Fast fully automatic segmentation of the severely abnormal human right ventricle from cardiovascular magnetic resonance images using a multi-scale 3D convolutional neural network

Cardiac magnetic resonance (CMR) is regarded as the reference examination for cardiac morphology in tetralogy of Fallot (ToF) patients allowing images of high spatial resolution and high contrast. The detailed knowledge of the right ventricular anatomy is critical in ToF management. The segmentation of the right ventricle (RV) in CMR images from ToF patients is a challenging task due to the high shape and image quality variability. In this paper we propose a fully automatic deep learning-based framework to segment the RV from CMR anatomical images of the whole heart. We adopt a 3D multi-scale deep convolutional neural network to identify pixels that belong to the RV. Our robust segmentation framework was tested on 26 ToF patients achieving a Dice similarity coefficient of 0.8281±0.1010 with reference to manual annotations performed by expert cardiologists. The proposed technique is also computationally efficient, which may further facilitate its adoption in the clinical routine

Native T1 mapping in the diagnosis of cardiac allograft rejection: A prospective histologically validated study

OBJECTIVES: This study aimed to determine the role of T1 mapping in identifying cardiac allograft rejection. BACKGROUND: Endomyocardial biopsy (EMBx), the current gold standard to diagnose cardiac allograft rejection, is associated with potentially serious complications. Cardiac magnetic resonance (CMR)-based T1 mapping detects interstitial edema and fibrosis, which are important markers of acute and chronic rejection. Therefore, T1 mapping can potentially diagnose cardiac allograft rejection noninvasively. METHODS: Patients underwent CMR within 24 h of EMBx. T1 maps were acquired at 1.5-T. EMBx-determined rejection was graded according to International Society of Heart and Lung Transplant (ISHLT) criteria. RESULTS: Of 112 biopsies with simultaneous CMR, 60 were classified as group 0 (ISHLT grade 0), 35 as group 1 (ISHLT grade 1R), and 17 as group 2 (2R, 3R, clinically diagnosed rejection, antibody-mediated rejection). Native T1 values in patients with grade 0 biopsies and left ventricular ejection fraction >60% (983 ± 42 ms; 95% confidence interval: 972 to 994) were comparable to values in nontransplant healthy control subjects (974 ± 45 ms; 95% confidence interval: 962 to 987). T1 values were significantly higher in group 2 (1,066 ± 78 ms) versus group 0 (984 ± 42 ms; p = 0.0001) and versus group 1 (1,001 ± 54 ms; p = 0.001). After excluding patients with an estimated glomerular filtration rate <50 ml/min/m2, there was a moderate correlation of log-transformed native T1 with high-sensitivity troponin T (r = 0.54, p < 0.0001) and pro-B-type natriuretic peptide (r = 0.67, p < 0.0001). Using a T1 cutoff value of 1,029 ms, the sensitivity, specificity, and negative predictive value were 93%, 79%, and 99%, respectively. CONCLUSIONS: Myocardial tissue characterization with T1 mapping displays excellent negative predictive capacity for the noninvasive detection of cardiac allograft rejection and holds promise to reduce substantially the EMBx requirement in cardiac transplant rejection surveillance