Method for time-resolved magnetic resonance fingerprinting

Abstract

Methods, systems, and software are provided for generating quantitative maps and synthetic parameter-weighted images using time-resolved magnetic resonance fingerprinting. The subject methods simultaneously measure multiple quantitative parameters, including T1, T2, T2*, and proton density along with quantitative susceptibility mapping, and can generate multiple synthetic parameter-weighted images, including T1-weighted, T2-weighted, proton density-weighted, and susceptibility-weighted images from a single scan. Time-resolved magnetic resonance fingerprinting schemes are provided to implement an accelerated echo planar sampling of a hybrid space spanned by the phase encoding dimension and the temporal dimension. T2* quantification is added with minimal extra scan time, which also enables simultaneous quantitative susceptibility mapping and synthetic T2* weighted / susceptibility weighted imaging. Multi-parametric quantification can be acquired from time-resolved magnetic resonance fingerprinting within the time of a single breath-hold of a subject, which greatly reduces the impact of inspiration motion.

Description

METHOD FOR TIME-RESOLVED MAGNETIC RESONANCE FINGERPRINTINGCROSS-REFERENCE TO ELATED APPLICATION

[0001] Pursuant to 35 U.S.C. § 119(e), this application claims priority to the filing elates of United States Provisional Patent Application Serial No. 63 / 638,018 filed April 24, 2024, the disclosure of which application is herein incorporated by reference in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under Grant No. U01 EB026976 and Grant No. R01 CA249909 awarded by the National Institutes of Health. The government has certain rights in the invention.BACKGROUND OF THE INVENTION

[0003] Magnetic resonance fingerprinting (MRF) (Ma et al., Nature (2013) 495(7440):187-192; Jiang et al., Magnetic Resonance in Medicine 74.6 (2015): 1621 -1631 ) is a quantitative magnetic resonance imaging (MRI) technique for simultaneous quantification of tissue or material properties such as spin-lattice relaxation Ti , spin-spin relaxation T2, proton density. Despite the fast acquisition of MRF compared with conventional quantitative MRI methods, involving more properties e.g., T27quantitative susceptibility mapping (“QSM”) in MRF quantification would significantly increase the scan time and compromise the image quality.

[0004] It is beneficial to simultaneously acquire Ti, T2, T2* and proton density to reduce total scan time and avoid co-registration for motion. In an MRF sequence, T2* quantification relies on the signal change from variable echo times (TE). As signal changes caused by T2and T2* cab behave similarly, it can be difficult to quantify T2and T2* simultaneously in conventional MRF. Previous methods (Hong et al., Magn Reson Med. (2019) 81 :2614-2623; Wyatt et al., NMR in Biomedicine. 2018;31 :e3951 ) have tried to add T2* quantification in MRF by combining MRF blocks with gradient echo ("GRE”) blocks with long TE, which greatly reduces the acquisition efficiency.

[0005] When Bofield inhomogeneity is severe and / or readout trajectory is long, MRF suffers from distortion and blurring caused by Bofield inhomogeneity and T2* decay (Rieger et al., Magnetic Resonance in Mmedicine 78.5 (2017): 1724-1733). Although a supplementary Bomap can be acquired separately for correction, it is possible to have co-registration problems due to Bomap changes caused by motion (Lee et al., Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 55.5 (2006): 1197-1201 ), typically in body imaging.

[0006] Subspace modeling (Tamir et al., Magnetic Resonance in Medicine 77.1 (2017): 180-195) is often used in MRF to reduce the condition number in iterative reconstruction. In the subspace reconstruction, especially in presence of T2* decay and off resonance induced phase, the processing tends to be computationally time and resource intensive.SUMMARY OF THE INVENTION

[0007] Methods, systems, and software are provided for generating quantitative maps and synthetic parameter-weighted images using time-resolved magnetic resonance fingerprinting. The subject methods simultaneously measure multiple quantitative parameters, including Ti, T2, T2*, and proton density along with quantitative susceptibility mapping, and can generate multiple synthetic parameter-weighted images, including Ti-weighted, T2-weighted, proton density-weighted, and susceptibility-weighted images from a single scan. Time-resolved magnetic resonance fingerprinting schemes are provided to implement an accelerated echo planar sampling of a hybrid space spanned by the phase encoding dimension and the temporal dimension. T2* quantification is added with minimal extra scan time, which also enables simultaneous quantitative susceptibility mapping and synthetic T2* weighted / susceptibility weighted imaging. Multi-parametric quantification can be acquired from time-resolved magnetic resonance fingerprinting within the time of a single breathhold of a subject, which greatly reduces the impact of inspiration motion.

[0008] In one aspect, a method of performing time-resolved magnetic resonance fingerprinting (TRMRF) using a magnetic resonance imaging (MRI) scanner is provided, the method comprising: acquiring TRMRF data for a subject with the MRI scanner using an inversion recovery unbalanced steady state free precession (SSFP) gradient echo magnetic resonance pulse sequence having a variable flip angle pattern and an echo planar imaging (EPI) readout with segmented Poisson disk samplings in a phase encoding (PE)-time domain, wherein the SSFP gradient echo magnetic resonance pulse sequence comprises a plurality of radio frequency (RF) pulses, wherein each of the plurality of RF pulses are separated from an adjacent RF pulse by a repetition time (TR), wherein the Poisson disk is divided into a plurality of segments having a plurality of samples in each segment, wherein one of the plurality of segments is acquired during each TR, wherein sampling density is inversely proportional to the radius to k-space center in the PE axes and is constant along a time axis, wherein Ti, T2, T2*, and proton density values are acquired simultaneously; performing time- resolved subspace reconstruction using a 2-dimensional decomposition, wherein signal evolution is represented by Ti -weighted and T2-weighted inter-TR signals, and wherein signal decay is represented by T2*-weighted intra-TR signals, measuring static magnetic field inhomogeneity (Bo) using separate inter-TR subspace reconstructions for each echo with only the first subspace order,wherein Boinhomogeneity is corrected in the subspace reconstructions to generate corrected subspace reconstructions; generating subspace images from the corrected subspace reconstructions; producing quantitative maps from the subspace images using fingerprint dictionary matching and quantitative susceptibility mapping performed with susceptibility source separation, wherein the inter-TR signals are used to map the Ti, T2, and proton density values, and wherein the intra-TR signals are used to map the T2* values, wherein a synthetic Ti-weighted image, a synthetic T2-weighted image, a synthetic T2*-weighted image, a synthetic proton density-weighted image, and a synthetic susceptibility-weighted image are produced.

[0009] In certain embodiments, the PE-time domain is a ky-t domain for 2-dimensional (2D) magnetic resonance fingerprinting or a ky-kz-t domain for three-dimensional (3D) magnetic resonance fingerprinting.

[0010] In certain embodiments, the Poisson disk is divided into a plurality of segments having an identical number of samples in each segment.

[0011] In certain embodiments, the fingerprint dictionary matching uses extended phase graph (EPG) simulations for subspace reconstruction and parametric matching.

[0012] In certain embodiments, T2* is matched or fitted separately from other parameters during the fingerprint dictionary matching.

[0013] In certain embodiments, the method further comprises performing low rank regularization locally to resolve residual artifacts.

[0014] In certain embodiments, the time-resolved subspace reconstruction further comprises calculating:wherein y is k space data, a is subspace images, <t> is an off resonance and eddy current induced phase calculated from each echo planar imaging echo, A 2rllFr(«)||* is a locally low rank regularization term,is a subspace transform operator for the inter-TR signals, t / 2is a subspace transform operator for the intra-TR signals, and wherein the subspace image is solved using an alternating direction method of multipliers (ADMM).

[0015] In certain embodiments, the method further comprises: generating a Boinhomogeneity induced phase map from the separate inter-TR reconstructions for each echo with only the first subspace order; and correcting the subspace reconstructions, wherein the Boinhomogeneity induced phase map is used to correct off-resonance effects and Nyquist ghosts in the subspace reconstructions.

[0016] In certain embodiments, the method further comprises fitting an off-resonance map to the Boinhomogeneity induced phase map of all echoes according to exact echo time for each echo planar imaging lobe.

[0017] In certain embodiments, the quantitative susceptibility mapping performed with susceptibility source separation further comprises generating paramagnetic positive susceptibility maps and diamagnetic negative susceptibility maps.

[0018] In certain embodiments, the TRMRF data is acquired within a time period of a single breathhold of the subject.

[0019] In certain embodiments, the TRMRF data is acquired within 25 seconds or less.

[0020] In certain embodiments, the TRMRF data is acquired for a region-of-interest in the subject.

[0021] In certain embodiments, the method further comprises generating a multiparametric quantitative map of the region-of-interest using two or more parameters selected from the group consisting of Ti, T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility. In some embodiments, the two or more parameters comprise Ti, T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility.

[0022] In certain embodiments, the TRMRF data is acquired for the whole body of the subject.

[0023] In certain embodiments, the method further comprises detecting a biomarker in the syntheticTi-weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image, or any combination thereof.

[0024] In certain embodiments, the method further comprises monitoring location, uptake, or efficacy of a drug by visual analysis of the synthetic T i-weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image, or any combination thereof, wherein the drug is administered to the subject prior to said acquiring the TRMRF data for the subject.

[0025] In certain embodiments, the method further comprises diagnosing a disease in the subject by visual analysis of the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility- weighted image, or any combination thereof for disease-related pathology.

[0026] In certain embodiments, the method further comprises using the Ti, T2, T2*, proton density, and susceptibility values that are acquired from TRMRF to adjust settings for performing contrast enhanced qualitative MRI of the subject to improve delineation of lesions in qualitative MRI images.

