Investigating and reducing the effects of confounding factors for robust T1 and T2 mapping with cardiac MR fingerprinting

Deep graph embedding based on Laplacian eigenmaps for MR fingerprinting reconstruction

Magnetic resonance fingerprinting (MRF) is a promising technique for fast quantitative imaging of multiple tissue parameters. However, the highly undersampled schemes utilized in MRF typically lead to noticeable aliasing artifacts in reconstructed images. Existing model-based methods can mitigate aliasing artifacts and enhance reconstruction quality but suffer from long reconstruction times. In addition, data priors used in these methods, such as low-rank and total variation, make it challenging to incorporate non-local and non-linear redundancies in MRF data. Furthermore, existing deep learning-based methods for MRF often lack interpretability and struggle with the high computational overhead caused by the high dimensionality of MRF data. To address these issues, we introduce a novel deep graph embedding framework based on the Laplacian eigenmaps for improved MRF reconstruction. Our work first models the acquired high-dimensional MRF data and the corresponding parameter maps as graph data nodes. Then, we propose an MRF reconstruction framework based on the graph embedding framework, retaining intrinsic graph structures between parameter maps and MRF data. To improve the accuracy of the estimated graph structure and the computational efficiency of the proposed framework, we unroll the iterative optimization process into a deep neural network, incorporating a learned graph embedding module to adaptively learn the Laplacian eigenmaps. By introducing the graph embedding framework into the MRF reconstruction, the proposed method can effectively exploit non-local and non-linear correlations in MRF data. Numerical experiments demonstrate that our approach can reconstruct high-quality MRF data and multiple parameter maps within a significantly reduced computational cost.

Simultaneous multislice cardiac multimapping based on locally low-rank and sparsity constraints

BACKGROUND

Although quantitative myocardial T1 and T2 mappings are clinically used to evaluate myocardial diseases, their application needs a minimum of six breath-holds to cover three short-axis slices. The purpose of this work is to simultaneously quantify multislice myocardial T1 and T2 across three short-axis slices in one breath-hold by combining simultaneous multislice (SMS) with multimapping.

METHODS

An SMS-Multimapping sequence with multiband radiofrequency (RF) excitations and Cartesian fast low-angle shot readouts was developed for data acquisition. When 3 slices are simultaneously acquired, the acceleration rate is around 12-fold, causing a highly ill-conditioned reconstruction problem. To mitigate image artifacts and noise caused by the ill-conditioning, a reconstruction algorithm based on locally low-rank and sparsity (LLRS) constraints was developed. Validation was performed in phantoms and in vivo imaging, with 20 healthy subjects and 4 patients, regarding regional mean, precision, and scan-rescan reproducibility.

RESULTS

The phantom imaging shows that SMS-Multimapping with locally low-rank (LLRS) accurately reconstructed multislice T1 and T2 maps despite a six-fold acceleration of scan time. Healthy subject imaging shows that the proposed LLRS algorithm substantially improved image quality relative to split slice-generalized autocalibrating partially parallel acquisition. Compared with modified look-locker inversion recovery (MOLLI), SMS-Multimapping exhibited higher T1 mean (1118 ± 43 ms vs 1190 ± 49 ms, P < 0.01), lower precision (67 ± 17 ms vs 90 ± 17 ms, P < 0.01), and acceptable scan-rescan reproducibility measured by 2 scans 10-min apart (bias = 1.4 ms for MOLLI and 9.0 ms for SMS-Multimapping). Compared with balanced steady-state free precession (bSSFP) T2 mapping, SMS-Multimapping exhibited similar T2 mean (43.5 ± 3.3 ms vs 43.0 ± 3.5 ms, P = 0.64), similar precision (4.9 ± 2.1 ms vs 5.1 ± 1.0 ms, P = 0.93), and acceptable scan-rescan reproducibility (bias = 0.13 ms for bSSFP T2 mapping and 0.55 ms for SMS-Multimapping). In patients, SMS-Multimapping clearly showed the abnormality in a similar fashion as the reference methods despite using only one breath-hold.

CONCLUSION

SMS-Multimapping with the proposed LLRS reconstruction can measure multislice T1 and T2 maps in one breath-hold with good accuracy, reasonable precision, and acceptable reproducibility, achieving a six-fold reduction of scan time and an improvement of patient comfort.

