0inhomogeneity PSF, which can be resampled to any given interval, for example, the original Z-spectral frequency interval.

FIGS.4A-Dshows an example of the application of the CIVIC method according to embodiments.FIG.4Ashows an example of a simulated Z-spectrum without B0inhomogeneity.FIG.4Bshows an example of a simulated symmetric B0inhomogeneity dispersion within a single CEST MM voxel or ROI.FIG.4Cshows the apparent Z-spectrum in the presence of symmetric B0inhomogeneity dispersion. As shown in this figure, the routine interpolation and shift-based B0inhomogeneity correction approach fails to correct symmetric B0inhomogeneity dispersion.FIG.4Dshows that using the method shown inFIG.3, the original Z spectrum has been recovered and is in agreement withFIG.4A.

FIGS.5A-Fshows another example of the application of the CIVIC method according to embodiments.FIG.5Ashows an example of a B0field inhomogeneity map determined from water saturation shift referencing (WASSR) approach.FIG.5Bshows an example of a high-resolution B0field inhomogeneity map.FIG.5Cshows that the per-pixel B0field inhomogeneity (FIG.5A) and averaged subvoxel B0field inhomogeneity (FIG.5B) per voxel are highly correlated in this example.FIGS.5D and5Eshow CEST images (MTRasym) obtained from conventional interpolation correction and the CIVIC correction, respectively.FIG.5Fshows the correlation of the corrections. As shown in this figure, both corrections are highly correlated.

FIGS.6A-Cillustrate another example of the application of the CIVIC method according to embodiments.FIG.6Ashows an example of a Z spectrum from a single voxel.FIG.6Bshows the subvoxel B0field inhomogeneity for the pixel shown inFIG.6A. In this example, there are 10 subvoxels for each voxel.FIG.6Cshows the reconstructed Z-spectrum after correction using the subvoxel inhomogeneity correction coefficient according to the method300. As shown inFIG.6C, the CIVIC correction resets the center of Z-spectrum to 0 ppm. In addition, the signal intensity at 0 ppm (FIG.6C) is much closer to 0 than the raw CEST Z-spectrum (FIG.6A). These markers show improved subvoxel inhomogeneity correction.

FIG.7shows an example700of a high-resolution field map-based inhomogeneity correction (CIVIC) method for single-voxel MRS or MRS imaging (MRSI) according to embodiments.

As shown inFIG.7, the method700may include a step710of acquiring a B0inhomogeneity field map and a step720of acquiring MRS/MRSI using any available methods. In some embodiments, the B0inhomogeneity field map may be a high resolution. The high resolution (“HR”) B0inhomogeneity field map may be acquired or constructed using any available methods. For example, the B0inhomogeneity field map is acquired using available methods.

By way of example, step710may include acquiring field inhomogeneity map and step720may include acquiring MRS/MRSI.

In some embodiments, the method700may include a step730of registering the (reconstructed, high resolution) B0inhomogeneity field map to the MRS/MRSI using available methods.

The method700may further include a step740of determining the intravoxel inhomogeneity correction coefficient and a step750of constructing the MRS/MRSI spectrum using available methods. For example, the step740may include constructing a point-spread-function (PSF) from the HR subvoxel field inhomogeneity map, per voxel of MRS/MRSI spectrum for the region of interest.

Next, the method700may include a step760of correcting the MRS/MRSI spectrum using the intravoxel inhomogeneity correction coefficient. By way of example, the step760may be performed on real and/or imaginary signals of the MR spectrum (MRS). In some embodiments, the step760may depend on the complex signal of the MR spectrum with automated and/or manual phase adjustment. In some embodiments, the step760may include deconvolving the B0PSF from the constructed spectrum. For example, the deconvolving the subvoxel field inhomogeneity PSF may be for MRS, per voxel/ROI of MRSI.

For example, the correcting (step760) can be performed on a phased spectrum, performed on real and imaginary signals independently, among others, or a combination thereof. After the MRS signal (real, imaginary, complex and/or phase-corrected magnitude) is corrected, the signal may be reconstructed to correct for field inhomogeneity.

By way of example, the processing can be performed on the MRS magnitude spectrum after phase adjustment. In this example, MRSapp=MRSorig⊙PSF, where MRSappand MRSorigare the apparent MRS signal with B0heterogeneity and “genuine” MRS signal without B0heterogeneity, respectively, in which PSF is subvoxel field inhomogeneity function.

