Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge.

PURPOSE: T1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains. METHODS: The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed. RESULTS: Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org. CONCLUSION: The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo.

Accelerated liver water T1 mapping using single-shot continuous inversion-recovery spiral imaging.

PURPOSE: Liver T1 mapping techniques typically require long breath holds or long scan time in free-breathing, need correction for B 1 + inhomogeneities and process composite (water and fat) signals. The purpose of this work is to accelerate the multi-slice acquisition of liver water selective T1 (wT1) mapping in a single breath hold, improving the k-space sampling efficiency. METHODS: The proposed continuous inversion-recovery (IR) Look-Locker methodology combines a single-shot gradient echo spiral readout, Dixon processing and a dictionary-based analysis for liver wT1 mapping at 3 T. The sequence parameters were adapted to obtain short scan times. The influence of fat, B 1 + inhomogeneities and TE on the estimation of T1 was first assessed using simulations. The proposed method was then validated in a phantom and in 10 volunteers, comparing it with MRS and the modified Look-Locker inversion-recovery (MOLLI) method. Finally, the clinical feasibility was investigated by comparing wT1 maps with clinical scans in nine patients. RESULTS: The phantom results are in good agreement with MRS. The proposed method encodes the IR-curve for the liver wT1 estimation, is minimally sensitive to B 1 + inhomogeneities and acquires one slice in 1.2 s. The volunteer results confirmed the multi-slice capability of the proposed method, acquiring nine slices in a breath hold of 11 s. The present work shows robustness to B 1 + inhomogeneities ( wT 1 , No B 1 + = 1.07 wT 1 , B 1 + - 45.63 , R 2 = 0.99 ) , good repeatability ( wT 1 , 2 ° = 1 . 0 wT 1 , 1 ° - 2.14 , R 2 = 0.96 ) and is in better agreement with MRS ( wT 1 = 0.92 wT 1 MRS + 103.28 , R 2 = 0.38 ) than is MOLLI ( wT 1 MOLLI = 0.76 wT 1 MRS + 254.43 , R 2 = 0.44 ) . The wT1 maps in patients captured diverse lesions, thus showing their clinical feasibility. CONCLUSION: A single-shot spiral acquisition can be combined with a continuous IR Look-Locker method to perform rapid repeatable multi-slice liver water T1 mapping at a rate of 1.2 s per slice without a B 1 + map. The proposed method is suitable for nine-slice liver clinical applications acquired in a single breath hold of 11 s.

AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models.

BACKGROUND: Magnetic Resonance acquisition is a time consuming process, making it susceptible to patient motion during scanning. Even motion in the order of a millimeter can introduce severe blurring and ghosting artifacts, potentially necessitating re-acquisition. Magnetic Resonance Imaging (MRI) can be accelerated by acquiring only a fraction of k-space, combined with advanced reconstruction techniques leveraging coil sensitivity profiles and prior knowledge. Artificial intelligence (AI)-based reconstruction techniques have recently been popularized, but generally assume an ideal setting without intra-scan motion. PURPOSE: To retrospectively detect and quantify the severity of motion artifacts in undersampled MRI data. This may prove valuable as a safety mechanism for AI-based approaches, provide useful information to the reconstruction method, or prompt for re-acquisition while the patient is still in the scanner. METHODS: We developed a deep learning approach that detects and quantifies motion artifacts in undersampled brain MRI. We demonstrate that synthetically motion-corrupted data can be leveraged to train the convolutional neural network (CNN)-based motion artifact estimator, generalizing well to real-world data. Additionally, we leverage the motion artifact estimator by using it as a selector for a motion-robust reconstruction model in case a considerable amount of motion was detected, and a high data consistency model otherwise. RESULTS: Training and validation were performed on 4387 and 1304 synthetically motion-corrupted images and their uncorrupted counterparts, respectively. Testing was performed on undersampled in vivo motion-corrupted data from 28 volunteers, where our model distinguished head motion from motion-free scans with 91% and 96% accuracy when trained on synthetic and on real data, respectively. It predicted a manually defined quality label ('Good', 'Medium' or 'Bad' quality) correctly in 76% and 85% of the time when trained on synthetic and real data, respectively. When used as a selector it selected the appropriate reconstruction network 93% of the time, achieving near optimal SSIM values. CONCLUSIONS: The proposed method quantified motion artifact severity in undersampled MRI data with high accuracy, enabling real-time motion artifact detection that can help improve the safety and quality of AI-based reconstructions.

