Simultaneous multislice cardiac magnetic resonance fingerprinting using low rank reconstruction

Self-calibrated subspace reconstruction for multidimensional MR fingerprinting for simultaneous relaxation and diffusion quantification

PURPOSE

To propose a new reconstruction method for multidimensional MR fingerprinting (mdMRF) to address shading artifacts caused by physiological motion-induced measurement errors without navigating or gating.

METHODS

The proposed method comprises two procedures: self-calibration and subspace reconstruction. The first procedure (self-calibration) applies temporally local matrix completion to reconstruct low-resolution images from a subset of under-sampled data extracted from the k-space center. The second procedure (subspace reconstruction) utilizes temporally global subspace reconstruction with pre-estimated temporal subspace from low-resolution images to reconstruct aliasing-free, high-resolution, and time-resolved images. After reconstruction, a customized outlier detection algorithm was employed to automatically detect and remove images corrupted by measurement errors. Feasibility, robustness, and scan efficiency were evaluated through in vivo human brain imaging experiments.

RESULTS

The proposed method successfully reconstructed aliasing-free, high-resolution, and time-resolved images, where the measurement errors were accurately represented. The corrupted images were automatically and robustly detected and removed. Artifact-free T1, T2, and ADC maps were generated simultaneously. The proposed reconstruction method demonstrated robustness across different scanners, parameter settings, and subjects. A high scan efficiency of less than 20 s per slice has been achieved.

CONCLUSION

The proposed reconstruction method can effectively alleviate shading artifacts caused by physiological motion-induced measurement errors. It enables simultaneous and artifact-free quantification of T1, T2, and ADC using mdMRF scans without prospective gating, with robustness and high scan efficiency.

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.

Improving motion robustness of 3D MR fingerprinting with a fat navigator

PURPOSE

To develop a 3D MR fingerprinting (MRF) method in combination with fat navigators to improve its motion robustness for neuroimaging.

METHODS

A rapid fat navigator was developed using the stack-of-spirals acquisition and non-Cartesian spiral GRAPPA. The fat navigator module was implemented in the 3D MRF sequence with high scan efficiency. The developed method was first validated in phantoms and five healthy subjects with intentional head motion. The method was further applied to infants with neonatal opioid withdrawal symptoms. The 3D MRF scans with fat navigators acquired with and without acceleration along the partition-encoding direction were both examined in the study.

RESULTS

Both phantom and in vivo results demonstrated that the added fat navigator modules did not influence the quantification accuracy in MRF. In combination with non-Cartesian spiral GRAPPA, a rapid fat navigator sampling with whole-brain coverage was achieved in ˜0.5 s at 3T, reducing its sensitivity to potential motion. Based on the motion waveforms extracted from fat navigators, the motion robustness of the 3D MRF was largely improved. With the proposed method, the motion-corrupted MRF datasets yielded T1 and T2 maps with significantly reduced artifacts and high correlations with measurements from the reference motion-free MRF scans.

CONCLUSION

We developed a 3D MRF method coupled with rapid fat navigators to improve its motion robustness for quantitative neuroimaging. Our results demonstrate that (1) accurate tissue quantification was preserved with the fat navigator modules and (2) the motion robustness for quantitative tissue mapping was largely improved with the developed method.

Motion-compensated T1 mapping in cardiovascular magnetic resonance imaging: a technical review

T 1 mapping is becoming a staple magnetic resonance imaging method for diagnosing myocardial diseases such as ischemic cardiomyopathy, hypertrophic cardiomyopathy, myocarditis, and more. Clinically, most T 1 mapping sequences acquire a single slice at a single cardiac phase across a 10 to 15-heartbeat breath-hold, with one to three slices acquired in total. This leaves opportunities for improving patient comfort and information density by acquiring data across multiple cardiac phases in free-running acquisitions and across multiple respiratory phases in free-breathing acquisitions. Scanning in the presence of cardiac and respiratory motion requires more complex motion characterization and compensation. Most clinical mapping sequences use 2D single-slice acquisitions; however newer techniques allow for motion-compensated reconstructions in three dimensions and beyond. To further address confounding factors and improve measurement accuracy, T 1 maps can be acquired jointly with other quantitative parameters such as T 2 , T 2 ∗ , fat fraction, and more. These multiparametric acquisitions allow for constrained reconstruction approaches that isolate contributions to T 1 from other motion and relaxation mechanisms. In this review, we examine the state of the literature in motion-corrected and motion-resolved T 1 mapping, with potential future directions for further technical development and clinical translation.

