Water-fat Dixon cardiac magnetic resonance fingerprinting

Simultaneous 3D T 1 $$ {\mathrm{T}}_1 $$ , T 2 $$ {\mathrm{T}}_2 $$ , and fat-signal-fraction mapping with respiratory-motion correction for comprehensive liver tissue characterization at 0.55 T

PURPOSE

To develop a framework for simultaneous three-dimensional (3D) mapping ofT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat signal fraction in the liver at 0.55 T.

METHODS

The proposed sequence acquires four interleaved 3D volumes with a two-echo Dixon readout.T 1 $$ {\mathrm{T}}_1 $$ andT 2 $$ {\mathrm{T}}_2 $$ are encoded into each volume via preparation modules, and dictionary matching allows simultaneous estimation ofT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , andM 0 $$ {M}_0 $$ for water and fat separately. 2D image navigators permit respiratory binning, and motion fields from nonrigid registration between bins are used in a nonrigid respiratory-motion-corrected reconstruction, enabling 100% scan efficiency from a free-breathing acquisition. The integrated nature of the framework ensures the resulting maps are always co-registered.

RESULTS

T 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat-signal-fraction measurements in phantoms correlated strongly (adjustedr 2 > 0 . 98 $$ {r}^2>0.98 $$ ) with reference measurements. Mean liver tissue parameter values in 10 healthy volunteers were427 ± 22 $$ 427\pm 22 $$ ,47 . 7 ± 3 . 3   ms $$ 47.7\pm 3.3\;\mathrm{ms} $$ , and7 ± 2 % $$ 7\pm 2\% $$ forT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat signal fraction, giving biases of71 $$ 71 $$ ,- 30 . 0   ms $$ -30.0\;\mathrm{ms} $$ , and- 5 $$ -5 $$ percentage points, respectively, when compared to conventional methods.

CONCLUSION

A novel sequence for comprehensive characterization of liver tissue at 0.55 T was developed. The sequence provides co-registered 3DT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat-signal-fraction maps with full coverage of the liver, from a single nine-and-a-half-minute free-breathing scan. Further development is needed to achieve accurate proton-density fat fraction (PDFF) estimation 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.

Bi-component dictionary matching for MR fingerprinting for efficient quantification of fat fraction and water T1 in skeletal muscle

PURPOSE

To propose an efficient bi-component MR fingerprinting (MRF) fitting method using a Variable Projection (VARPRO) strategy, applied to the quantification of fat fraction (FF) and water T1 (T 1 H 2 0 $$ \mathrm{T}{1}_{{\mathrm{H}}_20} $$ ) in skeletal muscle tissues.

METHODS

The MRF signals were analyzed in a two-step process by comparing them to the elements of separate water and fat dictionaries (bi-component dictionary matching). First, each pair of water and fat dictionary elements was fitted to the acquired signal to determine an optimal FF that was used to merge the fingerprints in a combined water/fat dictionary. Second, standard dictionary matching was applied to the combined dictionary for determining the remaining parameters. A clustering method was implemented to further accelerate the fitting. Accuracy, precision, and matching time of this approach were evaluated on both numerical and in vivo datasets, and compared to the reference dictionary-matching approach that includes FF as a dictionary parameter.

RESULTS

In numerical phantoms, all MRF parameters showed high correlation with ground truth for the reference and the bi-component method (R2  > 0.98). In vivo, the estimated parameters from the proposed method were highly correlated with those from the reference approach (R2  > 0.997). The bi-component method achieved an acceleration factor of up to 360 compared to the reference dictionary matching.

CONCLUSION

The proposed bi-component fitting approach enables a significant acceleration of the reconstruction of MRF parameter maps for fat-water imaging, while maintaining comparable precision and accuracy to the reference on FF andT 1 H 2 0 $$ \mathrm{T}{1}_{{\mathrm{H}}_20} $$ estimation.

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review

Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.

Synthetic multi-contrast late gadolinium enhancement imaging using post-contrast magnetic resonance fingerprinting

Late gadolinium enhancement (LGE) MRI is the non-invasive reference standard for identifying myocardial scar and fibrosis but has limitations, including difficulty delineating subendocardial scar and operator dependence on image quality. The purpose of this work is to assess the feasibility of generating multi-contrast synthetic LGE images from post-contrast T1 and T2 maps acquired using magnetic resonance fingerprinting (MRF). Fifteen consecutive patients with a history of prior ischemic cardiomyopathy (12 men; mean age 63  ±  13 years) were prospectively scanned at 1.5 T between Oct 2020 and May 2021 using conventional LGE and MRF after injection of gadolinium contrast. Three classes of synthetic LGE images were derived from MRF post-contrast T1 and T2 maps: bright-blood phase-sensitive inversion recovery (PSIR), black- and gray-blood T2 -prepared PSIR (T2 -PSIR), and a novel "tissue-optimized" image to enhance differentiation among scar, viable myocardium, and blood. Image quality was assessed on a 1-5 Likert scale by two cardiologists, and contrast was quantified as the mean absolute difference (MAD) in pixel intensities between two tissues, with different methods compared using Kruskal-Wallis with Bonferroni post hoc tests. Per-patient and per-segment scar detection rates were evaluated using conventional LGE images as reference. Image quality scores were highest for synthetic PSIR (4.0) and reference images (3.8), followed by synthetic tissue-optimized (3.3), gray-blood T2 -PSIR (3.0), and black-blood T2 -PSIR (2.6). Among synthetic images, PSIR yielded the highest myocardium/scar contrast (MAD = 0.42) but the lowest blood/scar contrast (MAD = 0.05), and vice versa for T2 -PSIR, while tissue-optimized images achieved a balance among all tissues (myocardium/scar MAD = 0.16, blood/scar MAD = 0.26, myocardium/blood MAD = 0.10). Based on reference mid-ventricular LGE scans, 13/15 patients had myocardial scar. The per-patient sensitivity/accuracy for synthetic images were the following: PSIR, 85/87%; black-blood T2 -PSIR, 62/53%; gray-blood T2 -PSIR, 100/93%; tissue optimized, 100/93%. Synthetic multi-contrast LGE images can be generated from post-contrast MRF data without additional scan time, with initial feasibility shown in ischemic cardiomyopathy patients.