Digital reference objects for evaluating algorithm performance in MR image formation.

PURPOSE: Digital Reference Objects (DROs) are mathematical phantoms that can serve as a basis for evaluating MR image quality (IQ) in an objective way. Their main purpose is to facilitate the establishment of fully automated and perfectly reproducible IQ metrics to objectively compare different algorithms in MR image formation in a standardized manner. They also allow to re-build parts of standard phantoms. METHODS: We sample DROs directly in k-space, using analytical formulas for the continuous Fourier transform of primitive shapes. We demonstrate this DRO approach by applying a state-of-the-art CNN-based denoising algorithm that is robust to varying noise levels to noisy images of the resolution section of the well-known ACR phantom for IQ assessment, reconstructed from both measured and simulated k-space data. RESULTS: Applying the CNN-based denoising algorithm to the measured and simulated version of the ACR phantom resolution section produced virtually identical results, as confirmed by visual and quantitative comparison. CONCLUSIONS: DROs can help guide technology selection during the development of new algorithms in MR image formation, e.g., via deep learning. This could be an important step towards reproducible MR image formation.

High-fidelity direct contrast synthesis from magnetic resonance fingerprinting.

PURPOSE: This work was aimed at proposing a supervised learning-based method that directly synthesizes contrast-weighted images from the Magnetic Resonance Fingerprinting (MRF) data without performing quantitative mapping and spin-dynamics simulations. METHODS: To implement our direct contrast synthesis (DCS) method, we deploy a conditional generative adversarial network (GAN) framework with a multi-branch U-Net as the generator and a multilayer CNN (PatchGAN) as the discriminator. We refer to our proposed approach as N-DCSNet. The input MRF data are used to directly synthesize T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) images through supervised training on paired MRF and target spin echo-based contrast-weighted scans. The performance of our proposed method is demonstrated on in vivo MRF scans from healthy volunteers. Quantitative metrics, including normalized root mean square error (nRMSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID), were used to evaluate the performance of the proposed method and compare it with others. RESULTS: In-vivo experiments demonstrated excellent image quality with respect to that of simulation-based contrast synthesis and previous DCS methods, both visually and according to quantitative metrics. We also demonstrate cases in which our trained model is able to mitigate the in-flow and spiral off-resonance artifacts typically seen in MRF reconstructions, and thus more faithfully represent conventional spin echo-based contrast-weighted images. CONCLUSION: We present N-DCSNet to directly synthesize high-fidelity multicontrast MR images from a single MRF acquisition. This method can significantly decrease examination time. By directly training a network to generate contrast-weighted images, our method does not require any model-based simulation and therefore can avoid reconstruction errors due to dictionary matching and contrast simulation (code available at:https://github.com/mikgroup/DCSNet).

Quantitative longitudinal mapping of radiation-treated prostate cancer using MR fingerprinting with radial acquisition and subspace reconstruction.