[0027] In another aspect, a computer-implemented method for generating quantitative maps and synthetic parameter-weighted images from time-resolved magnetic resonance fingerprinting(TRMRF) data is provided, the computer performing steps comprising: receiving the TRMRF data, wherein the TRMRF data is acquired for a subject with a magnetic resonance imaging (MRI) scanner using an inversion recovery unbalanced steady state free precession (SSFP) gradient echo magnetic resonance pulse sequence having a variable flip angle pattern and an echo planar imaging (EPI) readout with segmented Poisson disk samplings in a phase encoding (PE)-time domain, wherein the SSFP gradient echo magnetic resonance pulse sequence comprises a plurality of radio frequency (RF) pulses, wherein each of the plurality of RF pulses are separated from an adjacent RF pulse by a repetition time (TR), wherein the Poisson disk is divided into a plurality of segments having a plurality of samples in each segment, wherein one of the plurality of segments is acquired during each TR, wherein sampling density is inversely proportional to the radius to k-space center in the PE axes and is constant along a time axis, wherein Ti , T2, T2*, and proton density values are acquired simultaneously; performing time-resolved subspace reconstruction using a 2-dimensional decomposition, wherein signal evolution is represented by Ti-weighted and T2-weighted inter-TR signals, and wherein signal decay is represented by T2*-weighted intra-TR signals, measuring static magnetic field inhomogeneity (Bo) using separate inter-TR subspace reconstructions for each echo with only the first subspace order, wherein Boinhomogeneity is corrected in the subspace reconstructions to generate corrected subspace reconstructions; generating subspace images from the corrected subspace reconstructions; producing quantitative maps from the subspace images using fingerprint dictionary matching and quantitative susceptibility mapping performed with susceptibility source separation, wherein the inter-TR signals are used to map the Ti, T2, and proton density values, and wherein the intra-TR signals are used to map the T2* values, wherein a synthetic Ti-weighted image, a synthetic T2-weighted image, a synthetic T2* -weighted image, a synthetic proton density-weighted image, and a synthetic susceptibility-weighted image are produced; and displaying the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*- weighted image, the synthetic proton density-weighted image, and the synthetic susceptibility- weighted image.

[0028] In certain embodiments, the PE-time domain is a ky-t domain for 2-dimensional (2D) magnetic resonance fingerprinting or a ky-kz-t domain for three-dimensional (3D) magnetic resonance fingerprinting.

[0029] In certain embodiments, the Poisson disk is divided into a plurality of segments having an identical number of samples in each segment.

[0030] In certain embodiments, the fingerprint dictionary matching uses extended phase graph (EPG) simulations for subspace reconstruction and parametric matching.

[0031] In certain embodiments, T2* is matched or fitted separately from other parameters during the fingerprint dictionary matching.

[0032] In certain embodiments, the computer-implemented method further comprises performing low rank regularization locally to resolve residual artifacts.

[0033] In certain embodiments, the time-resolved subspace reconstruction further comprises calculating:wherein y is k space data, a is subspace images, is an off resonance and eddy current induced phase calculated from each echo planar imaging echo, Sr||Kr(a)||* is a locally low rank regularization term, I7i is a subspace transform operator for the inter-TR signals, U2 is a subspace transform operator for the intra-TR signals, and wherein the subspace image is solved using an alternating direction method of multipliers (ADMM).

[0034] In certain embodiments, the computer-implemented method further comprises generating a Bo inhomogeneity induced phase map from the separate inter-TR reconstructions for each echo with only the first subspace order; and correcting the subspace reconstructions, wherein the Boinhomogeneity induced phase map is used to correct off-resonance effects and Nyquist ghosts in the subspace reconstructions.

[0035] In certain embodiments, the computer-implemented method further comprises fitting an off- resonance map to the Boinhomogeneity induced phase map of all echoes according to exact echo time for each echo planar imaging lobe.

[0036] In certain embodiments, the quantitative susceptibility mapping performed with susceptibility source separation further comprises generating paramagnetic positive susceptibility maps and diamagnetic negative susceptibility maps.

[0037] In certain embodiments, the TRMRF data is acquired within a time period of a single breathhold of the subject.

[0038] In certain embodiments, the TRMRF data is acquired within 25 seconds or less.

[0039] In certain embodiments, the TRMRF data is acquired for a region-of-interest in the subject.

[0040] In certain embodiments, the computer-implemented method further comprises generating a multiparametric quantitative map of the region-of-interest using two or more parameters selected from the group consisting of T1, T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility. In some embodiments, the two or more parameters comprise T1, T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility.

[0041] In certain embodiments, the computer-implemented method further comprises instructing an MRI scanner, configured to acquire TRMRF data for a subject, to select an inversion recovery unbalanced steady state free precession (SSFP) gradient echo magnetic resonance pulse sequence having a variable flip angle pattern and an echo planar imaging (EPI) readout with segmented Poisson disk samplings in a phase encoding (PE)-time domain, wherein the SSFP gradient echo magnetic resonance pulse sequence comprises a plurality of radio frequency (RF) pulses, wherein each of the plurality of RF pulses are separated from an adjacent RF pulse by a repetition time (TR), wherein the Poisson disk is divided into a plurality of segments having a plurality of samples in each segment, wherein one of the plurality of segments is acquired during each TR, wherein sampling density is inversely proportional to the radius to k-space center in the PE axes and is constant along a time axis, wherein Ti, T2, T2*, and proton density values are acquired simultaneously.

[0042] In certain embodiments, the computer-implemented method further comprises displaying the subspace reconstructions.

[0043] In certain embodiments, the computer-implemented method further comprises displaying a table comprising the Ti, T2, T2*, proton density, or susceptibility values, or any combination thereof.

[0044] In certain embodiments, the computer-implemented method further comprises displaying the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image with a boundary line around a region of interest.

[0045] In another aspect, a system for generating quantitative maps and synthetic parameter- weighted images from time-resolved magnetic resonance fingerprinting (TRMRF) data is provided, the system comprising: (a) a storage component for storing data, wherein the storage component has instructions for generating the quantitative maps and the synthetic parameter-weighted images from the TRMRF data stored therein; (b) a computer processor programmed to generate the quantitative maps and the synthetic parameter-weighted images from the TRMRF data, wherein the computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component in order to receive the inputted TRMRF data and analyze the TRMRF data according to a computer implemented method described herein; and (c) a display component for displaying one or more of the quantitative maps and the synthetic parameter-weighted images.

[0046] In certain embodiments, the system further comprises a magnetic resonance imaging (MRI) scanner configured to acquire TRMRF data for a subject.

[0047] In certain embodiments, the display further displays the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2* -weighted image, the synthetic proton density- weighted image, or the synthetic susceptibility-weighted image, or any combination thereof.

[0048] In certain embodiments, the display further displays the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2* -weighted image, the synthetic proton density- weighted image, or the synthetic susceptibility-weighted image with a boundary line around a region of interest.

[0049] In certain embodiments, the display further displays the subspace reconstructions.

[0050] In certain embodiments, the display further displays a table comprising the Ti, T2, T2*, proton density, or susceptibility values, or any combination thereof.

[0051] In another aspect, a non-transitory computer-readable medium is provided, the non-transitory computer-readable medium comprising program instructions that, when executed by a processor in a computer, causes the processor to perform a computer implemented method described herein.

[0052] In another aspect, a kit comprising the non-transitory computer-readable medium described herein and instructions for generating quantitative maps and synthetic parameter-weighted images from time-resolved magnetic resonance fingerprinting (TRMRF) data is provided.BRIEF DESCRIPTION OF THE DRAWINGS

[0053] FIGS. 1A-1 D. Diagram of an exemplary TRMRF pulse sequence showing consecutive TRs (FIG. 1A), flip angle trains (FIG. 1 B), sampling order (FIG. 1C), and Poison ky-t sampling pattern (FIG. 1 D).

[0054] FIG. 2. Pipeline of TRMRF. With the designed TRMRF sequence, acquired signals can be represented by inter-TR signal evolution and intra-TR decay. This representation was used for subspace reconstruction. Quantitative maps were then acquired with subspace images by dictionary matching and QSM post-processing, including susceptibility source separation.

[0055] FIG. 3. Quantitative maps from human brain study.

[0056] FIG. 4. Quantitative maps of the same volunteer in two separate studies for repeatability evaluation. Note that in study 1 , breath-hold was at inspiration phases and in study 2, breath-hold was at expiration phase. The table shows the measured quantitative parameters from the ROI of lower liver, renal cortex, renal medulla, and veins in the liver.

[0057] FIG. 5. Zoomed-in multiparametric quantitative maps covering the liver and kidney. The liver showed significantly higher R2*, as well as higher positive and negative susceptibility maps compared to the kidney. Due to the deoxygenated hemoglobin, the veins showed high positive susceptibility than the liver tissue.

[0058] FIG. 6. Synthetic PD-weighted, Ti-weighted, T2-weighted, T2*-weighted and susceptibility- weighted images calculated from subspace images.

[0059] FIG. 7. Two different T2-FLAIR acquired using varying parameters for a 3-year-old male subject with cortical dysplasia.

[0060] FIG. 8. Protocol of TRMRF-guided patient customized MRI scan.DETAILED DESCRIPTION OF THE INVENTION

[0061] Methods, systems, and software are provided for generating quantitative maps and synthetic parameter-weighted images using time-resolved magnetic resonance fingerprinting. The subject methods simultaneously measure multiple quantitative parameters, including Ti, T2, T2*, and proton density along with quantitative susceptibility mapping, and can generate multiple synthetic parameter-weighted images, including Ti-weighted, T2-weighted, proton density-weighted, and susceptibility-weighted images from a single scan. Time-resolved magnetic resonance fingerprinting schemes are provided to implement an accelerated echo planar sampling of a hybrid space spanned by the phase encoding dimension and the temporal dimension. T2* quantification is added with minimal extra scan time, which also enables simultaneous quantitative susceptibility mapping and synthetic T2* weighted / susceptibility weighted imaging.

[0062] Before the present methods, systems, and software are described, it is to be understood that this invention is not limited to particular methods, systems, and software described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0063] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range is also specifically disclosed. Each smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in that stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range, and each range where either, neither or both limits are included in the smaller ranges is also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0064] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used inthe practice or testing of the present invention, some potential and preferred methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. It is understood that the present disclosure supersedes any disclosure of an incorporated publication to the extent there is a contradiction.