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.

Free-breathing 3D whole-heart joint T1/T2 mapping and water/fat imaging at 0.55 T

PURPOSE

To develop and validate a highly efficient motion compensated free-breathing isotropic resolution 3D whole-heart joint T1/T2 mapping sequence with anatomical water/fat imaging at 0.55 T.

METHODS

The proposed sequence takes advantage of shorter T1 at 0.55 T to acquire three interleaved water/fat volumes with inversion-recovery preparation, no preparation, and T2 preparation, respectively. Image navigators were used to facilitate nonrigid motion-compensated image reconstruction. T1 and T2 maps were jointly calculated by a dictionary matching method. Validations were performed with simulation, phantom, and in vivo experiments on 10 healthy volunteers and 1 patient. The performance of the proposed sequence was compared with conventional 2D mapping sequences including modified Look-Locker inversion recovery and T2-prepared balanced steady-SSFP sequence.

RESULTS

The proposed sequence has a good T1 and T2 encoding sensitivity in simulation, and excellent agreement with spin-echo reference T1 and T2 values was observed in a standardized T1/T2 phantom (R2 = 0.99). In vivo experiments provided good-quality co-registered 3D whole-heart T1 and T2 maps with 2-mm isotropic resolution in a short scan time of about 7 min. For healthy volunteers, left-ventricle T1 mean and SD measured by the proposed sequence were both comparable with those of modified Look-Locker inversion recovery (640 ± 35 vs. 630 ± 25 ms [p = 0.44] and 49.9 ± 9.3 vs. 54.4 ± 20.5 ms [p = 0.42]), whereas left-ventricle T2 mean and SD measured by the proposed sequence were both slightly lower than those of T2-prepared balanced SSFP (53.8 ± 5.5 vs. 58.6 ± 3.3 ms [p < 0.01] and 5.2 ± 0.9 vs. 6.1 ± 0.8 ms [p = 0.03]). Myocardial T1 and T2 in the patient measured by the proposed sequence were in good agreement with conventional 2D sequences and late gadolinium enhancement.

CONCLUSION

The proposed sequence simultaneously acquires 3D whole-heart T1 and T2 mapping with anatomical water/fat imaging at 0.55 T in a fast and efficient 7-min scan. Further investigation in patients with cardiovascular disease is now warranted.

Fast quantitative MRI: Spiral Acquisition Matching-Based Algorithm (SAMBA) for Robust T1 and T2 Mapping

Conventional diagnostic images from Magnetic Resonance Imaging (MRI) are typically qualitative and require subjective interpretation. Alternatively, quantitative MRI (qMRI) methods have become more prevalent in recent years with multiple clinical and preclinical imaging applications. Quantitative MRI studies on preclinical MRI scanners are being used to objectively assess tissues and pathologies in animal models and to evaluate new molecular MRI contrast agents. Low-field preclinical MRI scanners (≤3.0T) are particularly important in terms of evaluating these new MRI contrast agents at human MRI field strengths. Unfortunately, these low-field preclinical qMRI methods are challenged by long acquisition times, intrinsically low MRI signal levels, and susceptibility to motion artifacts. In this study, we present a new rapid qMRI method for a preclinical 3.0T MRI scanner that combines a Spiral Acquisition with a Matching-Based Algorithm (SAMBA) to rapidly and quantitatively evaluate MRI contrast agents. In this initial development, we compared SAMBA with gold-standard Spin Echo MRI methods using Least Squares Fitting (SELSF) in vitro phantoms and demonstrated shorter scan times without compromising measurement accuracy or repeatability. These initial results will pave the way for future in vivo qMRI studies using state-of-the-art chemical probes.

Deep image prior cine MR fingerprinting with B1 + spin history correction

PURPOSE

To develop a deep image prior (DIP) reconstruction for B1 + -corrected 2D cine MR fingerprinting (MRF).

