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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., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: Respiratory motion-corrected coronary MR angiography (CMRA) has shown promise for assessing coronary disease. By incorporating coronal 2D image navigators (iNAVs), respiratory motion can be corrected for in a beat-to-beat basis using translational correction in the foot-head (FH) and right-left (RL) directions and in a bin-to-bin basis using non-rigid motion correction addressing the remaining FH, RL and anterior-posterior (AP) motion. However, with this approach beat-to-beat AP motion is not corrected for. In this work we investigate the effect of remaining beat-to-beat AP motion and propose a virtual 3D iNAV that exploits autofocus motion correction to enable beat-to-beat AP and improved RL intra-bin motion correction., Methods: Free-breathing 3D whole-heart CMRA was acquired using a 3-fold undersampled variable-density Cartesian trajectory. Beat-to-beat 3D translational respiratory motion was estimated from the 2D iNAVs in FH and RL directions, and in AP direction with autofocus assuming a linear relationship between FH and AP movement of the heart. Furthermore, motion in RL was also refined using autofocus. This virtual 3D (v3D) iNAV was incorporated in a non-rigid motion correction (NRMC) framework. The proposed approach was tested in 12 cardiac patients, and visible vessel length and vessel sharpness for the right (RCA) and left (LAD) coronary arteries were compared against 2D iNAV-based NRMC., Results: Average vessel sharpness and length in v3D iNAV NRMC was improved compared to 2D iNAV NRMC (vessel sharpness: RCA: 56 ± 1% vs 52 ± 11%, LAD: 49 ± 8% vs 49 ± 7%; visible vessel length: RCA: 5.98 ± 1.37 cm vs 5.81 ± 1.62 cm, LAD: 5.95 ± 1.85 cm vs 4.83 ± 1.56 cm), however these improvements were not statistically significant., Conclusion: The proposed virtual 3D iNAV NRMC reconstruction further improved NRMC CMRA image quality by reducing artefacts arising from residual AP motion, however the level of improvement was subject-dependent., (Copyright © 2022. Published by Elsevier Inc.)

Purpose: To accelerate non-rigid motion corrected coronary MR angiography (CMRA) reconstruction by developing a deep learning based non-rigid motion estimation network and combining this with an efficient implementation of the undersampled motion corrected reconstruction., Methods: Undersampled and respiratory motion corrected CMRA with overall short scans of 5 to 10 min have been recently proposed. However, image reconstruction with this approach remains lengthy, since it relies on several non-rigid image registrations to estimate the respiratory motion and on a subsequent iterative optimization to correct for motion during the undersampled reconstruction. Here we introduce a self-supervised diffeomorphic non-rigid respiratory motion estimation network, DiRespME-net, to speed up respiratory motion estimation. We couple this with an efficient GPU-based implementation of the subsequent motion-corrected iterative reconstruction. DiRespME-net is based on a U-Net architecture, and is trained in a self-supervised fashion, with a loss enforcing image similarity and spatial smoothness of the motion fields. Motion predicted by DiRespME-net was used for GPU-based motion-corrected CMRA in 12 test subjects and final images were compared to those produced by state-of-the-art reconstruction. Vessel sharpness and visible length of the right coronary artery (RCA) and the left anterior descending (LAD) coronary artery were used as metrics of image quality for comparison., Results: No statistically significant difference in image quality was found between images reconstructed with the proposed approach (MC:DiRespME-net) and a motion-corrected reconstruction using cubic B-splines (MC:Nifty-reg). Visible vessel length was not significantly different between methods (RCA: MC:Nifty-reg 5.7 ± 1.7 cm vs MC:DiRespME-net 5.8 ± 1.7 cm, P = 0.32; LAD: MC:Nifty-reg 7.0 ± 2.6 cm vs MC:DiRespME-net 6.9 ± 2.7 cm, P = 0.81). Similarly, no statistically significant difference between methods was observed in terms of vessel sharpness (RCA: MC:Nifty-reg 60.3 ± 7.2% vs MC:DiRespME-net 61.0 ± 6.8%, P = 0.19; LAD: MC:Nifty-reg 57.4 ± 7.9% vs MC:DiRespME-net 58.1 ± 7.5%, P = 0.27). The proposed approach achieved a 50-fold reduction in computation time, resulting in a total reconstruction time of approximately 20 s., Conclusions: The proposed self-supervised learning-based motion corrected reconstruction enables fast motion-corrected CMRA image reconstruction, holding promise for integration in clinical routine., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Dixon cardiac magnetic resonance fingerprinting (MRF) has been recently introduced to simultaneously provide water T 1 , water T 2 , 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), T 2 gradient spin echo (T 2 -GRASE), 6-echo gradient rewound echo (GRE), and Dixon cardiac MRF., Assessment: Dixon cardiac MRF precision was assessed through repeated scans against conventional MOLLI, T 2 -GRASE, and PDFF in phantom and 11 healthy subjects. Dixon cardiac MRF native T 1 , T 2 , FF, postcontrast T 1 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 T 1 , ~2.2 msec for T 2 , 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 T 2 -GRASE, respectively. No statistically significant differences in postcontrast T 1 (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 T 1 , T 2 , 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., (© 