Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection

Objectives Parametric tissue mapping enables quantitative cardiac tissue characterization but is limited by inter-observer variability during manual delineation. Traditional approaches relying on average relaxation values and single cutoffs may oversimplify myocardial complexity. This study evaluates whether deep learning (DL) can achieve segmentation accuracy comparable to inter-observer variability, explores the utility of statistical features beyond mean T1/T2 values, and assesses whether machine learning (ML) combining multiple features enhances disease detection. Materials & Methods T1 and T2 maps were manually segmented. The test subset was independently annotated by two observers, and inter-observer variability was assessed. A DL model was trained to segment left ventricle blood pool and myocardium. Average (A), lower quartile (LQ), median (M), and upper quartile (UQ) were computed for the myocardial pixels and employed in classification by applying cutoffs or in ML. Dice similarity coefficient (DICE) and mean absolute percentage error evaluated segmentation performance. Bland-Altman plots assessed inter-user and model-observer agreement. Receiver operating characteristic analysis determined optimal cutoffs. Pearson correlation compared features from model and manual segmentations. F1-score, precision, and recall evaluated classification performance. Wilcoxon test assessed differences between classification methods, with p < 0.05 considered statistically significant. Results 144 subjects were split into training (100), validation (15) and evaluation (29) subsets. Segmentation model achieved a DICE of 85.4%, surpassing inter-observer agreement. Random forest applied to all features increased F1-score (92.7%, p < 0.001). Conclusion DL facilitates segmentation of T1/ T2 maps. Combining multiple features with ML improves disease detection.

Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery

Purpose: Static cardiac imaging such as late gadolinium enhancement, mapping, or 3-D coronary angiography require prior information, e.g., the phase during a cardiac cycle with least motion, called resting phase (RP). The purpose of this work is to propose a fully automated framework that allows the detection of the right coronary artery (RCA) RP within CINE series. Methods: The proposed prototype system consists of three main steps. First, the localization of the regions of interest (ROI) is performed. Second, as CINE series are time-resolved, the cropped ROI series over all time points are taken for tracking motions quantitatively. Third, the output motion values are used to classify RPs. In this work, we focused on the detection of the area with the outer edge of the cross-section of the RCA as our target. The proposed framework was evaluated on 102 clinically acquired dataset at 1.5T and 3T. The automatically classified RPs were compared with the ground truth RPs annotated manually by a medical expert for testing the robustness and feasibility of the framework. Results: The predicted RCA RPs showed high agreement with the experts annotated RPs with 92.7% accuracy, 90.5% sensitivity and 95.0% specificity for the unseen study dataset. The mean absolute difference of the start and end RP was 13.6 ${\pm}$ 18.6 ms for the validation study dataset (n=102). Conclusion: In this work, automated RP detection has been introduced by the proposed framework and demonstrated feasibility, robustness, and applicability for diverse static imaging acquisitions.

2-D Respiration Navigation Framework for 3-D Continuous Cardiac Magnetic Resonance Imaging

Continuous protocols for cardiac magnetic resonance imaging enable sampling of the cardiac anatomy simultaneously resolved into cardiac phases. To avoid respiration artifacts, associated motion during the scan has to be compensated for during reconstruction. In this paper, we propose a sampling adaption to acquire 2-D respiration information during a continuous scan. Further, we develop a pipeline to extract the different respiration states from the acquired signals, which are used to reconstruct data from one respiration phase. Our results show the benefit of the proposed workflow on the image quality compared to no respiration compensation, as well as a previous 1-D respiration navigation approach.