Cardiac MR Fingerprinting: Overview, Technical Developments, and Applications

Cardiovascular magnetic resonance (CMR) is an established imaging modality with proven utility in assessing cardiovascular diseases. The ability of CMR to characterize myocardial tissue using T1 - and T2 -weighted imaging, parametric mapping, and late gadolinium enhancement has allowed for the non-invasive identification of specific pathologies not previously possible with modalities like echocardiography. However, CMR examinations are lengthy and technically complex, requiring multiple pulse sequences and different anatomical planes to comprehensively assess myocardial structure, function, and tissue composition. To increase the overall impact of this modality, there is a need to simplify and shorten CMR exams to improve access and efficiency, while also providing reproducible quantitative measurements. Multiparametric MRI techniques that measure multiple tissue properties offer one potential solution to this problem. This review provides an in-depth look at one such multiparametric approach, cardiac magnetic resonance fingerprinting (MRF). The article is structured as follows. First, a brief review of single-parametric and (non-Fingerprinting) multiparametric CMR mapping techniques is presented. Second, a general overview of cardiac MRF is provided covering pulse sequence implementation, dictionary generation, fast k-space sampling methods, and pattern recognition. Third, recent technical advances in cardiac MRF are covered spanning a variety of topics, including simultaneous multislice and 3D sampling, motion correction algorithms, cine MRF, synthetic multicontrast imaging, extensions to measure additional clinically important tissue properties (proton density fat fraction, T2 *, and T1ρ ), and deep learning methods for image reconstruction and parameter estimation. The last section will discuss potential clinical applications, concluding with a perspective on how multiparametric techniques like MRF may enable streamlined CMR protocols. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

SPARCQ: A new approach for fat fraction mapping using asymmetries in the phase-cycled balanced SSFP signal profile

PURPOSE

To develop SPARCQ (Signal Profile Asymmetries for Rapid Compartment Quantification), a novel approach to quantify fat fraction (FF) using asymmetries in the phase-cycled balanced SSFP (bSSFP) profile.

METHODS

SPARCQ uses phase-cycling to obtain bSSFP frequency profiles, which display asymmetries in the presence of fat and water at certain TRs. For each voxel, the measured signal profile is decomposed into a weighted sum of simulated profiles via multi-compartment dictionary matching. Each dictionary entry represents a single-compartment bSSFP profile with a specific off-resonance frequency and relaxation time ratio. Using the results of dictionary matching, the fractions of the different off-resonance components are extracted for each voxel, generating quantitative maps of water and FF and banding-artifact-free images for the entire image volume. SPARCQ was validated using simulations, experiments in a water-fat phantom and in knees of healthy volunteers. Experimental results were compared with reference proton density FFs obtained with 1 H-MRS (phantoms) and with multiecho gradient-echo MRI (phantoms and volunteers). SPARCQ repeatability was evaluated in six scan-rescan experiments.

RESULTS

Simulations showed that FF quantification is accurate and robust for SNRs greater than 20. Phantom experiments demonstrated good agreement between SPARCQ and gold standard FFs. In volunteers, banding-artifact-free quantitative maps and water-fat-separated images obtained with SPARCQ and ME-GRE demonstrated the expected contrast between fatty and non-fatty tissues. The coefficient of repeatability of SPARCQ FF was 0.0512.

CONCLUSION

SPARCQ demonstrates potential for fat quantification using asymmetries in bSSFP profiles and may be a promising alternative to conventional FF quantification techniques.

MR Fingerprinting for Contrast Agent-free and Quantitative Characterization of Focal Liver Lesions

Purpose To evaluate the feasibility of liver MR fingerprinting (MRF) for quantitative characterization and diagnosis of focal liver lesions. Materials and Methods This single-site, prospective study included 89 participants (mean age, 62 years ± 15 [SD]; 45 women, 44 men) with various focal liver lesions who underwent MRI between October 2021 and August 2022. The participants underwent routine clinical MRI, non-contrast-enhanced liver MRF, and reference quantitative MRI with a 1.5-T MRI scanner. The bias and repeatability of the MRF measurements were assessed using linear regression, Bland-Altman plots, and coefficients of variation. The diagnostic capability of MRF-derived T1, T2, T2*, proton density fat fraction (PDFF), and a combination of these metrics to distinguish benign from malignant lesions was analyzed according to the area under the receiver operating characteristic curve (AUC). Results Liver MRF measurements showed moderate to high agreement with reference measurements (intraclass correlation = 0.94, 0.77, 0.45, and 0.61 for T1, T2, T2*, and PDFF, respectively), with underestimation of T2 values (mean bias in lesion = -0.5%, -29%, 5.8%, and -8.2% for T1, T2, T2*, and PDFF, respectively). The median coefficients of variation for repeatability of T1, T2, and T2* values were 2.5% (IQR, 3.6%), 3.1% (IQR, 5.6%), and 6.6% (IQR, 13.9%), respectively. After considering multicollinearity, a combination of MRF measurements showed a high diagnostic performance in differentiating benign from malignant lesions (AUC = 0.92 [95% CI: 0.86, 0.98]). Conclusion Liver MRF enabled the quantitative characterization of various focal liver lesions in a single breath-hold acquisition. Keywords: MR Imaging, Abdomen/GI, Liver, Imaging Sequences, Technical Aspects, Tissue Characterization, Technology Assessment, Diagnosis, Liver Lesions, MR Fingerprinting, Quantitative Characterization Supplemental material is available for this article. © RSNA, 2023.

Quantitative MRI of diffuse liver diseases: techniques and tissue-mimicking phantoms

Quantitative magnetic resonance imaging (MRI) techniques are emerging as non-invasive alternatives to biopsy for assessment of diffuse liver diseases of iron overload, steatosis and fibrosis. For testing and validating the accuracy of these techniques, phantoms are often used as stand-ins to human tissue to mimic diffuse liver pathologies. However, currently, there is no standardization in the preparation of MRI-based liver phantoms for mimicking iron overload, steatosis, fibrosis or a combination of these pathologies as various sizes and types of materials are used to mimic the same liver disease. Liver phantoms that mimic specific MR features of diffuse liver diseases observed in vivo are important for testing and calibrating new MRI techniques and for evaluating signal models to accurately quantify these features. In this study, we review the liver morphology associated with these diffuse diseases, discuss the quantitative MR techniques for assessing these liver pathologies, and comprehensively examine published liver phantom studies and discuss their benefits and limitations.

Magnetic Resonance Fingerprinting: A Review of Clinical Applications

ABSTRACT

Magnetic resonance fingerprinting (MRF) is an approach to quantitative magnetic resonance imaging that allows for efficient simultaneous measurements of multiple tissue properties, which are then used to create accurate and reproducible quantitative maps of these properties. As the technique has gained popularity, the extent of preclinical and clinical applications has vastly increased. The goal of this review is to provide an overview of currently investigated preclinical and clinical applications of MRF, as well as future directions. Topics covered include MRF in neuroimaging, neurovascular, prostate, liver, kidney, breast, abdominal quantitative imaging, cardiac, and musculoskeletal applications.

Cardiac Magnetic Resonance Fingerprinting: Potential Clinical Applications

PURPOSE OF REVIEW

Cardiac magnetic resonance fingerprinting (cMRF) has developed as a technique for rapid, multi-parametric tissue property mapping that has potential to both improve cardiac MRI exam efficiency and expand the information captured. In this review, we describe the cMRF technique, summarize technical developments and in vivo reports, and highlight potential clinical applications.