2020 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: To combine a 3D saturation-recovery-based myocardial T 1 mapping (3D SASHA) sequence with a 2D image navigator with fat excitation (fat-iNAV) to allow 3D T 1 maps with 100% respiratory scan efficiency and predictable scan time., Methods: Data from T 1 phantom and 10 subjects were acquired at 1.5T. For respiratory motion compensation, a 2D fat-iNAV was acquired before each 3D SASHA k-space segment to correct for 2D translational motion in a beat-to-beat fashion. The effect of the fat-iNAV on the 3D SASHA T1 estimation was evaluated on the T 1 phantom. For 3 representative subjects, the proposed free-breathing 3D SASHA with fat-iNAV was compared to the original implementation with the diaphragmatic navigator. The 3D SASHA with fat-iNAV was compared to the breath-hold 2D SASHA sequence in terms of accuracy and precision., Results: In the phantom study, the Bland-Altman plot shows that the 2D fat-iNAVs does not affect the T 1 quantification of the 3D SASHA acquisition (0 ± 12.5 ms). For the in vivo study, the 2D fat-iNAV permits to estimate the respiratory motion of the heart, while allowing for 100% scan efficiency, improving the precision of the T 1 measurement compared to non-motion-corrected 3D SASHA. However, the image quality achieved with the proposed 3D SASHA with fat-iNAV is lower compared to the original implementation, with reduced delineation of the myocardial borders and papillary muscles., Conclusions: We demonstrate the feasibility to combine the 3D SASHA T 1 mapping imaging sequence with a 2D fat-iNAV for respiratory motion compensation, allowing 100% respiratory scan efficiency and predictable scan time., (© 2019 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: To develop a free-running framework for 3D isotropic simultaneous myocardial T1/T2 mapping and cine imaging., Methods: Continuous data acquisition with 3D golden angle radial trajectory is used in conjunction with T2 preparation of varying echo times and inversion recovery (IR) pulses to enable simultaneous myocardial T1/T2 mapping and cine imaging. Data acquisition is retrospectively synchronized with ECG signal, and 1D respiratory self-navigation signal is extracted from the k-space center of all radial spokes. Respiratory binning is performed based on the estimated respiratory signal, enabling estimation and correction of 3D translational respiratory motion. Using high-dimensionality patch-based undersampled reconstruction with dictionary-based low-rank inversion, whole-heart T1/T2 maps and cine images can be generated with 2 mm isotropic spatial resolution. The proposed technique was validated in a standardised phantom and ten healthy subjects in comparison to conventional 2D imaging techniques., Results: Phantom T1 and T2 measurements demonstrated good agreement with 2D spin echo techniques. Septal T1 estimated with the proposed technique (1185.6 ± 49.8 ms) was longer than with a conventional breath-hold 2D IR-prepared sequence (1044.3 ± 26.7 ms), whereas T2 measurements (47.6 ± 2.5 ms) were lower than a breath-hold 2D gradient spin echo sequence (52.0 ± 1.8 ms). Precision of the proposed 3D mapping was higher than conventional 2D mapping techniques. Ejection fraction measured with the proposed 3D approach (63.8 ± 6.8%) agreed well with conventional breath-held multi-slice 2D cine (62.3 ± 6.4%)., Conclusions: The proposed technique provides co-registered 3D T1/T2 maps and cine images with isotropic spatial resolution from a single free-breathing scan, thereby providing a promising imaging tool for whole-heart myocardial tissue characterization and functional evaluation., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Purpose: To develop a free-running (free-breathing, retrospective cardiac gating) 3D myocardial T 1 mapping with isotropic spatial resolution., Methods: The free-running sequence is inversion recovery (IR)-prepared followed by continuous 3D golden angle radial data acquisition. 1D respiratory motion signal is extracted from the k-space center of all spokes and used to bin the k-space data into different respiratory states, enabling estimation and correction of 3D translational respiratory motion, whereas cardiac motion is recorded using electrocardiography and synchronized with data acquisition. 3D translational respiratory motion compensated T 1 maps at diastole and systole were generated with 1.5 mm isotropic spatial resolution with low-rank inversion and high-dimensionality patch-based undersampled reconstruction. The technique was validated against conventional methods in phantom and 9 healthy subjects., Results: Phantom results demonstrated good agreement (R 2 = 0.99) of T 1 estimation with reference method. Homogeneous systolic and diastolic 3D T 1 maps were reconstructed from the proposed technique. Diastolic septal T 1 estimated with the proposed method (1140 ± 36 ms) was comparable to the saturation recovery single-shot acquisition (SASHA) sequence (1153 ± 49 ms), but was higher than the modified Look-Locker inversion recovery (MOLLI) sequence (1037 ± 33 ms). Precision of the proposed method (42 ± 8 ms) was comparable to MOLLI (41 ± 7 ms) and improved with respect to SASHA (87 ± 19 ms)., Conclusions: The proposed free-running whole heart T 1 mapping method allows for reconstruction of isotropic resolution 3D T 1 maps at different cardiac phases, serving as a promising tool for whole heart myocardial tissue characterization., (© 2019 International Society for Magnetic Resonance in Medicine.)