METHODS

The proposed method combines low-rank (LR) modeling with a DIP to generate cardiac phase-resolved parameter maps without motion correction, employing self-supervised training to enforce consistency with undersampled spiral k-space data. Two implementations were tested: one approach (DIP) for cine T1 , T2 , and M0 mapping, and a second approach (DIP with effective B1 + estimation [DIP-B1]) that also generated an effective B1 + map to correct for errors due to RF transmit inhomogeneities, through-plane motion, and blood flow. Cine MRF data were acquired in 14 healthy subjects and four reconstructions were compared: LR, low-rank motion-corrected (LRMC), DIP, and DIP-B1. Results were compared to diastolic ECG-triggered MRF, MOLLI, and T2 -prep bSSFP. Additionally, bright-blood and dark-blood images calculated from cine MRF maps were used to quantify ventricular function and compared to reference cine measurements.

RESULTS

DIP and DIP-B1 outperformed other cine MRF reconstructions with improved noise suppression and delineation of high-resolution details. Within-segment variability in the myocardium (reported as the coefficient of variation for T1 /T2 ) was lowest for DIP-B1 (2.3/8.3%) followed by DIP (2.7/8.7%), LRMC (3.5/10.5%), and LR (15.3/39.6%). Spatial homogeneity improved with DIP-B1 having the lowest intersegment variability (2.6/4.1%). The mean bias in ejection fraction was -1.1% compared to reference cine scans.

CONCLUSION

A DIP reconstruction for 2D cine MRF enabled cardiac phase-resolved mapping of T1 , T2 , M0 , and the effective B1 + with improved noise suppression and precision compared to LR and LRMC.

The future of cardiovascular magnetic resonance: All-in-one vs. real-time (Part 1)

Cardiovascular magnetic resonance (CMR) protocols can be lengthy and complex, which has driven the research community to develop new technologies to make these protocols more efficient and patient-friendly. Two different approaches to improving CMR have been proposed, specifically "all-in-one" CMR, where several contrasts and/or motion states are acquired simultaneously, and "real-time" CMR, in which the examination is accelerated to avoid the need for breathholding and/or cardiac gating. The goal of this two-part manuscript is to describe these two different types of emerging rapid CMR. To this end, the vision of each is described, along with techniques which have been devised and tested along the pathway of clinical implementation. The pros and cons of the different methods are presented, and the remaining open needs of each are detailed. Part 1 will tackle the "all-in-one" approaches, and Part 2 the "real-time" approaches along with an overall summary of these emerging methods.

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.

Cardiovascular magnetic resonance (CMR) and positron emission tomography (PET) imaging in the diagnosis and follow-up of patients with acute myocarditis and chronic inflammatory cardiomyopathy : A review paper with practical recommendations on behalf of the European Society of Cardiovascular Radiology (ESCR)

Advanced cardiac imaging techniques such as cardiovascular magnetic resonance (CMR) and positron emission tomography (PET) are widely used in clinical practice in patients with acute myocarditis and chronic inflammatory cardiomyopathies (I-CMP). We aimed to provide a review article with practical recommendations from the European Society of Cardiovascular Radiology (ESCR), in order to guide physicians in the use and interpretation of CMR and PET in clinical practice both for acute myocarditis and follow-up in chronic forms of I-CMP.

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.

Assessment of MRF for simultaneous T1 and T2 quantification and water-fat separation in the liver at 0.55 T

OBJECTIVE

The goal of this work was to assess the feasibility of performing MRF in the liver on a 0.55 T scanner and to examine the feasibility of water-fat separation using rosette MRF at 0.55 T.

MATERIALS AND METHODS

Spiral and rosette MRF sequences were implemented on a commercial 0.55 T scanner. The accuracy of both sequences in T1 and T2 quantification was validated in the ISMRM/NIST system phantom. The efficacy of rosette MRF in water-fat separation was evaluated in simulations and water/oil phantoms. Both spiral and rosette MRF were performed in the liver of healthy subjects.

RESULTS

In the ISMRM/NIST phantom, both spiral and rosette MRF achieved good agreement with reference values in T1 and T2 measurements. In addition, rosette MRF enables water-fat separation and can generate water- and fat- specific T1 maps, T2 maps, and proton density images from the same dataset for a spatial resolution of 1.56 × 1.56 × 5mm3 within the acquisition time of 15 s.

CONCLUSION

It is feasible to measure T1 and T2 simultaneously in the liver using MRF on a 0.55 T system with lower performance gradients compared to state-of-the-art 1.5 T and 3 T systems within an acquisition time of 15 s. In addition, rosette MRF enables water-fat separation along with T1 and T2 quantification with no time penalty.

