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for invention","granted":true,"earliest_filing_date":"2016-12-22","grant_date":"2019-02-26","anticipated_term_date":"2037-06-27","has_disclaimer":false,"patent_status":"ACTIVE","publication_count":3,"has_spc":false,"has_grant_event":true,"has_entry_into_national_phase":false},"abstract":{"en":[{"text":"Methods of obtaining quantitative MRI images of a subject that includes fitting a theoretical model that accounts for tissue-specific relaxation properties and magnetization transfer effects to MRI measured data is disclosed.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"}]},"abstract_lang":["en"],"has_abstract":true,"claim":{"en":[{"text":"1. A method of performing quantitative MM imaging of a subject, the method comprising: acquiring a plurality of MR data using an RF coil from the subject using a GRE sequence with a series of read-out gradient pulses with a plurality of flip angles; combining a plurality of MRI image datasets obtained from the subject to form a single dataset, each MRI image dataset comprising a plurality of imaging voxels and an image value set associated with each imaging voxel, each image value set comprising multiple image values, each image value reconstructed from k-space MRI data obtained from one RF channel of the RF coil at one combination of read-out gradient pulse and flip angle of the GRE sequence; fitting a theoretical model S(α, TR, TE) to each image value set associated with each imaging voxel of the single dataset, the theoretical model S(α, TR, TE) characterized by five quantitative tissue-specific MRI parameters, the quantitative tissue-specific MM parameters comprising: S 0 representing a spin density, R 1 representing a longitudinal relaxation rate constant, R 2 *; representing a transverse relaxation rate constant, k f ′ representing a cross-relaxation rate constant, and λ representing a magnetization transfer-related relaxation parameter, wherein α is a flip angle, TR is a repetition time, and TE is a gradient echo time of the GRE sequence; and producing at least one quantitative image (map) comprising each imaging voxel and at least one corresponding value of S 0 , R 1 , R 2 * , k f ′ and λ determined from fitting the theoretical model S(α, TR, TE).","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"2. The method of claim 1 , wherein combining the plurality of MRI image datasets comprises summing M GRE signals S m obtained by M RF channels for each imaging voxel according to the relation: wherein: S* is a complex conjugate of S, η m is a weighting factor, and σ m is an r.m.s. noise amplitude.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"3. The method of claim 1 , wherein the theoretical model S(α, TR, TE) further comprises: Δω is a frequency shift, F(TE) is a correction factor for B0 macroscopic field inhomogeneities, and q is a correction factor for B1 radio frequency transmitter field inhomogeneities.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"4. The method of claim 3 , wherein F(TE) is obtained using a voxel spread function approach.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"5. The method of claim 3 , wherein the theoretical model S(α, TR, TE) is further characterized by q, and q is determined during fitting the theoretical model S(α, TR, TE) to each image value set associated with each imaging voxel of the single dataset.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"6. The method of claim 5 , wherein each value of q at each imaging voxel is corrected by averaging the q values within a surrounding region determined for each imaging voxel.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"7. The method of claim 3 , wherein q is determined by: obtaining a first plurality of MR signals produced in response to a first pair of successive and orthogonal excitation RF pulses comprising a first initial excitation RF pulse with a first initial flip angle α and a first final excitation RF pulse produced orthogonal to the first initial excitation RF pulse with a first final flip angle β; obtaining a second plurality of MR signals produced in response to a second pair of successive and orthogonal excitation RF pulses comprising a second initial excitation RF pulse with a second initial flip angle α and a second final excitation RF pulse produced orthogonal to the second initial excitation RF pulse excitation with a second final flip angle −β; and obtaining a phase difference Δφ between each portion of the first and second plurality of MR signals corresponding to each imaging voxel according to the relation: wherein: M is the number of RF channels of an RF coil used to obtain the first and second plurality of MR signals, N is the number of echo times, S m (1) is an MR signal from the portion of the first plurality of MR signals, and S m (2) * is a complex conjugate of the portion of the second plurality of MR signals; and obtaining q from the relationship: wherein α is a flip angle of a first excitation RF pulse corresponding to the first plurality of MR signals, β is a flip angle of a second excitation RF pulse corresponding to the second plurality of MR signals, p=Δω·τ, Ωα=((αq) 2 +p 2 ) 1/2 , Ωβ=((βq) 2 +p 2 ) 1/2 .","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"8. The method of claim 7 , wherein α=β and the method further comprises obtaining q from the relationship wherein Ω=((αq) 2 +p 2 ) 1/2 .","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"9. The method of claim 1 , further comprising calculating a Tissue Damage Score (TDS) for each imaging voxel according to TDS R =( R C −R )/ R C wherein R c is a position of a peak center in a Gaussian distribution of one of the five quantitative tissue-specific MRI parameters associated with a substructure within a tissue of the subject, and R is a value of the one quantitative tissue-specific MM parameter at each imaging voxel.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"10. The method of claim 1 , wherein an MM image dataset is acquired independently at each different flip angle and all MRI image datasets are combined to produce the single dataset.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"11. The method of claim 1 , wherein each MM image dataset acquires all flip angles in an interleaved manner at each phase encoding step.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"12. The method of claim 1 , further comprising acquiring the k-space MM data from a plurality of RF channels using a Gradient Recalled Echo (GRE) sequence, the GRE sequence comprising multiple gradient echoes and multiple flip angles.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"13. The method of claim 12 , further comprising removing an artifact associated with physiological fluctuations of the subject during acquisition of the k-space MRI data.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"14. A method of mapping a B1 radio-frequency field within a region of interest of an MM scanning device, the method comprising: obtaining a first plurality of MR signals produced in response to a first pair of successive and orthogonal excitation RF pulses comprising a first initial excitation RF pulse with a first initial flip angle α and a first final excitation RF pulse produced orthogonal to the first initial excitation RF pulse with a first final flip angle β; obtaining a second plurality of MR signals produced in response to a second pair of successive and orthogonal excitation RF pulses comprising a second initial excitation RF pulse with a second initial flip angle α and a second final excitation RF pulse produced orthogonal to the second initial excitation RF pulse excitation with a second final flip angle −β; and analyzing the first and second plurality of MR signals to obtain a map of a B1-encoded MR signal phase and a B0-dependent signal frequency.