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.

A fast MR fingerprinting simulator for direct error estimation and sequence optimization

Magnetic resonance fingerprinting (MRF) is a novel quantitative MR technique that simultaneously provides multiple tissue property maps. When optimizing MRF scans, modeling undersampling errors and field imperfections in cost functions for direct measurement of quantitative errors will make the optimization results more practical and robust. However, optimizing such cost function is computationally expensive and impractical for MRF optimization with tens of thousands of iterations. Here, we introduce a fast MRF simulator to simulate aliased images from actual scan scenarios including undersampling and system imperfections, which substantially reduces computational time and allows for direct error estimation of the quantitative maps and efficient sequence optimization. We evaluate the performance and computational speed of the proposed approach by simulations and in vivo experiments. The simulations from the proposed method closely approximate the signals and MRF maps from in vivo scans, with 158 times shorter processing time than the conventional simulation method using Non-uniform Fourier transform. We also demonstrate the power of applying the fast MRF simulator in MRF sequence optimization. The optimized sequences are validated with in vivo scans to assess the image quality and accuracy. The optimized sequences produce artifact-free T1 and T2 maps in 2D and 3D scans with equivalent mapping accuracy as the human-designed sequence but at shorter scan times. Incorporating the proposed simulator in the MRF optimization framework makes direct estimation of undersampling errors during the optimization process feasible, and provide optimized MRF sequences that are robust against undersampling artifacts and field inhomogeneity.

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.

Dynamic Cardiac Magnetic Resonance Fingerprinting During Vasoactive Breathing Maneuvers: First Results

BACKGROUND

Cardiac magnetic resonance fingerprinting (cMRF) enables simultaneous mapping of myocardial T1 and T2 with very short acquisition times. Breathing maneuvers have been utilized as a vasoactive stress test to dynamically characterize myocardial tissue in vivo. We tested the feasibility of sequential, rapid cMRF acquisitions during breathing maneuvers to quantify myocardial T1 and T2 changes.

METHODS

We measured T1 and T2 values using conventional T1 and T2-mapping techniques (modified look locker inversion [MOLLI] and T2-prepared balanced-steady state free precession), and a 15 heartbeat (15-hb) and rapid 5-hb cMRF sequence in a phantom and in 9 healthy volunteers. The cMRF_5-hb sequence was also used to dynamically assess T1 and T2 changes over the course of a vasoactive combined breathing maneuver.

RESULTS

In healthy volunteers, the mean myocardial T1 of the different mapping methodologies were: MOLLI 1,224 ± 81 ms, cMRF_15-hb 1,359 ± 97 ms, and cMRF_5-hb 1,357 ± 76 ms. The mean myocardial T2 measured with the conventional mapping technique was 41.7 ± 6.7 ms, while for cMRF_15-hb 29.6 ± 5.8 ms and cMRF_5-hb 30.5 ± 5.8 ms. T2 was reduced with vasoconstriction (post-hyperventilation compared to a baseline resting state) (30.15 ± 1.53 ms vs. 27.99 ± 2.07 ms, p = 0.02), while T1 did not change with hyperventilation. During the vasodilatory breath-hold, no significant change of myocardial T1 and T2 was observed.

CONCLUSIONS

cMRF_5-hb enables simultaneous mapping of myocardial T1 and T2, and may be used to track dynamic changes of myocardial T1 and T2 during vasoactive combined breathing maneuvers.