FIGS.8A and8BandFIGS.9A-Cshow examples of the application of high-resolution field map-based inhomogeneity correction (CIVIC) method for single-voxel MRS according to embodiments.FIG.8Ashows the high-resolution field map with higher-order gradient shimming condition andFIG.8Bshows high-resolution field map without higher-order gradient shimming condition. A single voxel MRS was acquired from a voxel of 1 cm×1 cm×1 cm under these two shimming conditions.

FIGS.9A-Cshow the effectiveness of CIVIC in improving MRS measurements from a creatine-gel phantom.FIG.9Ashows MRS that acquired under high-order gradient shimming condition (FIG.8A) andFIG.9Bshows the single-voxel MRS that was acquired without high order gradient shimming condition (FIG.8B). In this example, the 3D high-resolution field maps were obtained from a field of view of 5 cm×5 cm×9.6 cm with a matrix size of 64×64×96. The high-resolution field maps have a spatial resolution of 0.78 mm×0.78 mm×1 mm. The single MRS voxel (1 cm×1 cm×1 cm) corresponds to 13×13×10 subvoxels (i.e., 1690) of high-resolution field map.FIG.9Cshows the MRS after correction using the subvoxel inhomogeneity correction coefficient according to the method700. As shown,FIG.9Chas a higher signal intensity and narrower spectral width than that ofFIG.9B, closer to the MRS spectrum obtained under the good shimming condition (FIG.9A).

FIG.10shows an example of a method1000of a field map-based voxel inhomogeneity correction method for CEST imaging or MT imaging without the need of Z-spectrum according to embodiments. By not requiring Z-spectral scans, this example can result in a substantial reduction in scan time, which can be critical for the emergency setting such as acute stroke imaging. In this example, the regression between the apparent CEST effect and B0inhomogeneity can be used to establish the CEST-B0response spectrum, from which, field inhomogeneity can be corrected without Z-spectral scans. In some embodiments, the method1000may be performed with and/or without a method to correct for intravoxel inhomogeneity, such as the method300described inFIG.3. By way of example, the output of the method1000may be used as an input (step320) for the method300. In another example, the method1000may be performed without the method300.

As shown inFIG.10, the method1000may include a step1010of acquiring a B0inhomogeneity field map and a step1020of acquiring CEST/MT (non Z-spectrum) image(s)/spectrum. In some embodiments, the B0inhomogeneity field map may be high resolution. The high resolution (“HR”) B0inhomogeneity field map may be acquired or constructed using any available methods.

For example, the B0inhomogeneity field map may be acquired using available methods. By way of example, the B0inhomogeneity field map can be constructed by interpolation of a Water saturation shift referencing (“WASSR”) map, non-high resolution (or routine) B0inhomogeneity field map resolution field map, directly from an acquired high resolution B0inhomogeneity field map, among others, or a combination thereof. For example, the B0inhomogeneity field map may be determined using the CEST/MT Z-spectrum image(s). By way of example, the lowest point of the WAS SR Z-spectrum can be determined either by symmetry analysis or fitting, which is taken as the bulk water resonance frequency, and the difference between the WAS SR resonance frequency and that from the water resonance may be considered as the B0field inhomogeneity.

In some embodiments, the CEST/MT (non Z-spectrum) image(s)/spectrum may be acquired using available methods. For example, the CEST/MT images (in the absence of Z spectrum) may be acquired by calculating CEST map using

CESTR=Iref-IlabelI0,
where I0is the control image without RF irradiation and Iref,labelare the label and reference images with RF irradiation applied at label and reference frequency offsets, respectively. In other embodiments, other methods may be used. By way of example, it can be other means of CEST effect quantification, such as CEST-specific magnetization transfer and relaxation normalized APT (MRAPT) image (Guo et al. Neuroimage 2016; 141:242-9; Wang et al. Neuroimage 2019; 191:610-7).

In some embodiments, the method1000may include a step1030of registering the (reconstructed, high resolution) B0inhomogeneity field map to the non Z-spectrum CEST/MT image(s)/spectrum using any available methods.

The method1000may further include a step1040of determining one or more subregions that should have a uniform MRI effect (also referred to as “reference region(s)”) of the region of interest. For example, one or more subregions may include a region of known normal tissue, such as intact brain white matter, gray matter, and/or a combination thereof.