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"15. The method of claim 14 , wherein analyzing the first and second plurality of MR signals comprises: obtaining a phase difference Δφ between each portion of the first and second plurality of MR signals corresponding to each imaging voxel according to the relation: wherein: M is the number of RF channels of an RF coil used to obtain the first and second plurality of MR signals, N is the number of echo times, S m (i) is an MR signal from the portion of the first plurality of MR signals, and S m (2) * is a complex conjugate of the portion of the second plurality of MR signals; and obtaining q from the relationship: wherein α is a flip angle of a first excitation RF pulse corresponding to the first plurality of MR signals, β is a flip angle of a second excitation RF pulse corresponding to the second plurality of MR signals, p=Δω·τ, Ωα=((αq) 2 +p 2 ) 1/2 , Ωβ=((βq) 2 +p 2 ) 1/2 , and q represents inhomogeneities in a B1 radio frequency transmitter field.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"16. The method of claim 15 , wherein α=β and the method further comprises obtaining q from the relationship wherein Ω=((αq) 2 p 2 ) 1/2 .","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"17. A method of performing quantitative MM imaging of a subject, the method comprising: acquiring a plurality of MR data using an RF coil from the subject using a GRE sequence with a series of read-out gradient pulses with a plurality of flip angles; combining a plurality of MRI image datasets obtained from the subject to form a single dataset, each MM image dataset comprising a plurality of imaging voxels and an image value set associated with each imaging voxel, each image value set comprising multiple image values, each image value reconstructed from k-space Mill data obtained from one RF channel of the RF coil at one combination of read-out gradient pulse and flip angle of the GRE sequence; fitting a theoretical model S(α, TR, TE) to each image value set associated with each imaging voxel of the single dataset, the theoretical model S(α, TR, TE) characterized by five quantitative tissue-specific MM parameters, the quantitative tissue-specific MM parameters comprising: S 0 representing a spin density, R 1 representing a longitudinal relaxation rate constant, R 2 * representing a transverse relaxation rate constant, k f ′ representing a cross-relaxation rate constant, and λ representing a magnetization transfer-related relaxation parameter, and an additional parameter q representing inhomogeneities in a B1 radio frequency transmitter field wherein α is a flip angle, TR is a repetition time, and TE is a gradient echo time of the GRE sequence; and producing at least one quantitative image (map) comprising each imaging voxel and at least one corresponding value of S 0 , R 1 , R 2 *, k f ′ and λ determined from fitting the theoretical model S(α, TR, TE).","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"},{"text":"18. The method of claim 17 , wherein combining the plurality of MRI image datasets comprises summing M GRE signals S m obtained by M RF channels for each imaging voxel according to the relation: wherein: S* is a complex conjugate of 5, η m is a weighting factor, and σ m is an r.m.s. noise amplitude.","lang":"en","source":"USPTO_FULLTEXT","data_format":"ORIGINAL"}]},"claim_lang":["en"],"has_claim":true,"description":{"en":{"text":"CROSS REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application Ser. No. 62/354,469 entitled “ROBUST MODEL-INDEPENDENT B1 MAPPING WITH PHASED-ARRAY RF COILS: A MULTI-GRADIENT-ECHO APPROACH WITH ORTHOGONAL EXCITATION RF PULSES AND B0 CORRECTION” filed on Jun. 24, 2016, the entirety of which is hereby incorporated by reference. This application also claims the benefit of U.S. Provisional Patent Application Ser. No. 62/271,165 entitled “METHODS FOR SIMULTANEOUS MULTI-ANGULAR RELAXOMETRY OF TISSUE USING MAGNETIC RESONANCE IMAGING” filed on Dec. 22, 2015, the entirety of which is hereby incorporated by reference. FIELD OF THE INVENTION This disclosure relates to methods of MRI imaging of biological tissues. In particular, this disclosure relates to methods of obtaining quantitative MRI images of tissue-specific parameters describing relaxation properties and magnetization transfer effects, by fitting a theoretical model that accounts for these tissue-specific parameters, to MRI measured data. BACKGROUND Quantitative measurements of relaxation parameters characterizing MRI signal, such as longitudinal (R 1 ), transverse (R 2 and R 2 *) and cross-relaxation rate constants have always been an important task in the field of quantitative MRI. Conventional inversion recovery (IR) and spin echo (SE) sequences have been used as the gold standard to measure R 1 and R 2 , respectively. However, the long acquisition times have greatly hindered their clinical applications. Compared to conventional IR techniques, variable flip angle (VFA) R 1 mapping is based on imaging in steady state and can greatly reduce scan time, thus making 3D high-resolution R 1 mapping of the whole brain feasible. Information on cross-relaxation parameters is usually obtained using off-resonance magnetization transfer (MT) saturation pulses. It was also realized that MT effects can greatly affect gradient echo signals, even in the absence of off-resonance MT saturation pulses thus causing systematic errors in VFA measurements. Recently, a Gradient Echo Plural Contrast Imaging (GEPCI) technique based on a Gradient Recalled Echo (GRE) sequence with multiple gradient echoes has enabled simultaneous generation of naturally co-registered multi-contrast images (T 1 -weighted or spin density images, R 2 * maps and frequency maps) from a single MR scan. R 2 * mapping using GRE sequences has many advantages. First, the acquisition is fast, and therefore suffers from fewer motion artifacts. Second, due to the low flip angle used in GRE sequences, they have lower RF power deposition and are more suitable for high-field MRI. GRE sequences may also be sensitive to tissue-specific magnetic susceptibility effects, and hence may provide separate information on tissue cellular and hemodynamic properties. Different methods have been used in the past to map the B1 field. The magnitude-based methods strongly rely on specific theoretical models that fail to account for complexity of biological tissues. As a result, previous methods are subject to biases and errors due to the limitations of the models imperfections and usually require a low-pass filter to eliminate noise in the data. To minimize this effect, long repetition times to suppress the T 1 dependence of the signal can be used but demand very long acquisition times. The phase-based approaches are less dependent on a model than amplitude-based methods. SUMMARY In one aspect, a method of performing quantitative MRI imaging of a subject is disclosed. The method includes acquiring a plurality of MR data using an RF coil from the subject using a GRE sequence with a series of read-out gradient pulses with a plurality of flip angles. The method also includes combining a plurality of MRI image datasets obtained from the subject to form a single dataset. Each MRI image dataset includes a plurality of imaging voxels and an image value set associated with each imaging voxel. Each image