Joint Cardiac T1 Mapping and Cardiac Cine Using Manifold Modeling

The main focus of this work is to introduce a single free-breathing and ungated imaging protocol to jointly estimate cardiac function and myocardial T1 maps. We reconstruct a time series of images corresponding to k-space data from a free-breathing and ungated inversion recovery gradient echo sequence using a manifold algorithm. We model each image in the time series as a non-linear function of three variables: cardiac and respiratory phases and inversion time. The non-linear function is realized using a convolutional neural networks (CNN) generator, while the CNN parameters, as well as the phase information, are estimated from the measured k-t space data. We use a dense conditional auto-encoder to estimate the cardiac and respiratory phases from the central multi-channel k-space samples acquired at each frame. The latent vectors of the auto-encoder are constrained to be bandlimited functions with appropriate frequency bands, which enables the disentanglement of the latent vectors into cardiac and respiratory phases, even when the data are acquired with intermittent inversion pulses. Once the phases are estimated, we pose the image recovery as the learning of the parameters of the CNN generator from the measured k-t space data. The learned CNN generator is used to generate synthetic data on demand by feeding it with appropriate latent vectors. The proposed approach capitalizes on the synergies between cine MRI and T1 mapping to reduce the scan time and improve patient comfort. The framework also enables the generation of synthetic breath-held cine movies with different inversion contrasts, which improves the visualization of the myocardium. In addition, the approach also enables the estimation of the T1 maps with specific phases, which is challenging with breath-held approaches.

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.

Recent technologies in cardiac imaging

Cardiac imaging allows physicians to view the structure and function of the heart to detect various heart abnormalities, ranging from inefficiencies in contraction, regulation of volumetric input and output of blood, deficits in valve function and structure, accumulation of plaque in arteries, and more. Commonly used cardiovascular imaging techniques include x-ray, computed tomography (CT), magnetic resonance imaging (MRI), echocardiogram, and positron emission tomography (PET)/single-photon emission computed tomography (SPECT). More recently, even more tools are at our disposal for investigating the heart's physiology, performance, structure, and function due to technological advancements. This review study summarizes cardiac imaging techniques with a particular interest in MRI and CT, noting each tool's origin, benefits, downfalls, clinical application, and advancement of cardiac imaging in the near future.

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.

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.

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.

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.

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.

MR fingerprinting of the prostate

Multiparametric magnetic resonance imaging (mpMRI) has been adopted as the key tool for detection, localization, characterization, and risk stratification of patients suspected to have prostate cancer. Despite advantages over systematic biopsy, the interpretation of prostate mpMRI has limitations including a steep learning curve, leading to considerable interobserver variation. There is growing interest in clinical translation of quantitative imaging techniques for more objective lesion assessment. However, traditional mapping techniques are slow, precluding their use in the clinic. Magnetic resonance fingerprinting (MRF) is an efficient approach for quantitative maps of multiple tissue properties simultaneously. The T1 and T2 values obtained with MRF have been validated with phantom studies as well as in normal volunteers and patients. Studies have shown that MRF-derived T1 and T2 along with ADC values are all significant independent predictors in the differentiation between normal prostate tissue and prostate cancer, and hold promise in differentiating low and intermediate/high-grade cancers. This review seeks to introduce the basics of the prostate MRF technique, discuss the potential applications of prostate MRF for the characterization of prostate cancer, and describes ongoing areas of research.

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.

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.

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.

Generalized low-rank nonrigid motion-corrected reconstruction for MR fingerprinting

PURPOSE

Develop a novel low-rank motion-corrected (LRMC) reconstruction for nonrigid motion-corrected MR fingerprinting (MRF).

METHODS

Generalized motion-corrected (MC) reconstructions have been developed for steady-state imaging. Here we extend this framework to enable nonrigid MC for transient imaging applications with varying contrast, such as MRF. This is achieved by integrating low-rank dictionary-based compression into the generalized MC model to reconstruct MC singular images, reducing motion artifacts in the resulting parametric maps. The proposed LRMC reconstruction was applied for cardiac motion correction in 2D myocardial MRF (T₁ and T₂ ) with extended cardiac acquisition window (~450 ms) and for respiratory MC in free-breathing 3D myocardial and 3D liver MRF. Experiments were performed in phantom and 22 healthy subjects. The proposed approach was compared with reference spin echo (phantom) and with 2D electrocardiogram-triggered/breath-hold MOLLI and T₂ gradient-and-spin echo conventional maps (in vivo 2D and 3D myocardial MRF).