Purpose: To develop a motion-corrected 3D flow-insensitive imaging approach interleaved T 2 prepared-inversion recovery (iT 2 prep-IR) for simultaneous lumen and wall visualization of the great thoracic vessels and cardiac structures., Methods: A 3D flow-insensitive approach for simultaneous cardiovascular lumen and wall visualization (iT 2 prep) has been previously proposed. This approach requires subject-dependent weighted subtraction to completely null the arterial blood signal in the black-blood volume. Here, we propose an (T 2 prep-IR) approach to improve wall visualization and remove need for weighted subtraction. The proposed sequence is based on the acquisition and direct subtraction of 2 interleaved 3D whole-heart data sets acquired with and without T 2 prep-IR preparation. Image navigators are acquired before data acquisition to enable 2D translational and 3D non-rigid motion correction allowing 100% respiratory scan efficiency. The proposed approach was evaluated in 10 healthy subjects and compared with the conventional 2D double inversion recovery (DIR) sequence and the 3D iT 2 prep sequence. Additionally, 5 patients with congenital heart disease were acquired to test the clinical feasibility of the proposed approach., Results: The proposed iT 2 prep-IR sequence showed improved blood nulling compared to both DIR and iT 2 prep techniques in terms of SNR (SNR blood = 6.9, 12.2, and 18.2, respectively) and contrast-to-noise-ratio (CNR myoc-blood = 28.4, 15.4, and 15.3, respectively). No statistical difference was observed between iT 2 prep-IR, iT 2 prep and DIR atrial and ventricular wall thickness quantification., Conclusion: The proposed interleaved T 2 prep-IR sequence enables the simultaneous lumen and wall visualization of cardiac structures and shows promising results in terms of SNR, CNR, and wall thickness measurement., (© 2019 International Society for Magnetic Resonance in Medicine.)

Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: To develop a free‐running (free‐breathing, retrospective cardiac gating) 3D myocardial T1 mapping with isotropic spatial resolution. Methods: The free‐running sequence is inversion recovery (IR)‐prepared followed by continuous 3D golden angle radial data acquisition. 1D respiratory motion signal is extracted from the k‐space center of all spokes and used to bin the k‐space data into different respiratory states, enabling estimation and correction of 3D translational respiratory motion, whereas cardiac motion is recorded using electrocardiography and synchronized with data acquisition. 3D translational respiratory motion compensated T1 maps at diastole and systole were generated with 1.5 mm isotropic spatial resolution with low‐rank inversion and high‐dimensionality patch‐based undersampled reconstruction. The technique was validated against conventional methods in phantom and 9 healthy subjects. Results: Phantom results demonstrated good agreement (R2 = 0.99) of T1 estimation with reference method. Homogeneous systolic and diastolic 3D T1 maps were reconstructed from the proposed technique. Diastolic septal T1 estimated with the proposed method (1140 ± 36 ms) was comparable to the saturation recovery single‐shot acquisition (SASHA) sequence (1153 ± 49 ms), but was higher than the modified Look‐Locker inversion recovery (MOLLI) sequence (1037 ± 33 ms). Precision of the proposed method (42 ± 8 ms) was comparable to MOLLI (41 ± 7 ms) and improved with respect to SASHA (87 ± 19 ms). Conclusions: The proposed free‐running whole heart T1 mapping method allows for reconstruction of isotropic resolution 3D T1 maps at different cardiac phases, serving as a promising tool for whole heart myocardial tissue characterization. [ABSTRACT FROM AUTHOR]