value set includes multiple image values, and each image value is reconstructed from k-space MRI data obtained from one RF channel of the RF coil at one combination of read-out gradient pulse and flip angle of the GRE sequence. The method also includes fitting a theoretical model S(α, TR, TE) to each image value set associated with each imaging voxel of the single dataset. The theoretical model S(α, TR, TE) is characterized by five quantitative tissue-specific MRI parameters. The quantitative tissue-specific MRI parameters include: S 0 representing a spin density, R 1 representing a longitudinal relaxation rate constant, R 2 * representing a transverse relaxation rate constant, k f ′ representing a cross-relaxation rate constant, and λ representing a magnetization transfer-related relaxation parameter. In addition, α is a flip angle, TR is a repetition time, and TE is a gradient echo time of the GRE sequence. The method further includes producing at least one quantitative image (map) that includes each imaging voxel and at least one corresponding value of S 0 , R 1 , R 2 *, k f ′, and λ determined from fitting the theoretical model S(α, TR, TE). In another aspect, a method of mapping a B1 radio-frequency field within a region of interest of an MRI scanning device is disclosed. The method includes obtaining a first plurality of MR signals produced in response to a first pair of successive and orthogonal excitation RF pulses that include a first initial excitation RF pulse with a first initial flip angle α, as well as a first final excitation RF pulse produced orthogonal to the first initial excitation RF pulse with a first final flip angle β. The method also includes obtaining a second plurality of MR signals produced in response to a second pair of successive and orthogonal excitation RF pulses that include a second initial excitation RF pulse with a second initial flip angle α as well as a second final excitation RF pulse produced orthogonal to the second initial excitation RF pulse excitation with a second final flip angle −β. The method also includes analyzing the first and second plurality of MR signals to obtain a map of a B1-encoded MR signal phase and a B0-dependent signal frequency. In an additional aspect, a method of performing quantitative MRI imaging of a subject is disclosed. The method includes acquiring a plurality of MR data using an RF coil from the subject using a GRE sequence with a series of read-out gradient pulses with a plurality of flip angles. The method also includes combining a plurality of MRI image datasets obtained from the subject to form a single dataset. Each MRI image dataset includes a plurality of imaging voxels and an image value set associated with each imaging voxel. Each image value set includes multiple image values, and each image value is reconstructed from k-space MRI data obtained from one RF channel of the RF coil at one combination of read-out gradient pulse and flip angle of the GRE sequence. The method also includes fitting a theoretical model S(α, TR, TE) to each image value set associated with each imaging voxel of the single dataset. The theoretical model S(α, TR, TE) is characterized by five quantitative tissue-specific MRI parameters. The quantitative tissue-specific MRI parameters include: S 0 representing a spin density, R 1 representing a longitudinal relaxation rate constant, R 2 * representing a transverse relaxation rate constant, k f ′ representing a cross-relaxation rate constant, and λ representing a magnetization transfer-related relaxation parameter, as well as an additional parameter q representing inhomogeneities in a B1 radio frequency transmitter field. In addition, α is a flip angle, TR is a repetition time, and TE is a gradient echo time of the GRE sequence. The method further includes producing at least one quantitative image (map) that includes each imaging voxel and at least one corresponding value of S 0 , R 1 , R 2 *, k f ′, and λ determined from fitting the theoretical model S(α, TR, TE). BRIEF DESCRIPTION OF THE DRAWINGS The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. FIG. 1 is a flow chart illustrating a SMART MRI quantitative MRI imaging method according to one aspect. FIG. 2 is a flow chart illustrating a method of calculating the parameter q used for compensation of B1 field inhomogeneities according to one aspect. FIG. 3 is a schematic block diagram of a MR pulse sequence corresponding to the consecutive data acquisition. FIG. 4 is a schematic block diagram of a MR pulse sequence corresponding to the interleaved data acquisition. FIG. 5 contains images of SMART MRI maps of S 0 , R 1 , R 1 (f) , k f ′, λ, and R 2 * from a healthy subject. FIG. 6A is an MPRAGE image from a healthy subject. FIG. 6B is a SMART MRI map of R 1 from a healthy subject. FIG. 6C is a SMART MRI map of R 1 (f) from a healthy subject. FIG. 6D is a SMART MRI map of k f ′ from a healthy subject. FIG. 6E is a graph containing a histogram of the MPRAGE signals from the image of FIG. 6A . FIG. 6F is a graph containing a histogram of R 1 signals from the map of FIG. 6B . FIG. 6G is a graph containing a histogram of the R 1 (f) signals from the map of FIG. 6C . FIG. 6H is a graph containing a histogram of the k f ′ signals from the map of FIG. 6D . FIG. 7 contains a series of images of multi-parametric SMART MRI maps of S 0 , R 1 , R 1 (f) , k′ f and R 2 * (top row), as well the multi-parametric SMART MRI maps of S 0 , R 1 , R 1 (f) , k f ′ and R 2 * superimposed with the corresponding TDS scores of an MS subject (bottom row). FIG. 8 contains a series of images (top row) and corresponding magnifications (bottom row) of multi-parametric SMART MRI maps of S 0 , R 1 , R 1 (f) , k f ′ and R 2 * (top row) with MS lesions indicated by superimposed yellow arrows in the thalamus region of the SMART MRI magnified maps. FIG. 9 is a comparison of multi-parametric SMART MRI maps of S 0 , R 1 , R 1 (f) , k f ′ and R 2 * in the cerebellum region from a healthy subject (top row) and an MS subject (middle row); corresponding magnifications of the multi-parametric SMART MRI maps of the MS subject (bottom row) include a superimposed yellow arrows indicating an MS lesion. FIG. 10 is a graph of the ratio R 1 (app) /R 1 (f) as a function of the repetition time TR at different values of the exchange rate constant K (in sec −1 , shown by numbers by the lines); R 1 (f) =0.8 s −1 , R 1 (b) =1 s −1 , ζ b =0.2. FIG. 11 is graph of the function Ψ(α,TR) as a function of the flip angle α for two different values of k f ′. The black line corresponds to k f ′=0.35 s −1 , TR=18 ms, v=1, τ=0.4 ms, R 1 =1.2 s −1 , k′ f =0.35 s −1 , and λ=6·10 4 s −1 . The red line corresponds to k f ′=0, TR=18 ms, v=1, τ=0.4 ms, R 1 =1.2 s −1 , and λ=6·10 4 s −1 . FIG. 12A is a graph of the uncertainty per unit measurement of R 2 * (δR 2 *) as a function of maximal flip angle (α max ) for different minimal flip angles α max =1°, 5°, 9° (black, red, and green lines, respectively), obtained using a 2° flip angle interval, TR=18 ms, and three gradient echoes (TE=2, 6, and 10 ms). FIG. 12B is a graph of the uncertainty per unit measurement of R 1 (δR 1 ) as a function of maximal flip angle (α max ) for different minimal flip angles α min =1°, 5°, 9° (black, red, and green lines, respectively), obtained using a 2° flip angle interval, TR=18 ms, and three gradient echoes (TE=2, 6, and 10 ms). FIG. 12C is a graph of the uncertainty per unit measurement of k f ′ (δk f ′) as a function of maximal flip angle (α max ) for different minimal flip angles α min =1°, 5°, 9° (black, red, and green lines, respectively), obtained