Journal of Cardiovascular Magnetic Resonance最新文献

Background: Carotid atherosclerosis is a major contributor in the etiology of ischemic stroke. Although intraplaque hemorrhage (IPH) is known to increase stroke risk and plaque burden, its long-term effects on plaque dynamics remain unclear. This study aimed to evaluate the long-term impact of IPH on carotid plaque burden progression using deep learning-based segmentation on multi-contrast magnetic resonance vessel wall imaging (VWI).

Methods: Twenty-eight asymptomatic subjects with carotid atherosclerosis underwent an average of 4.7 ± 0.6 VWI scans over 5.8 ± 1.1 years. Deep learning pipelines were used to segment the carotid vessel walls and IPH. Bilateral plaque progression was analyzed using correlation coefficients and generalized estimating equations. Associations between IPH occurrence, IPH volume, and plaque burden (%WV) progression were evaluated using linear mixed-effect models.

Results: IPH was detected in 23/50 of arteries at any time point. Of arteries without IPH at baseline, 11/39 developed new IPH that persisted, while 5/11 arteries with baseline IPH exhibited it throughout the study. Bilateral plaque growth was significantly correlated (r = 0.54, p < 0.001), but this symmetry was weakened in cases with IPH (r = 0.1, p = 0.62). Moreover, IPH presence or development at any point was associated with a 2.3% absolute increase in %WV on average within the affected artery (p < 0.001). The volume of IPH was also positively associated with increased %WV (p = 0.005).

Conclusions: Deep learning-based segmentation pipelines were utilized to identify IPH, quantify IPH volume, and measure their effects on carotid plaque burden during long-term follow-up. Findings demonstrated that IPH may persist for extended periods. While arteries without IPH demonstrated minimal progression under contemporary treatment, presence of IPH and greater IPH volume significantly accelerated long-term plaque growth.

Background: Late gadolinium enhancement (LGE) imaging is considered the imaging reference standard for the diagnosis of myocardial infarction and scarring. The aim of this study is to evaluate a free-breathing high-resolution 3D Dixon LGE imaging prototype with image navigation (iNAV) in chronic myocardial infarction on a 3T system.

Methods: Consecutive myocardial infarction patients were enrolled to undergo CMR examination between February 2024 and January 2025. LGE protocols included breath-hold 2D PSIR and free-breathing iNAV 3D Dixon acquisitions. Radiologist image quality scoring, contrast ratio (CR), quantitative LGE assessment, and scan time were obtained and reported. Paired t-tests, Wilcoxon signed-rank tests, and repeated measures ANOVA were used for the comparison.

Results: A total of 32 participants (50 years ± 11; 31 male, 1 female) were included. 3D LGE reduced scan time by 2m9s (3D: 4m34s [3m50s, 5m17s], 2D: 6m43s [5m17s, 7m41s], P < 0.001). Overall image quality showed no difference (3D: 4 [3, 4], 2D: 4 [3, 5], P = 0.474). 3D LGE showed a trend toward higher image quality scores (3D: 4 [3, 4], 2D: 3 [2, 4], P = 0.053) in patients with respiratory motion artifacts on 2D images. LGE-to-blood CR was significantly higher in the 3D LGE than the 2D LGE images (P < 0.001). LGE mass (P = 0.11) and LGE extent (P = 0.02) showed no significant difference between the 3D and 2D LGE datasets.

Conclusion: Free-breathing iNAV 3D Dixon LGE is feasible at 3T, achieving comparable image quality and scar quantification to 2D PSIR within shorter scan times. It improves CR and enables simultaneous assessment of myocardial fibrosis and fat infiltration.

Objectives: Cardiac Magnetic Resonance Imaging (CMRI), the gold standard approach for characterizing myocardial infarction (MI), frequently relies on Late Gadolinium Enhancement (LGE) using gadolinium-based contrast agents (GBCA). Whereas novel GBCAs targeting specific molecules have not yet entered clinical practice, chemical exchange saturation transfer (CEST) MRI shows promise for detecting various endogenous molecules. This study explored the potential of natural D-glucose as a biodegradable MRI contrast agent for imaging MI on day 7 by employing glucose-weighted CEST MRI (glucoCEST).