The method1000may include a step1050of determining the field inhomogeneity correction coefficient for each voxel of the one or more reference regions or subregions. The voxel-wise inhomogeneity correction coefficient may be determined using linear regression. By way of example, the correction coefficient may be determined in simple phantoms where there is one exchange group with water (2-pool) model. In another example, for example, pH imaging, white matter (WM)/gray matter (GM) heterogeneity may be determined because they have little pH difference (see for example, Guo et al. Neuroimage 2016; 141:242-9; Wang et al. Neuroimage 2019; 191:610-7). By modeling the B0inhomogeneity effect using a regression function, their effect can be accounted for and be used to restore the original CEST effect using CESTRapp≈CESTRorig+[C1·Δωs+C2·Δωs2] or numerical fitting CESTRapp≈CESTRorig+F[Δωs, Δωs2]. In other embodiments, the voxel field inhomogeneity correction coefficient may be determined using other methods/function than this linear regression.

By establishing the voxel-wise dependence of B0and CEST/MT MRI signal, it can be treated as a segment of the spectrum, which the CIVIC correction applies.

In some embodiments, the method1000may include a step1060of correcting the other (sub)regions of the region of interest and/or the entire region of interest (e.g., brain) of the B0map (step1010) and CEST map (step1020) using this coefficient.

FIGS.11A-Fand12A-H show examples of the application of the regression B0correction method using non-Z spectrum CEST (MRAPTR) images according to embodiments.FIGS.11A-Fshows an application of fast B0inhomogeneity correction without Z-spectrum in a dual pH phantom.FIG.11Ashows a raw MTR asymmetry (MTRasym) image calculated from an asymmetry analysis, with noticeable B0inhomogeneity artifact.FIG.11Bshows the B0inhomogeneity map determined from the WASSR scan.FIG.11Cshows a regression analysis between B0inhomogeneity and raw MTRasym, per pixel for each ROI (interior and exterior compartment).FIG.11Dshows fast B0inhomogeneity-corrected MTRasym(fast) map.FIG.11Eshows interpolation-based B0inhomogeneity-corrected MTRasym(intrpl) map.FIG.11Fshows regression analysis between MTRasym(fast) and MTRasym(intrpl), per pixel.

FIGS.12A-Hshow an example of in vivo fast B0inhomogeneity correction in a representative normal adult rat brain regression B0correction method according to embodiments.FIG.12Ashows an example of an acquired mean magnetization transfer ratio (MMTR) at ±3.5 ppm.FIG.12Bshows an example of an acquired pH-sensitive MTRasymmap.FIG.12Cshows pH-specific ΔMRAPTR map without B0inhomogeneity correction.FIG.12Dshows a pH map determined from ΔMRAPTR map without B0inhomogeneity correction. B0inhomogeneity map determined from the WASSR scan is shown inFIG.12E. The regression analysis between B0inhomogeneity and raw ΔMRAPTR, per pixel, is shown inFIG.12F.FIG.12Gshows ΔMRAPTR map with the proposed fast B0inhomogeneity correction, with the corresponding pH map shown inFIG.12H.

FIG.13shows an example1300of a standardization method for corrected MR images/spectrum (e.g., CEST/MT Z-spectral, CEST/MT (non Z-spectrum) image(s) and/or CEST MRS/MRSI)), for example, from step150, according to embodiments. In some embodiments, the corrected MR images/spectrum may correspond to corrected CEST/MT Z-spectral images, for example, using the method300(seeFIG.3) and/or corrected CEST/MT (non Z-spectrum) image(s) according to embodiments, for example, using the method1000(seeFIG.10). In some embodiments, the inhomogeneity for CEST/MT Z-spectral, CEST/MT (non Z-spectrum) image(s) and/or CEST MRS/MRSI may be corrected using other methods. In some embodiments, the standardization method may be applied per voxel for each saturation offset.

The standardization method can improve sensitivity without needing a long-scan time. The standardization can derive the quasi-steady-state even when the experimental saturation duration and relaxation delay are generally not long. This can enable a reduction of the scan time without the loss of the magnitude of the CEST contrast.

In some embodiments, the step1300may include a step1310of acquiring the scan parameters, such as saturation duration and relaxation delay, associated with the corrected MR images/spectrum (step1320), for example, from step150(e.g., corrected CEST/MT images/spectrum).