using a 2° flip angle interval, TR=18 ms, and three gradient echoes (TE=2, 6, and 10 ms). FIG. 12D is a graph of the uncertainty per unit measurement of λ (δλ) as a function of maximal flip angle (α max ) for different minimal flip angles α min =1°, 5°, 9° (black, red, and green lines, respectively), obtained using a 2° flip angle interval, TR=18 ms, and three gradient echoes (TE=2, 6, and 10 ms). FIG. 13 is a schematic block diagram of a SMART MRI imaging system in one aspect. FIG. 14 is a schematic block diagram of an example server system. FIG. 15 is a block diagram of an example computing device. FIG. 16A is a graph of a second flip angle, β * =β * (α), corresponding to the minimum of the function F(α, β) with respect to β at a given α. FIG. 16B is a graph of an estimation error, δq, corresponding to the flip angle pair (α, β * (α)) as a function of flip angle α. FIG. 16C is a graph of SAR (specific absorption rate) for the flip angle pair (α, β * (α)) as a function of flip angle α. Both the pulses are assumed to have the same duration. The system and sequence parameters are: T1=1 s, TR=30 ms, SNR=50 for the sequence with α=β=90°. FIG. 17 is a graph of the ratio α′/α, as a function of the frequency shift Δω for α=π/2 and τ=0.4 msec. FIG. 18 contains schematic diagrams illustrating a pulse sequence for B1 mapping, including radio frequency pulses (RF, top graph), read-out pulses (RO, middle graph) and phase encoding pulses (PE, bottom graph). FIG. 19A is a magnitude image obtained using B1 mapping of a small spherical phantom. FIG. 19B is a map of the frequency offset (Δf) due to B0 field inhomogeneities obtained using B1 mapping of a small spherical phantom. FIG. 19C is a map of the phase difference Δφ between the two scans ((X, Y) and (X, −Y)) obtained using B1 mapping of a small spherical phantom. FIG. 19D is a map of the parameter q′ describing B1 field inhomogeneities, calculated with Δf=0 obtained using B1 mapping of a small spherical phantom. FIG. 19E is a map of the parameter q calculated with Δf from the map of FIG. 19B . FIG. 19F is a graph showing histograms of the parameters q and q′ from FIG. 19D and FIG. 19E , respectively; the histograms are undistinguishable from one another. FIG. 19G is a magnitude image obtained using B1 mapping of a large spherical phantom. FIG. 19H is a map of the frequency offset (Δf) due to B0 field inhomogeneities obtained using B1 mapping of a large spherical phantom. FIG. 19I is a map of the phase difference Δφ between the two scans ((X, Y) and (X, −Y)) obtained using B1 mapping of a large spherical phantom. FIG. 19J is a map of the parameter q′ describing B1 field inhomogeneities, calculated with Δf=0 obtained using B1 mapping of a large spherical phantom. FIG. 19K is a map of the parameter q calculated with Δf from the map of FIG. 19H . FIG. 19L is a graph showing histograms of the parameters q and q′ from FIG. 19J and FIG. 19K , respectively; the histograms are undistinguishable from one another. FIG. 20A is a magnitude image obtained using B1 mapping of a healthy volunteer. FIG. 20B is a map of the frequency offset (Δf) due to B0 field inhomogeneities obtained using B1 mapping of a healthy volunteer. FIG. 20C is a map of the phase difference Δφ between the two scans ((X, Y) and (X, −Y)) obtained using B1 mapping of a healthy volunteer. FIG. 20D is a map of the parameter q′ describing B1 field inhomogeneities, calculated with Δf=0 obtained using B1 mapping of a healthy volunteer. FIG. 20E is a map of the parameter q calculated with Δf from the map of FIG. 20B . FIG. 20F is a graph showing histograms of the parameters q and q′ from FIG. 20D and FIG. 20E , respectively; the histograms are undistinguishable from one another. FIG. 21 contains a series of SMART MRI maps of the parameters S 0 , R 1 , R 1 (f) , k f ′ and R 2 * obtained without B1 correction (top row) and with B1 correction (middle row), as well as histograms of each corresponding parameter obtained without B1 correction (dashed lines) and with B1 correction (solid lines). Corresponding reference characters and labels indicate corresponding elements among the views of the drawings. The headings used in the figures should not be interpreted to limit the scope of the claims. Aspects of the invention may be better understood by referring to the following description in conjunction with the accompanying drawings. DETAILED DESCRIPTION While the making and using of various embodiments of the invention are discussed in detail below, it should be appreciated that the presently described embodiments provide many applicable inventive concepts that may be embodied in a wide variety of contexts. The specific embodiments discussed herein are merely illustrative of exemplary ways to make and use embodiments of the invention and do not delimit the scope of the invention. To facilitate the understanding of this invention, a number of terms are defined below. Terms defined herein have meanings as commonly understood by a person of ordinary skill in the areas relevant to the embodiments of the invention. Terms such as “a,” “an” and “the” are not intended to refer to only a singular entity, but include the general class of which a specific example may be used for illustration and are intended to mean that there are one or more of the elements. The terminology herein is used to describe specific embodiments of the invention, but their usage does not delimit the invention, except as outlined in the claims. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. The order of execution or performance of the operations in embodiments of the invention illustrated and described herein is not essential, unless otherwise specified. That is, the operations may be performed in any order, unless otherwise specified, and embodiments of the invention may include additional or fewer operations than those disclosed herein. For example, it is contemplated that executing or performing a particular operation before, contemporaneously with, or after another operation is within the scope of aspects of the invention. The terminology herein is used to describe embodiments of the invention, but their usage does not delimit the invention. All numerical results, calculated for specific sets of parameters and displayed in the graphs included herein for illustrative purpose only and do not restrict or limit implications of the methods for other set of parameters unless otherwise is clearly stated. In one aspect, a technique of providing quantitative measurements of tissue-specific spin density, relaxation and cross-relaxation parameters is disclosed. Typically, the measurements used to obtain tissue-specific relaxation and cross-relaxation parameters may include uncertainties due to artifacts created by inhomogeneities of macroscopic (B0) and radio-frequency (B1) fields, as well as the effects of physiological fluctuations and MRI system instabilities. In an aspect, a method for correction of artifacts related to B1 inhomogeneity effects is disclosed herein below. The method in this aspect not only enables the accurate measurement of B1 Radio-Frequency (RF) fields, but further provides additional capabilities for other MRI applications, such as RF pulse design. In one aspect, a phase-sensitive approach for B1 mapping that relies on a multi-gradient-echo sequence with two successive orthogonal RF pulses is disclosed. In another aspect, a theoretical expression relating the MR signal phase to the B1 and B0 fields is disclosed that enables B1 evaluation based on measured MR signal phase and frequency. In an additional aspect, a theoretical