[0065] As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present invention. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

[0066] It must be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a parameter" includes a plurality of such parameters and reference to "the image" includes reference to one or more images and equivalents thereof, known to those skilled in the art, and so forth.

[0067] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.Definitions

[0068] The term "about," particularly in reference to a given quantity, is meant to encompass deviations of plus or minus five percent.

[0069] The terms “individual”, “subject”, and “patient”, are used interchangeably herein and refer to any mammalian subject, particularly humans. Mammalian subjects include human and non-human mammals such as non-human primates, including chimpanzees and other apes and monkey species; laboratory animals such as mice, rats, rabbits, hamsters, guinea pigs, and chinchillas; domestic animals such as dogs and cats; and farm animals such as sheep, goats, pigs, horses, and cows.

[0070] The term “user” as used herein refers to a person that interacts with a device and / or system disclosed herein for performing one or more steps of the presently disclosed methods. The user may be a radiologist or MRI technologist operating an MRI scanner to perform time-resolved magnetic resonance fingerprinting, as described herein.Time-Resolved Magnetic Resonance Fingerprinting

[0071] In one aspect, a method is provided for generating quantitative maps and synthetic parameter-weighted images using time-resolved magnetic resonance fingerprinting. The method comprises: acquiring TRMRF data for a subject with an MRI scanner using an inversion recovery unbalanced steady state free precession (SSFP) gradient echo magnetic resonance pulse sequence having a variable flip angle pattern and an echo planar imaging (EPI) readout with segmented Poisson disk samplings in a phase encoding (PE)-time domain. The SSFP gradient echo magnetic resonance pulse sequence comprises a plurality of radio frequency (RF) pulses, wherein each of the plurality of RF pulses are separated from an adjacent RF pulse by a repetition time (TR). The Poisson disk is divided into a plurality of segments having a plurality of samples in each segment. One segment is acquired during each TR, wherein sampling density is inversely proportional to the radius to k-space center in the PE axes and is constant along a time axis. In certain embodiments, the Poisson disk is divided into a plurality of segments having an identical number of samples in each segment.

[0072] A diagram of an exemplary TRMRF pulse sequence is shown in FIG. 1A. The figure shows a pulse sequence comprising a number of RF excitation pulses with each pulse separated from an adjacent RF pulse by a TR. Further shown are the variable flip angle trains (FIG. 1 B), sampling order (FIG. 1 C), and segmented Poison ky-t sampling pattern (FIG. 1 D). In addition, FIG. 2 provides a schematic overview of the TRMRF method of data acquisition and processing. First, the TRMRF data is acquired using a pulse sequence such as illustrated in FIG. 1A, wherein the Ti, T2, T2*, and proton density values are acquired simultaneously from a single scan by an MRI scanner. The acquired signals can be represented by inter-TR signal evolution and intra-TR decay. This representation is used for subspace reconstruction from which quantitative maps are generated by dictionary matching and QSM post-processing, including susceptibility source separation.

[0073] Time-resolved subspace reconstruction is performed using a 2-dimensional decomposition, wherein signal evolution is represented by Ti-weighted and T2-weighted inter-TR signals, and wherein signal decay is represented by T2*-weighted intra-TR signals. Static magnetic field inhomogeneity (Bo) is measured using separate inter-TR subspace reconstructions for each echo with only the first subspace order to allow for subspace reconstructions to be corrected for Bo. Subspace images are generated from the corrected subspace reconstructions. Quantitative maps can be produced from the subspace images using magnetic resonance fingerprint dictionary matching and quantitative susceptibility mapping performed with susceptibility source separation. Inter-TR signals are used to map the Ti , T2, and proton density values, and intra-TR signals are usedto map the T2* values. This method is capable of producing a variety of parameter-weighted synthetic images from a single scan, including a synthetic Ti-weighted image, a synthetic T2-weighted image, a synthetic T2*-weighted image, a synthetic proton density-weighted image, and a synthetic susceptibility-weighted image. In certain embodiments, the quantitative susceptibility mapping performed with susceptibility source separation further comprises generating paramagnetic positive susceptibility maps and diamagnetic negative susceptibility maps.

[0074] Magnetic resonance fingerprinting data may be captured in a two-dimensional (2D) manner or a three-dimensional (3D) manner. For 2D magnetic resonance fingerprinting, the PE-time domain is a ky-t domain, whereas for 3D magnetic resonance fingerprinting, the PE-time domain is a ky-kz- t domain. In certain embodiments, magnetic resonance fingerprint dictionary matching uses extended phase graph (EPG) simulations for subspace reconstruction and parametric matching. For a description of methods of using an EPG model for magnetic resonance fingerprint dictionary generation, see, e.g., Wang et al. (2020) Magn. Reson. Imaging 66:248-256; Hennig et al. (2004) Magn. Reson. Med. 51 (1 ):68-80, Cohen et al. (2018) Magn. Reson. Med. 80(3):885-894, Emmerich et al. (2019) J. Magn. Reson. Imaging 49(5):1253-1262, and Liu et al. (2021 ) NMR Biomed. 34(7):e4527; herein incorporated by reference in their entireties. In certain embodiments, T2* is matched or fitted separately from other parameters during the fingerprint dictionary matching.

[0075] In certain embodiments, the time-resolved subspace reconstruction further comprises calculating:wherein y is k space data, a is subspace images, $ is an off resonance and eddy current induced phase calculated from each echo planar imaging echo, A £,.|| / ?,.(«) II* isalocally low rank regularization term, Ui is a subspace transform operator for the inter-TR signals, U2is a subspace transform operator for the intra-TR signals, and wherein the subspace image is solved using an alternating direction method of multipliers (Boyd et al. (2011) Foundations and Trends® in Machine Learning 3(1 ):1 -22; herein incorporated by reference in its entirety). The low rank regularization term, A £r|| / ?r(a) II*. can be used for low rank regularization locally to resolve residual artifacts.

[0076] In certain embodiments, the method further comprises generating a Boinhomogeneity induced phase map from the separate inter-TR reconstructions for each echo with only the first subspace order; and correcting the subspace reconstructions, wherein the Boinhomogeneity induced phase map is used to correct off-resonance effects and Nyquist ghosts in the subspace reconstructions. In some embodiments, the method further comprises fitting an off-resonance mapto the Bo inhomogeneity induced phase map of all echoes according to exact echo time for each echo planar imaging lobe.

[0077] In certain embodiments, the TRMRF data is acquired within 60 seconds or less, 50 seconds or less, 40 seconds or less, 30 seconds or less, 25 seconds or less, or 20 seconds or less. In some embodiments, the TRMRF data is acquired within a time ranging from 10 seconds to 60 seconds, 20 seconds to 40 seconds, or 25 seconds to 30 seconds, including any amount of time within these ranges such as 10 seconds, 15 seconds, 20 seconds, 21 seconds, 22 seconds, 23 seconds, 24 seconds, 25 seconds, 26 seconds, 27 seconds, 28 seconds, 29 seconds, 30 seconds, 35 seconds, 40 seconds, 45 seconds, 50 seconds, 55 seconds, or 60 seconds. In certain embodiments, the TRMRF data is acquired within a time period of a single breath-hold of the subject. In some embodiments, the breath-hold is at an inspiration phase or an expiration phase.

[0078] In certain embodiments, the TRMRF data is acquired for a region-of-interest in the subject to produce images that can be used for clinical diagnosis. The region of interest may comprise a lesion, which may include, for example, without limitation, abnormal or damaged tissue, a tumor, a plaque, an abnormal deposit, or other disease pathology. In some embodiments, TRMRF data is acquired for the whole body of the subject.

[0079] In some embodiments, the method further comprises detecting a biomarker in a synthetic Ti- weighted image, synthetic T2-weighted image, synthetic T2*-weighted image, synthetic proton density-weighted image, or synthetic susceptibility-weighted image, or any combination thereof. The biomarker may be a molecular biomarker (e.g., protein, nucleic acid, lipid, carbohydrate, or metabolite), cellular biomarker (e.g., surface marker, tumor antigen), or imaging biomarker (e.g., an extensive variable such as spatial dimensions or volume of a structure in a region of interest, an intensive variable such as a quantity or parameter in a region of interest that can be mapped, or a characteristic used for staging a disease). Biomarkers can be used to monitor normal biological processes, pathogenic processes, or a response to a therapeutic intervention. Detection of a biomarker in the synthetic images, generated by TRMRF, may be used for example, in diagnosing a disease or predicting clinical outcome. In some embodiments, a synthetic Ti-weighted image, synthetic T2-weighted image, synthetic T2*-weighted image, synthetic proton density-weighted image, or synthetic susceptibility-weighted image, or any combination thereof, produced by the TRMRF methods described herein, is analyzed for disease-related pathology or used for monitoring location, uptake, or efficacy of a drug administered to the subject.

[0080] In certain embodiments, the method further comprises using the Ti, T2, T2*, proton density, and susceptibility values that are acquired from TRMRF to adjust settings for performing MRI scans at higher resolution. For example, parameter values acquired from TRMRF may be used to adjustsettings for contrast enhanced qualitative MRI of a subject to improve delineation of lesions in qualitative MRI images, as further described in Example 2.System and Computer Implemented Methods

[0081] The present disclosure also provides systems and computer implemented methods which find use in practicing the subject methods. The subject methods are implemented using a magnetic resonance imaging (MRI) scanner. Any suitable MRI scanner may be used to acquire TRMF data, including any commercially available programmable machine. Exemplary MRI scanners suitable for the practice of the subject methods include 3 tesla (T) and 7T scanners. Such MRI scanners are commercially available, for example, from GE Healthcare (Chicago, Illinois), Siemens Medical Solutions, Inc. (Malvern, PA), and Philips (Amsterdam, Netherlands). The MRI scanner typically includes an operator workstation comprising a display, one or more input devices such as a keyboard and mouse, or the like, and a processor. The operator workstation provides an operator interface that enables scan parameters to be entered for operating the MRI scanner.