MR fingerprinting (MRF) enables fast multiparametric quantitative imaging with a single acquisition and has been shown to improve diagnosis of prostate cancer. However, most prostate MRF studies were performed with spiral acquisitions that are sensitive to B0 inhomogeneities and consequent blurring. In this work, a radial MRF acquisition with a novel subspace reconstruction technique was developed to enable fast T1/T2 mapping in the prostate in under 4 min. The subspace reconstruction exploits the extensive temporal correlations in the MRF dictionary to pre-compute a low dimensional space for the solution and thus reduce the number of radial spokes to accelerate the acquisition. Iterative reconstruction with the subspace model and additional regularization of the signal representation in the subspace is performed to minimize the number of spokes and maintain matching quality and SNR. Reconstruction accuracy was assessed using the ISMRM NIST phantom. In-vivo validation was performed on two healthy subjects and two prostate cancer patients undergoing radiation therapy. The longitudinal repeatability was quantified using the concordance correlation coefficient (CCC) in one of the healthy subjects by repeated scans over 1 year. One prostate cancer patient was scanned at three time points, before initiating therapy and following brachytherapy and external beam radiation. Changes in the T1/T2 maps obtained with the proposed method were quantified. The prostate, peripheral and transitional zones, and visible dominant lesion were delineated for each study, and the statistics and distribution of the quantitative mapping values were analyzed. Significant image quality improvements compared with standard reconstruction methods were obtained with the proposed subspace reconstruction method. A notable decrease in the spread of the T1/T2 values without biasing the estimated mean values was observed with the subspace reconstruction and agreed with reported literature values. The subspace reconstruction enabled visualization of small differences in T1/T2 values in the tumor region within the peripheral zone. Longitudinal imaging of a volunteer subject yielded CCC of 0.89 for MRF T1, and 0.81 for MRF T2 in the prostate gland. Longitudinal imaging of the prostate patient confirmed the feasibility of capturing radiation treatment related changes. This work is a proof-of-concept for a high resolution and fast quantitative mapping using golden-angle radial MRF combined with a subspace reconstruction technique for longitudinal treatment response assessment in subjects undergoing radiation treatment.

Feasibility study of 2D Dixon-Magnetic Resonance Fingerprinting (MRF) of breast cancer.

Purpose: Application of MRF to evaluate the feasibility of 2D Dixon blurring-corrected MRF (2DDb-cMRF) to differentiate breast cancer (BC) from normal fibroglandular tissue (FGT). Methods: Prospective study on 14 patients with unilateral BC on 1.5 T system/axial T2w-TSE sequence, 2DDb-cMRF, B1 map, dynamic contrast-enhanced (DCE) T1-w GE-series. Mean T1 and T2 values and standard deviations were computed in the BC-/FGT-ROI on pre-/post-contrast MRF-maps and their differences were tested by two-tailed student t-test.Accuracy and repeatability of MRF were evaluated in a phantom experiment with gelatin with Primovist surrounded by fat.The T1 reduction between pre-/post-contrast MRF-maps was correlated to DCE signal enhancement in the last image post-contrast through the Pearson´s correlation coefficient (r) and for the phantom validation experiment through the Lin's concordance correlation coefficient (CCC).Visual evaluation of cancers on MRF-Maps was performed by rating each MRF-Map by 3 radiologists. Results: T1- and T2-MRF values of BC vs. FGT were for T1 and T2 pre-contrast respectively: 1147 ± 1 ms vs. 1052 ± 9 ms (p = 0.007) and 83 ± 1 ms vs. 73 ± 1 ms (p = 0.03); post-contrast respectively: 367.3 ± 121.5 ms vs. 690.3 ± 200.3 ms (p = 0.0005) and 76.9 ± 11.5 ms vs. 69.8 ± 15.2 ms (p = 0.12). r was positive (FGT r = 0.7; BC r = 0.6). CCC was 0.999 for T1 and 0.994 for T2. In the T1- and T2-MRF-Maps before contrast respectively (7,7,8)/14 and (5,9,8)/14 cancers were visible to the readers; afterwards, (11,12,12)/14 and (5,6,11)/14. Conclusions: MRF is promising for distinction between BC and FGT as well as for analyzing pre-/post-contrast T1 changes. However, its potential for differential diagnosis warrants further studies.

Comparing myelin-sensitive magnetic resonance imaging measures and resulting g-ratios in healthy and multiple sclerosis brains.