In some embodiments, the method1300may include a step1330of determining T1 map and B1 using the corrected MR images/spectrum and/or scan parameters. In some embodiments, the T1 map and B1 may be determined using known methods. For example, the T1 map may be determined using inversion recovery methods, saturation recovery methods, look-locker methods, MR fingerprint (MRF) techniques methods, among others, or any combination thereof. For example, B1 may correspond to the amplitude of RF saturation when B1 field is homogenous, may be determined from a B1 map (e.g., determined from known methods such as double angle method (DAM) if B1 field is not homogenous, may be determined using other methods, or any combination thereof.

For example, if standardization is applied per voxel with CEST scan (one offset a time), the corresponding T1 and B1 values may be either direct measurement of the voxel(s). If standardization is applied to CEST MRS/MRSI, corresponding T1 and B1 values may be either direct measurement of the voxel(s) or co-registered from images (MRS voxel is often larger than image voxel).

In some embodiments, the method1300may further include a step1340of determining the tilt angle (θ) for each pixel/voxel. In some embodiments, the title angle (θ) may be determined using the gyromagnetic ratio (γ), amplitude of RF saturation pulse (B1), and offset of the RF saturation pulse (Δω). For example, the title angle (θ) may be determined using the following:

θ=a⁢tan⁡(γ⁢B1Δ⁢ω).(1)

After which, the method1300may include a step1350of determining a steady state spinlock relaxation rate (R1ρ) using the calculated tilt angle (θ). Because the inhomogeneity has been corrected in spectrum/image(s), the calculated tilt angle (θ) may be effectively used to calculate a steady state spinlock relaxation rate (R1ρ).

In some embodiments, the steady state spinlock relaxation rate (R1ρ) for each pixel or voxel may be determined using the calculated tilt angle (θ), scan parameters, parameters determined from the T1 map, B1 map, among others, or any combination thereof.

For example, the steady state spinlock relaxation rate (R1ρ) may be determined using the following:

IsatappI0app·{1-e-R1⁢w·(Ts+Td)1-e-R1⁢w·Td}=e-R1⁢ρ·Ts+{R1⁢wR1⁢ρ·(1-e-R1⁢w·Td)⁢cos2⁢θ)·(1-e-R1⁢ρ·Ts).(2)

In equation (2), Isatappcorresponds to saturated scan acquired from the CEST image/spectrum, I0appcorresponds to unsaturated control scan acquired from the CEST image/spectrum, Ts corresponds to saturation time provided in the scan parameters, Td corresponds to relaxation delay provided in the scan parameters, θ corresponds to the calculated tilt angle, and R1wcorresponds to the bulk water transverse relaxation rate acquired from the T1 map from the step1330(e.g., T1wmap (i.e., R1w=1/T1w)). In some embodiments, R1wand/or R1ρfrom Eq. 2 may be determined using other methods/techniques, such as numerical solution, modified MR fingerprinting (MRF) and/or machine learning.

In some embodiments, the method1300may include a step1360of determining a quasi-steady state signal (I/I0) for each voxel or component for each saturation offset using the calculated tilt angle (θ), the calculated spinlock relaxation rate (R1ρ), and the calculated bulk water transverse relaxation rate (R1w). For example, the quasi-steady state signal (I/I0) may be determined using the following:
I/I0=(R1w/R1ρ)cos2θ  (3).

In some embodiments, the method1300may include a step1370of generating/determining quantitative information. In some embodiments, the quantitative information may include one or more measurements determined based on the standardized voxel/component (the quasi-steady state signal (I/I0) from step1360) for each saturation offset; one or more quantitative images using the one or more measurements, the standardized voxel/component; among others; or any combination thereof. In some embodiments, the one or more measurements may include but is not limited to CEST asymmetry (CESTR) image, the CEST exchange effect (Rex), labile proton concentration, labile proton exchange rate, among others, or any combination thereof. For example, the CESTR may be determined using the following:

CESTR⁢=Ir⁢e⁢fI0-Il⁢a⁢b⁢e⁢lI0,where⁢Ir⁢e⁢fI0⁢and⁢Il⁢a⁢b⁢e⁢lI0
are the quasi-steady-state CEST signals at the reference and label frequency offsets, respectively.