RECENT FINDINGS

Technical developments in cMRF continue to progress rapidly, including motion compensated reconstruction, additional tissue property quantification, signal time course analysis, and synthetic LGE image generation. Such technical developments can enable simplified CMR protocols by combining multiple evaluations into a single protocol and reducing the number of breath-held scans. cMRF continues to be reported for use in a range of pathologies; however barriers to clinical implementation remain. Technical developments are described in this review, followed by a focus on potential clinical applications that they may support. Clinical translation of cMRF could shorten protocols, improve CMR accessibility, and provide additional information as compared to conventional cardiac parametric mapping methods. Current needs for clinical implementation are discussed, as well as how those needs may be met in order to bring cMRF from its current research setting to become a viable tool for patient care.

Quantitative MRI in cardiometabolic disease: From conventional cardiac and liver tissue mapping techniques to multi-parametric approaches

Cardiometabolic disease refers to the spectrum of chronic conditions that include diabetes, hypertension, atheromatosis, non-alcoholic fatty liver disease, and their long-term impact on cardiovascular health. Histological studies have confirmed several modifications at the tissue level in cardiometabolic disease. Recently, quantitative MR methods have enabled non-invasive myocardial and liver tissue characterization. MR relaxation mapping techniques such as T₁, T_1ρ, T₂ and T₂* provide a pixel-by-pixel representation of the corresponding tissue specific relaxation times, which have been shown to correlate with fibrosis, altered tissue perfusion, oedema and iron levels. Proton density fat fraction mapping approaches allow measurement of lipid tissue in the organ of interest. Several studies have demonstrated their utility as early diagnostic biomarkers and their potential to bear prognostic implications. Conventionally, the quantification of these parameters by MRI relies on the acquisition of sequential scans, encoding and mapping only one parameter per scan. However, this methodology is time inefficient and suffers from the confounding effects of the relaxation parameters in each single map, limiting wider clinical and research applications. To address these limitations, several novel approaches have been proposed that encode multiple tissue parameters simultaneously, providing co-registered multiparametric information of the tissues of interest. This review aims to describe the multi-faceted myocardial and hepatic tissue alterations in cardiometabolic disease and to motivate the application of relaxometry and proton-density cardiac and liver tissue mapping techniques. Current approaches in myocardial and liver tissue characterization as well as latest technical developments in multiparametric quantitative MRI are included. Limitations and challenges of these novel approaches, and recommendations to facilitate clinical validation are also discussed.

MR Fingerprinting for Liver Tissue Characterization: A Histopathologic Correlation Study

Background Liver MR fingerprinting (MRF) enables simultaneous quantification of T1, T2, T2*, and proton density fat fraction (PDFF) maps in single breath-hold acquisitions. Histopathologic correlation studies are desired for its clinical use. Purpose To compare liver MRF-derived metrics with separate reference quantitative MRI in participants with diffuse liver disease, evaluate scan-rescan repeatability of liver MRF, and validate MRF-derived measurements for histologic grading of liver biopsies. Materials and Methods This prospective study included participants with diffuse liver disease undergoing MRI from July 2021 to January 2022. Participants underwent two-dimensional single-section liver MRF and separate reference quantitative MRI. Linear regression, Bland-Altman plots, and coefficients of variation were used to assess the bias and repeatability of liver MRF measurements. For participants undergoing liver biopsy, the association between mapping and histologic grading was evaluated by using the Spearman correlation coefficient. Results Fifty-six participants (mean age, 59 years ± 15 [SD]; 32 women) were included to compare mapping techniques and 23 participants were evaluated with liver biopsy (mean age, 52.7 years ± 12.7; 14 women). The linearity of MRF with reference measurements in participants with diffuse liver disease (R² value) for T1, T2, T2*, and PDFF maps was 0.86, 0.88, 0.54, and 0.99, respectively. The overall coefficients of variation for repeatability in the liver were 3.2%, 5.5%, 7.1%, and 4.6% for T1, T2, T2*, and PDFF maps, respectively. MRF-derived metrics showed high diagnostic performance in differentiating moderate or severe changes from mild or no changes (area under the receiver operating characteristic curve for fibrosis, inflammation, steatosis, and siderosis: 0.62 [95% CI: 0.52, 0.62], 0.92 [95% CI: 0.88, 0.92], 0.97 [95% CI: 0.96, 0.97], and 0.74 [95% CI: 0.57, 0.74], respectively). Conclusion Liver MR fingerprinting provided repeatable T1, T2, T2*, and proton density fat fraction maps in high agreement with reference quantitative mapping and may correlate with pathologic grades in participants with diffuse liver disease. © RSNA, 2022 Online supplemental material is available for this article.

Optimisation of data acquisition towards continuous cardiac Magnetic Resonance Fingerprinting applications

PURPOSE

Assess and optimise acquisition parameters for continuous cardiac Magnetic Resonance Fingerprinting (MRF).

METHODS

Different acquisition schemes (flip angle amplitude, lobe size, T2-preparation pulses) for cardiac MRF were assessed in simulations and phantom and demonstrated in one healthy volunteer. Three different experimental designs were evaluated using central composite and fractional factorial designs. Relative errors for T1 and T2 were calculated for a wide range of realistic T1 and T2 value combinations. The effect of different designs on the accuracy of T1 and T2 was assessed using response surface modelling and Cohen's f calculations.

RESULTS

Larger flip angle amplitudes lead to an improvement of T2 accuracy and precision for simulations and phantom experiments. Similar effects could also be shown qualitatively in in-vivo scans. Accuracy and precision of T1 were robust to different design parameters with improved values for faster flip angle variation. Cohen's f showed that T2-preparation pulses influence the accuracy of T2. The number of pulses used is the most important parameter. Without T2-preparation pulses, RMSE were 3.0 ± 8.09 % for T1 and 16.24 ± 14.47 % for T2. Using those pulses reduced the RMSE to 2.3 ± 8.4 % for T1 and 14.11 ± 13.46 % for T2. Nonetheless, even if the improvement is significant, RMSE are still too high for reliable quantification.

CONCLUSION

In contrast to previous study using triggered MRF sequences using < 30° flip angles, large flip angle amplitudes led to better results for continuous cardiac MRF sequences. T2-preparation pulse can improve the accuracy of T2 estimation but lead to longer scan times.

Free-breathing, non-ECG, simultaneous myocardial T1 , T2 , T2 *, and fat-fraction mapping with motion-resolved cardiovascular MR multitasking

PURPOSE

To develop a free-breathing, non-electrocardiogram technique for simultaneous myocardial T1 , T2 , T2 *, and fat-fraction (FF) mapping in a single scan.

METHODS

The MR Multitasking framework is adapted to quantify T1 , T2 , T2 *, and FF simultaneously. A variable TR scheme is developed to preserve temporal resolution and imaging efficiency. The underlying high-dimensional image is modeled as a low-rank tensor, which allows accelerated acquisition and efficient reconstruction. The accuracy and/or repeatability of the technique were evaluated on static and motion phantoms, 12 healthy volunteers, and 3 patients by comparing to the reference techniques.