The myelin concentration and the degree of myelination of nerve fibers can provide valuable information on the integrity of human brain tissue. Magnetic resonance imaging (MRI) of myelin-sensitive parameters can help to non-invasively evaluate demyelinating diseases such as multiple sclerosis (MS). Several different myelin-sensitive MRI methods have been proposed to determine measures of the degree of myelination, in particular the g-ratio. However, variability in underlying physical principles and different biological models influence measured myelin concentrations, and consequently g-ratio values. We therefore investigated similarities and differences between five different myelin-sensitive MRI measures and their effects on g-ratio mapping in the brains of both MS patients and healthy volunteers. We compared two different estimates of the myelin water fraction (MWF) as well as the inhomogeneous magnetization transfer ratio (ihMTR), magnetization transfer saturation (MTsat), and macromolecular tissue volume (MTV) in 13 patients with MS and 14 healthy controls. In combination with diffusion-weighted imaging, we derived g-ratio parameter maps for each of the five different myelin measures. The g-ratio values calculated from different myelin measures varied strongly, especially in MS lesions. While, compared to normal-appearing white matter, MTsat and one estimate of the MWF resulted in higher g-ratio values within lesions, ihMTR, MTV, and the second MWF estimate resulted in lower lesion g-ratio values. As myelin-sensitive measures provide rough estimates of myelin content rather than absolute myelin concentrations, resulting g-ratio values strongly depend on the utilized myelin measure and model used for g-ratio mapping. When comparing g-ratio values, it is, thus, important to utilize the same MRI methods and models or to consider methodological differences. Particular caution is necessary in pathological tissue such as MS lesions.

High-Resolution Free-Breathing Quantitative First-Pass Perfusion Cardiac MR Using Dual-Echo Dixon With Spatio-Temporal Acceleration.

Introduction: To develop and test the feasibility of free-breathing (FB), high-resolution quantitative first-pass perfusion cardiac MR (FPP-CMR) using dual-echo Dixon (FOSTERS; Fat-water separation for mOtion-corrected Spatio-TEmporally accelerated myocardial peRfuSion). Materials and Methods: FOSTERS was performed in FB using a dual-saturation single-bolus acquisition with dual-echo Dixon and a dynamically variable Cartesian k-t undersampling (8-fold) approach, with low-rank and sparsity constrained reconstruction, to achieve high-resolution FPP-CMR images. FOSTERS also included automatic in-plane motion estimation and T 2 * correction to obtain quantitative myocardial blood flow (MBF) maps. High-resolution (1.6 x 1.6 mm2) FB FOSTERS was evaluated in eleven patients, during rest, against standard-resolution (2.6 x 2.6 mm2) 2-fold SENSE-accelerated breath-hold (BH) FPP-CMR. In addition, MBF was computed for FOSTERS and spatial wavelet-based compressed sensing (CS) reconstruction. Two cardiologists scored the image quality (IQ) of FOSTERS, CS, and standard BH FPP-CMR images using a 4-point scale (1-4, non-diagnostic - fully diagnostic). Results: FOSTERS produced high-quality images without dark-rim and with reduced motion-related artifacts, using an 8x accelerated FB acquisition. FOSTERS and standard BH FPP-CMR exhibited excellent IQ with an average score of 3.5 ± 0.6 and 3.4 ± 0.6 (no statistical difference, p > 0.05), respectively. CS images exhibited severe artifacts and high levels of noise, resulting in an average IQ score of 2.9 ± 0.5. MBF values obtained with FOSTERS presented a lower variance than those obtained with CS. Discussion: FOSTERS enabled high-resolution FB FPP-CMR with MBF quantification. Combining motion correction with a low-rank and sparsity-constrained reconstruction results in excellent image quality.

A unifying view on extended phase graphs and Bloch simulations for quantitative MRI.

Quantitative MRI methods and learning-based algorithms require exact forward simulations. One critical factor to correctly describe magnetization dynamics is the effect of slice-selective RF pulses. While contemporary simulation techniques correctly capture their influence, they only provide final magnetization distributions, require to be run for each parameter set separately, and make it hard to derive general theoretical conclusions and to generate a fundamental understanding of echo formation in the presence of slice-profile effects. This work aims to provide a mathematically exact framework, which is equally intuitive as extended phase graphs (EPGs), but also considers slice-profiles through their natural spatial representation. We show, through an analytical, hybrid Bloch-EPG formalism, that the spatially-resolved EPG approach allows to exactly predict the signal dependency on off-resonance, spoiling moment, microscopic dephasing, and echo time. We also demonstrate that our formalism allows to use the same phase graph to simulate both gradient-spoiled and balanced SSFP-based MR sequences. We present a derivation of the formalism and identify the connection to existing methods, i.e. slice-selective Bloch, slice-selective EPG, and the partitioned EPG. As a use case, the proposed hybrid Bloch-EPG framework is applied to MR Fingerprinting.