[0082] In certain embodiments, a computer-implemented method is used for instructing an MRI scanner, configured to acquire TRMRF data for a subject, to select an inversion recovery unbalanced steady state free precession (SSFP) gradient echo magnetic resonance pulse sequence having a variable flip angle pattern and an echo planar imaging (EPI) readout with segmented Poisson disk samplings in a phase encoding (PE)-time domain, wherein the SSFP gradient echo magnetic resonance pulse sequence comprises a plurality of radio frequency (RF) pulses, wherein each of the plurality of RF pulses are separated from an adjacent RF pulse by a repetition time (TR), wherein the Poisson disk is divided into a plurality of segments having a plurality of samples in each segment, wherein one of the plurality of segments is acquired during each TR, wherein sampling density is inversely proportional to the radius to k-space center in the PE axes and is constant along a time axis, wherein Ti, T2, T2*, and proton density values are acquired simultaneously.

[0083] In some embodiments, a computer implemented method is provided for generating quantitative maps and synthetic parameter-weighted images from TRMRF data acquired with an MRI scanner. A processor can be programmed to perform steps of a computer implemented method comprising: receiving the TRMRF data, wherein the TRMRF data is acquired for a subject with a magnetic resonance imaging (MRI) scanner using an inversion recovery unbalanced steady state free precession (SSFP) gradient echo magnetic resonance pulse sequence having a variable flip angle pattern and an echo planar imaging (EPI) readout with segmented Poisson disk samplings in a phase encoding (PE)-time domain, wherein the SSFP gradient echo magnetic resonance pulse sequence comprises a plurality of radio frequency (RF) pulses, wherein each of the plurality of RFpulses are separated from an adjacent RF pulse by a repetition time (TR), wherein the Poisson disk is divided into a plurality of segments having a plurality of samples in each segment, wherein one of the plurality of segments is acquired during each TR, wherein sampling density is inversely proportional to the radius to k-space center in the PE axes and is constant along a time axis, wherein Ti, T2, T2*, and proton density values are acquired simultaneously; performing time-resolved subspace reconstruction using a 2-dimensional decomposition, wherein signal evolution is represented by Ti -weighted and T2-weighted inter-TR signals, and wherein signal decay is represented by T2*-weighted intra-TR signals, measuring static magnetic field inhomogeneity (Bo) using separate inter-TR subspace reconstructions for each echo with only the first subspace order, wherein Boinhomogeneity is corrected in the subspace reconstructions to generate corrected subspace reconstructions; generating subspace images from the corrected subspace reconstructions; producing quantitative maps from the subspace images using fingerprint dictionary matching and quantitative susceptibility mapping performed with susceptibility source separation, wherein the inter-TR signals are used to map the Ti, T2, and proton density values, and wherein the intra-TR signals are used to map the T2* values, wherein a synthetic Ti-weighted image, a synthetic T2-weighted image, a synthetic T2*-weighted image, a synthetic proton density-weighted image, and a synthetic susceptibility-weighted image are produced; and displaying the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, and the synthetic susceptibility-weighted image.

[0084] In certain embodiments, the PE-time domain is a ky-t domain for 2-dimensional (2D) magnetic resonance fingerprinting or a ky-kz-t domain for three-dimensional (3D) magnetic resonance fingerprinting.

[0085] In certain embodiments, the Poisson disk is divided into a plurality of segments having an identical number of samples in each segment.

[0086] In certain embodiments, magnetic resonance fingerprint dictionary matching uses extended phase graph (EPG) simulations for subspace reconstruction and parametric matching. For a description of methods of using an EPG model for magnetic resonance fingerprint dictionary generation, see, e.g., Wang et al. (2020) Magn. Reson. Imaging 66:248-256; Hennig et al. (2004) Magn. Reson. Med. 51 (1 ):68-80, Cohen et al. (2018) Magn. Reson. Med. 80(3):885-894, Emmerich et al. (2019) J. Magn. Reson. Imaging 49(5):1253-1262, and Liu et al. (2021 ) NMR Biomed. 34(7):e4527; herein incorporated by reference in their entireties. In certain embodiments, T2* is matched or fitted separately from other parameters during the fingerprint dictionary matching.

[0087] In certain embodiments, the time-resolved subspace reconstruction further comprises calculating:

[0088] wherein y is k space data, a is subspace images, is an off resonance and eddy current induced phase calculated from each echo planar imaging echo, A Zr||flr(a) II* is a locally low rank regularization term, I7i is a subspace transform operator for the inter-TR signals, t / 2is a subspace transform operator for the intra-TR signals, and wherein the subspace image is solved using an alternating direction method of multipliers (Boyd et al. (2011) Foundations and Trends® in Machine Learning 3(1 ):1 -22; herein incorporated by reference in its entirety). The low rank regularization term, £r||Kr(a) ||*, can be used for low rank regularization locally to resolve residual artifacts.

[0089] In certain embodiments, the computer-implemented method further comprises generating a Bo inhomogeneity induced phase map from the separate inter-TR reconstructions for each echo with only the first subspace order; and correcting the subspace reconstructions, wherein the Boinhomogeneity induced phase map is used to correct off-resonance effects and Nyquist ghosts in the subspace reconstructions.

[0090] In certain embodiments, the computer-implemented method further comprises fitting an off- resonance map to the Boinhomogeneity induced phase map of all echoes according to exact echo time for each echo planar imaging lobe.

[0091] In certain embodiments, the quantitative susceptibility mapping performed with susceptibility source separation further comprises generating paramagnetic positive susceptibility maps and diamagnetic negative susceptibility maps.

[0092] In certain embodiments, the computer-implemented method further comprises generating a multiparametric quantitative map of a region-of-interest using two or more parameters selected from the group consisting of T1, T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility. In some embodiments, the two or more parameters comprise T1, T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility.

[0093] In certain embodiments, the computer implemented method further comprises storing the TRMRF data that is acquired for the subject in a database.

[0094] The methods can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware. The disclosed and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, a data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or any combination thereof.

[0095] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

[0096] In a further aspect, the system for performing the computer implemented method, as described, may include a processor, a storage component (i.e., memory), a display component, and other components typically present in general purpose computers. In some embodiments, the processor is provided by a computer or handheld device (e.g., a cell phone or tablet). The storage component stores information accessible by the processor, including instructions that may be executed by the processor and data that may be retrieved, manipulated or stored by the processor.

[0097] The storage component includes instructions. For example, the storage component includes instructions for generating quantitative maps and synthetic parameter-weighted images from the TRMRF data stored therein according to the methods described herein. In some embodiments, the storage component further includes instructions for controlling operation of an MRI scanner, including instructions on selecting an SSFP gradient echo magnetic resonance pulse sequence for performing TRMRF of a subject. The computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component in order to receive the inputted TRMRF data and analyze the TRMRF data according to a computer implemented method described herein. In some embodiments, the computer processor and / or storage component are coupled to an operator workstation of an MRI scanner through a wired or wireless connection.

[0098] The processor and / or memory may be operably connected to a display device, for example, via a wired, such as a Universal Serial Bus (USB) connection, or wireless connection, such as a Bluetooth connection. Any convenient display device, such as a liquid crystal display (LCD), lightemitting diode (LED) display, plasma (PDP) display, quantum dot (QLED) display or cathode ray tube display device may be used. The display component may display one or more quantitative maps and / or synthetic parameter-weighted images. In some embodiments, the display displays thesynthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image, or any combination thereof. In some embodiments, the display further adds a boundary line around a region of interest to a displayed quantitative map and / or synthetic parameter-weighted image. In some embodiments, the display further displays a subspace reconstruction image. In some embodiments, the display further displays a table comprising the Ti , T2, T2*, proton density, or susceptibility values, or any combination thereof.

[0099] The storage component may be of any type capable of storing information accessible by the processor, such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, USB Flash drive, write- capable, and read-only memories. The processor may be a general purpose processor, a graphics processor unit, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor can be a microprocessor, but in the alternative, the processor can be a controller, microcontroller, or state machine, combinations of the same, or the like. A processor can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, a processor can also include primarily analog components. A computing environment can include any type of computer system, including, but not limited to, a computer system based on a microprocessor, a graphics processor unit, a mainframe computer, a digital signal processor, a portable computing device, a personal organizer, a device controller, and a computational engine within an appliance, to name a few.

[0100] The steps of a method, process, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module, engine, and associated databases can reside in memory resources such as in RAM memory, FRAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of non-transitory computer-readable storage medium, media, or physical computer storage known in the art. An exemplary storage medium can be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

[0101] The instructions may be any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by the processor. In that regard, the terms "instructions," "steps" and "programs" may be used interchangeably herein. The instructions may be stored in object code form for direct processing by the processor, or in any other computer language including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance.

[0102] Data may be retrieved, stored or modified by the processor in accordance with the instructions. For instance, although the system is not limited by any particular data structure, the data may be stored in computer registers, in a relational database as a table having a plurality of different fields and records, XML documents, or flat files. The data may also be formatted in any computer-readable format such as, but not limited to, binary values, ASCII or Unicode. Moreover, the data may comprise any information sufficient to identify the relevant information, such as numbers, descriptive text, proprietary codes, pointers, references to data stored in other memories (including other network locations) or information which is used by a function to calculate the relevant data.

[0103] In certain embodiments, the processor and storage component may comprise multiple processors and storage components that may or may not be stored within the same physical housing. For example, some of the instructions and data may be stored on removable CD-ROM and others within a read-only computer chip. Some or all of the instructions and data may be stored in a location physically remote from, yet still accessible by, the processor. Similarly, the processor may comprise a collection of processors which may or may not operate in parallel.

[0104] In some embodiments, the method can be performed using a cloud computing system. In these embodiments, the TRMRF data, acquired for a subject, and the programming can be exported to a cloud computer, which runs the program, and returns an output to the user.Kits

[0105] Also provided are kits containing any of the devices, systems, or software described herein for use in generating quantitative maps and synthetic parameter-weighted images of a subject using time-resolved magnetic resonance fingerprinting. In certain embodiments, the kit comprises software for carrying out the computer implemented methods, described herein. In some embodiments, the kit comprises a non-transitory computer-readable medium comprising program instructions that, when executed by a processor in a computer, causes the processor to perform a computer implemented method described herein.