RESULTS

In static and motion phantoms, T1 /T2 /T2 */FF measurements showed substantial consistency (R > 0.98) and excellent agreement (intraclass correlation coefficient > 0.93) with reference measurements. In human subjects, the proposed technique yielded repeatable T1 , T2 , T2 *, and FF measurements that agreed with those from references.

CONCLUSIONS

The proposed free-breathing, non-electrocardiogram, motion-resolved Multitasking technique allows simultaneous quantification of myocardial T1 , T2 , T2 *, and FF in a single 2.5-min scan.

Artificial intelligence in cardiac magnetic resonance fingerprinting

Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T₁ and T₂ mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.

Cardiac MRF using rosette trajectories for simultaneous myocardial T1, T2, and proton density fat fraction mapping

The goal of this work is to extend prior work on cardiac MR Fingerprinting (cMRF) using rosette k-space trajectories to enable simultaneous T₁, T₂, and proton density fat fraction (PDFF) mapping in the heart. A rosette trajectory designed for water-fat separation at 1.5T was used in a 2D ECG-triggered 15-heartbeat cMRF sequence. Water and fat specific T₁ and T₂ maps were generated from the cMRF data. A PDFF map was also retrieved using Hierarchical IDEAL by segmenting the rosette cMRF data into multiple echoes. The accuracy of rosette cMRF in T₁, T₂, and PDFF quantification was validated in the ISMRM/NIST phantom and an in-house built fat fraction phantom, respectively. The proposed method was also applied for myocardial tissue mapping of healthy subjects and cardiac patients at 1.5T. T₁, T₂, and PDFF values measured using rosette cMRF in the ISMRM/NIST phantom and the fat fraction phantom agreed well with the reference values. In 16 healthy subjects, rosette cMRF yielded T₁ values which were 80~90 ms higher than spiral cMRF and MOLLI. T₂ values obtained using rosette cMRF were ~3 ms higher than spiral cMRF and ~5 ms lower than conventional T₂-prep bSSFP method. Rosette cMRF was also able to detect abnormal T₁ and T₂ values in cardiomyopathy patients and may provide more accurate maps due to effective fat suppression. In conclusion, this study shows that rosette cMRF has the potential for efficient cardiac tissue characterization through simultaneous quantification of myocardial T₁, T₂, and PDFF.

Accuracy and repeatability of joint sparsity multi-component estimation in MR Fingerprinting

MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estimation for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmentations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL- and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray matter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter.

Clinical evaluation of the Multimapping technique for simultaneous myocardial T1 and T2 mapping

The Multimapping technique was recently proposed for simultaneous myocardial T₁ and T₂ mapping. In this study, we evaluate its correlation with clinical reference mapping techniques in patients with a range of cardiovascular diseases (CVDs) and compare image quality and inter- and intra-observer repeatability. Multimapping consists of an ECG-triggered, 2D single-shot bSSFP readout with inversion recovery and T₂ preparation modules, acquired across 10 cardiac cycles. The sequence was implemented at 1.5T and compared to clinical reference mapping techniques, modified Look-Locker inversion recovery (MOLLI) and T₂ prepared bSSFP with four echo times (T₂bSSFP), and compared in 47 patients with CVD (of which 44 were analyzed). In diseased myocardial segments (defined as the presence of late gadolinium enhancement), there was a high correlation between Multimapping and MOLLI for native myocardium T₁ (r² = 0.73), ECV (r² = 0.91), and blood T₁ (r² = 0.88), and Multimapping and T₂bSSFP for native myocardial T₂ (r² = 0.80). In healthy myocardial segments, a bias for native T₁ (Multimapping = 1,116 ± 21 ms, MOLLI = 1,002 ± 21, P < 0.001), post-contrast T₁ (Multimapping = 479 ± 31 ms, MOLLI = 426 ± 27 ms, 0.001), ECV (Multimapping = 21.5 ± 1.9%, MOLLI = 23.7 ± 2.3%, P = 0.001), and native T₂ (Multimapping = 48.0 ± 3.0 ms, T₂bSSFP = 53.9 ± 3.5 ms, P < 0.001) was observed. The image quality for Multimapping was scored as higher for all mapping techniques (native T₁, post-contrast T₁, ECV, and T₂bSSFP) compared to the clinical reference techniques. The inter- and intra-observer agreements were excellent (intraclass correlation coefficient, ICC > 0.9) for most measurements, except for inter-observer repeatability of Multimapping native T₁ (ICC = 0.87), post-contrast T₁ (ICC = 0.73), and T₂bSSFP native T₂ (ICC = 0.88). Multimapping shows high correlations with clinical reference mapping techniques for T₁, T₂, and ECV in a diverse cohort of patients with different cardiovascular diseases. Multimapping enables simultaneous T₁ and T₂ mapping and can be performed in a short breath-hold, with image quality superior to that of the clinical reference techniques.

A Self-Supervised Deep Learning Reconstruction for Shortening the Breathhold and Acquisition Window in Cardiac Magnetic Resonance Fingerprinting

The aim of this study is to shorten the breathhold and diastolic acquisition window in cardiac magnetic resonance fingerprinting (MRF) for simultaneous T1, T2, and proton spin density (M0) mapping to improve scan efficiency and reduce motion artifacts. To this end, a novel reconstruction was developed that combines low-rank subspace modeling with a deep image prior, termed DIP-MRF. A system of neural networks is used to generate spatial basis images and quantitative tissue property maps, with training performed using only the undersampled k-space measurements from the current scan. This approach avoids difficulties with obtaining in vivo MRF training data, as training is performed de novo for each acquisition. Calculation of the forward model during training is accelerated by using GRAPPA operator gridding to shift spiral k-space data to Cartesian grid points, and by using a neural network to rapidly generate fingerprints in place of a Bloch equation simulation. DIP-MRF was evaluated in simulations and at 1.5 T in a standardized phantom, 18 healthy subjects, and 10 patients with suspected cardiomyopathy. In addition to conventional mapping, two cardiac MRF sequences were acquired, one with a 15-heartbeat(HB) breathhold and 254 ms acquisition window, and one with a 5HB breathhold and 150 ms acquisition window. In simulations, DIP-MRF yielded decreased nRMSE compared to dictionary matching and a sparse and locally low rank (SLLR-MRF) reconstruction. Strong correlation (R2 &gt; 0.999) with T1 and T2 reference values was observed in the phantom using the 5HB/150 ms scan with DIP-MRF. DIP-MRF provided better suppression of noise and aliasing artifacts in vivo, especially for the 5HB/150 ms scan, and lower intersubject and intrasubject variability compared to dictionary matching and SLLR-MRF. Furthermore, it yielded a better agreement between myocardial T1 and T2 from 15HB/254 ms and 5HB/150 ms MRF scans, with a bias of −9 ms for T1 and 2 ms for T2. In summary, this study introduces an extension of the deep image prior framework for cardiac MRF tissue property mapping, which does not require pre-training with in vivo scans, and has the potential to reduce motion artifacts by enabling a shortened breathhold and acquisition window.