T1, T2, and Fat Fraction Cardiac MR Fingerprinting: Preliminary Clinical Evaluation.

BACKGROUND: Dixon cardiac magnetic resonance fingerprinting (MRF) has been recently introduced to simultaneously provide water T1 , water T2 , and fat fraction (FF) maps. PURPOSE: To assess Dixon cardiac MRF repeatability in healthy subjects and its clinical feasibility in a cohort of patients with cardiovascular disease. POPULATION: T1MES phantom, water-fat phantom, 11 healthy subjects and 19 patients with suspected cardiovascular disease. STUDY TYPE: Prospective. FIELD STRENGTH/SEQUENCE: 1.5T, inversion recovery spin echo (IRSE), multiecho spin echo (MESE), modified Look-Locker inversion recovery (MOLLI), T2 gradient spin echo (T2 -GRASE), 6-echo gradient rewound echo (GRE), and Dixon cardiac MRF. ASSESSMENT: Dixon cardiac MRF precision was assessed through repeated scans against conventional MOLLI, T2 -GRASE, and PDFF in phantom and 11 healthy subjects. Dixon cardiac MRF native T1 , T2 , FF, postcontrast T1 and synthetic extracellular volume (ECV) maps were assessed in 19 patients in comparison to conventional sequences. Measurements in patients were performed in the septum and in late gadolinium enhanced (LGE) areas and assessed using mean value distributions, correlation, and Bland-Altman plots. Image quality and diagnostic confidence were assessed by three experts using 5-point scoring scales. STATISTICAL TESTS: Paired Wilcoxon rank signed test and paired t-tests were applied. Statistical significance was indicated by *(P < 0.05). RESULTS: Dixon cardiac MRF showed good overall precision in phantom and in vivo. Septal average repeatability was ~23 msec for T1 , ~2.2 msec for T2 , and ~1% for FF. Biases in healthy subjects/patients were measured at +37 msec*/+60 msec* and -8.8 msec*/-8 msec* when compared to MOLLI and T2 -GRASE, respectively. No statistically significant differences in postcontrast T1 (P = 0.17) and synthetic ECV (P = 0.19) measurements were observed in patients. DATA CONCLUSION: Dixon cardiac MRF attained good overall precision in phantom and healthy subjects, while providing coregistered T1 , T2 , and fat fraction maps in a single breath-hold scan with similar or better image quality than conventional methods in patients. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 2.

Confounding factors in breast magnetic resonance fingerprinting: B1+ , slice profile, and diffusion effects.

PURPOSE: Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-plane B1+ , slice profile and diffusion effects on T1 and T2 estimation in the female breast at 1.5T. METHODS: Simulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE. RESULTS: Theoretic analysis predicts T1 under-/T2 overestimation if the nominal flip angles are underestimated and inversely, T1 under-/T2 overestimation if omitting slice profile correction, and T1 under-/T2 underestimation if omitting diffusion in the signal model. Averaged over repeated signal simulations, including spiral aliasing artefacts affected precision more than accuracy. Strong in-plane B1+ effects occurred in vivo, causing T2 left-right inhomogeneity between both breasts. Their correction decreased the T2 difference from 29 to 5 ms in FGT and from 29 to 9 ms in FT. Slice profile correction affected FGT T2 most strongly, resulting in -22% smaller values. For the employed spoiler gradient strengths, diffusion did not affect the parameter maps, corresponding well with theoretic predictions. CONCLUSION: Understanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.

A pilot study of magnetic resonance fingerprinting in Parkinson's disease.