Cardiac MR fingerprinting with a short acquisition window in consecutive patients referred for clinical CMR and healthy volunteers

Cardiac Magnetic Resonance Fingerprinting (cMRF) has been demonstrated to enable robust and accurate T₁ and T₂ mapping for the detection of myocardial fibrosis and edema. However, the relatively long acquisition window (250 ms) used in previous cMRF studies might leave it vulnerable to motion artifacts in patients with high heart rates. The goal of this study was therefore to compare cMRF with a short acquisition window (154 ms) and low-rank reconstruction to routine cardiac T₁ and T₂ mapping at 1.5 T. Phantom studies showed that the proposed cMRF had a high T₁ and T₂ accuracy over a wider range than routine mapping techniques. In 9 healthy volunteers, the proposed cMRF showed small but significant myocardial T₁ and T₂ differences compared to routine mapping (ΔT₁ = 1.5%, P = 0.031 and ΔT₂ = - 7.1%, P < 0.001). In 61 consecutive patients referred for CMR, the native T₁ values were slightly lower (ΔT₁ = 1.6%; P = 0.02), while T₂ values did not show statistical difference (ΔT₂ = 4.3%; P = 0.11). However, the difference was higher in post-contrast myocardial T₁ values (ΔT₁ = 12.3%; P < 0.001), which was reflected in the extracellular volume (ΔECV = 2.4%; P < 0.001). Across all subjects, the proposed cMRF had a lower precision when compared to routine techniques, although its higher spatial resolution enabled the visualization of smaller details.

MR Fingerprinting with b-Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan

PURPOSE

Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time.

METHODS

We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps.

RESULTS

T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts.

CONCLUSION

We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice. The map quality and quantitative values are consistent with the reference scans.

Simultaneous multi-parametric acquisition and reconstruction techniques in cardiac magnetic resonance imaging: Basic concepts and status of clinical development

Simultaneous multi-parametric acquisition and reconstruction techniques (SMART) are gaining attention for their potential to overcome some of cardiovascular magnetic resonance imaging's (CMR) clinical limitations. The major advantages of SMART lie within their ability to simultaneously capture multiple "features" such as cardiac motion, respiratory motion, T1/T2 relaxation. This review aims to summarize the overarching theory of SMART, describing key concepts that many of these techniques share to produce co-registered, high quality CMR images in less time and with less requirements for specialized personnel. Further, this review provides an overview of the recent developments in the field of SMART by describing how they work, the parameters they can acquire, their status of clinical testing and validation, and by providing examples for how their use can improve the current state of clinical CMR workflows. Many of the SMART are in early phases of development and testing, thus larger scale, controlled trials are needed to evaluate their use in clinical setting and with different cardiac pathologies.

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.

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.

A Slice-Low-Rank Plus Sparse (slice-L + S) Reconstruction Method for k-t Undersampled Multiband First-Pass Myocardial Perfusion MRI

Purpose

The synergistic use of k‐t undersampling and multiband (MB) imaging has the potential to provide extended slice coverage and high spatial resolution for first‐pass perfusion MRI. The low‐rank plus sparse (L + S) model has shown excellent performance for accelerating single‐band (SB) perfusion MRI.

Methods

A MB data consistency method employing ESPIRiT maps and through‐plane coil information was developed. This data consistency method was combined with the temporal L + S constraint to form the slice‐L + S method. Slice‐L + S was compared to SB L + S and the sequential operations of split slice‐GRAPPA and SB L + S (seq‐SG‐L + S) using synthetic data formed from multislice SB images. Prospectively k‐t undersampled MB data were also acquired and reconstructed using seq‐SG‐L + S and slice‐L + S.

Results

Using synthetic data with total acceleration rates of 6–12, slice‐L + S outperformed SB L + S and seq‐SG‐L + S (N = 7 subjects) with respect to normalized RMSE and the structural similarity index (P < 0.05 for both). For the specific case with MB factor = 3 and rate 3 undersampling, or for SB imaging with rate 9 undersampling (N = 7 subjects), the normalized RMSE values were 0.037 ± 0.007, 0.042 ± 0.005, and 0.031 ± 0.004; and the structural similarity index values were 0.88 ± 0.03, 0.85 ± 0.03, and 0.89 ± 0.02 for SB L + S, seq‐SG‐L + S, and slice‐L + S, respectively (P < 0.05 for both). For prospectively undersampled MB data, slice‐L + S provided better image quality than seq‐SG‐L + S for rate 6 (N = 7) and rate 9 acceleration (N = 7) as scored by blinded experts.