Constructing Dixon Matrix for Sparse Polynomial Equations Based on Hybrid and Heuristics Scheme

Solving polynomial equations inevitably faces many severe challenges, such as easily occupying storage space and demanding prohibitively expensive computation resources. There has been considerable interest in exploiting the sparsity to improve computation efficiency, since asymmetry phenomena are prevalent in scientific and engineering fields, especially as most of the systems in real applications have sparse representations. In this paper, we propose an efficient parallel hybrid algorithm for constructing a Dixon matrix. This approach takes advantage of the asymmetry (i.e., sparsity) in variables of the system and introduces a heuristics strategy. Our method supports parallel computation and has been implemented on a multi-core system. Through time-complexity analysis and extensive benchmarks, we show our new algorithm has significantly reduced computation and memory overhead. In addition, performance evaluation via the Fermat–Torricelli point problem demonstrates its effectiveness in combinatorial geometry optimizations.

Myocardial T1, T2, T2*, and fat fraction quantification via low-rank motion-corrected cardiac MR fingerprinting

Purpose

Develop a novel 2D cardiac MR fingerprinting (MRF) approach to enable simultaneous T1, T2, T2*, and fat fraction (FF) myocardial tissue characterization in a single breath‐hold scan.

Methods

Simultaneous, co‐registered, multi‐parametric mapping of T1, T2, and FF has been recently achieved with cardiac MRF. Here, we further incorporate T2* quantification within this approach, enabling simultaneous T1, T2, T2*, and FF myocardial tissue characterization in a single breath‐hold scan. T2* quantification is achieved with an eight‐echo readout that requires a long cardiac acquisition window. A novel low‐rank motion‐corrected (LRMC) reconstruction is exploited to correct for cardiac motion within the long acquisition window. The proposed T1/T2/T2*/FF cardiac MRF was evaluated in phantom and in 10 healthy subjects in comparison to conventional mapping techniques.

Results

The proposed approach achieved high quality parametric mapping of T1, T2, T2*, and FF with corresponding normalized RMS error (RMSE) T1 = 5.9%, T2 = 9.6% (T2 values <100 ms), T2* = 3.3% (T2* values <100 ms), and FF = 0.8% observed in phantom scans. In vivo, the proposed approach produced higher left‐ventricular myocardial T1 values than MOLLI (1148 vs 1056 ms), lower T2 values than T2‐GraSE (42.8 vs 50.6 ms), lower T2* values than eight‐echo gradient echo (GRE) (35.0 vs 39.4 ms), and higher FF values than six‐echo GRE (0.8 vs 0.3 %) reference techniques. The proposed approach achieved considerable reduction in motion artifacts compared to cardiac MRF without motion correction, improved spatial uniformity, and statistically higher apparent precision relative to conventional mapping for all parameters.

Conclusion

The proposed cardiac MRF approach enables simultaneous, co‐registered mapping of T1, T2, T2*, and FF in a single breath‐hold for comprehensive myocardial tissue characterization, achieving higher apparent precision than conventional methods.

Cartesian dictionary-based native T1 and T2 mapping of the myocardium

PURPOSE

To implement and evaluate a new dictionary-based technique for native myocardial T₁ and T₂ mapping using Cartesian sampling.

METHODS

The proposed technique (Multimapping) consisted of single-shot Cartesian image acquisitions in 10 consecutive cardiac cycles, with inversion pulses in cycle 1 and 5, and T₂ preparation (TE: 30 ms, 50 ms, and 70 ms) in cycles 8-10. Multimapping was simulated for different T₁ and T₂ , where entries corresponding to the k-space centers were matched to acquired data. Experiments were performed in a phantom, 16 healthy subjects, and 3 patients with cardiovascular disease.

RESULTS

Multimapping phantom measurements showed good agreement with reference values for both T₁ and T₂ , with no discernable heart-rate dependency for T₁ and T₂ within the range of myocardium. In vivo mean T₁ in healthy subjects was significantly higher using Multimapping (T₁ = 1114 ± 14 ms) compared to the reference (T₁ = 991 ± 26 ms) (p < 0.01). Mean Multimapping T₂ (47.1 ± 1.3 ms) and T₂ spatial variability (5.8 ± 1.0 ms) was significantly lower compared to the reference (T₂ = 54.7 ± 2.2 ms, p < 0.001; spatial variability = 8.4 ± 2.0 ms, p < 0.01). Increased T₁ and T₂ was detected in all patients using Multimapping.

CONCLUSIONS

Multimapping allows for simultaneous native myocardial T₁ and T₂ mapping with a conventional Cartesian trajectory, demonstrating promising in vivo image quality and parameter quantification results.

Free-breathing multitasking multi-echo MRI for whole-liver water-specific T1 , proton density fat fraction, and R2∗ quantification

PURPOSE

To develop a 3D multitasking multi-echo (MT-ME) technique for the comprehensive characterization of liver tissues with 5-min free-breathing acquisition; whole-liver coverage; a spatial resolution of 1.5 × 1.5 × 6 mm³ ; and simultaneous quantification of T₁ , water-specific T₁ (T_1w ), proton density fat fraction (PDFF), and R2∗ .

METHODS

Six-echo bipolar spoiled gradient echo readouts following inversion recovery preparation was performed to generate T₁ , water/fat, and R2∗ contrast. MR multitasking was used to reconstruct the MT-ME images with 3 spatial dimensions: 1 T₁ recovery dimension, 1 multi-echo dimension, and 1 respiratory dimension. A basis function-based approach was developed for T_1w quantification, followed by the estimation of R2∗ and T₁ -corrected PDFF. The intrasession repeatability and agreement against references of MT-ME measurements were tested on a phantom and 15 clinically healthy subjects. In addition, 4 patients with confirmed liver diseases were recruited, and the agreement between MT-ME measurements and references was assessed.

RESULTS

MT-ME produced high-quality, coregistered T₁ , T_1w , PDFF, and R2∗ maps with good intrasession repeatability and substantial agreement with references on phantom and human studies. The intra-class coefficients of T₁ , T_1w , PDFF, and R2∗ from the repeat MT-ME measurements on clinically healthy subjects were 0.989, 0.990, 0.999, and 0.988, respectively. The intra-class coefficients of T₁ , PDFF, and R2∗ between the MT-ME and reference measurements were 0.924, 0.987, and 0.975 in healthy subjects and 0.980, 0.999, and 0.998 in patients. The T_1w was independent to PDFF (R = -0.029, P = .904).

CONCLUSION

The proposed MT-ME technique quantifies T₁ , T_1w , PDFF, and R2∗ simultaneously and is clinically promising for the comprehensive characterization of liver tissue properties.

Simultaneous T1 , T2 , and T1ρ cardiac magnetic resonance fingerprinting for contrast agent-free myocardial tissue characterization

PURPOSE

To develop a simultaneous T₁ , T₂ , and T_1ρ cardiac magnetic resonance fingerprinting (MRF) approach to enable comprehensive contrast agent-free myocardial tissue characterization in a single breath-hold scan.