[0106] In certain embodiments, the kit comprises a system for generating quantitative maps and synthetic parameter-weighted images from TRMRF data, wherein the system comprises: (a) a storage component for storing data, wherein the storage component has instructions for generating the quantitative maps and the synthetic parameter-weighted images from the TRMRF data stored therein; (b) a computer processor programmed to generate the quantitative maps and the synthetic parameter-weighted images from the TRMRF data, wherein the computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component in order to receive the inputted TRMRF data and analyze the TRMRF data according to a computer implemented method described herein; and (c) a display component for displaying one or more of the quantitative maps and the synthetic parameter-weighted images. In some embodiments, the system further comprises a magnetic resonance imaging (MRI) scanner configured to acquire TRMRF data for a subject, as described herein.

[0107] In addition to the above components, the subject kits may further include (in certain embodiments) instructions for practicing the subject methods. For example, the kit may include instructions for performing TRMRF using an MRI scanner according to the methods described herein to generate quantitative maps and synthetic parameter-weighted images. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, and the like. Yet another form of these instructions is a computer readable medium, e.g., diskette, compact disk (CD), DVD, Blu-ray, flash drive, and the like, on which the information has been recorded. Yet another form of these instructions that may be present is a website address which may be used via the internet to access the information at a removed site.Utility

[0108] The methods and systems, described herein, for performing TRMRF improve the efficiency of magnetic resonance acquisition and generate high-quality multi-parametric quantitative maps and multi-contrast images for clinical diagnosis in a single scan, Magnetic resonance acquisition can be performed within the time of a single breath-hold of a patient, which reduces the impact of inspiration motion and patient discomfort during MRI scanning. Radiologists benefit from the variety of quantitative parameter maps and synthetic weighted images the method produces for clinical diagnosis.

[0109] In addition, the parameters obtained from a quick TRMRF scan can be used to tailor MRI protocols for individual patients for generation of higher resolution images. In routine clinical scans,imaging protocols are optimized for lesion contrast based on disease type and anatomic locations based on previous experience with other patients; therefore, the acquired images of an individual patient are often not obtained with the best contrast that MRI can provide. Ti, T2, T2*, proton density, and / or susceptibility values that are acquired from a quick TRMRF scan of a patient can be used to optimize settings for performing higher resolution MRI scans of the patient to improve delineation of lesions in qualitative MRI images.Examples of Non-Limiting Aspects of the Disclosure

[0110] Aspects, including embodiments, of the present subject matter described above may be beneficial alone or in combination, with one or more other aspects or embodiments. Without limiting the foregoing description, certain non-limiting aspects of the disclosure numbered 1-47 are provided below. As will be apparent to those of skill in the art upon reading this disclosure, each of the individually numbered aspects may be used or combined with any of the preceding or following individually numbered aspects. This is intended to provide support for all such combinations of aspects and is not limited to combinations of aspects explicitly provided below:1. A method of performing time-resolved magnetic resonance fingerprinting (TRMRF) using a magnetic resonance imaging (MRI) scanner, the method comprising: acquiring TRMRF data for a subject with the MRI scanner using an inversion recovery unbalanced steady state free precession (SSFP) gradient echo magnetic resonance pulse sequence having a variable flip angle pattern and an echo planar imaging (EPI) readout with segmented Poisson disk samplings in a phase encoding (PE)-time domain, wherein the SSFP gradient echo magnetic resonance pulse sequence comprises a plurality of radio frequency (RF) pulses, wherein each of the plurality of RF pulses are separated from an adjacent RF pulse by a repetition time (TR), wherein the Poisson disk is divided into a plurality of segments having a plurality of samples in each segment, wherein one of the plurality of segments is acquired during each TR, wherein sampling density is inversely proportional to the radius to k-space center in the PE axes and is constant along a time axis, wherein Ti, T2, T2*, and proton density values are acquired simultaneously; performing time-resolved subspace reconstruction using a 2-dimensional decomposition, wherein signal evolution is represented by Ti-weighted and T2-weighted inter-TR signals, and wherein signal decay is represented by T2*-weighted intra-TR signals, measuring static magnetic field inhomogeneity (Bo) using separate inter-TR subspace reconstructions for each echo with only the first subspace order, wherein Boinhomogeneity is corrected in the subspace reconstructions to generate corrected subspace reconstructions;generating subspace images from the corrected subspace reconstructions; producing quantitative maps from the subspace images using fingerprint dictionary matching and quantitative susceptibility mapping performed with susceptibility source separation, wherein the inter-TR signals are used to map the Ti, T2, and proton density values, and wherein the intra-TR signals are used to map the T2* values, wherein a synthetic T1 -weighted image, a synthetic T2- weighted image, a synthetic T2*-weighted image, a synthetic proton density-weighted image, and a synthetic susceptibility-weighted image are produced.2. The method of aspect 1 , wherein the PE-time domain is a ky-t domain for 2- dimensional (2D) magnetic resonance fingerprinting or a ky-kz-t domain for three-dimensional (3D) magnetic resonance fingerprinting.3. The method of aspect 1 or 2, wherein the Poisson disk is divided into a plurality of segments having an identical number of samples in each segment.4. The method of any one of aspects 1-3, wherein the fingerprint dictionary matching uses extended phase graph (EPG) simulations for subspace reconstruction and parametric matching.5. The method of any one of aspects 1 -4, wherein T2* is matched or fitted separately from other parameters during the fingerprint dictionary matching.6. The method of any one of aspects 1 -5, further comprising performing low rank regularization locally to resolve residual artifacts.7. The method of any one of aspects 1 -6, wherein the time-resolved subspace reconstruction further comprises calculating:wherein y is k space data, a is subspace images, is an off resonance and eddy current induced phase calculated from each echo planar imaging echo, A £r||Rr(a)||* is a locally low rank regularization term, L7i is a subspace transform operator for the inter-TR signals, U2is a subspacetransform operator for the intra-TR signals, and wherein the subspace image is solved using an alternating direction method of multipliers (ADMM).8. The method of any one of aspects 1-7, further comprising: generating a Boinhomogeneity induced phase map from the separate inter-TR reconstructions for each echo with only the first subspace order; and correcting the subspace reconstructions, wherein the Boinhomogeneity induced phase map is used to correct off-resonance effects and Nyquist ghosts in the subspace reconstructions.9. The method of aspect 8, further comprising fitting an off-resonance map to the Boinhomogeneity induced phase map of all echoes according to exact echo time for each echo planar imaging lobe.10. The method of any one of aspects 1 -9, wherein the quantitative susceptibility mapping performed with susceptibility source separation further comprises generating paramagnetic positive susceptibility maps and diamagnetic negative susceptibility maps.11. The method of any one of aspects 1 -10, wherein said acquiring TRMRF data is performed within a time period of a single breath-hold of the subject.12. The method of any one of aspects 1 -11 , wherein said acquiring TRMRF data is performed within 25 seconds or less.13. The method of any one of aspects 1 -12, wherein the TRMRF data is acquired for a region-of-interest in the subject.14. The method of aspect 13, further comprising generating a multiparametric quantitative map of the region-of-interest using two or more parameters selected from the group consisting of Ti , T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility.15. The method of aspect 14, wherein the two or more parameters comprise Ti, T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility.16. The method of any one of aspects 1-1 , wherein the TRMRF data is acquired for whole body of the subject.17. The method of any one of aspects 1 -16, further comprising detecting a biomarker in the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image, or any combination thereof.18. The method of any one of aspects 1-17, further comprising monitoring location, uptake, or efficacy of a drug by visual analysis of the synthetic Ti-weighted image, the synthetic T2- weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image, or any combination thereof, wherein the drug is administered to the subject prior to said acquiring the TRMRF data for the subject.19. The method of any one of aspects 1 -18, further comprising diagnosing a disease in the subject by visual analysis of the synthetic Ti -weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image, or any combination thereof for disease-related pathology.20. The method of any one of aspects 1 -19, further comprising using theTi, T2, T2*, proton density, and susceptibility values that are acquired from the TRMRF to adjust settings for performing contrast enhanced qualitative MRI of the subject to improve delineation of lesions in qualitative MRI images.21 . A computer-implemented method for generating quantitative maps and synthetic parameter-weighted images from time-resolved magnetic resonance fingerprinting (TRMRF) data, the computer performing steps comprising: receiving the TRMRF data, wherein the TRMRF data is acquired for a subject with a magnetic resonance imaging (MRI) scanner using an inversion recovery unbalanced steady state free precession (SSFP) gradient echo magnetic resonance pulse sequence having a variable flip angle pattern and an echo planar imaging (EPI) readout with segmented Poisson disk samplings in a phase encoding (PE)-time domain, wherein the SSFP gradient echo magnetic resonance pulse sequence comprises a plurality of radio frequency (RF) pulses, wherein each of the plurality of RF pulses are separated from an adjacent RF pulse by a repetition time (TR), wherein the Poisson disk is dividedinto a plurality of segments having a plurality of samples in each segment, wherein one of the plurality of segments is acquired during each TR, wherein sampling density is inversely proportional to the radius to k-space center in the PE axes and is constant along a time axis, wherein Ti , T2, T2*, and proton density values are acquired simultaneously; performing time-resolved subspace reconstruction using a 2-dimensional decomposition, wherein signal evolution is represented by Ti-weighted and T2-weighted inter-TR signals, and wherein signal decay is represented by T2*-weighted intra-TR signals, measuring static magnetic field inhomogeneity (Bo) using separate inter-TR subspace reconstructions for each echo with only the first subspace order, wherein Boinhomogeneity is corrected in the subspace reconstructions to generate corrected subspace reconstructions; generating subspace images from the corrected subspace reconstructions; producing quantitative maps from the subspace images using fingerprint dictionary matching and quantitative susceptibility mapping performed with susceptibility source separation, wherein the inter-TR signals are used to map the Ti, T2, and proton density values, and wherein the intra-TR signals are used to map the T2* values, wherein a synthetic Ti -weighted image, a synthetic T2- weighted image, a synthetic T2*-weighted image, a synthetic proton density-weighted image, and a synthetic susceptibility-weighted image are produced; and displaying the synthetic Ti -weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, and the synthetic susceptibility- weighted image.22. The computer-implemented method of aspect 21 , wherein the PE-time domain is a ky-t domain for 2-dimensional (2D) magnetic resonance fingerprinting or a ky-kz-t domain for three- dimensional (3D) magnetic resonance fingerprinting.23. The computer-implemented method of aspect 21 or 22, wherein the Poisson disk is divided into a plurality of segments having an identical number of samples in each segment.24. The computer-implemented method of any one of aspects 21 -23 wherein the fingerprint dictionary matching uses extended phase graph (EPG) simulations for subspace reconstruction and parametric matching.25. The computer-implemented method of any one of aspects 21 -24, wherein T2* is matched or fitted separately from other parameters during the fingerprint dictionary matching.26. The computer-implemented method of any one of aspects 21 -25, further comprising performing low rank regularization locally to resolve residual artifacts.27. The computer-implemented method of any one of aspects 21 -26, wherein the time- resolved subspace reconstruction further comprises calculating:wherein y is k space data, a is subspace images, 0 is an off resonance and eddy current induced phase calculated from each echo planar imaging echo, A £,.|| / ?,.(“) II* isalocally low rank regularization term, t / i is a subspace transform operator for the inter-TR signals, U2is a subspace transform operator for the intra-TR signals, and wherein the subspace image is solved using an alternating direction method of multipliers (ADMM).28. The computer-implemented method of any one of aspects 21-27, further comprising: generating a Boinhomogeneity induced phase map from the separate inter-TR reconstructions for each echo with only the first subspace order; and correcting the subspace reconstructions, wherein the Boinhomogeneity induced phase map is used to correct off-resonance effects and Nyquist ghosts in the subspace reconstructions.29. The computer-implemented method of aspect 28, further comprising fitting an off- resonance map to the Boinhomogeneity induced phase map of all echoes according to exact echo time for each echo planar imaging lobe.30. The computer-implemented method of any one of aspects 21 -29, wherein the quantitative susceptibility mapping performed with susceptibility source separation further comprises generating paramagnetic positive susceptibility maps and diamagnetic negative susceptibility maps.31. The computer-implemented method of any one of aspects 21 -30, wherein said acquiring TRMRF data is performed within a time period of a single breath-hold of the subject.32. The computer-implemented method of any one of aspects 21 -31 , wherein said acquiring TRMRF data is performed within 25 seconds or less.33. The computer-implemented method of any one of aspects 21 -32, wherein the TRMRF data is acquired for a region-of-interest in the subject.34. The computer-implemented method of aspect 33, further comprising generating a multiparametric quantitative map of the region-of-interest using two or more parameters selected from the group consisting of Ti, T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility.35. The computer-implemented method of aspect 34, wherein the two or more parameters comprise Ti, T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility.36. The computer-implemented method of any one of aspects 21 -35, further comprising instructing an MRI scanner, configured to acquire TRMRF data for a subject, to select an inversion recovery unbalanced steady state free precession (SSFP) gradient echo magnetic resonance pulse sequence having a variable flip angle pattern and an echo planar imaging (EPI) readout with segmented Poisson disk samplings in a phase encoding (PE)-time domain, wherein the SSFP gradient echo magnetic resonance pulse sequence comprises a plurality of radio frequency (RF) pulses, wherein each of the plurality of RF pulses are separated from an adjacent RF pulse by a repetition time (TR), wherein the Poisson disk is divided into a plurality of segments having a plurality of samples in each segment, wherein one of the plurality of segments is acquired during each TR, wherein sampling density is inversely proportional to the radius to k-space center in the PE axes and is constant along a time axis, wherein Ti, T2, T2*, and proton density values are acquired simultaneously.37. The computer-implemented method of any one of aspects 21 -36, further comprising displaying the subspace reconstructions.38. The computer-implemented method of any one of aspects 21 -37, further comprising displaying a table comprising the Ti, T2, T2*, proton density, or susceptibility values, or any combination thereof.39. The computer-implemented method of any one of aspects 21 -38, further comprising displaying the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*- weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility- weighted image with a boundary line around a region of interest.40. A system for generating quantitative maps and synthetic parameter-weighted images from time-resolved magnetic resonance fingerprinting (TRMRF) data, the system comprising:(a) a storage component for storing data, wherein the storage component has instructions for generating the quantitative maps and the synthetic parameter-weighted images from the TRMRF data stored therein;(b) a computer processor programmed to generate the quantitative maps and the synthetic parameter-weighted images from the TRMRF data, wherein the computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component in order to receive the inputted TRMRF data and analyze the TRMRF data according to the computer implemented method of any one of aspects 21 -39; and(c) a display component for displaying one or more of the quantitative maps and the synthetic parameter-weighted images.41 . The system of aspect 40, further comprising a magnetic resonance imaging (MRI) scanner configured to acquire TRMRF data for a subject.42. The system of aspect 40 or 41 , wherein the display further displays the synthetic Ti- weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image, or any combination thereof.43. The system of any one of aspects 40-42, wherein the display further displays the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image with a boundary line around a region of interest.44. The system of any one of aspects 40-43, wherein the display further displays the subspace reconstructions.45. The system of any one of aspects 40-44, wherein the display further displays a table comprising the Ti, T2, T2*, proton density, or susceptibility values, or any combination thereof.46. A non-transitory computer-readable medium comprising program instructions that, when executed by a processor in a computer, causes the processor to perform the computer implemented method of any one of aspects 21 -39.47. A kit comprising the non-transitory computer-readable medium of aspect 46 and instructions for generating quantitative maps and synthetic parameter- weighted images from time- resolved magnetic resonance fingerprinting (TRMRF) data.