Methods and results: In vivo, the application of cardiac glucoCEST MTR_asym (asymmetric magnetization transfer ratio) mapping delineated distinct pre- and post-glucose infusion states in both healthy (n=8) and MI-induced mice (n=6) at 9.4T. This approach resulted in significant alterations in glucoCEST contrast, effectively identifying MI regions analogous to conventional LGE and immunohistochemical staining. Ex vivo mass spectrometry imaging confirmed elevated ¹³C-glucose and gadolinium accumulation in the MI region after exogenous administration, suggesting the potential of glucoCEST MRI for MI detection.

Conclusion: Our preclinical study on MI demonstrated that cardiac glucoCEST MRI utilizing natural D-glucose as a biodegradable contrast agent effectively differentiates between MI regions, Remote myocardium (RM), and healthy myocardium. The results were comparable to those obtained using LGE imaging.

Objective: Phosphorus-31 magnetic resonance spectroscopy (³¹P MRS) is the only non-invasive imaging modality that directly quantifies myocardial energy metabolism in vivo. While extensively studied, its readiness for clinical application in heart failure remains uncertain. This meta-analysis aimed to evaluate the association between myocardial phosphocreatine-to-ATP (PCr/ATP) ratio, measured by ³¹P MRS, and heart failure, as a step toward assessing its translational potential as a clinical biomarker.

Methods: We systematically reviewed studies published from Jan 1, 1990, to Dec 31, 2024, using PubMed, Embase, and Web of Science. Eligible studies included cohort studies and randomised controlled trials reporting PCr/ATP ratios in heart failure patients and healthy controls. A random-effects model was used to estimate pooled odds ratios. Risk of bias was assessed using the ROBINS-E tool.

Findings: Twenty-six observational studies met inclusion criteria; no randomised trials were identified. Meta-analysis showed a directionally consistent and substantial association between reduced PCr/ATP ratio and heart failure (odds ratio 7.62, 95% CI 4.90-11.85), with moderate between-study heterogeneity (I²=60% and the prediction interval is (1.35-42.92)). Studies at field strengths >1.5T showed reduced heterogeneity (I²=18.8%) with a comparable effect size (odds ratio 7.69, 95% CI 5.17-11.43). Pre-specified meta-regression did not identify significant moderators (age, female proportion, ejection fraction, NYHA class, field strength, or blood-pool correction; all p≥0.18).

Interpretation: These findings support a clinically sizable but heterogeneous association between impaired myocardial energy metabolism-measured by reduced PCr/ATP ratio using ³¹P MRS-and heart failure. The consistency across subgroups, including HFpEF, suggests potential utility in early-stage or diagnostically challenging heart failure. The results support inclusion of PCr/ATP in prospective studies aimed at validating its clinical utility and advancing metabolic imaging toward routine cardiovascular care.

Background: Cardiac magnetic resonance (CMR) is a reference-standard modality for heart diseases, although its clinical application is restricted by prolonged acquisition times. Recently, artificial intelligence (AI), particularly deep learning (DL), has exhibited the potential to accelerate the CMR acquisition through technological advances. Prospective validation of its diagnostic performance across multiple clinical sequences remains underexplored. This research aims to assess the functions of the compressed sensing artificial intelligence (CSAI) algorithm in accelerating CMR acquisition, enhancing image quality, and maintaining diagnostic accuracy versus conventional sensitivity encoding (SENSE) reconstruction.

Methods: A total of 105 participants scheduled for clinical CMR between February and August 2024 underwent both SENSE and CSAI-accelerated sequences containing Cine, T2 short TI inversion recovery (STIR), and late gadolinium enhancement (LGE) during a single session. The subjective image quality was assessed by a 5-point Likert scale. Quantitative image quality metrics were evaluated including SNR, CNR, edge sharpness, ventricular function, T2 signal intensity (SI) ratio, and LGE percentage.

Results: Using standard resolution, the acquisition time of CSAI-CMR was 57.4% lower than that of SENSE CMR (159.2 ± 22.4seconds vs 277.1 ± 30.4seconds; P <.001). Higher subjective scores could be found in CSAI-Cine and CSAI-T2 STIR compared with SENSE CMR (P <.001). Quantitative analyses demonstrated superior SNR and CNR across CSAI sequences in comparison with SENSE-CMR including Cine (SNR: 109.16 ± 20.36 vs. 102.52 ± 21.93, CNR: 58.98 ± 24.43 vs. 51.08 ± 23.37; both P<.001), T2 STIR (SNR: 98.17 ± 6.20 vs. 81.77 ± 11.15; P<.001), and LGE (SNR: 38.02 ± 7.90 vs. 32.54 ± 7.72, CNR: 22.24 ± 5.15 vs. 19.09 ± 4.22; both P<.001). Edge sharpness was significantly improved by using CSAI-CMR (0.141 ± 0.06 pixel⁻¹ vs 0.105 ± 0.04 pixel⁻¹; P <.01). Functional parameters, both T2 SI ratio and LGE percentage were comparable relatively (all P >.05).