METHODS

A 2D gradient-echo electrocardiogram-triggered cardiac MRF sequence with low flip angles, varying magnetization preparation, and spiral trajectory was acquired at 1.5 T to encode T₁ , T₂ , and T_1⍴ simultaneously. The MRF images were reconstructed using low-rank inversion, regularized with a multicontrast patch-based higher-order reconstruction. Parametric maps were generated and matched in the singular value domain to extended phase graph-based dictionaries. The proposed approach was tested in phantoms and 10 healthy subjects and compared against conventional methods in terms of coefficients of determination and best fits for the phantom study, and in terms of Bland-Altman agreement, average values and coefficient of variation of T₁ , T₂ , and T_1⍴ for the healthy subjects study.

RESULTS

The T₁ , T₂ , and T_1⍴ MRF values showed excellent correlation with conventional spin-echo and clinical mapping methods in phantom studies (r² > 0.97). Measured MRF values in myocardial tissue (mean ± SD) were 1133 ± 33 ms, 38.8 ± 3.5 ms, and 52.0 ± 4.0 ms for T₁ , T₂ and T1_⍴ , respectively, against 1053 ± 47 ms, 50.4 ± 3.9 ms, and 55.9 ± 3.3 ms for T₁ modified Look-Locker inversion imaging, T₂ gradient and spin echo, and T_1⍴ turbo field echo, respectively.

CONCLUSION

A cardiac MRF approach for simultaneous quantification of myocardial T₁ , T₂ , and T_1ρ in a single breath-hold MR scan of about 16 seconds has been proposed. The approach has been investigated in phantoms and healthy subjects showing good agreement with reference spin echo measurements and conventional clinical maps.

Simultaneous comprehensive liver T1 , T2 , T 2 ∗ , T1ρ , and fat fraction characterization with MR fingerprinting

PURPOSE

To develop a novel simultaneous co-registered T₁ , T₂ , T 2 ∗ , T_1ρ , and fat fraction abdominal MR fingerprinting (MRF) approach for fully comprehensive liver-tissue characterization in a single breath-hold scan.

METHODS

A gradient-echo liver MRF sequence with low fixed flip angle, multi-echo radial readout, and varying magnetization preparation pulses for multiparametric encoding is performed at 1.5 T. The T 2 ∗ and fat fraction are estimated from a graph/cut water/fat separation method using a six-peak fat model. Water/fat singular images obtained are then matched to an MRF dictionary, estimating water-specific T₁ , T₂ , and T_1ρ . The proposed approach was tested in phantoms and 10 healthy subjects and compared against conventional sequences.

RESULTS

For the phantom studies, linear fits show excellent coefficients of determination (r² > 0.9) for every parametric map. For in vivo studies, the average values measured within regions of interest drawn on liver, spleen, muscle, and fat are statistically different from the reference scans (p < 0.05) for T₁ , T₂ , and T_1⍴ but not for T 2 ∗ and fat fraction, whereas correlation between MRF and reference scans is excellent for each parameter (r² > 0.92 for every parameter).

CONCLUSION

The proposed multi-echo inversion-recovery, T₂ , and T_1⍴ prepared liver MRF sequence presented in this work allows for quantitative T₁ , T₂ , T 2 ∗ , T_1⍴ , and fat fraction liver-tissue characterization in a single breath-hold scan of 18 seconds. The approach showed good agreement and correlation with respect to reference clinical maps.

Magnetic Resonance Fingerprinting of the Pediatric Brain

Magnetic resonance fingerprinting (MRF) is increasingly being used to evaluate brain development and differentiate normal and pathologic tissues in children. MRF can provide reliable and accurate intrinsic tissue properties, such as T1 and T2 relaxation times. MRF is a powerful tool in evaluating brain disease in pediatric population. MRF is a new quantitative MR imaging technique for rapid and simultaneous quantification of multiple tissue properties.

Toward magnetic resonance fingerprinting for low-field MR-guided radiation therapy

PURPOSE

The acquisition of multiparametric quantitative magnetic resonance imaging (qMRI) is becoming increasingly important for functional characterization of cancer prior to- and throughout the course of radiation therapy. The feasibility of a qMRI method known as magnetic resonance fingerprinting (MRF) for rapid T₁ and T₂ mapping was assessed on a low-field MR-linac system.

METHODS

A three-dimensional MRF sequence was implemented on a 0.35T MR-guided radiotherapy system. MRF-derived measurements of T₁ and T₂ were compared to those obtained with gold standard single spin echo methods, and the impacts of the radiofrequency field homogeneity and scan times ranging between 6 and 48 min were analyzed by acquiring between 1 and 8 spokes per time point in a standard quantitative system phantom. The short-term repeatability of MRF was assessed over three measurements taken over a 10-h period. To evaluate transferability, MRF measurements were acquired on two additional MR-guided radiotherapy systems. Preliminary human volunteer studies were performed.

RESULTS

The phantom benchmarking studies showed that MRF is capable of mapping T₁ and T₂ values within 8% and 10% of gold standard measures, respectively, at 0.35T. The coefficient of variation of T₁ and T₂ estimates over three repeated scans was < 5% over a broad range of relaxation times. The T₁ and T₂ times derived using a single-spoke MRF acquisition across three scanners were near unity and mean percent errors in T₁ and T₂ estimates using the same phantom were < 3%. The mean percent differences in T₁ and T₂ as a result of truncating the scan time to 6 min over the large range of relaxation times in the system phantom were 0.65% and 4.05%, respectively.

CONCLUSIONS

The technical feasibility and accuracy of MRF on a low-field MR-guided radiation therapy device has been demonstrated. MRF can be used to measure accurate T₁ and T₂ maps in three dimensions from a brief 6-min scan, offering strong potential for efficient and reproducible qMRI for future clinical trials in functional plan adaptation and tumor/normal tissue response assessment.

Free-breathing simultaneous T1 , T2 , and T2∗ quantification in the myocardium

PURPOSE

To implement a free-breathing sequence for simultaneous quantification of T 1 , T 2 , and T 2 ∗ for comprehensive tissue characterization of the myocardium in a single scan using a multi-gradient-echo readout with saturation and T 2 preparation pulses.

METHODS

In the proposed Saturation And T 2 -prepared Relaxometry with Navigator-gating (SATURN) technique, a series of multi-gradient-echo (GRE) images with different magnetization preparations was acquired during free breathing. A total of 35 images were acquired in 26.5 ± 14.9 seconds using multiple saturation times and T 2 preparation durations and with imaging at 5 echo times. Bloch simulations and phantom experiments were used to validate a 5-parameter fit model for accurate relaxometry. Free-breathing simultaneous T 1 , T 2 , and T 2 ∗ measurements were performed in 10 healthy volunteers and 2 patients using SATURN at 3T and quantitatively compared to conventional single-parameter methods such as SASHA for T 1 , T 2 -prepared bSSFP, and multi-GRE for T 2 ∗ .