[0111] It will be apparent to one of ordinary skill in the art that various changes and modifications can be made without departing from the spirit or scope of the invention.EXPERIMENTAL

[0112] The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as their invention nor are they intended to represent that the experiments below are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g. amounts, temperature, efc.) but some experimental errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, molecular weight is average molecular weight, temperature is in degrees Centigrade, and pressure is at or near atmospheric.

[0113] All publications and patent applications cited in this specification are herein incorporated by reference as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference.

[0114] The present invention has been described in terms of particular embodiments found or proposed by the present inventor to comprise preferred modes for the practice of the invention. It will be appreciated by those of skill in the art that, in light of the present disclosure, numerous modifications and changes can be made in the particular embodiments exemplified without departing from the intended scope of the invention. All such modifications are intended to be included within the scope of the appended claims.Example 1Time-Resolved Magnetic Resonance FingerprintingINTRODUCTION

[0115] Time-resolved MR fingerprinting (“TRMRF”) with an echo planar imaging (“EPI”) readout and segmented Poisson disk samplings in a phase encoding (“PE”)-time domain (ky-t domain for 2D MRF and ky-kz-t for 3D MRF) was used in the pulse sequence. Specifically, sampling density is inversely proportional to the radius to k-space center in PE axes, and is constant along the time axis. The Poisson disk is taken into segments with identical number of samples, and one of the segments is acquired within each repetition time (“TR”).

[0116] A 2-dimensional decomposition is used in the subspace reconstruction due to the independence of the global signal variation and the local signal variation. The signal evolution can be divided into two independent parts - Ti and T2weighted inter-TR signal and T2* weighted intra- TR signal due to the existence of spoiler, and the image series can be represented by subspace coefficients from inter-TR and intra-TR domain. This decomposition and representation reduce the reconstruction time and memory required. A locally low rank regularization term was utilized to resolve the residual artifacts. Boinhomogeneity can be measured by separated inter-TR reconstructions for each echo with only the first subspace order, and corrected in the subspace reconstruction. After reconstruction, inter-TR signals were used to map Ti, T2and proton density, and intra-TR signals were used to map T2*. QSM and multi-contrast synthetic images are generated accordingly.

[0117] The novelty of the invention arises as (1 ) T2* quantification is added in MRF scan with minimal extra scan time, which also enables simultaneous QSM and synthetic T2* weighted / Susceptibility weighted imaging (“SWI”); (2) multi-parametric quantification can be acquired in body imaging within a breath-hold, which greatly reduced the impact of inspiration motion; (3) reconstruction method was designed by utilizing a 2-dimonsional decomposition to reduce time-computation complexity and improve the reconstructed image quality.DETAILED DESCRIPTION

[0118] The acquisition framework of time-resolved magnetic resonance fingerprinting (“TRMRF”) is shown in FIG. 1. The sequence is based on an inversion recovery unbalanced steady state free precession (“SSFP”) MRF sequence, FIGS. 1A-1 B show the sequence diagram of consecutive timeframes with a variable flip angle pattern. A Poison disk sampling pattern in the ky-t plane with echo planar imaging (“EPI”) readout is shown in FIGS. 1 C-1 D. Specifically, sampling density is inversely proportional to the radius to k-space center in the PE axes, and is constant along the time axis as in FIG. 1 D. The Poisson disk is taken into segments with an identical number of samples, and one of the segments is acquired within each repetition time (“TR”) as shown in FIG. 10.

[0119] FIG. 2 shows the image processing pipeline. Due to intra-TR T2* decay being independent to inter-TR Ti and T2weighted signal evolution, acquired temporal signal can be represented by these two parts, enabling the simultaneous quantification of T2and T2*. Two dictionaries were accordingly built with extended phase graph (“EPG”) for both subspace reconstruction and parametric matching. The time-resolved subspace reconstruction was performed with:where y is the k space data, a is the subspace images, is the off resonance and eddy current induced phase calculated from each EPI echo, and / t £r|| / ?r(«)ll* is the locally low rank regularization term. 171 and t / 2are subspace transform operators for inter-TR and intra-TR signals, respectively. The subspace image can be solved with alternating direction method of multipliers (“ADMM”)8. A 2- dimensional decomposition is used in the subspace reconstruction due to the independence of the global signal variation and the local signal variation. The signal evolution can be divided into two independent parts - Ti and T2weighted inter-TR signal and T2* weighted intra-TR signal due to the existence of spoiler, and the image series can be represented by subspace coefficients from inter- TR and intra-TR domain. A locally low rank regularization term was utilized to resolve the residual artifacts.