analysis is disclosed that enables the selection of MR sequence parameters based on minimization of the B1 measurement error. In another additional aspect, a method for combining multi-channel data for optimal parameters estimation is disclosed. Provided herein is a technique, SMART MRI, which provides quantitative maps of naturally co-registered tissue MRI parameters: spin density, longitudinal (R 1 ) and transverse (R 2 *) relaxation rate constants as well as essential parameters characterizing MT (magnetization transfer) effects within a single MRI experiment. Acquiring GRE data with multiple gradient echoes and multiple flip angles and accounting for cross-relaxation effects between “free” (intra- and extra-cellular) and “bound” (attached to macromolecules) water enables quantitative mapping of not only tissue R 2 * relaxation rate but also tissue spin density, R 1 relaxation rate and some essential MT effects. The SMART MRI method disclosed herein may be used in various clinical applications including, but not limited to, obtaining comparative data from healthy subjects and patients afflicted with various disorders including, but not limited to, multiple sclerosis (MS), traumatic brain injury, Alzheimer disease, psychiatric disorders, and other disorders. An accurate determination of R 1 , R 2 * and MT relaxation rate parameters has always been an important task in the field of quantitative MRI as these parameters may be used as biomarkers characterizing “health status” of biological tissues. Without being limited to any particular theory, it is thought that MT exchange effect between “free” water protons and “bound” water protons (cross-relaxation) can greatly affect the results of R 1 mapping, even in the absence of off-resonance MT saturation pulse, thus making R 1 mapping sequence dependent. To address this effect, the SMART MRI method disclosed herein makes use of a theoretical model that accounts for cross-relaxation effects and provides quantitative expressions describing the dependence of the GRE signal on tissue parameters characterizing free water spin density S 0 and internal relaxation rate R 1 (f) . This model also provides information on other parameters characterizing cross-relaxation effects, thus allowing MT imaging without MT pulses. This theoretical model, combined with the GRE sequence with multiple gradient echoes and multiple flip angles, may be used to simultaneously map multiple relaxation parameters describing free water and its interaction with bound water (i.e., water bounded to macromolecules). This approach generalizes an existing GEPCI approach, thereby enabling simultaneous generation of R 2 * maps and T 1 -weighted images. SMART MRI enables quantitative mapping of R 2 *, T 1 =1/R 1 (instead of T 1 -weighted), tissue density S 0 , local frequency shift Δω, free water relaxation rate R 1 (f) , and additional parameters characterizing cross-relaxation MT effects including, but not limited to, k′ f and λ (defined in the Summary and Eq. [3] below). Without being limited to any particular theory, the accuracy of measurements of relaxation parameters may be influenced by B1 (RF) field inhomogeneities. In one aspect, a phase-based B1 mapping method is disclosed that enables fast, accurate B1 mapping using a multi-channel phased-array coils. This method was used to perform measurements on two spherical phantoms of different sizes and on a human subject as described in the examples provided herein below. In addition, accurate quantification of human brain tissue relaxation and magnetization transfer parameters using the disclosed SMART MRI technique was also demonstrated in the examples provided herein below. I. SMART MRI Quantitative MRI Imaging Method In various aspects, a SMART MRI quantitative imaging method analyzes GRE (Gradient Recalled Echo) data with multiple gradient echoes and multiple flip angles obtained using widely available MRI scanning devices to simultaneously obtain naturally co-registered maps of spin density (S 0 ), longitudinal (R 1 ) and transverse (R 2 *) relaxation rate constants, as well as MT (magnetization transfer) related relaxation parameters (k′ f and λ). The SMART MRI method enables high quality quantitative imaging data to be acquired in a relatively brief scan time without need for specialized MRI scanning equipment. By way of non-limiting example, high resolution (1×1×1 mm 3 ) data covering the whole brain and upper spinal cord of a human subject may be acquired within 18 minutes using a standard clinical 3 T scanner. The SMART MRI method may be used to image healthy brains and brains of patients with brain disorders, such as MS, to detect and evaluate brain damage in patients with brain disorders by means of multiple quantitative relaxometric maps, thereby enhancing the available diagnostic tools available to a clinician. By way of non-limiting example, all relaxation parameters determined using the SMART MRI method are typically characterized by lower parameter values in normal-appearing white and gray matter of MS subjects compared to healthy controls and thus enable effective contrast of MS lesions, including in the cerebellum of MS patients. Other diseases may be characterized by parameters values that are increased relative to corresponding parameter values of healthy controls. In some aspects, Tissue Damage Score (TDS) maps, based on quantitative relaxometric parameters produced by SMART MRI, can be created using the SMART MRI method disclosed herein. Tissue Damage Score (TDS) maps may highlight details in the structure of WM lesions in MS patients, and may further reveal heterogeneity within WM lesions such as ring structures within some lesions. This ring structure likely reflects known pathologic features of some MS lesions, where the center is often less cellular and inactive, but with disease activity at the periphery. Thus, SMART MRI provides noninvasive insights into the dynamic pathology within some lesions. FIG. 1 is a flow chart illustrating the SMART-MRI method 100 in one aspect. Referring to FIG. 1 , the method 100 includes obtaining GRE data with multiple gradient echoes and multiple flip angles at step 102 . Any suitable MRI scanner capable of performing GRE imaging may be used to obtain the GRE data at step 102 . Non-limiting examples of suitable MRI scanners include closed MRI scanners, open MRI scanners, sitting MRI scanners, and standing MRI scanners. In addition, suitable MRI scanners may have any magnet field strength including, but not limited to a magnetic field strength from about 0.5 Tesla (T) to about 3 T or higher. By way of non-limiting example, a 3 T Trio MRI scanner (Siemens, Erlangen, Germany) equipped with a 32-channel phased-array head coil may be used to obtain GRE data at step 102 . The GRE data may be acquired at step 102 using a multi-slice two dimensional (2D) or a three dimensional (3D) multi-gradient-echo sequence with at least two flip angles. In various aspects, a variety of flip angles, gradient echo times and GRE sequence repetition times can be used to perform SMART MRI. Since a fitting routine is used to determine relaxometry parameters, the number of measurements should be bigger than the number of measured parameters. In various embodiments, the GRE data may be acquired at step 102 using a three dimensional (3D) multi-gradient-echo sequence with at least three flip angles, at least four flip angles, at least five flip angles, at least six flip angles, at least seven flip angles, at least eight flip angles, at least nine flip angles, and at least ten flip angles. The flip angles may range