Parkinson's disease (PD) affects more than six million people, but reliable MRI biomarkers with which to diagnose patients have not been established. Magnetic resonance fingerprinting (MRF) is a recent quantitative technique that can provide relaxometric maps from a single sequence. The purpose of this study is to assess the potential of MRF to identify PD in patients and their disease severity, as well as to evaluate comfort during MRF. Twenty-five PD patients and 25 matching controls underwent 3 T MRI, including an axial 2D spoiled gradient echo MRF sequence. T1 and T2 maps were generated by voxel-wise matching the measured MRF signal to a precomputed dictionary. All participants also received standard inversion recovery T1 and multi-echo T2 mapping. An ROI-based analysis of relaxation times was performed. Differences between patients and controls as well as techniques were determined by logistic regression, Spearman correlation and t-test. Patients were asked to estimate the subjective comfort of the MRF sequence. Both MRF-based T1 and T2 mapping discriminated patients from controls: T1 relaxation times differed most in cortical grey matter (PD 1337 ± 38 vs. control 1386 ± 37 ms; mean ± SD; P = .0001) and, in combination with normal-appearing white matter, enabled correct discrimination in 85.7% of cases (sensitivity 83.3%; specificity 88.0%; receiver-operating characteristic [ROC]) area under the curve [AUC] 0.87), while for T2 mapping the left putamen was the strongest classifier (40.54 ± 6.28 vs. 34.17 ± 4.96 ms; P = .0001), enabling differentiation of groups in 84.0% of all cases (sensitivity 80.0%; specificity 88.0%; ROC AUC 0.87). Relaxation time differences were not associated with disease severity. Standard mapping techniques generated significantly different relaxation time values and identified other structures as different between groups other than MRF. Twenty-three out of 25 PD patients preferred the MRF examination instead of a standard MRI. MRF-based mapping can identify PD patients with good comfort but needs further assessment regarding disease severity identification and its potential for comparability with standard mapping technique results.

SURE-based automatic parameter selection for ESPIRiT calibration.

PURPOSE: ESPIRiT is a parallel imaging method that estimates coil sensitivity maps from the auto-calibration region (ACS). This requires choosing several parameters for the optimal map estimation. While fairly robust to these parameter choices, occasionally, poor selection can result in reduced performance. The purpose of this work is to automatically select parameters in ESPIRiT for more robust and consistent performance across a variety of exams. METHODS: By viewing ESPIRiT as a denoiser, Stein's unbiased risk estimate (SURE) is leveraged to automatically optimize parameter selection in a data-driven manner. The optimum parameters corresponding to the minimum true squared error, minimum SURE as derived from densely sampled, high-resolution, and non-accelerated data and minimum SURE as derived from ACS are compared using simulation experiments. To avoid optimizing the rank of ESPIRiT's auto-calibrating matrix (one of the parameters), a heuristic derived from SURE-based singular value thresholding is also proposed. RESULTS: Simulations show SURE derived from the densely sampled, high-resolution, and non-accelerated data to be an accurate estimator of the true mean squared error, enabling automatic parameter selection. The parameters that minimize SURE as derived from ACS correspond well to the optimal parameters. The soft-threshold heuristic improves computational efficiency while providing similar results to an exhaustive search. In-vivo experiments verify the reliability of this method. CONCLUSIONS: Using SURE to determine ESPIRiT parameters allows for automatic parameter selections. In-vivo results are consistent with simulation and theoretical results.

3D free-breathing cardiac magnetic resonance fingerprinting.

PURPOSE: To develop a novel respiratory motion compensated three-dimensional (3D) cardiac magnetic resonance fingerprinting (cMRF) approach for whole-heart myocardial T1 and T2 mapping from a free-breathing scan. METHODS: Two-dimensional (2D) cMRF has been recently proposed for simultaneous, co-registered T1 and T2 mapping from a breath-hold scan; however, coverage is limited. Here we propose a novel respiratory motion compensated 3D cMRF approach for whole-heart myocardial T1 and T2 tissue characterization from a free-breathing scan. Variable inversion recovery and T2 preparation modules are used for parametric encoding, respiratory bellows driven localized autofocus is proposed for beat-to-beat translation motion correction and a subspace regularized reconstruction is employed to accelerate the scan. The proposed 3D cMRF approach was evaluated in a standardized T1 /T2 phantom in comparison with reference spin echo values and in 10 healthy subjects in comparison with standard 2D MOLLI, SASHA and T2 -GraSE mapping techniques at 1.5 T. RESULTS: 3D cMRF T1 and T2 measurements were generally in good agreement with reference spin echo values in the phantom experiments, with relative errors of 2.9% and 3.8% for T1 and T2 (T2 < 100 ms), respectively. in vivo left ventricle (LV) myocardial T1 values were 1054 ± 19 ms for MOLLI, 1146 ± 20 ms for SASHA and 1093 ± 24 ms for the proposed 3D cMRF; corresponding T2 values were 51.8 ± 1.6 ms for T2-GraSE and 44.6 ± 2.0 ms for 3D cMRF. LV coefficients of variation were 7.6 ± 1.6% for MOLLI, 12.1 ± 2.7% for SASHA and 5.8 ± 0.8% for 3D cMRF T1 , and 10.5 ± 1.4% for T2-GraSE and 11.7 ± 1.6% for 3D cMRF T2 . CONCLUSION: The proposed 3D cMRF can provide whole-heart, simultaneous and co-registered T1 and T2 maps with accuracy and precision comparable to those of clinical standards in a single free-breathing scan of about 7 min.