Conclusion

Slice‐L + S outperformed SB‐L + S and seq‐SG‐L + S and provides 9 slice coverage of the left ventricle with a spatial resolution of 1.5 mm × 1.5 mm with good image quality.

The Road Toward Reproducibility of Parametric Mapping of the Heart: A Technical Review

Parametric mapping of the heart has become an essential part of many cardiovascular magnetic resonance imaging exams, and is used for tissue characterization and diagnosis in a broad range of cardiovascular diseases. These pulse sequences are used to quantify the myocardial T₁, T₂, T2*, and T_1ρ relaxation times, which are unique surrogate indices of fibrosis, edema and iron deposition that can be used to monitor a disease over time or to compare patients to one another. Parametric mapping is now well-accepted in the clinical setting, but its wider dissemination is hindered by limited inter-center reproducibility and relatively long acquisition times. Recently, several new parametric mapping techniques have appeared that address both of these problems, but substantial hurdles remain for widespread clinical adoption. This review serves both as a primer for newcomers to the field of parametric mapping and as a technical update for those already well at home in it. It aims to establish what is currently needed to improve the reproducibility of parametric mapping of the heart. To this end, we first give an overview of the metrics by which a mapping technique can be assessed, such as bias and variability, as well as the basic physics behind the relaxation times themselves and what their relevance is in the prospect of myocardial tissue characterization. This is followed by a summary of routine mapping techniques and their variations. The problems in reproducibility and the sources of bias and variability of these techniques are reviewed. Subsequently, novel fast, whole-heart, and multi-parametric techniques and their merits are treated in the light of their reproducibility. This includes state of the art segmentation techniques applied to parametric maps, and how artificial intelligence is being harnessed to solve this long-standing conundrum. We finish up by sketching an outlook on the road toward inter-center reproducibility, and what to expect in the future.

3D MR fingerprinting using Seiffert spirals

PURPOSE

Seiffert spirals were recently explored as an efficient way to traverse 3D k-space compared to traditional 3D techniques. Several studies have shown the ability of 3D MR fingerprinting (MRF) techniques to acquire T₁ and T₂ relaxation maps in a short period of time. However, these sequences do not sample across a large region of 3D k-space every TR, especially in the way that Seiffert trajectories can.

METHODS

A 3D MRF sequence was designed using 8 Seiffert spirals rotated in 3D k-space, with flip angle modulation for T₁ and T₂ sensitivity. The sequence was compared to an MRF sequence using a 2D spiral rotated in 3D k-space using the tiny golden angle acquisition with similar resolution/readout duration. Both sequences were evaluated using simulations, phantom validation, and in vivo imaging.

RESULTS

In all experiments, the Seiffert spiral MRF sequence performed similar to if not better than the multi-axis 2D spiral MRF sequence. Strong intraclass correlation coefficients (> 0.9) were found between conventional and MRF sequences in phantoms, whereas the in vivo results showed slightly less aliasing artifact with the Seiffert trajectory.

CONCLUSION

In this study, Seiffert spirals were used within the MRF framework to acquire high-resolution T₁ and T₂ relaxation time maps in less than 2.5 min. The reduced aliasing artifacts seen with the Seiffert sequence suggests that sampling over 3D k-space evenly each TR can improve quantification or shorten scan times.

Simultaneous Multi-Slice Cardiac MR Multitasking for Motion-Resolved, Non-ECG, Free-Breathing T1–T2 Mapping