RESULTS

Phantom results were in general agreement with reference spin-echo measurements, presenting relative errors of approximately 5.4% and 5.5% for T₁ and short T₂ (<100 ms), respectively. The proposed LRMC MRF reduced residual blurring artifacts with respect to no MC for cardiac or respiratory motion in all cases (2D and 3D myocardial, 3D abdominal). In 2D myocardial MRF, left-ventricle T₁ values were 1150 ± 41 ms for LRMC MRF and 1010 ± 56 ms for MOLLI; T₂ values were 43.8 ± 2.3 ms for LRMC MRF and 49.5 ± 4.5 ms for T₂ gradient and spin echo. Corresponding measurements for 3D myocardial MRF were 1085 ± 30 ms and 1062 ± 29 ms for T₁ , and 43.5 ± 1.9 ms and 51.7 ± 1.7 ms for T₂ . For 3D liver, LRMC MRF measured liver T₁ at 565 ± 44 ms and liver T₂ at 35.4 ± 2.4 ms.

CONCLUSION

The proposed LRMC reconstruction enabled generalized (nonrigid) MC for 2D and 3D MRF, both for cardiac and respiratory motion. The proposed approach reduced motion artifacts in the MRF maps with respect to no motion compensation and achieved good agreement with reference measurements.

Quantitative T1 and T2 mapping by magnetic resonance fingerprinting (MRF) of the placenta before and after maternal hyperoxia

INTRODUCTION

MR relaxometry has been used to assess placental exchange function, but methods to date are not sufficiently fast to be robust to placental motion. Magnetic resonance fingerprinting (MRF) permits rapid, voxel-wise, intrinsically co-registered T₁ and T₂ mapping. After characterizing measurement error, we scanned pregnant women during air and oxygen breathing to demonstrate MRF's ability to detect placental oxygenation changes.

METHODS

The accuracy of FISP-based, sliding-window reconstructed MRF was tested on phantoms. MRF scans in 9-s breath holds were acquired at 3T in 31 pregnant women during air and oxygen breathing. A mixed effects model was used to test for changes in placenta relaxation times between physiological states, to assess the dependency on gestational age (GA), and the impact of placental motion.

RESULTS

MRF estimates of known phantom relaxation times resulted in mean absolute errors for T₁ of 92 ms (4.8%), but T₂ was less accurate at 16 ms (13.6%). During normoxia, placental T₁ = 1825 ± 141 ms (avg ± standard deviation) and T₂ = 60 ± 16 ms (gestational age range 24.3-36.7, median 32.6 weeks). In the statistical model, placental T₂ rose and T₁ remained contant after hyperoxia, and no GA dependency was observed for T₁ or T₂.

DISCUSSION

Well-characterized, motion-robust MRF was used to acquire T₁ and T₂ maps of the placenta. Changes with hyperoxia are consistent with a net increase in oxygen saturation. Toward the goal of whole-placenta quantitative oxygenation imaging over time, we aim to implement 3D MRF with integrated motion correction to improve T₂ accuracy.

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.