Conclusion: DL reconstruction of CMR sequences reduced acquisition times by 57% and enhanced image quality compared to conventional SENSE reconstruction, while maintaining consistent quantitative parameters and diagnostic accuracy across all sequences. These results advocate integrating DL-accelerated workflows into clinical practice.

Background: Measurements of cardiac size and function drive clinical decisions. Left ventricle (LV) metrics can be derived from cardiac MR images by delineating the blood pool and myocardium, by either drawing a rounded contour to approximate the compacted myocardial border, or by delineating the papillary muscles and trabeculae (trabecular segmentation). There is no consensus as to which is best, particularly in the emergent AI era. We developed machine-learning (ML) approaches for both and compared them for clinically important metrics (error rate, precision, and prognosis).

Methods: Separate ML models were developed for rounded and trabecular segmentation, using U-net models trained on 1,923 subjects (mixed pathology, multiple scanners, multiple centres). Blood and myocardial volumes for each segmentation method were compared on 4,118 healthy UK biobank subjects. Model segmentation quality was evaluated subjectively on a real-world clinical dataset of 1,594 consecutive CMR scans, with all scans included regardless of image quality and artefacts. Scan-rescan precision was measured on a multi-centre, multi-disease dataset of 109 subjects scanned twice and compared to human performance. Finally, prognostication ability was evaluated on 1,215 clinical patients, using a primary outcome of all-cause mortality and hospitalisation with heart failure.

Results: Error rates (where a human disagreed by >1ml) were the same, occurring in 0.6% of images and 3.6% (1 in 28) of patients. In health, the mean EF was 4% higher for trabecular vs rounded segmentation. On test-retest data, there was no difference between rounded and trabecular ML models for precision, apart from end-diastolic and end systolic volume, which was better for rounded segmentations. ML rounded and trabecular precision exceeded clinician performance for EF. There were marginal differences in prognostication between rounded and trabecular models.

Conclusion: We developed an automated method for annotating papillary muscles and trabeculae from cardiac MR images with low error rates. We found higher precision than clinicians in ejection fraction. There was similar precision and prognostication to an ML rounded model with similarly low error rates. Findings support the feasibility of automated trabecular segmentation in clinical care and clinical trials.

Purpose: To develop a reconstruction framework for 3D real-time cine cardiovascular magnetic resonance (CMR) from highly undersampled data without requiring fully sampled training datasets.

Methods: We developed a multi-dynamic low-rank deep image prior (ML-DIP) framework that models spatial image content and deformation fields using separate neural networks. These networks are optimized per scan to reconstruct the dynamic image series directly from undersampled k-space data. ML-DIP was evaluated on (i) a 3D cine digital phantom with simulated premature ventricular contractions (PVCs), (ii) ten healthy subjects (including two scanned during both rest and exercise), and (iii) 12 patients with a history of PVCs. Phantom results were assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). In vivo performance was evaluated by comparing left-ventricular function quantification (against 2D real-time cine) and image quality (against 2D real-time cine and binning-based 5D-Cine).

Results: In the phantom study, ML-DIP achieved PSNR  > 29 dB and SSIM  > 0.90 for scan times as short as two minutes, while recovering cardiac motion, respiratory motion, and PVC events. In healthy subjects, ML-DIP yielded functional measurements comparable to 2D cine and higher image quality than 5D-Cine, including during exercise with high heart rates and bulk motion. In PVC patients, ML-DIP preserved beat-to-beat variability and reconstructed irregular beats, whereas 5D-Cine showed motion artifacts and information loss due to binning.

Conclusion: ML-DIP enables high-quality 3D real-time CMR with acceleration factors exceeding 1, 000 by learning low-rank spatial and motion representations from undersampled data, without relying on external fully sampled training datasets.