from about 1° to about 180° in various aspects. In one exemplary aspect, the GRE data may be acquired at step 102 using a three dimensional (3D) multi-gradient-echo sequence with five flip angles of 5°, 10°, 20°, 40° and 60°. In one aspect, the MR pulse sequence may enable consecutive data acquisition, as illustrated schematically in FIG. 3 . Referring to FIG. 3 , MR data are collected separately for each flip angle and the m th block corresponds to a flip angle α m (m=1, 2, . . . M). The first row in FIG. 3 shows a position of the RF pulse (rectangle) and positions of picked-up signals (rhombi). The second row displays the position of phase encoding gradients (the first trapezoid with horizontal lines), phase-compensated gradients (the second trapezoid), and the crasher gradient (star). The phase encoding gradients serve to encode images for 2D or 3D acquisition. The third row shows a profile of read-out gradients. The last, (N+1) th , signal (phase-stabilization signal) is acquired after applying the phase-compensated gradient and serves to correct images for physiological fluctuations. The fourth line shows the echo times around which the signals are acquired: TE 1 , TE 2 , . . . TE N , TE N+1 . In another aspect, the MR pulse sequence may enable interleaved data acquisition, as illustrated schematically in FIG. 4 . As illustrated in FIG. 4 , MR data are collected separately for each line of phase-encoding and phase-compensated gradients and then combined in the whole data set. The m th block corresponding to a flip angle α m (m=1, 2, . . . M). The first row shows a position of the RF pulse (rectangle) and positions of picked-up signals (rhombi). The second row displays the position of phase encoding gradients (shown as a line within the first trapezoid with horizontal lines), phase-compensated gradients (shown as a line within the second trapezoid), and the crasher gradient (star). The third row shows a profile of read-out gradients. The structure of the last, M th , block differs from the others: the first M−1 blocks do not contain the phase-stabilization signal acquisition (at TE N+1 ). Only the last (M th ) block contains the phase-stabilization signal acquisition after applying the phase-compensated gradient. The fourth line shows the moments (echo times) at which the signal are acquired: TE 1 , TE 2 , . . . TE N , TE N+1 . This MR pulse sequence illustrated in FIG. 4 allows faster acquisition because it uses only one phase-stabilization readout for all flip angles. In various aspects, the GRE data acquired at step 102 can be under-sampled k space data. In this case, a full k-space data should be reconstructed prior to further analysis by different methods including, but not limited to, a Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) package. Referring again to FIG. 1 , the method 100 may further include removing artifacts associated with the acquisition of the GRE data at step 104 including, but not limited to, artifacts caused by physiological fluctuations of the subject during MRI scanning. The method 100 may further include Fourier transforming the k-space data at step 106 for each separate channel, and combining the data from different channels of the MRI scanning device at step 108 . Combining data from different channels may enhance the estimation of quantitative parameters, such as MR signal decay rate constants, and may additionally remove any initial phase incoherence between channels. In one aspect, data from different channels are combined for each voxel into a single data set at step 108 using the strategy expressed in Eq. [1]: where the summation is taken over all the channels (m), S* denotes complex conjugate of S, λ m is a weighting parameter, σ m is a noise amplitude (r.m.s.); the index corresponding to voxel position is omitted for clarity. In one aspect, data from different scans are registered to minimize artifacts caused by possible inter-scan motions. By way of non-limiting example, multi-flip-angle scans may be registered using a FLIRT tool in FSL. Referring again to FIG. 1 , a function F(TE) is calculated at step 110 to account for macroscopic B0 field inhomogeneities. In one aspect, the function F(TE) can be obtained by a variety of methods including, but not limited to, the voxel spread function approach (VSF). Referring again to FIG. 1 , a theoretical model is fit at step 112 to the combined data from step 108 to determine at least one quantitative MRI parameter chosen from: spin density (S 0 ), longitudinal (R 1 ) and transverse (R 2 ) relaxation rate constants, as well as MT related relaxation parameters (k f ′ and λ). The combined data are analysed on a voxel-by-voxel basis using the theoretical model that takes into account cross-relaxation effects between “free” (intra- and extra-cellular) and “bound” (attached to macromolecules) water. In one aspect, the theoretical model used to determine at least one quantitative parameter is expressed by Eq. [2] and Eq. [3]: In Eq. [2] and [3], the quantity S 0 is proportional to a spin density in the free water compartment free water; F(TE) represents the factor describing the signal decay due to the macroscopic field inhomogeneities (herein a voxel spread function (VSF) approach was used to compensate for this effect at step 110 ); superscripts/subscripts ‘f’ and “b” indicate “free” and “bound” water, respectively, R 1 , R 2 and k are longitudinal, transverse, and cross-relaxation rate constants respectively; Δω is the local frequency shift, τ is the RF pulse duration, and the v is a factor that depends on the RF pulse shape (for the rectangular RF pulses, v=1). By fitting the theoretic model as summarized in Eq. [2] and Eq. [3] to the multi-flip-angle and multi-gradient-echo data, six parameters—S 0 , R 1 , k f ′, λ, R 2 *, and Δω are obtained at step 112 in one aspect. The value of the parameter λ estimated at step 112 may have an associated estimation error. In an aspect, this estimation error may be corrected at step 112 using an average λ-value within a region surrounding each voxel with a predetermined size. Without being limited to any particular theory, this predetermined size may be selected to compromise between small characteristic sizes of tissue structures (i.e. on the order of a few mm) and the need to increase SNR and thereby decrease measurement error. In one aspect, this predetermined size may range from about 1 mm to about 100 mm. Referring again to FIG. 1 , a correction for B1 field inhomogeneities may be incorporated into the theoretical model used at step 112 . In various aspects, the correction coefficient q can be obtained by different methods including, but not limited to, the method disclosed herein. In one aspect, the correction coefficient q may be obtained at step 111 using a method 200 as described in FIG. 2 and as disclosed in detail herein. Referring again to FIG. 1 , at step 114 the parameters determined at step 112 (S 0 , R 1 , k f ′, λ, R 2 *, and Δω) may be mapped and used to visualize structures including, but not limited to, structures within the brains of healthy subjects and subjects with brain disorders. These maps may further be used to diagnose and/or characterize a disorder in a patient including, but not limited to a brain disorder or disorders of any other organs that may be imaged using the SMART MRI method disclosed herein. FIG. 2 is a flow chart illustrating a method 200 for obtaining the parameter q for each voxel in one aspect. The method 200 may include a phase-based B1 mapping method that enables fast, accurate B1 mapping using a multi-channel phased-array coil. The