The aim of this study is to simultaneously quantify T1/T2 across three slices of the left-ventricular myocardium without breath-holds or ECG monitoring, all within a 3 min scan. Radial simultaneous multi-slice (SMS) encoding, self-gating, and image reconstruction was incorporated into the cardiovascular magnetic resonance (CMR) Multitasking framework to simultaneously image three short-axis slices. A T2prep-IR FLASH sequence with two flip angles was designed and implemented to allow B1+-robust T1 and T2 mapping. The proposed Multitasking-SMS method was validated in a standardized phantom and 10 healthy volunteers, comparing T1 and T2 measurements and scan-rescan repeatability against corresponding reference methods in one layer of phantom vials and in 16 American Heart Association (AHA) myocardial segments. In phantom, Multitasking-SMS T1/T2 measurements showed substantial correlation (R² > 0.996) and excellent agreement [intraclass correlation coefficients (ICC) ≥ 0.999)] with reference measurements. In healthy volunteers, Multitasking-SMS T1/T2 maps reported similar myocardial T1/T2 values (1,215 ± 91.0/41.5 ± 6.3 ms) to the reference myocardial T1/T2 values (1,239 ± 67.5/42.7 ± 4.1 ms), with P = 0.347 and P = 0.296, respectively. Bland–Altman analyses also demonstrated good in vivo repeatability in both the multitasking and references, with segment-wise coefficients of variation of 4.7% (multitasking T1), 8.9% (multitasking T2), 2.4% [modified look-locker inversion recovery (MOLLI)], and 4.6% (T2-prep FLASH), respectively. In summary, multitasking-SMS is feasible for free-breathing, non-ECG, myocardial T1/T2 quantification in 16 AHA segments over 3 short-axis slices in 3 min. The method shows the great potential for reducing exam time for quantitative CMR without ECG or breath-holds.

Cardiac MR: From Theory to Practice

Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.

3D MR fingerprinting (MRF) for simultaneous T1 and T2 quantification of the bone metastasis: Initial validation in prostate cancer patients

PURPOSE

To investigate the feasibility of using 3-dimensional MRF for bone marrow evaluation in the field of view of prostate MRI for T1 and T2 quantification of prostate cancer bone metastases, as well as comparing it to the ADC value.

METHODS

In this retrospective study, 30 prostate MRIs were included: 14 cases with prostate cancer bone metastasis and 16 cases without prostate cancer (control). MRF was obtained twice before (nonenhanced [NE] MRF) and after contrast injection (contrast-enhanced [CE] MRF), and T1 and T2 maps were generated from each MRF. Two radiologists independently drew regions of interest (ROIs) on the MRF maps and the ADC maps. Mann-Whitney U tests and the area under the receiver operating characteristic curve (AUROC) evaluated the two-reader means of T1, T2 and ADC values between bone metastasis and normal bone.

RESULTS

There were 83 ROIs, including 39 bone metastases and 44 normal bone. The two-reader average ADC, NE T2 and CE T2 values were significantly lower and NE T1 and CE T1 values were significantly higher in metastatic bone compared with normal bone (P < 0.001). The AUROC of the ADC was lowest (0.685), which was significantly lower than those of NE T1 (1.0, P = 0.001), NE T2 (0.932, P = 0.004), and CE T2 (0.876, P = 0.031).

CONCLUSION

MRF to assess the pelvic bone during a prostate gland evaluation provides a reliable parametric map for skeletal work-up. With higher diagnostic performance than the ADC value, NE MRF is a potential alternative for quantifying bone marrow metastases in prostate cancer patients.

Technical overview of magnetic resonance fingerprinting and its applications in radiation therapy

Magnetic resonance fingerprinting (MRF) is an emerging imaging technique for rapid and simultaneous quantification of multiple tissue properties. The technique has been developed for quantitative imaging of different organs. The obtained quantitative measures have the potential to improve multiple steps of a typical radiotherapy workflow and potentially further improve integration of magnetic resonance imaging guided clinical decision making. In this review paper, we first provide a technical overview of the MRF method from data acquisition to postprocessing, along with recent development in advanced reconstruction methods. We further discuss critical aspects that could influence its usage in radiation therapy, such as accuracy and precision, repeatability and reproducibility, geometric distortion, and motion robustness. Finally, future directions for MRF application in radiation therapy are discussed.