Rapid B1-Insensitive MR Fingerprinting for Quantitative Kidney Imaging

Background MR fingerprinting (MRF) provides rapid and simultaneous quantification of multiple tissue parameters in a single scan. Purpose To evaluate a rapid kidney MRF technique at 3.0 T in phantoms, healthy volunteers, and patients. Materials and Methods A 15-second kidney MRF acquisition was designed with 12 acquisition segments, a range of low flip angles (5°-12°), multiple magnetization preparation schema (T1, T2, and fat suppression), and an undersampled spiral trajectory. This technique was first validated in vitro using standardized T1 and T2 phantoms. Kidney T1 and T2 maps were then obtained for 10 healthy adult volunteers (mean age ± standard deviation, 35 years ± 13; six men) and three pediatric patients with autosomal recessive polycystic kidney disease (ARPKD) (mean age, 10 years ± 3; two boys) between August 2019 and October 2020 to evaluate the method in vivo. Results Results in nine phantoms showed good agreement with spin-echo-based T1 and T2 values (R2 > 0.99). In vivo MRF kidney T1 and T2 assessments in healthy adult volunteers (cortex: T1, 1362 msec ± 5; T2, 64 msec ± 5; medulla: T1, 1827 msec ± 94; T2, 69 msec ± 3) were consistent with values in the literature but with improved precision in comparison with prior MRF implementations. In vivo MRF-based kidney T1 and T2 values with and without B1 correction were in good agreement (R2 > 0.96, P < .001), demonstrating limited sensitivity to B1 field inhomogeneities. Additional MRF reconstructions using the first nine segments of the MRF profiles (11-second acquisition time) were in good agreement with the reconstructions using 12 segments (15-second acquisition time) (R2 > 0.87, P < .001). Repeat kidney MRF scans for the three patients with ARPKD on successive days also demonstrated good reproducibility (T1 and T2: <3% difference). Conclusion A kidney MR fingerprinting method provided in vivo kidney T1 and T2 maps at 3.0 T in a single breath hold with improved precision and no need for B1 correction. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Laustsen in this issue.

Magnetic resonance fingerprinting: an overview

Magnetic resonance fingerprinting (MRF) is an evolving quantitative MRI framework consisting of unique data acquisition, processing, visualization, and interpretation steps. MRF is capable of simultaneously producing multiple high-resolution property maps including T1, T2, M0, ADC, and T2* measurements. While a relatively new technology, MRF has undergone rapid development for a variety of clinical applications from brain tumor characterization and epilepsy imaging to characterization of prostate cancer, cardiac imaging, among others. This paper will provide a brief overview of current state of MRF technology including highlights of technical and clinical advances. We will conclude with a brief discussion of the challenges that need to be overcome to establish MRF as a quantitative imaging biomarker.

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.

Magnetic resonance fingerprinting: from evolution to clinical applications

In 2013, Magnetic Resonance Fingerprinting (MRF) emerged as a method for fast, quantitative Magnetic Resonance Imaging. This paper reviews the current status of MRF up to early 2020 and aims to highlight the advantages MRF can offer medical imaging professionals. By acquiring scan data as pseudorandom samples, MRF elicits a unique signal evolution, or 'fingerprint', from each tissue type. It matches 'randomised' free induction decay acquisitions against pre-computed simulated tissue responses to generate a set of quantitative images of T1 , T2 and proton density (PD) with co-registered voxels, rather than as traditional relative T1 - and T2 -weighted images. MRF numeric pixel values retain accuracy and reproducibility between 2% and 8%. MRF acquisition is robust to strong undersampling of k-space. Scan sequences have been optimised to suppress sub-sampling artefacts, while artificial intelligence and machine learning techniques have been employed to increase matching speed and precision. MRF promises improved patient comfort with reduced scan times and fewer image artefacts. Quantitative MRF data could be used to define population-wide numeric biomarkers that classify normal versus diseased tissue. Certification of clinical centres for MRF scan repeatability would permit numeric comparison of sequential images for any individual patient and the pooling of multiple patient images across large, cross-site imaging studies. MRF has to date shown promising results in early clinical trials, demonstrating reliable differentiation between malignant and benign prostate conditions, and normal and sclerotic hippocampal tissue. MRF is now undergoing small-scale trials at several sites across the world; moving it closer to routine clinical application.

Fast and accurate calculation of myocardial T1 and T2 values using deep learning Bloch equation simulations (DeepBLESS)

PURPOSE

To propose and evaluate a deep learning model for rapid and accurate calculation of myocardial T1 /T2 values based on a previously proposed Bloch equation simulation with slice profile correction (BLESSPC) method.

METHODS

Deep learning Bloch equation simulations (DeepBLESS) models are proposed for rapid and accurate T1 estimation for the MOLLI T1 mapping sequence with balanced SSFP readouts and T1 /T2 estimation for a radial simultaneous T1 and T2 mapping (radial T1 -T2 ) sequence. The DeepBLESS models were trained separately based on simulated radial T1 -T2 and MOLLI data, respectively. The DeepBLESS T1 -T2 estimation accuracy was evaluated based on simulated data with different noise levels. The DeepBLESS model was compared with BLESSPC in simulation, phantom, and in vivo studies for the MOLLI sequence at 1.5 T and radial T1 -T2 sequence at 3 T.

