Josefa González-Carrillo

Cardiac magnetic resonance (CMR) is a non-invasive imaging technique that plays a crucial role in the diagnosis, risk stratification, and management of a broad spectrum of cardiovascular diseases. Its high spatial resolution and ability to provide multiparametric tissue characterization make it uniquely suited for evaluating both structural and functional cardiac abnormalities. This review provides a comprehensive and clinically oriented overview of CMR applications in adult cardiology, structured into six main areas: (1) myocardial scarring in ischemic and non-ischemic cardiomyopathies, (2) infiltrative myocardial diseases, (3) adult congenital heart disease, (4) valvular heart disease, (5) pulmonary hypertension and right ventricular morpho-functional evaluation, and (6) cardio-oncology. In addition, technical considerations are also discussed. Finally, recommendations from recent guidelines issued by main international societies are integrated to support clinical decision-making.

Background: Accurate segmentation of the left ventricular myocardium in cardiac MRI is essential for developing reliable deep learning models to diagnose left ventricular non-compaction cardiomyopathy (LVNC). This work focuses on improving the segmentation database used to train these models, enhancing the quality of myocardial segmentation for more precise model training. Methods: We present a semi-automatic framework that refines segmentations through three fundamental approaches: (1) combining neural network outputs with expert-driven corrections, (2) implementing a blob-selection method to correct segmentation errors and neural network hallucinations, and (3) employing a cross-validation process using the baseline U-Net model. Results: Applied to datasets from three hospitals, these methods demonstrate improved segmentation accuracy, with the blob-selection technique boosting the Dice coefficient for the Trabecular Zone by up to 0.06 in certain populations. Conclusions: Our approach enhances the dataset’s quality, providing a more robust foundation for future LVNC diagnostic models.

Accurate segmentation of the left ventricular myocardium in cardiac MRI is essential for developing reliable deep learning models to diagnose Left Ventricular Non-Compaction Cardiomyopathy (LVNC). This work focuses on improving the segmentation database used to train these models, enhancing the quality of myocardial segmentation for more precise model training. We present a semi-automatic framework that refines segmentations through three fundamental approaches: 1) Combining neural network outputs with expert-driven corrections, 2) Implementing a blob-selection method to correct segmentation errors and neural network hallucinations, and \textcolor{black}{3) Employing a cross-validation process using the baseline U-Net model. Applied to datasets from three hospitals, these methods demonstrate improved segmentation accuracy, with the blob-selection technique boosting the Dice coefficient for the trabecular zone up to 0.06 in certain populations. Our approach enhances the dataset’s quality, providing a more robust foundation for future LVNC diagnostic models.

Accurate diagnosis of Left Ventricular Noncompaction Cardiomyopathy (LVNC) is critical for proper patient treatment but remains challenging. This work improves LVNC detection by improving left ventricle segmentation in cardiac MR images. Trabeculated left ventricle indicates LVNC, but automatic segmentation is difficult. We present techniques to improve segmentation and evaluate their impact on LVNC diagnosis. Three main methods are introduced: (1) using full 800 × 800 MR images rather than 512 × 512; (2) a clustering algorithm to eliminate neural network hallucinations; (3) advanced network architectures including Attention U-Net, MSA-UNet, and U-Net++.Experiments utilize cardiac MR datasets from three different hospitals. U-Net++ achieves the best segmentation performance using 800 × 800 images, and it improves the mean segmentation Dice score by 0.02 over the baseline U-Net, the clustering algorithm improves the mean Dice score by 0.06 on the images it affected, and the U-Net++ provides an additional 0.02 mean Dice score over the baseline U-Net. For LVNC diagnosis, U-Net++ achieves 0.896 accuracy, 0.907 precision, and 0.912 F1-score outperforming the baseline U-Net. Proposed techniques enhance LVNC detection, but differences between hospitals reveal problems in improving generalization. This work provides validated methods for precise LVNC diagnosis.

Left ventricular non-compaction (LVNC) is defined by an increase of trabeculations in left ventricular (LV) endomyocardium. Although LVNC can be in isolation, an increase in hypertrabeculation often accompanies genetic cardiomyopathies. Current methods for quantification of LV trabeculae have limitations. Several improvements are proposed and implemented to enhance a software tool to quantify the trabeculae degree in the LV myocardium in an accurate and automatic way for a population of patients with genetic cardiomyopathies (QLVTHCI). The software tool is developed and evaluated for a population of 59 patients (470 end-diastole cardiac magnetic resonance images). This tool produces volumes of the compact sector and the trabecular area, the proportion between these volumes, and the left ventricular and trabeculated masses. Substantial enhancements are obtained over the manual process performed by cardiologists, so saving important diagnosis time. The parallelization of the detection of the external layer is proposed to ensure real-time processing of a patient, obtaining speed-ups from 7.5 to 1500 with regard to QLVTHCI and the manual process used traditionally by cardiologists. Comparing the method proposed with the fractal proposal to differentiate LVNC and non-LVNC patients among 27 subjects with previously diagnosed cardiomyopathies, QLVTHCI presents a full diagnostic accuracy, while the fractal criteria achieve 78%. Moreover, QLTVHCI can be installed and integrated in hospitals on request, whereas the high cost of the license of the fractal method per year of this tool has prevented reproducibility by other medical centers.

Aims: Hypertrophic cardiomyopathy (HCM) is often accompanied by increased trabeculated myocardium (TM)—which clinical relevance is unknown. We aim to measure the left ventricular (LV) mass and proportion of trabeculation in an HCM population and to analyze its clinical implication. Methods and Results: We evaluated 211 patients with HCM (mean age 47.8 ± 16.3 years, 73.0% males) with cardiac magnetic resonance (CMR) studies. LV trabecular and compacted mass were measured using dedicated software for automatic delineation of borders. Mean compacted myocardium (CM) was 160.0 ± 62.0 g and trabecular myocardium (TM) 55.5 ± 18.7 g. The percentage of trabeculated myocardium (TM%) was 26.7% ± 6.4%. Females had significantly increased TM% compared to males (29.7 ± 7.2 vs. 25.6 ± 5.8, p < 0.0001). Patients with LVEF < 50% had significantly higher values of TM% (30.2% ± 6.0% vs. 26.6% ± 6.4%, p = 0.02). Multivariable analysis showed that female gender and neutral pattern of hypertrophy were directly associated with TM%, while dynamic obstruction, maximal wall thickness and LVEF% were inversely associated with TM%. There was no association between TM% with arterial hypertension, physical activity, or symptoms. Atrial fibrillation and severity of hypertrophy were the only variables associated with cardiovascular death. Multivariable analysis failed to demonstrate any correlation between TM% and arrhythmias. Conclusions: Approximately 25% of myocardium appears non-compacted and can automatically be measured in HCM series. Proportion of non-compacted myocardium is increased in female, non-obstructives, and in those with lower contractility. The amount of trabeculation might help to identify HCM patients prone to systolic heart failure.