Quantitative MR relaxation using MR fingerprinting with fractional-order signal evolution

The fractional-order Bloch equations have been shown to describe a wider range of experimental situations involving heterogeneous, porous, or composite materials. This paper introduces a novel dictionary of quantitative MR fingerprinting generated by signal evolution model with fractional-order Bloch equations to describe magnetic resonance (MR) relaxation. Here, the fractional-order relaxation models are implemented into Bloch equations through phase transitions using EPG simulation. In the phantom experiments, the fractional-order analysis showed smaller root mean squared error (T1: RMSE = 5.21%, T2: RMSE=3.75%) using the proposed method compared to using conventional method. Among the in vivo experiments of human brains, the estimated T1 and T2 values (mean ± SD) were 843 ± 46.3 ms and 70 ± 4.7 ms in white matter, 1323 ± 28.5 ms and 95 ± 3.8 ms in gray matter. So the proposed method can provide well extensions of current MR fingerprinting and has shown potential to apply into the phantom experiments and the in vivo applications to approach the standard methods for quantitative imaging.

Free-Breathing Abdominal Magnetic Resonance Fingerprinting Using a Pilot Tone Navigator

BACKGROUND

Quantitative T1 and T2 mapping in the abdomen provides valuable information in tissue characterization but is technically challenging due to respiratory motions. The proposed technique integrates magnetic resonance fingerprinting (MRF) and pilot tone (PT) navigator with retrospective gating to provide simultaneous quantification of multiple tissue properties in a single acquisition without breath-holding or patient set-up.

PURPOSE

To develop a free-breathing abdominal MRF technique for quantitative mapping in the abdomen.

STUDY TYPE

Prospective.

POPULATION

Twelve healthy volunteers.

FIELD STRENGTH/SEQUENCE

A 3 T, two-dimensional (2D) and three-dimensional (3D) spiral MRF sequence with fast imaging with steady-state free precession (FISP) readout.

ASSESSMENT

The PT navigator was compared to standard respiratory belt performance. The T1 and T2 values acquired using 2D and 3D MRF with and without PT were obtained in a phantom and compared to reference values. Digital phantom simulation was performed to evaluate PT MRF reconstruction with varying breathing patterns. In the in vivo studies, T1 and T2 values derived from PT 2D MRF were compared to 2D breath-hold MRF. T1 and T2 values derived from PT 3D MRF were compared to published values.

STATISTICAL TESTS

Principal component analysis (PCA), linear regression, relative error, Pearson correlation, paired Student's t-test, Bland-Altman Analysis.

RESULTS

The phantom study showed PT MRF T1 values had a mean difference of 0.2% ± 0.1%, and T2 values had a mean difference of 0.1% ± 0.4% when compared to no-PT MRF values. The digital phantom experiment suggested the T1 and T2 maps at both end-exhalation and end-inhalation states resemble the corresponding ground-truth maps.

DATA CONCLUSION

The phantom study showed good agreement between MRF T1 and T2 values and with reference values. In vivo studies demonstrated that 2D and 3D quantitative imaging in the abdomen could be achieved with integration of PT navigation with MRF reconstruction using retrospective gating of respiratory motion. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 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.

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.

Cardiac cine magnetic resonance fingerprinting for combined ejection fraction, T1 and T2 quantification

This study introduces a technique called cine magnetic resonance fingerprinting (cine-MRF) for simultaneous T₁ , T₂ and ejection fraction (EF) quantification. Data acquired with a free-running MRF sequence are retrospectively sorted into different cardiac phases using an external electrocardiogram (ECG) signal. A low-rank reconstruction with a finite difference sparsity constraint along the cardiac motion dimension yields images resolved by cardiac phase. To improve SNR and precision in the parameter maps, these images are nonrigidly registered to the same phase and matched to a dictionary to generate T₁ and T₂ maps. Cine images for computing left ventricular volumes and EF are also derived from the same data. Cine-MRF was tested in simulations using a numerical relaxation phantom. Phantom and in vivo scans of 19 subjects were performed at 3 T during a 10.9 seconds breath-hold with an in-plane resolution of 1.6 x 1.6 mm² and 24 cardiac phases. Left ventricular EF values obtained with cine-MRF agreed with the conventional cine images (mean bias -1.0%). Average myocardial T₁ times in diastole/systole were 1398/1391 ms with cine-MRF, 1394/1378 ms with ECG-triggered cardiac MRF (cMRF) and 1234/1212 ms with MOLLI; and T₂ values were 30.7/30.3 ms with cine-MRF, 32.6/32.9 ms with ECG-triggered cMRF and 37.6/41.0 ms with T₂ -prepared FLASH. Cine-MRF and ECG-triggered cMRF relaxation times were in good agreement. Cine-MRF T₁ values were significantly longer than MOLLI, and cine-MRF T₂ values were significantly shorter than T₂ -prepared FLASH. In summary, cine-MRF can potentially streamline cardiac MRI exams by combining left ventricle functional assessment and T₁ -T₂ mapping into one time-efficient acquisition.