RESULTS

After DeepBLESS was trained, in phantom studies, DeepBLESS and BLESSPC achieved similar accuracy and precision in T1 -T2 estimations for both MOLLI and radial T1 -T2 (P > .05). For in vivo, DeepBLESS and BLESSPC generated similar myocardial T1 /T2 values for radial T1 -T2 at 3 T (T1 : 1366 ± 31 ms for both methods, P > .05; T2 : 37.4 ms ± 0.9 ms for both methods, P > .05), and similar myocardial T1 values for the MOLLI sequence at 1.5 T (1044 ± 20 ms for both methods, P > .05). DeepBLESS generated a T1 /T2 map in less than 1 second.

CONCLUSION

The DeepBLESS model offers an almost instantaneous approach for estimating accurate T1 /T2 values, replacing BLESSPC for both MOLLI and radial T1 -T2 sequences, and is promising for multiparametric mapping in cardiac MRI.

Confounding factors in breast magnetic resonance fingerprinting: B 1 + , slice profile, and diffusion effects

PURPOSE

Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-plane B 1 + , slice profile and diffusion effects on T₁ and T₂ estimation in the female breast at 1.5T.

METHODS

Simulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE.

RESULTS

Theoretic analysis predicts T₁ under-/T₂ overestimation if the nominal flip angles are underestimated and inversely, T₁ under-/T₂ overestimation if omitting slice profile correction, and T₁ under-/T₂ underestimation if omitting diffusion in the signal model. Averaged over repeated signal simulations, including spiral aliasing artefacts affected precision more than accuracy. Strong in-plane B 1 + effects occurred in vivo, causing T₂ left-right inhomogeneity between both breasts. Their correction decreased the T₂ difference from 29 to 5 ms in FGT and from 29 to 9 ms in FT. Slice profile correction affected FGT T₂ most strongly, resulting in -22% smaller values. For the employed spoiler gradient strengths, diffusion did not affect the parameter maps, corresponding well with theoretic predictions.

CONCLUSION

Understanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.

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.

Deep learning reconstruction for cardiac magnetic resonance fingerprinting T1 and T2 mapping

PURPOSE

To develop a deep learning method for rapidly reconstructing T₁ and T₂ maps from undersampled electrocardiogram (ECG) triggered cardiac magnetic resonance fingerprinting (cMRF) images.

METHODS

A neural network was developed that outputs T₁ and T₂ values when given a measured cMRF signal time course and cardiac RR interval times recorded by an ECG. Over 8 million cMRF signals, corresponding to 4000 random cardiac rhythms, were simulated for training. The training signals were corrupted by simulated k-space undersampling artifacts and random phase shifts to promote robust learning. The deep learning reconstruction was evaluated in Monte Carlo simulations for a variety of cardiac rhythms and compared with dictionary-based pattern matching in 58 healthy subjects at 1.5T.

RESULTS

In simulations, the normalized root-mean-square error (nRMSE) for T₁ was below 1% in myocardium, blood, and liver for all tested heart rates. For T₂ , the nRMSE was below 4% for myocardium and liver and below 6% for blood for all heart rates. The difference in the mean myocardial T₁ or T₂ observed in vivo between dictionary matching and deep learning was 3.6 ms for T₁ and -0.2 ms for T₂ . Whereas dictionary generation and pattern matching required more than 4 min per slice, the deep learning reconstruction only required 336 ms.

CONCLUSION

A neural network is introduced for reconstructing cMRF T₁ and T₂ maps directly from undersampled spiral images in under 400 ms and is robust to arbitrary cardiac rhythms, which paves the way for rapid online display of cMRF maps.

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.

Prospective correction of patient-specific respiratory motion in myocardial T1 and T2 mapping

PURPOSE

Respiratory motion in cardiovascular MRI presents a challenging problem with many potential solutions. Current approaches require breath-holds, apply retrospective image registration, or significantly increase scan time by respiratory gating. Myocardial T₁ and T₂ mapping techniques are particularly sensitive to motion as they require multiple source images to be accurately aligned prior to the estimation of tissue relaxation. We propose a patient-specific prospective motion correction (PROCO) strategy that corrects respiratory motion on the fly with the goal of reducing the spatial variation of myocardial parametric mapping techniques.