[0120] Bo inhomogeneity induced phase map can be measured by separated inter-TR reconstructions for each echo with only the first subspace order, and corrected in the subspace reconstruction. The phase map corrects both off resonance effect and Nyquist ghost in reconstruction. Furthermore, off resonance map can be fitted with the phase map of all echoes according to the exact echo time for each EPI lobe.

[0121] In multi-parametric quantification, inter-TR signals were used to map Ti, T2and proton density (PD), and intra-TR signals were used to map T2*. The STAR-QSM method9was performed to estimate the QSM and APART-QSM method10was performed for susceptibility source separation in paramagnetic positive and diamagnetic negative susceptibility maps.

[0122] The acquisition and reconstruction method were demonstrated in a brain volunteer study and an abdominal volunteer study. All the scans were performed on a GE 3T scanner (MR750, Waukesha, Wl) with a 32-channel coil. In the brain volunteer study, the parameters include TR = 40 ms, Tl = 3.65 ms, FOV = 224 x 224, resolution = 1 mm2, echo train length (ETL) = 28, echo spacing = 1.2 ms, time frame = 400, and scan time is 16 s / slice. In the abdominal study, the parameters include TR = 40 ms, coronal FOV = 400 x 400 mm2, resolution = 1 .67 x 1 .67 x 5 mm3, ETL = 28, and echo spacing = 0.8 ms, time frame = 400. The scan time is 16 s / slice, achieving scan for each slice within a breath-hold.

[0123] FIG. 3 shows the quantitative maps of brain Ti , T2, T2*, off resonance, proton density and Bi + scale generated from the reconstruction pipeline in FIG. 2. T2* can be matched or fitted separately from other parameters to reduce the searching time, and the result is identity to that from joint matching with other parameters.

[0124] The volunteer in abdominal study was scanned in two separate studies to evaluate the repeatability, shown in FIG. 4. The table in FIG. 4 shows the measured quantitative parameters from the region-of-interest (ROI) of lower liver, renal cortex and renal medulla in both studies. In both studies, the Ti , T2, and susceptibility had similar values. Though T2* relaxation times were different between studies, potentially caused by the different breath-holding stages in two studies, renal cortex showed longer T2* relaxation than renal medulla and liver in both studies. Notably, the upper liver showed a higher susceptibility parameter in the first study, which was possibly caused by different breath-holding stages. It will be validated with future studies.

[0125] FIG.5 shows the zoomed-in multiparametric quantitative maps covering the liver and kidney from the volunteer. QSM showed both liver and kidney had small bulk susceptibility differences. However, susceptibility source separation showed significant positive and negative susceptibility maps in the liver. This is consistent with higher R2* due to local magnetic susceptibility but that the susceptibility sources cancel out and thus there is no bulk QSM shift in the liver. This is likely is due to hepatic iron concentrations in the liver. The kidneys, which do not have such high iron concentration, had smaller positive and negative susceptibility values. Due to the deoxygenated hemoglobin, the veins showed high positive susceptibility compared to liver tissue with little negative susceptibility.

[0126] The synthetic PD-weighted, Ti-weighted, T2-weighted, T2*-weighted and susceptibility- weighted images are shown in FIG. 6, which were calculated from the subspace images. The quality of synthetic images is comparable to traditional contrast images, while the efficiency is much higher.REFERENCES

[0127] 1. Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. 2013;495(7440):187-192.

[0128] 2. Jiang, Yun, et al. "MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout." Magnetic resonance in medicine 74.6 (2015): 1621 -1631 .

[0129] 3. Hong T, Han D, Kim D-H. Simultaneous estimation of PD, T1 , T2, T2*, and ABOusing magnetic resonance fingerprinting with background gradient compensation. Magn Reson Med.2019;81 :2614-2623.

[0130] 4. Wyatt CR, Smith TB, Sammi MK, Rooney WD, Guimaraes AR. Multi-parametric T2* magnetic resonance finger-printing using variable echo times. NMR in Biomedicine. 2018;31 :e3951 .

[0131] 5. Rieger, Benedikt, et al. "Magnetic resonance fingerprinting using echo-planar imaging:Joint quantification of T 1 and relaxation times." Magnetic resonance in medicine 78.5 (2017): 1724- 1733.

[0132] 6. Lee, Jongho, et al. "Respiration-induced B0 field fluctuation compensation in balancedSSFP: real-time approach for transition-band SSFP fMRI." Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 55.5 (2006): 1197- 1201.

[0133] 7. Tamir, Jonathan I., et al. "T2 shuffling: sharp, multicontrast, volumetric fast spin-echo imaging." Magnetic resonance in medicine 77 A (2017): 180-195.

[0134] 8. Boyd S, Parikh N, Chu E, Peleato B, Eckstein J. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine learning. 2011 Jul 25;3(1 ):1-22.

[0135] 9. Li W, Wu B, Liu C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. Neuroimage. 2011 ;55(4):1645-1656.

[0136] 10. Li Z, Feng R, Liu Q, et al. APART-QSM: An improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method. Neuroimage. 2023 ;274.Example 2TRMRF-Guided Optimization and Customization of MRI Scanning for Individual Patients

[0137] In addition to generating quantitative parameter maps and synthetic weighted images for direct diagnostic use, an important application of the provided TRMRF methods is to tailor the imaging protocol for individualized scans. In routine clinical scans, imaging protocols are optimized only for the lesion contrast from previous experiences based on disease type and anatomic locations,therefore, the acquired images often do not have the best contrast that MRI can provide. It’s rarely updated for each patient due to workflow issues. This application aims to utilize the fact that lesion contrast could be affected by protocol settings for clinical qualitative imaging. For the example in FIG.1 , while the image on the right has excellent gray and white matter contrast, the image on the left shows better delineation of the cortical abnormality, even though both sequences are acquired using T2-FLAIR (fluid-attenuated inversion recovery) contrast using 3D FSE with slightly different TE / TR. It demonstrates the need of customizing acquisition parameters to better highlight lesions instead of using one size fits all imaging parameters that are predetermined. The provided method achieves quantifications of different biomarkers with accelerated scan and processing time, so it has the ability to guide the routine scans with different contrast, including Ti-weighted, T2-weighted, FLAIR, proton density weighted, T2* weighted and susceptibility weighted imaging.

[0138] A TRMRF-guided protocol is illustrated in FIG.2. A fast TRMRF scan is performed at the beginning of the protocol with the quantification of T1, T2, T2* and proton density maps and the analysis with the signal evolution of lesion and surrounding tissues. An algorithm is used that is designed to optimize the scan protocol to improve delineation of lesions in the qualitative images to be used for clinical diagnosis. It also has the potential to indicate whether additional imaging biomarkers should be acquired to provide sufficient contrast for lesion identification. This application would be compatible with deep learning or Al for even faster reconstruction and protocol optimization.

Claims

What is claimed is:

1. A method of performing time-resolved magnetic resonance fingerprinting (TRMRF) using a magnetic resonance imaging (MRI) scanner, the method comprising: acquiring TRMRF data for a subject with the MRI scanner using an inversion recovery unbalanced steady state free precession (SSFP) gradient echo magnetic resonance pulse sequence having a variable flip angle pattern and an echo planar imaging (EPI) readout with segmented Poisson disk samplings in a phase encoding (PE)-time domain, wherein the SSFP gradient echo magnetic resonance pulse sequence comprises a plurality of radio frequency (RF) pulses, wherein each of the plurality of RF pulses are separated from an adjacent RF pulse by a repetition time (TR), wherein the Poisson disk is divided into a plurality of segments having a plurality of samples in each segment, wherein one of the plurality of segments is acquired during each TR, wherein sampling density is inversely proportional to the radius to k-space center in the PE axes and is constant along a time axis, wherein Ti, T2, T2*, and proton density values are acquired simultaneously; performing time-resolved subspace reconstruction using a 2-dimensional decomposition, wherein signal evolution is represented by Ti-weighted and T2-weighted inter-TR signals, and wherein signal decay is represented by T2*-weighted intra-TR signals, measuring static magnetic field inhomogeneity (Bo) using separate inter-TR subspace reconstructions for each echo with only the first subspace order, wherein Boinhomogeneity is corrected in the subspace reconstructions to generate corrected subspace reconstructions; generating subspace images from the corrected subspace reconstructions; producing quantitative maps from the subspace images using fingerprint dictionary matching and quantitative susceptibility mapping performed with susceptibility source separation, wherein the inter-TR signals are used to map the Ti, T2, and proton density values, and wherein the intra-TR signals are used to map the T2* values, wherein a synthetic Ti -weighted image, a synthetic T2- weighted image, a synthetic T2*-weighted image, a synthetic proton density-weighted image, and a synthetic susceptibility-weighted image are produced.

2. The method of claim 1 , wherein the PE-time domain is a ky-t domain for 2-dimensional (2D) magnetic resonance fingerprinting or a ky-kz-t domain for three-dimensional (3D) magnetic resonance fingerprinting.

3. The method of claim 1 or 2, wherein the Poisson disk is divided into a plurality of segments having an identical number of samples in each segment.

4. The method of any one of claims 1 -3, wherein the fingerprint dictionary matching uses extended phase graph (EPG) simulations for subspace reconstruction and parametric matching.

5. The method of any one of claims 1 -4, wherein T 2* is matched or fitted separately from other parameters during the fingerprint dictionary matching.

6. The method of any one of claims 1-5, further comprising performing low rank regularization locally to resolve residual artifacts.

7. The method of any one of claims 1 -6, wherein the time-resolved subspace reconstruction further comprises calculating:wherein y is k space data, a is subspace images, O is an off resonance and eddy current induced phase calculated from each echo planar imaging echo, AII* isalocally low rank regularization term, t / i is a subspace transform operator for the inter-TR signals, U2is a subspace transform operator for the intra-TR signals, and wherein the subspace image is solved using an alternating direction method of multipliers (ADMM).