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.

T1ρ magnetic resonance fingerprinting

T_1ρ relaxation imaging is a quantitative imaging technique that has been used to assess cartilage integrity, liver fibrosis, tumors, cardiac infarction, and Alzheimer's disease. T₁ , T₂ , and T_1ρ relaxation time constants have each demonstrated different degrees of sensitivity to several markers of fibrosis and inflammation, allowing for a potential multi-parametric approach to tissue quantification. Traditional magnetic resonance fingerprinting (MRF) has been shown to provide quick, quantitative mapping of T₁ and T₂ relaxation time constants. In this study, T_1ρ relaxation is added to the MRF framework using spin lock preparations. An MRF sequence involving an RF-spoiled sequence with T_R , flip angle, T_1ρ , and T₂ preparation variation is described. The sequence is then calibrated against conventional T₁ , T₂ , and T_1ρ relaxation mapping techniques in agar phantoms and the abdomens of four healthy volunteers. Strong intraclass correlation coefficients (ICC > 0.9) were found between conventional and MRF sequences in phantoms and also in healthy volunteers (ICC > 0.8). The highest ICC correlation values were seen in T₁ , followed by T_1ρ and then T₂ . In this study, T_1ρ relaxation has been incorporated into the MRF framework by using spin lock preparations, while still fitting for T₁ and T₂ relaxation time constants. The acquisition of these parameters within a single breath hold in the abdomen alleviates the issues of movement between breath holds in conventional techniques.

Magnetic resonance fingerprinting review part 2: Technique and directions

Magnetic resonance fingerprinting (MRF) is a general framework to quantify multiple MR-sensitive tissue properties with a single acquisition. There have been numerous advances in MRF in the years since its inception. In this work we highlight some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:993-1007.

From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction

Cardiac magnetic resonance (CMR) imaging is an important tool for the non-invasive assessment of cardiovascular disease. However, CMR suffers from long acquisition times due to the need of obtaining images with high temporal and spatial resolution, different contrasts, and/or whole-heart coverage. In addition, both cardiac and respiratory-induced motion of the heart during the acquisition need to be accounted for, further increasing the scan time. Several undersampling reconstruction techniques have been proposed during the last decades to speed up CMR acquisition. These techniques rely on acquiring less data than needed and estimating the non-acquired data exploiting some sort of prior information. Parallel imaging and compressed sensing undersampling reconstruction techniques have revolutionized the field, enabling 2- to 3-fold scan time accelerations to become standard in clinical practice. Recent scientific advances in CMR reconstruction hinge on the thriving field of artificial intelligence. Machine learning reconstruction approaches have been recently proposed to learn the non-linear optimization process employed in CMR reconstruction. Unlike analytical methods for which the reconstruction problem is explicitly defined into the optimization process, machine learning techniques make use of large data sets to learn the key reconstruction parameters and priors. In particular, deep learning techniques promise to use deep neural networks (DNN) to learn the reconstruction process from existing datasets in advance, providing a fast and efficient reconstruction that can be applied to all newly acquired data. However, before machine learning and DNN can realize their full potentials and enter widespread clinical routine for CMR image reconstruction, there are several technical hurdles that need to be addressed. In this article, we provide an overview of the recent developments in the area of artificial intelligence for CMR image reconstruction. The underlying assumptions of established techniques such as compressed sensing and low-rank reconstruction are briefly summarized, while a greater focus is given to recent advances in dictionary learning and deep learning based CMR reconstruction. In particular, approaches that exploit neural networks as implicit or explicit priors are discussed for 2D dynamic cardiac imaging and 3D whole-heart CMR imaging. Current limitations, challenges, and potential future directions of these techniques are also discussed.