Myelin water imaging from multi-echo T2 MR relaxometry data using a joint sparsity constraint.

Demyelination is the key pathological process in multiple sclerosis (MS). The extent of demyelination can be quantified with magnetic resonance imaging by assessing the myelin water fraction (MWF). However, long computation times and high noise sensitivity hinder the translation of MWF imaging to clinical practice. In this work, we introduce a more efficient and noise robust method to determine the MWF using a joint sparsity constraint and a pre-computed B1+-T2 dictionary. A single component analysis with this dictionary is used in an initial step to obtain a B1+ map. The T2 distribution is then determined from a reduced dictionary corresponding to the estimated B1+ map using a combination of a non-negativity and a joint sparsity constraint. The non-negativity constraint ensures that a feasible solution with non-negative contribution of each T2 component is obtained. The joint sparsity constraint restricts the T2 distribution to a small set of T2 relaxation times shared between all voxels and reduces the noise sensitivity. The applied Sparsity Promoting Iterative Joint NNLS (SPIJN) algorithm can be implemented efficiently, reducing the computation time by a factor of 50 compared to the commonly used regularized non-negative least squares algorithm. The proposed method was validated in simulations and in 8 healthy subjects with a 3D multi-echo gradient- and spin echo scan at 3 â€‹T. In simulations, the absolute error in the MWF decreased from 0.031 to 0.013 compared to the regularized NNLS algorithm for SNR â€‹= â€‹250. The in vivo results were consistent with values reported in literature and improved MWF-quantification was obtained especially in the frontal white matter. The maximum standard deviation in mean MWF in different regions of interest between subjects was smaller for the proposed method (0.0193) compared to the regularized NNLS algorithm (0.0266). In conclusion, the proposed method for MWF estimation is less computationally expensive and less susceptible to noise compared to state of the art methods. These improvements might be an important step towards clinical translation of MWF measurements.

Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting.

PURPOSE: To develop an efficient algorithm for multi-component analysis of magnetic resonance fingerprinting (MRF) data without making a priori assumptions about the exact number of tissues or their relaxation properties. METHODS: Different tissues or components within a voxel are potentially separable in MRF because of their distinct signal evolutions. The observed signal evolution in each voxel can be described as a linear combination of the signals for each component with a non-negative weight. An assumption that only a small number of components are present in the measured field of view is usually imposed in the interpretation of multi-component data. In this work, a joint sparsity constraint is introduced to utilize this additional prior knowledge in the multi-component analysis of MRF data. A new algorithm combining joint sparsity and non-negativity constraints is proposed and compared to state-of-the-art multi-component MRF approaches in simulations and brain MRF scans of 11 healthy volunteers. RESULTS: Simulations and in vivo measurements show reduced noise in the estimated tissue fraction maps compared to previously proposed methods. Applying the proposed algorithm to the brain data resulted in 4 or 5 components, which could be attributed to different brain structures, consistent with previous multi-component MRF publications. CONCLUSIONS: The proposed algorithm is faster than previously proposed methods for multi-component MRF and the simulations suggest improved accuracy and precision of the estimated weights. The results are easier to interpret compared to voxel-wise methods, which combined with the improved speed is an important step toward clinical evaluation of multi-component MRF.