For example, the CEST exchange effect (Rex) may be determined using (standardized) spinlock relaxation rate (R1ρ) (determined using the standardized image and the tilt angle (θ). For example, the (Rex) may be determined using the following:
Rex=R1ρ−R1wcos2θ−R2wsin2θ  (4).

FIGS.14A-Cshows an example of a simulation of the effect of Td and Ts on the CEST MM effect.FIG.14Ashows three representative Z-spectra under short Td and Ts (i.e., Td=1 s/Ts=1 s, x markers), moderate Td, and Ts (i.e., 3 s/3 s, diamond markers), and long Td and Ts (i.e., 10 s/10 s, + markers). For the simulation, we assumed T1w=2 s. As shown inFIG.14A, the Z-spectral intensity drops at a long saturation duration and relaxation delay, as expected.FIG.14Bplots the corresponding Z-spectra asymmetry under the three representative Td and Ts, which increases with the saturation duration and relaxation delay.FIG.14Cshows the CEST effect as a function of saturation time and relaxation delay, indicating dependence on the saturation time and relaxation delay, as expected. Such a strong dependence of CESTR over Td and Ts suggests the need to account for such experimental choice of saturation duration and relaxation delay when comparing results obtained under different experimental conditions. For the Z-spectrum simulation, it was assumed a typical labile proton ratio and an exchange rate of 1:1000 and 100 s−1, respectively, with a labile proton chemical shift at 2 ppm for a magnetic field strength of 7 Tesla with RF saturation amplitude of 1 μT. The minimal and maximal CEST effect was 4.97% and 11.62%, respectively, with its mean and standard deviation being 10.34±1.86% and a coefficient of variance (COV) of 17.97%.

FIGS.15A-Cshow an example of a simulation of the quasi-steady-state CEST MM using the steady-state method (FIG.13) according to embodiments.FIG.15Aplots the quasi-steady-state R1ρsolved from three representative saturation time and relaxation delay, being 1 s/1 s (black cross markers), 3 s/3 s (black diamond markers), and 10 s/10 s (gray plus markers). As shown, the quasi-steady state R1ρnearly overlapped with the apparent R1ρ

(i.e.,I0appIsatapp·R1⁢w⁢cos2⁢θ)
under the condition of long saturation time and relaxation delay (10 s/10 s, black plus markers).FIGS.15B and15Cshow reconstructed quasi-steady-state Z-spectra and asymmetry Z-spectra (e.g., 1 s/1 s (cross markers), 3 s/3 s (diamond markers), and 10 s/10 s gray plus markers)) and those obtained under long saturation time and relaxation delay (10 s/10 s (black plus markers)).FIG.15Dshows the CEST effect as a function of saturation time and relaxation delay. As shown, there is little variation between CEST effect. The minimal and maximal CEST effect was 11.44% and 11.64%, respectively, with its mean and standard deviation being 11.60±0.05% and a coefficient of variance of 0.42%. As shown, the quasi-steady-state CEST Mill effect can be in excellent agreement with the CEST effect at the long saturation time and relaxation delay (i.e., 11.62%). The coefficient of variation (COV) of the quasi-steady-state solution (FIG.15D) is only 2.34% of that without correction (FIG.14C), showing that the quasi-steady-state CEST MM effect determined using the standardization method (FIG.13) according to embodiments can be effective in minimizing the effect of limited saturation duration and relaxation delay.

FIGS.16A-Dshows an example of a comparison of a numerally solved labile proton exchange rate and concentration as functions of the saturation duration and relaxation delay and determined using the steady-state method (FIG.13) according to embodiments.FIG.16Ashows the normalized exchange rate determined from the omega plot of the apparent CEST effect as a function of Td and Ts. As shown, the relative labile proton exchange rate (rksw) was determined from the apparent CEST effect. The exchange rate can be overestimated when Ts is not sufficiently long, while the dependence on Td is less prominent than the Ts dependence. Correspondingly,FIG.16Bshows the normalized labile proton concentration determined from the omega plot of the apparent CEST effect, as a function of Td and Ts. As shown, the relative labile proton ratio (rfs) was determined from the apparent CEST effect. It can be underestimated when Ts is not sufficiently long. In comparison,FIGS.16C and16Dshow the normalized labile proton exchange rate and ratio from the proposed quasi-steady-state solution. InFIG.16C, the relative labile proton exchange rate (rksw) was determined from the quasi-steady-state CEST effect using the standardization method (FIG.13) according to embodiments. InFIG.16D, the relative labile proton ratio (rfs) was determined from the quasi-steady-state CEST effect using the standardization method (FIG.13) according to embodiments. As shown inFIGS.16C and16D, there can little dependence on Ts and Td and therefore the standardization method (FIG.13) according to embodiments can reasonably account for the effect of limited saturation duration and recovery delay, permitting accurate quantification of the underlying CEST systems.