8. The method of any one of claims 1 -7, further comprising: generating a Boinhomogeneity induced phase map from the separate inter-TR reconstructions for each echo with only the first subspace order; and correcting the subspace reconstructions, wherein the Boinhomogeneity induced phase map is used to correct off-resonance effects and Nyquist ghosts in the subspace reconstructions.

9. The method of claim 8, further comprising fitting an off-resonance map to the Boinhomogeneity induced phase map of all echoes according to exact echo time for each echo planar imaging lobe.

10. The method of any one of claims 1 -9, wherein the quantitative susceptibility mapping performed with susceptibility source separation further comprises generating paramagnetic positive susceptibility maps and diamagnetic negative susceptibility maps.

11. The method of any one of claims 1 -10, wherein said acquiring TRMRF data is performed within a time period of a single breath-hold of the subject.

12. The method of any one of claims 1 -11 , wherein said acquiring TRMRF data is performed within 25 seconds or less.

13. The method of any one of claims 1 -12, wherein the TRMRF data is acquired for a region-of-interest in the subject.

14. The method of claim 13, further comprising generating a multiparametric quantitative map of the region-of-interest using two or more parameters selected from the group consisting of Ti , T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility.

15. The method of claim 14, wherein the two or more parameters comprise Ti , T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility.

16. The method of any one of claims 1 -12, wherein the TRMRF data is acquired for whole body of the subject.

17. The method of any one of claims 1-16, further comprising detecting a biomarker in the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image, or any combination thereof.

18. The method of any one of claims 1-17, further comprising monitoring location, uptake, or efficacy of a drug by visual analysis of the synthetic Ti -weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image, or any combination thereof, wherein the drug is administered to the subject prior to said acquiring the TRMRF data for the subject.

19. The method of any one of claims 1-18, further comprising diagnosing a disease in the subject by visual analysis of the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image, or any combination thereof for disease-related pathology.

20. The method of any one of claims 1 -19, further comprising using the Ti , T2, T2*, proton density, and susceptibility values that are acquired from the TRMRF to adjust settings for performing contrast enhanced qualitative MRI of the subject to improve delineation of lesions in qualitative MRI images.21 . A computer-implemented method for generating quantitative maps and synthetic parameter-weighted images from time-resolved magnetic resonance fingerprinting (TRMRF) data, the computer performing steps comprising: receiving the TRMRF data, wherein the TRMRF data is acquired for a subject with a magnetic resonance imaging (MRI) scanner using an inversion recovery unbalanced steady state free precession (SSFP) gradient echo magnetic resonance pulse sequence having a variable flip angle pattern and an echo planar imaging (EPI) readout with segmented Poisson disk samplings in a phase encoding (PE)-time domain, wherein the SSFP gradient echo magnetic resonance pulse sequence comprises a plurality of radio frequency (RF) pulses, wherein each of the plurality of RF pulses are separated from an adjacent RF pulse by a repetition time (TR), wherein the Poisson disk is divided into a plurality of segments having a plurality of samples in each segment, wherein one of the plurality of segments is acquired during each TR, wherein sampling density is inversely proportional to the radius to k-space center in the PE axes and is constant along a time axis, wherein Ti , T2, T2*, and proton density values are acquired simultaneously; performing time-resolved subspace reconstruction using a 2-dimensional decomposition, wherein signal evolution is represented by Ti-weighted and T2-weighted inter-TR signals, and wherein signal decay is represented by T2*-weighted intra-TR signals, measuring static magnetic field inhomogeneity (Bo) using separate inter-TR subspace reconstructions for each echo with only the first subspace order, wherein Boinhomogeneity is corrected in the subspace reconstructions to generate corrected subspace reconstructions; generating subspace images from the corrected subspace reconstructions; producing quantitative maps from the subspace images using fingerprint dictionary matching and quantitative susceptibility mapping performed with susceptibility source separation, wherein the inter-TR signals are used to map the Ti, T2, and proton density values, and wherein the intra-TRsignals are used to map the T2* values, wherein a synthetic Ti -weighted image, a synthetic T2- weighted image, a synthetic T2*-weighted image, a synthetic proton density-weighted image, and a synthetic susceptibility-weighted image are produced; and displaying the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, and the synthetic susceptibility- weighted image.

22. The computer-implemented method of claim 21 , wherein the PE-time domain is a ky- t domain for 2-dimensional (2D) magnetic resonance fingerprinting or a ky-kz-t domain for three- dimensional (3D) magnetic resonance fingerprinting.

23. The computer-implemented method of claim 21 or 22, wherein the Poisson disk is divided into a plurality of segments having an identical number of samples in each segment.

24. The computer-implemented method of any one of claims 21 -23 wherein the fingerprint dictionary matching uses extended phase graph (EPG) simulations for subspace reconstruction and parametric matching.

25. The computer-implemented method of any one of claims 21 -24, wherein T2* is matched or fitted separately from other parameters during the fingerprint dictionary matching.

26. The computer-implemented method of any one of claims 21 -25, further comprising performing low rank regularization locally to resolve residual artifacts.

27. The computer-implemented method of any one of claims 21 -26, wherein the time- resolved subspace reconstruction further comprises calculating:wherein y is k space data, a is subspace images, is an off resonance and eddy current induced phase calculated from each echo planar imaging echo, A £r||7?r(a) ||- is a locally low rank regularization term, t7i is a subspace transform operator for the inter-TR signals, l / 2is a subspacetransform operator for the intra-TR signals, and wherein the subspace image is solved using an alternating direction method of multipliers (ADMM).

28. The computer-implemented method of any one of claims 21 -27, further comprising: generating a Boinhomogeneity induced phase map from the separate inter-TR reconstructions for each echo with only the first subspace order; and correcting the subspace reconstructions, wherein the Boinhomogeneity induced phase map is used to correct off-resonance effects and Nyquist ghosts in the subspace reconstructions.

29. The computer-implemented method of claim 28, further comprising fitting an off- resonance map to the Boinhomogeneity induced phase map of all echoes according to exact echo time for each echo planar imaging lobe.

30. The computer-implemented method of any one of claims 21-29, wherein the quantitative susceptibility mapping performed with susceptibility source separation further comprises generating paramagnetic positive susceptibility maps and diamagnetic negative susceptibility maps.

31. The computer-implemented method of any one of claims 21 -30, wherein said acquiring TRMRF data is performed within a time period of a single breath-hold of the subject.

32. The computer-implemented method of any one of claims 21 -31 , wherein said acquiring TRMRF data is performed within 25 seconds or less.

33. The computer-implemented method of any one of claims 21 -32, wherein the TRMRF data is acquired for a region-of-interest in the subject.

34. The computer-implemented method of claim 33, further comprising generating a multiparametric quantitative map of the region-of-interest using two or more parameters selected from the group consisting of Ti, T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility.

35. The computer-implemented method of claim 34, wherein the two or more parameters comprise Ti, T2, T2*, proton density, paramagnetic positive susceptibility, and diamagnetic negative susceptibility.

36. The computer-implemented method of any one of claims 21 -35, further comprising instructing an MRI scanner, configured to acquire TRMRF data for a subject, to select an inversion recovery unbalanced steady state free precession (SSFP) gradient echo magnetic resonance pulse sequence having a variable flip angle pattern and an echo planar imaging (EPI) readout with segmented Poisson disk samplings in a phase encoding (PE)-time domain, wherein the SSFP gradient echo magnetic resonance pulse sequence comprises a plurality of radio frequency (RF) pulses, wherein each of the plurality of RF pulses are separated from an adjacent RF pulse by a repetition time (TR), wherein the Poisson disk is divided into a plurality of segments having a plurality of samples in each segment, wherein one of the plurality of segments is acquired during each TR, wherein sampling density is inversely proportional to the radius to k-space center in the PE axes and is constant along a time axis, wherein Ti, T2, T2*, and proton density values are acquired simultaneously.

37. The computer-implemented method of any one of claims 21 -36, further comprising displaying the subspace reconstructions.

38. The computer-implemented method of any one of claims 21 -37, further comprising displaying a table comprising the T1, T2, T2*, proton density, or susceptibility values, or any combination thereof.

39. The computer-implemented method of any one of claims 21 -38, further comprising displaying the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*- weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility- weighted image with a boundary line around a region of interest.

40. A system for generating quantitative maps and synthetic parameter-weighted images from time-resolved magnetic resonance fingerprinting (TRMRF) data, the system comprising:(a) a storage component for storing data, wherein the storage component has instructions for generating the quantitative maps and the synthetic parameter-weighted images from the TRMRF data stored therein;(b) a computer processor programmed to generate the quantitative maps and the synthetic parameter-weighted images from the TRMRF data, wherein the computer processor is coupled to the storage component and configured to execute the instructions stored in the storage componentin order to receive the inputted TRMRF data and analyze the TRMRF data according to the computer implemented method of any one of claims 21 -39; and(c) a display component for displaying one or more of the quantitative maps and the synthetic parameter-weighted images.41 . The system of claim 40, further comprising a magnetic resonance imaging (MRI) scanner configured to acquire TRMRF data for a subject.

42. The system of claim 40 or 41 , wherein the display further displays the synthetic Ti- weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image, or any combination thereof.

43. The system of any one of claims 40-42, wherein the display further displays the synthetic Ti-weighted image, the synthetic T2-weighted image, the synthetic T2*-weighted image, the synthetic proton density-weighted image, or the synthetic susceptibility-weighted image with a boundary line around a region of interest.

44. The system of any one of claims 40-43, wherein the display further displays the subspace reconstructions.

45. The system of any one of claims 40-44, wherein the display further displays a table comprising the Ti , T2, T2*, proton density, or susceptibility values, or any combination thereof.

46. A non-transitory computer-readable medium comprising program instructions that, when executed by a processor in a computer, causes the processor to perform the computer implemented method of any one of claims 21 -39.

47. A kit comprising the non-transitory computer-readable medium of claim 46 and instructions for generating quantitative maps and synthetic param eter-weighted images from time- resolved magnetic resonance fingerprinting (TRMRF) data.

Images