efficiency of this method 200 for accurate quantification of human brain tissue relaxation and magnetization transfer parameters using the SMART MRI technique is demonstrated in examples provided herein below. Referring again to FIG. 2 , the method 200 of correction for B1 field inhomogeneities in this aspect includes obtaining two MRI scans at step 202 , in which each MRI scan includes two orthogonal RF pulses. The method 200 may further include removing artifacts associated with the acquisition of the GRE data at step 204 including, but not limited to, artifacts caused by physiological fluctuations of the subject during MRI scanning. The method 200 may further include Fourier transforming the k-space data at step 206 for each separate channel, and combining the data from different channels of the MRI scanning device at step 208 . Combining data from different channels may enhance the estimation of quantitative parameters, such as MR signal decay rate constants, and may additionally remove any initial phase incoherence between channels. In one aspect, data from different channels are combined for each voxel into a single data set at step 208 using the strategy expressed in Eq. [1] disclosed herein above. In one aspect, data from different scans are registered to minimize artifacts caused by possible inter-scan motions. By way of non-limiting example, multi-flip-angle scans may be registered using a FLIRT tool in FSL. Referring again to FIG. 2 , at step 210 the phases of the MR signals for both pairs of RF pulses may be obtained, and the differences between these phases may be calculated on a voxel-by-voxel basis to provide B1 field mapping. Based on theoretical expressions disclosed herein, this phase difference may be used to calculate an actual flip angle at step 212 which can deviate from a nominal flip angle. The method for the B1 field correction disclosed herein is not limited to applications to the SMART MRI technique but can be used for any MR experiments requiring precise measurements of the B1 field. II. Theoretical Model for SMART MRI Imaging Method In various aspects, the SMART MRI method includes fitting a theoretical model to the assembled k-space data obtained using GRE imaging with multiple gradient echoes and multiple flip angles to obtain naturally co-registered maps of spin density (S 0 ), local frequency shift (Δω), longitudinal (R 1 ) and transverse (R 2 *) relaxation rate constants, MT (magnetization transfer) related relaxation parameters (k f ′ and λ). The theoretical model used to fit the GRE imaging data is an extension of a theoretical model derived using a Gradient Echo Plural Contrast Imaging (GEPCI) technique. The derivation of the theoretical model used in the SMART MRI imaging method is described in detail herein below. A system comprising a pool of free water molecules (f-pool) and a pool of water bounded to macromolecules (b-pool), characterized by the magnetization vectors M (f) and M (b) respectively, is considered. Without being limited to any particular theory, in the presence of exchange between these two pools, the time evolution of M (f) and M (b) may be described by a system of 6 coupled Bloch equations. As described herein below, a pulse sequence comprising repetitive short RF pulses of durations z separated by repetition time TR characterized by τ<
combining a plurality of MRI image datasets obtained from the subject to form a single dataset, each MRI image dataset comprising a plurality of imaging voxels and an image value set associated with each imaging voxel, each image value set comprising multiple image values, each image value reconstructed from k-space MRI data obtained from one RF channel of the RF coil at one combination of read-out gradient pulse and flip angle of the GRE sequence;\n
fitting a theoretical model S(α, TR, TE) to each image value set associated with each imaging voxel of the single dataset, the theoretical model S(α, TR, TE) characterized by five quantitative tissue-specific MRI parameters, the quantitative tissue-specific MM parameters comprising: S0 representing a spin density, R1 representing a longitudinal relaxation rate constant, R2*; representing a transverse relaxation rate constant, kf′ representing a cross-relaxation rate constant, and λ representing a magnetization transfer-related relaxation parameter, wherein α is a flip angle, TR is a repetition time, and TE is a gradient echo time of the GRE sequence; and\n
producing at least one quantitative image (map) comprising each imaging voxel and at least one corresponding value of S0, R1, R2* , kf′ and λ determined from fitting the theoretical model S(α, TR, TE)."],"number":1,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1, wherein combining the plurality of MRI image datasets comprises summing M GRE signals Sm obtained by M RF channels for each imaging voxel according to the relation:\nS(TE)=1M·∑m=1Mηm·S*(TE1)·Sm(TE),\nwherein:\nηm=1Mσm2·∑i=1Mσi2,\nS* is a complex conjugate of S, ηm is a weighting factor, and σm is an r.m.s. noise amplitude."],"number":2,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1, wherein the theoretical model S(α, TR, TE) further comprises:\nS(α,TR,TE)=S0·Ψ(α,TR)·exp(-R2*·TE)·exp(iΔω·TE)·F(TE)\nwherein:\nΨ(α,TR)=(1-E)-kf′·TR·(αq)2λ·(v·τ·TR)+(αq)21-E·cos(αq)·sin(αq),\nE=exp(-R1·TR),\nλ=R2(b)·(R1(b)+kb),R1=R1(f)+kf′,kf′=kf·(1+kb/R1(b))-1,\nΔω is a frequency shift, F(TE) is a correction factor for B0 macroscopic field inhomogeneities, and q is a correction factor for B1 radio frequency transmitter field inhomogeneities."],"number":3,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 3, wherein F(TE) is obtained using a voxel spread function approach."],"number":4,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 3, wherein the theoretical model S(α, TR, TE) is further characterized by q, and q is determined during fitting the theoretical model S(α, TR, TE) to each image value set associated with each imaging voxel of the single dataset."],"number":5,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 5, wherein each value of q at each imaging voxel is corrected by averaging the q values within a surrounding region determined for each imaging voxel."],"number":6,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 3, wherein q is determined by:\n
obtaining a first plurality of MR signals produced in response to a first pair of successive and orthogonal excitation RF pulses comprising a first initial excitation RF pulse with a first initial flip angle α and a first final excitation RF pulse produced orthogonal to the first initial excitation RF pulse with a first final flip angle β;\n
obtaining a second plurality of MR signals produced in response to a second pair of successive and orthogonal excitation RF pulses comprising a second initial excitation RF pulse with a second initial flip angle α and a second final excitation RF pulse produced orthogonal to the second initial excitation RF pulse excitation with a second final flip angle −β; and\n
obtaining a phase difference Δφ between each portion of the first and second plurality of MR signals corresponding to each imaging voxel according to the relation:\n\nΔφ=arg[1M·∑n=1N∑m=1Mλm·Sm(1)(TEn)·Sm(2)*(TEn)],\nwherein:M is the number of RF channels of an RF coil used to obtain the first and second plurality of MR signals, N is the number of echo times, Sm(1) is an MR signal from the portion of the first plurality of MR signals, and Sm(2)* is a complex conjugate of the portion of the second plurality of MR signals; and\n