FIG.17shows an example of a system1700for the determination of the correction coefficient. The system for carrying out the embodiments of the methods disclosed herein is not limited to the system shown inFIG.17. Other systems may also be used. It is also to be understood that the system1700may omit any of the modules illustrated and/or may include additional modules not shown. By way of example, the system1700may include the server1750and a computer system (e.g., processor1720, memory1730, and display1740) or the medical scanner with the computer system. In another example, the system is a computer or workstation instead of the medical scanner1710, instead of the server1750, or instead of both. In another example, the processor1720, memory1730, and display1740may be part of the medical scanner1710. In further example, the processor1720, memory1730, and display1740may be a part of an archival and/or image processing system, such as associated with a medical records database workstation or server, separate from the medical scanner1710. In other examples, the processor1720, memory1730, and display1740may be a personal computer, such as a desktop or laptop, a workstation, or combinations thereof. The image processor1710, display1740, and memory1730may be provided without other components for acquiring data by scanning a patient.

The system1700shown inFIG.17may include any number of modules that communicate with each other through electrical or data connections (not shown). In some embodiments, the modules may be connected via any network (e.g., wired network, wireless network, or any combination thereof).

In some embodiments, the system may include one or more processors1720. The processor(s)1720may include one or more processing units, which may be any known processor or a microprocessor. For example, the processor(s) may include any known central processing unit (CPU), imaging processing unit, graphical processing unit (GPU) (e.g., capable of efficient arithmetic on large matrices encountered in deep learning models), among others, or any combination thereof. The processor(s)1720may be coupled directly or indirectly to one or more computer-readable storage media (e.g., memory)1730. The memory1730may include random access memory (RAM), read-only memory (ROM), disk drive, tape drive, etc., or any combinations thereof. The memory1730may be configured to store programs and data, including data structures. In some embodiments, the memory1730may also include a frame buffer for storing data arrays.

In some embodiments, another system may assume the data analysis, image processing, or other functions of the processor(s)1720. In response to commands received from an input device, the programs or data stored in the memory1730may be archived in long term storage or may be further processed by the processor and presented on the display1740.

In some embodiments, the disclosed methods (e.g.,FIGS.1,3,7,10, and13) may be implemented using software applications that are stored in a memory and executed by the one or more processors (e.g., CPU and/or GPU). In some embodiments, the disclosed methods may be implemented using software applications that are stored in memories and executed by one or more processors distributed across the system.

As such, any of the modules of the system1700may be a general-purpose computer system, that becomes a specific purpose computer system when executing the routines and methods of the disclosure. The systems and/or modules of the system1700may also include an operating system and micro instruction code. The various processes and functions described herein may either be part of the micro instruction code or part of the application program or routine (or any combination thereof) that is executed via the operating system.

If written in a programming language conforming to a recognized standard, sequences of instructions designed to implement the methods may be compiled for execution on a variety of hardware systems and for interface to a variety of operating systems. In addition, embodiments are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement embodiments of the disclosure. An example of hardware for performing the described functions is shown inFIG.17. It is to be further understood that, because some of the constituent system components and method steps depicted in the accompanying figures can be implemented in software, the actual connections between the systems components (or the process steps) may differ depending upon the manner in which the disclosure is programmed. Given the teachings of the disclosure provided herein, one of ordinary skill in the related art will be able to contemplate these and similar implementations or configurations of the disclosure.

The disclosures of each and every publication cited herein are hereby incorporated herein by reference in their entirety.

While the disclosure has been described in detail with reference to exemplary embodiments, those skilled in the art will appreciate that various modifications and substitutions may be made thereto without departing from the spirit and scope of the disclosure as set forth in the appended claims. For example, elements and/or features of different exemplary embodiments may be combined with each other and/or substituted for each other within the scope of this disclosure and appended claims.