Water-fat Dixon cardiac magnetic resonance fingerprinting.

PURPOSE: Cardiac magnetic resonance fingerprinting (cMRF) has been recently introduced to simultaneously provide T1 , T2 , and M0 maps. Here, we develop a 3-point Dixon-cMRF approach to enable simultaneous water specific T1 , T2 , and M0 mapping of the heart and fat fraction (FF) estimation in a single breath-hold scan. METHODS: Dixon-cMRF is achieved by combining cMRF with several innovations that were previously introduced for other applications, including a 3-echo GRE acquisition with golden angle radial readout and a high-dimensional low-rank tensor constrained reconstruction to recover the highly undersampled time series images for each echo. Water-fat separation of the Dixon-cMRF time series is performed to allow for water- and fat-specific T1 , T2 , and M0 estimation, whereas FF estimation is extracted from the M0 maps. Dixon-cMRF was evaluated in a standardized T1 -T2 phantom, in a water-fat phantom, and in healthy subjects in comparison to current clinical standards: MOLLI, SASHA, T2 -GRASE, and 6-point Dixon proton density FF (PDFF) mapping. RESULTS: Dixon-cMRF water T1 and T2 maps showed good agreement with reference T1 and T2 mapping techniques (R2 > 0.99 and maximum normalized RMSE ~5%) in a standardized phantom. Good agreement was also observed between Dixon-cMRF FF and reference PDFF (R2 > 0.99) and between Dixon-cMRF water T1 and T2 and water selective T1 and T2 maps (R2 > 0.99) in a water-fat phantom. In vivo Dixon-cMRF water T1 values were in good agreement with MOLLI and water T2 values were slightly underestimated when compared to T2 -GRASE. Average myocardium septal T1 values were 1129 ± 38 ms, 1026 ± 28 ms, and 1045 ± 32 ms for SASHA, MOLLI, and the proposed water Dixon-cMRF. Average T2 values were 51.7 ± 2.2 ms and 42.8 ± 2.6 ms for T2 -GRASE and water Dixon-cMRF, respectively. Dixon-cMRF FF maps showed good agreement with in vivo PDFF measurements (R2 > 0.98) and average FF in the septum was measured at 1.3%. CONCLUSION: The proposed Dixon-cMRF allows to simultaneously quantify myocardial water T1 , water T2 , and FF in a single breath-hold scan, enabling multi-parametric T1 , T2 , and fat characterization. Moreover, reduced T1 and T2 quantification bias caused by water-fat partial volume was demonstrated in phantom experiments.

Spiral blurring correction with water-fat separation for magnetic resonance fingerprinting in the breast.

PURPOSE: Magnetic resonance fingerprinting (MRF) with spiral readout enables rapid quantification of tissue relaxation times. However, it is prone to blurring because of off-resonance effects. Hence, fat blurring into adjacent regions might prevent identification of small tumors by their quantitative T1 and T2 values. This study aims to correct for the blurring artifacts, thereby enabling fast quantitative mapping in the female breast. METHODS: The impact of fat blurring on spiral MRF results was first assessed by simulations. Then, MRF was combined with 3-point Dixon water-fat separation and spiral blurring correction based on conjugate phase reconstruction. The approach was assessed in phantom experiments and compared to Cartesian reference measurements, namely inversion recovery (IR), multi-echo spin echo (MESE), and Cartesian MRF, by normalized root-mean-square error (NRMSE) and SD calculations. Feasibility is further demonstrated in vivo for quantitative breast measurements of 6 healthy female volunteers, age range 24-31 y. RESULTS: In the phantom experiment, the blurring correction reduced the NRMSE per phantom vial on average from 16% to 8% for T1 and from 18% to 11% for T2 when comparing spiral MRF to IR/MESE sequences. When comparing to Cartesian MRF, the NRMSE reduced from 15% to 8% for T1 and from 12% to 7% for T2 . Furthermore, SDs decreased. In vivo, the blurring correction removed fat bias on T1 /T2 from a rim of ~7-8 mm width adjacent to fatty structures. CONCLUSION: The blurring correction for spiral MRF yields improved quantitative maps in the presence of water and fat.