obtaining q from the relationship:\n\ntan(Δφ)={2α·β·(p2+α2·cosΩα)· [4p2·Ωβ2·cosΩβ·sin2(Ωα2)·sin2(Ωβ2)+ +ΩαΩβ3· sinΩα·sinΩβ-2p2·Ωβ2·sin2(Ωα2)·sin2Ωβ]}/{p6·β2-α2·β4·Ωα2·sin2Ωα-2·p2·β2· cosΩβ·(p4+α2·Ωα2·sin2Ωα)- cos2ΩB·[-p6·β2+p2·α2·(4·Ωβ4·sin4(Ωα2)+p2·Ωα2·sin2Ωα)]+8·p2·α2·β2·· ΩαΩβ· sin2(Ωα2)sin2(Ωβ2)· sinΩα·sinΩβ-p2·Ωβ2·(p2·(α2-β2)+α2·Ωα2·sin2Ωα)·sin2Ωβ++2·p2·α2·cosΩα· [4p2·β2·sin4(Ωβ2)+Ωβ4·sin2Ωβ]+ +α2·cos2Ωα·[4p2·α2·β2·sin4 (Ωβ2)+(α2β2-p4)·Ωβ2·sin2Ωβ]}\nwherein α is a flip angle of a first excitation RF pulse corresponding to the first plurality of MR signals, β is a flip angle of a second excitation RF pulse corresponding to the second plurality of MR signals, p=Δω·τ, Ωα=((αq)2+p2)1/2, Ωβ=((βq)2+p2)1/2."],"number":7,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 7, wherein α=β and the method further comprises obtaining q from the relationship\nΔφ=arctan[2Ω2·(p2+(αq)2cosΩ)(αq)2·(1-cosΩ)·((αq)2+(p2+Ω2)·cosΩ)]\nwherein Ω=((αq)2+p2)1/2."],"number":8,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1, further comprising calculating a Tissue Damage Score (TDS) for each imaging voxel according to\n
\n\nTDSR=(RC−R)/RC \n\nwherein Rc is a position of a peak center in a Gaussian distribution of one of the five quantitative tissue-specific MRI parameters associated with a substructure within a tissue of the subject, and R is a value of the one quantitative tissue-specific MM parameter at each imaging voxel."],"number":9,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1, wherein an MM image dataset is acquired independently at each different flip angle and all MRI image datasets are combined to produce the single dataset."],"number":10,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1, wherein each MM image dataset acquires all flip angles in an interleaved manner at each phase encoding step."],"number":11,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1, further comprising acquiring the k-space MM data from a plurality of RF channels using a Gradient Recalled Echo (GRE) sequence, the GRE sequence comprising multiple gradient echoes and multiple flip angles."],"number":12,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 12, further comprising removing an artifact associated with physiological fluctuations of the subject during acquisition of the k-space MRI data."],"number":13,"annotation":false,"claim":true,"title":false},{"lines":["A method of mapping a B1 radio-frequency field within a region of interest of an MM scanning device, the method comprising:\n
obtaining a first plurality of MR signals produced in response to a first pair of successive and orthogonal excitation RF pulses comprising a first initial excitation RF pulse with a first initial flip angle α and a first final excitation RF pulse produced orthogonal to the first initial excitation RF pulse with a first final flip angle β;\n
obtaining a second plurality of MR signals produced in response to a second pair of successive and orthogonal excitation RF pulses comprising a second initial excitation RF pulse with a second initial flip angle α and a second final excitation RF pulse produced orthogonal to the second initial excitation RF pulse excitation with a second final flip angle −β; and\n
analyzing the first and second plurality of MR signals to obtain a map of a B1-encoded MR signal phase and a B0-dependent signal frequency."],"number":14,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 14, wherein analyzing the first and second plurality of MR signals comprises:\n
obtaining a phase difference Δφ between each portion of the first and second plurality of MR signals corresponding to each imaging voxel according to the relation:\n\nΔφ=arg[1M·∑n=1N∑m=1Mλm·Sm(1)(TEn)·Sm(2)*(TEn)],\nwherein:M is the number of RF channels of an RF coil used to obtain the first and second plurality of MR signals, N is the number of echo times, Sm(i) is an MR signal from the portion of the first plurality of MR signals, and Sm(2)* is a complex conjugate of the portion of the second plurality of MR signals; and\n
obtaining q from the relationship:\n\ntan(Δφ)={2α·β·(p2+α2·cosΩα)· [4p2·Ωβ2·cosΩβ·sin2(Ωα2)·sin2(Ωβ2)+ +ΩαΩβ3· sinΩα·sinΩβ-2p2·Ωβ2·sin2(Ωα2)·sin2Ωβ]}/{p6·β2-α2·β4·Ωα2·sin2Ωα-2·p2·β2· cosΩβ·(p4+α2·Ωα2·sin2Ωα)- cos2Ωβ·[-p6·β2+p2·α2·(4·Ωβ4·sin4(Ωα2)+p2·Ωα2·sin2Ωα)]+8·p2·α2·β2··ΩαΩβ· sin2(Ωα2)sin2(Ωβ2)·sinΩα·sinΩβ--p2·Ωβ2·(p2·(α2-β2)+α2·Ωα2·sin2Ωα)·sin2Ωβ++2·p2·α2·cosΩα·[4p2·β2·sin4(Ωβ2)+Ωβ4·sin2Ωβ]++α2·cos2Ωα·[4p2·α2·β2·sin4(Ωβ2)+(α2β2-p4)·Ωβ2·sin2Ωβ]}\nwherein α is a flip angle of a first excitation RF pulse corresponding to the first plurality of MR signals, β is a flip angle of a second excitation RF pulse corresponding to the second plurality of MR signals, p=Δω·τ, Ωα=((αq)2+p2)1/2, Ωβ=((βq)2+p2)1/2, and q represents inhomogeneities in a B1 radio frequency transmitter field."],"number":15,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 15, wherein α=β and the method further comprises obtaining q from the relationship\nΔφ=arctan[2Ω2·(p2+(αq)2cosΩ)(αq)2·(1-cosΩ)·((αq)2+(p2+Ω2)·cosΩ)]\nwherein Ω=((αq)2p2)1/2."],"number":16,"annotation":false,"claim":true,"title":false},{"lines":["A method of performing quantitative MM imaging of a subject, the method comprising:\n
acquiring a plurality of MR data using an RF coil from the subject using a GRE sequence with a series of read-out gradient pulses with a plurality of flip angles;\n
combining a plurality of MRI image datasets obtained from the subject to form a single dataset, each MM image dataset comprising a plurality of imaging voxels and an image value set associated with each imaging voxel, each image value set comprising multiple image values, each image value reconstructed from k-space Mill data obtained from one RF channel of the RF coil at one combination of read-out gradient pulse and flip angle of the GRE sequence;\n
fitting a theoretical model S(α, TR, TE) to each image value set associated with each imaging voxel of the single dataset, the theoretical model S(α, TR, TE) characterized by five quantitative tissue-specific MM parameters, the quantitative tissue-specific MM parameters comprising: S0 representing a spin density, R1 representing a longitudinal relaxation rate constant, R2* representing a transverse relaxation rate constant, kf′ representing a cross-relaxation rate constant, and λ representing a magnetization transfer-related relaxation parameter, and an additional parameter q representing inhomogeneities in a B1 radio frequency transmitter field wherein α is a flip angle, TR is a repetition time, and TE is a gradient echo time of the GRE sequence; and\n
producing at least one quantitative image (map) comprising each imaging voxel and at least one corresponding value of S0, R1, R2*, kf′ and λ determined from fitting the theoretical model S(α, TR, TE)."],"number":17,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 17, wherein combining the plurality of MRI image datasets comprises summing M GRE signals Sm obtained by M RF channels for each imaging voxel according to the relation:\nS(TE)=1M·∑m=1Mηm·S*(TE1)·Sm(TE),\nwherein:\nηm=1Mσm2·∑i=1Mσi2,\nS* is a complex conjugate of 5, ηm is a weighting factor, and σm is an r.m.s. noise amplitude."],"number":18,"annotation":false,"claim":true,"title":false}]}},"filters":{"npl":[],"notNpl":[],"applicant":[],"notApplicant":[],"inventor":[],"notInventor":[],"owner":[],"notOwner":[],"tags":[],"dates":[],"types":[],"notTypes":[],"j":[],"notJ":[],"fj":[],"notFj":[],"classIpcr":[],"notClassIpcr":[],"classNat":[],"notClassNat":[],"classCpc":[],"notClassCpc":[],"so":[],"notSo":[],"sat":[]},"sequenceFilters":{"s":"SEQIDNO","d":"ASCENDING","p":0,"n":10,"sp":[],"si":[],"len":[],"t":[],"loc":[]}}