METHODS

A rapid, patient-specific training scan was performed to characterize respiration-induced motion of the heart relative to a diaphragmatic navigator, and a parametric mapping pulse sequence utilized the resulting motion model to prospectively update the scan plane in real-time. Midventricular short-axis T₁ and T₂ maps were acquired under breath-hold or free-breathing conditions with and without PROCO in 7 healthy volunteers and 3 patients. T₁ and T₂ were measured in 6 segments and compared to reference standard breath-hold measurements using Bland-Altman analysis.

RESULTS

PROCO significantly reduced the spatial variation of parametric maps acquired during free-breathing, producing limits of agreement of -47.16 to 30.98 ms (T₁ ) and -1.35 to 4.02 ms (T₂ ), compared to -67.77 to 74.34 ms (T₁ ) and -2.21 to 5.62 ms (T₂ ) for free-breathing acquisition without PROCO.

CONCLUSION

Patient-specific respiratory PROCO method significantly reduced the spatial variation of myocardial T₁ and T₂ mapping, while allowing for 100% efficient free-breathing acquisitions.

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.

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.

Simultaneous Mapping of T1 and T2 Using Cardiac Magnetic Resonance Fingerprinting in a Cohort of Healthy Subjects at 1.5T

BACKGROUND

Cardiac MR fingerprinting (cMRF) is a novel technique for simultaneous T₁ and T₂ mapping.

PURPOSE

To compare T₁ /T₂ measurements, repeatability, and map quality between cMRF and standard mapping techniques in healthy subjects.

STUDY TYPE

Prospective.

POPULATION

In all, 58 subjects (ages 18-60). FIELD STRENGTH/SEQUENCE: cMRF, modified Look-Locker inversion recovery (MOLLI), and T₂ -prepared balanced steady-state free precession (bSSFP) at 1.5T.

ASSESSMENT

T₁ /T₂ values were measured in 16 myocardial segments at apical, medial, and basal slice positions. Test-retest and intrareader repeatability were assessed for the medial slice. cMRF and conventional mapping sequences were compared using ordinal and two alternative forced choice (2AFC) ratings.

STATISTICAL TESTS

Paired t-tests, Bland-Altman analyses, intraclass correlation coefficient (ICC), linear regression, one-way analysis of variance (ANOVA), and binomial tests.

RESULTS

Average T₁ measurements were: basal 1007.4±96.5 msec (cMRF), 990.0±45.3 msec (MOLLI); medial 995.0±101.7 msec (cMRF), 995.6±59.7 msec (MOLLI); apical 1006.6±111.2 msec (cMRF); and 981.6±87.6 msec (MOLLI). Average T₂ measurements were: basal 40.9±7.0 msec (cMRF), 46.1±3.5 msec (bSSFP); medial 41.0±6.4 msec (cMRF), 47.4±4.1 msec (bSSFP); apical 43.5±6.7 msec (cMRF), 48.0±4.0 msec (bSSFP). A statistically significant bias (cMRF T₁ larger than MOLLI T₁ ) was observed in basal (17.4 msec) and apical (25.0 msec) slices. For T₂ , a statistically significant bias (cMRF lower than bSSFP) was observed for basal (-5.2 msec), medial (-6.3 msec), and apical (-4.5 msec) slices. Precision was lower for cMRF-the average of the standard deviation measured within each slice was 102 msec for cMRF vs. 61 msec for MOLLI T₁ , and 6.4 msec for cMRF vs. 4.0 msec for bSSFP T₂ . cMRF and conventional techniques had similar test-retest repeatability as quantified by ICC (0.87 cMRF vs. 0.84 MOLLI for T₁ ; 0.85 cMRF vs. 0.85 bSSFP for T₂ ). In the ordinal image quality comparison, cMRF maps scored higher than conventional sequences for both T₁ (all five features) and T₂ (four features).

DATA CONCLUSION

This work reports on myocardial T₁ /T₂ measurements in healthy subjects using cMRF and standard mapping sequences. cMRF had slightly lower precision, similar test-retest and intrareader repeatability, and higher scores for map quality.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1 J. Magn. Reson. Imaging 2020;52:1044-1052.

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.

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.