RESULTS

Simulations confirmed accurate fitting with the 5-parameter model. Phantom measurements showed good agreement with the reference methods in the relevant range for in vivo measurements. Compared to single-parameter methods comparable accuracy was achieved. SATURN produced in vivo parameter maps that were visually comparable to single-parameter methods. No significant difference between T 1 , T 2 , and T 2 ∗ times acquired with SATURN and single-parameter methods was shown in quantitative measurements (SATURN T 1 = 1573 ± 86 ms , T 2 = 33.2 ± 3.6 ms , T 2 ∗ = 25.3 ± 6.1 ms ; conventional methods: T 1 = 1544 ± 107 ms , T 2 = 33.2 ± 3.6 ms , T 2 ∗ = 23.8 ± 5.5 ms ; P > . 2 ) CONCLUSION: SATURN enables simultaneous quantification of T 1 , T 2 , and T 2 ∗ in the myocardium for comprehensive tissue characterization with co-registered maps, in a single scan with good agreement to single-parameter methods.

Magnetization-prepared GRASP MRI for rapid 3D T1 mapping and fat/water-separated T1 mapping

PURPOSE

This study aimed to (i) develop Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI using a stack-of-stars trajectory for rapid free-breathing T1 mapping and (ii) extend MP-GRASP to multi-echo acquisition (MP-Dixon-GRASP) for fat/water-separated (water-specific) T1 mapping.

METHODS

An adiabatic non-selective 180° inversion-recovery pulse was added to a gradient-echo-based golden-angle stack-of-stars sequence for magnetization-prepared 3D single-echo or 3D multi-echo acquisition. In combination with subspace-based GRASP-Pro reconstruction, the sequence allows for standard T1 mapping (MP-GRASP) or fat/water-separated T1 mapping (MP-Dixon-GRASP), respectively. The accuracy of T1 mapping using MP-GRASP was evaluated in a phantom and volunteers (brain and liver) against clinically accepted reference methods. The repeatability of T1 estimation was also assessed in the phantom and volunteers. The performance of MP-Dixon-GRASP for water-specific T1 mapping was evaluated in a fat/water phantom and volunteers (brain and liver).

RESULTS

ROI-based mean T1 values are correlated between the references and MP-GRASP in the phantom (R2 = 1.0), brain (R2 = 0.96), and liver (R2 = 0.73). MP-GRASP achieved good repeatability of T1 estimation in the phantom (R2 = 1.0), brain (R2 = 0.99), and liver (R2 = 0.82). Water-specific T1 is different from in-phase and out-of-phase composite T1 (composite T1 when fat and water signal are mixed in phase or out of phase) both in the phantom and volunteers.

CONCLUSION

This work demonstrated the initial performance of MP-GRASP and MP-Dixon-GRASP MRI for rapid 3D T1 mapping and 3D fat/water-separated T1 mapping in the brain (without motion) and in the liver (during free breathing). With fat/water-separated T1 estimation, MP-Dixon-GRASP could be potentially useful for imaging patients with fatty-liver diseases.

Synergistic multi-contrast cardiac magnetic resonance image reconstruction

Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Cardiac magnetic resonance fingerprinting: Trends in technical development and potential clinical applications

Quantitative cardiac magnetic resonance has emerged in recent years as an approach for evaluating a range of cardiovascular conditions, with T1 and T2 mapping at the forefront of these developments. Cardiac Magnetic Resonance Fingerprinting (cMRF) provides a rapid and robust framework for simultaneous quantification of myocardial T1 and T2 in addition to other tissue properties. Since the advent of cMRF, a number of technical developments and clinical validation studies have been reported. This review provides an overview of cMRF, recent technical developments, healthy subject and patient studies, anticipated technical improvements, and potential clinical applications. Recent technical developments include slice profile and pulse efficiency corrections, improvements in image reconstruction, simultaneous multislice imaging, 3D whole-ventricle imaging, motion-resolved imaging, fat-water separation, and machine learning for rapid dictionary generation. Future technical developments in cMRF, such as B0 and B1 field mapping, acceleration of acquisition and reconstruction, imaging of patients with implanted devices, and quantification of additional tissue properties are also described. Potential clinical applications include characterization of infiltrative, inflammatory, and ischemic cardiomyopathies, tissue characterization in the left atrium and right ventricle, post-cardiac transplantation assessment, reduction of contrast material, pre-procedural planning for electrophysiology interventions, and imaging of patients with implanted devices.

Dixon-T2WI magnetic resonance imaging at 3 tesla outperforms conventional imaging for thyroid eye disease

OBJECTIVES

To determine the diagnostic performances of a single Dixon-T2-weighted imaging (WI) sequence compared to a conventional protocol including T1-, T2-, and fat-suppressed T2-weighted MRI at 3 T when assessing thyroid eye disease (TED).

MATERIALS AND METHODS

This IRB-approved prospective single-center study enrolled participants presenting with confirmed TED from April 2015 to October 2019. They underwent an MRI, including a conventional protocol and a Dixon-T2WI sequence. Two neuroradiologists, blinded to all data, read both datasets independently and randomly. They assessed the presence of extraocular muscle (EOM) inflammation, enlargement, fatty degeneration, or fibrosis as well as the presence of artifacts. The Wilcoxon signed-rank test was used.

RESULTS

Two hundred six participants were enrolled (135/206 [66%] women, 71/206 [34%] men, age 52.3 ± 13.2 years). Dixon-T2WI was significantly more likely to detect at least one inflamed EOM as compared to the conventional set (248/412 [60%] versus 228/412 [55%] eyes; (p = 0.02). Dixon-T2WI was more sensitive and specific than the conventional set for assessing muscular inflammation (100% versus 94.7% and 71.2% versus 68.5%, respectively). Dixon-T2WI was significantly less likely to show major or minor artifacts as compared to fat-suppressed T2WI (20/412 [5%] versus 109/412 [27%] eyes, p < 0.001, and 175/412 [42%] versus 257/412 [62%] eyes, p < 0.001). Confidence was significantly higher with Dixon-T2WI than with the conventional set (2.35 versus 2.24, p = 0.003).

CONCLUSION

Dixon-T2WI showed higher sensitivity and specificity and showed fewer artifacts than a conventional protocol when assessing thyroid eye disease, in addition to higher self-reported confidence.

KEY POINTS

• Dixon-T2WI has better sensitivity and specificity than a conventional protocol for assessing inflamed extraocular muscles in patients with thyroid eye disease. • Dixon-T2WI shows significantly fewer artifacts than fat-suppressed T2WI. • Dixon-T2WI is faster and is associated with significantly higher self-reported reader confidence as compared to a conventional protocol when assessing inflammatory extraocular muscles.

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.

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.

Myocardial T1 and T2 quantification and water-fat separation using cardiac MR fingerprinting with rosette trajectories at 3T and 1.5T

PURPOSE

This work aims to develop an approach for simultaneous water-fat separation and myocardial T₁ and T₂ quantification based on the cardiac MR fingerprinting (cMRF) framework with rosette trajectories at 3T and 1.5T.