Machine Learning for Rapid Magnetic Resonance Fingerprinting Tissue Property Quantification

Magnetic Resonance Fingerprinting (MRF) is an MRI-based method that can provide quantitative maps of multiple tissue properties simultaneously from a single rapid acquisition. Tissue property maps are generated by matching the complex signal evolutions collected at the scanner to a dictionary of signals derived using Bloch equation simulations. However, in some circumstances, the process of dictionary generation and signal matching can be time-consuming, reducing the utility of this technique. Recently, several groups have proposed using machine learning to accelerate the extraction of quantitative maps from MRF data. This article will provide an overview of current research that combines MRF and machine learning, as well as present original research demonstrating how machine learning can speed up dictionary generation for cardiac MRF.

Advances in MRI Applications to Diagnose and Manage Cardiomyopathies

PURPOSE OF REVIEW

The prevalence of heart failure continues to rise, and imaging characterization of the cardiomyopathic process is important for identifying myocardial disease, initiating appropriate treatment, and improving outcomes. We aimed to summarize recent advances in cardiac magnetic resonance imaging (CMR) applications for the diagnosis, characterization, and implications on management of various cardiomyopathies.

RECENT FINDINGS

Parametric mapping by CMR has emerged as an important advancement in quantification of myocardial fibrosis, increased extracellular space, and myocardial edema. In addition, improved assessment of myocardial function with myocardial strain assessment may provide early identification of patients at risk and determining responsiveness to therapeutic interventions. Novel MRI techniques and the advent of artificial intelligence may help to uncover important mechanistic insights into the cardiomyopathic process. Innovative CMR techniques continue to evolve, and it will be of interest to determine how these advances can be incorporated into clinical practice to improve diagnosis, treatment, and management of patients with cardiomyopathies.

Cardiac Magnetic Resonance Fingerprinting: Technical Developments and Initial Clinical Validation

PURPOSE OF REVIEW

Magnetic resonance imaging (MRI) has enabled non-invasive myocardial tissue characterization in a wide range of cardiovascular diseases by quantifying several tissue specific parameters such as T1, T2, and T2* relaxation times. Simultaneous assessment of these parameters has recently gained interest to potentially improve diagnostic accuracy and enable further understanding of the underlying disease. However, these quantitative maps are usually acquired sequentially and are not necessarily co-registered, making multi-parametric analysis challenging. Magnetic resonance fingerprinting (MRF) has been recently introduced to unify and streamline parametric mapping into a single simultaneous, multi-parametric, fully co-registered, and efficient scan. Feasibility of cardiac MRF has been demonstrated and initial clinical validation studies are ongoing. Provide an overview of the cardiac MRF framework, recent technical developments and initial undergoing clinical validation.

RECENT FINDINGS

Cardiac MRF has enabled the acquisition of co-registered T1 and T2 maps in a single, efficient scan. Initial results demonstrate feasibility of cardiac MRF in healthy subjects and small patient cohorts. Current in vivo results show a small bias and comparable precision in T1 and T2 with respect to conventional clinical parametric mapping approaches. This bias may be explained by several confounding factors such as magnetization transfer and field inhomogeneities, which are currently not included in the cardiac MRF model. Initial clinical validation for cardiac MRF has demonstrated good reproducibility in healthy subjects and heart transplant patients, reduced artifacts in inflammatory cardiomyopathy patients and good differentiation between hypertrophic cardiomyopathy and healthy controls. Cardiac MRF has emerged as a novel technique for simultaneous, multi-parametric, and co-registered mapping of different tissue parameters. Initial efforts have focused on enabling T1, T2, and fat quantification; however this approach has the potential of enabling quantification of several other parameters (such as T2*, diffusion, perfusion, and flow) from a single scan. Initial results in healthy subjects and patients are promising, thus further clinical validation is now warranted.