METHODS

Two 15-heartbeat cMRF sequences with different rosette trajectories designed for water-fat separation at 3T and 1.5T were implemented. Water T₁ and T₂ maps, water image, and fat image were generated with B₀ inhomogeneity correction using a B₀ map derived from the cMRF data themselves. The proposed water-fat separation rosette cMRF approach was validated in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI system phantom and water/oil phantoms. It was also applied for myocardial tissue mapping of healthy subjects at both 3T and 1.5T.

RESULTS

Water T₁ and T₂ values measured using rosette cMRF in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom agreed well with the reference values. In the water/oil phantom, oil was well suppressed in the water images and vice versa. Rosette cMRF yielded comparable T₁ but 2~3 ms higher T₂ values in the myocardium of healthy subjects than the original spiral cMRF method. Epicardial fat deposition was also clearly shown in the fat images.

CONCLUSION

Rosette cMRF provides fat images along with myocardial T₁ and T₂ maps with significant fat suppression. This technique may improve visualization of the anatomical structure of the heart by separating water and fat and could provide value in diagnosing cardiac diseases associated with fibrofatty infiltration or epicardial fat accumulation. It also paves the way toward comprehensive myocardial tissue characterization in a single scan.

Slice-selective extended phase graphs in gradient-crushed, transient-state free precession sequences: An application to MR fingerprinting

PURPOSE

Slice-selective, gradient-crushed, transient-state sequences such as those used in MR fingerprinting (MRF) relaxometry are sensitive to slice profile effects. Whereas balanced steady-state free precession MRF profile effects have been studied, less attention has been given to gradient-crushed MRF forms. Extensions of the extended phase graph (EPG) formalism, called slice-selective EPG (ssEPG), are proposed that model slice profile effects.

THEORY AND METHODS

The hard-pulse approximation to slice-selective excitation in the spatial domain is reformulated in k-space. Excitation is modeled by standard EPG shift and transition operators. This ssEPG modeling is validated against Bloch simulations and phantom slice profile measurements. ssEPG relaxometry accuracy and variability are compared with other EPG methods in phantoms and human leg in vivo. The role of ∆B₀ interactions with slice profile and gradient crushers is investigated.

RESULTS

Simulations and slice profile measurements show that ssEPG can model highly dynamic slice profile effects of gradient-crushed sequences. The MRF ssEPG T₂ estimates over 0 < T₂ < 100 ms improve accuracy by > 10 ms at some values relative to other modeling approaches. Small deviations in B₀ can produce substantial bias in T₂ estimations from a range of MRF sequence types, and these effects can be modeled and understood by ssEPG.

CONCLUSION

Transient-state, gradient-crushed sequences such as those used in MRF are sensitive to slice profile effects, and these effects depend on RF pulse choice, gradient crusher strength, and ∆B₀ . It was found ssEPG was the most accurate EPG-based means to model these effects.

3D whole-heart isotropic-resolution motion-compensated joint T1 /T2 mapping and water/fat imaging

PURPOSE

To develop a free-breathing isotropic-resolution whole-heart joint T₁ and T₂ mapping sequence with Dixon-encoding that provides coregistered 3D T₁ and T₂ maps and complementary 3D anatomical water and fat images in a single ~9 min scan.

METHODS

Four interleaved dual-echo Dixon gradient echo volumes are acquired with a variable density Cartesian trajectory and different preparation pulses: 1) inversion recovery-preparation, 2) and 3) no preparations, and 4) T₂ preparation. Image navigators are acquired to correct each echo for 2D translational respiratory motion; the 8 echoes are jointly reconstructed with a low-rank patch-based reconstruction. A water/fat separation algorithm is used to obtain water and fat images for each acquired volume. T₁ and T₂ maps are generated by matching the signal evolution of the water images to a simulated dictionary. Complementary bright-blood and fat volumes for anatomical visualization are obtained from the T₂ -prepared dataset. The proposed sequence was tested in phantom experiments and 10 healthy subjects and compared to standard 2D MOLLI T₁ mapping, 2D balance steady-state free precession T₂ mapping, and 3D T₂ -prepared Dixon coronary MR angiography.

RESULTS

High linear correlation was found between T₁ and T₂ quantification with the proposed approach and phantom spin echo measurements (y = 1.1 × -11.68, R² = 0.98; and y = 0.85 × +5.7, R² = 0.99). Mean myocardial values of T₁ /T₂ = 1116 ± 30.5 ms/45.1 ± 2.38 ms were measured in vivo. Biases of T₁ /T₂ = 101.8 ms/-0.77 ms were obtained compared to standard 2D techniques.

CONCLUSION

The proposed joint T₁ /T₂ sequence permitted the acquisition of motion-compensated isotropic-resolution 3D T₁ and T₂ maps and complementary coronary MR angiography and fat volumes, showing promising results in terms of T₁ and T₂ quantification and visualization of cardiac anatomy and pericardial fat.

Free-running cardiac magnetic resonance fingerprinting: Joint T1/T2 map and Cine imaging

PURPOSE

To develop and evaluate a novel non-ECG triggered 2D magnetic resonance fingerprinting (MRF) sequence allowing for simultaneous myocardial T₁ and T₂ mapping and cardiac Cine imaging.

METHODS

Cardiac MRF (cMRF) has been recently proposed to provide joint T₁/T₂ myocardial mapping by triggering the acquisition to mid-diastole and relying on a subject-dependent dictionary of MR signal evolutions to generate the maps. In this work, we propose a novel "free-running" (non-ECG triggered) cMRF framework for simultaneous myocardial T₁ and T₂ mapping and cardiac Cine imaging in a single scan. Free-running cMRF is based on a transient state bSSFP acquisition with tiny golden angle radial readouts, varying flip angle and multiple adiabatic inversion pulses. The acquired data is retrospectively gated into several cardiac phases, which are reconstructed with an approach that combines parallel imaging, low rank modelling and patch-based high-order tensor regularization. Free-running cMRF was evaluated in a standardized phantom and ten healthy subjects. Comparison with reference spin-echo, MOLLI, SASHA, T₂-GRASE and Cine was performed.

RESULTS

T₁ and T₂ values obtained with the proposed approach were in good agreement with reference phantom values (ICC(A,1) > 0.99). Reported values for myocardium septum T₁ were 1043 ± 48 ms, 1150 ± 100 ms and 1160 ± 79 ms for MOLLI, SASHA and free-running cMRF respectively and for T₂ of 51.7 ± 4.1 ms and 44.6 ± 4.1 ms for T₂-GRASE and free-running cMRF respectively. Good agreement was observed between free-running cMRF and conventional Cine 2D ejection fraction (bias = -0.83%).

CONCLUSION

The proposed free-running cardiac MRF approach allows for simultaneous assessment of myocardial T₁ and T₂ and Cine imaging in a single scan.

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.