Identification of patients who are at a high risk for right ventricular failure (RVF) after left ventricular assist device (LVAD) implantation is of critical importance. Conventional tools for predicting RVF, including two-dimensional echocardiography, right heart catheterization (RHC), and clinical parameters, generally have limited sensitivity and specificity. We retrospectively examined the ability of computed tomography (CT) ventricular volume measures to identify patients who experienced RVF after LVAD implantation. Between September 2017 and November 2021, 92 patients underwent LVAD surgery at our institution. Preoperative CT-derived ventricular volumes were obtained in 20 patients. Patients who underwent CT evaluation had a similar demographics and rate of RVF after LVAD as patients who did not undergo cardiac CT imaging. In the study cohort, seven of 20 (35%) patients experienced RVF (2 unplanned biventricular assist device, 5 prolonged inotropic support). Computed tomography-derived right ventricular enddiastolic and end-systolic volume indices were the strongest predictors of RVF compared with demographic, echocardiographic, and RHC data with areas under the receiver operating curve of 0.79 and 0.76, respectively. Computed tomography volumetric assessment of RV size can be performed in patients evaluated for LVAD treatment. RV measures of size provide a promising means of pre-LVAD assessment for postoperative RV failure.
Nets (1), can perform semantic segmentation of medical images, including three-dimensional (3D) CT data (2). However, 3D image volumes are challenging to analyze given their large size and their associated large memory footprint, which limits the implementation of 3D architectures on commercially available graphics processing units (GPUs) to shallow designs (3,4). Although previous approaches have analyzed image volumes as a series of twodimensional sections (5-7), this process irrevocably disrupts the 3D content and/or information. Further, 3D approaches downsample and/or crop image volumes to keep 3D information intact (3,4). However, cropping reduces the extent of information available to the network and can lead to multiple inferences that may require postprediction combination, whereas downsampling is a lossy operation that decreases image fidelity, thereby limiting the accuracy of the resulting segmentation (8). Although alternative image representations, such as point clouds (9,10) and sparse tensors (11) exist, there is no obvious way to apply CNNs to these data structures.In this study, we explored an octree-based representation for 3D CT images that provides high data compression without sacrificing 3D content or spatial resolution. The approach maintains a grid structure that enables application of convolution-based neural network architectures. To adapt the framework to medical imaging, we introduced an intensity tolerance parameter in the octree subdivision algorithm to govern image compression. We found that across a range of compression levels, the octree-based representation preserved image and segmentation features better than spatial downsampling. Further, the octree representation enabled semantic segmentation with a 3D U-Net architecture at the native image resolution, which improved segmentation accuracy, especially at the object border. We demonstrated these findings in semantic segmentation of left ventricular (LV) and left atrial (LA) blood pools on clinical cardiac CT angiograms.
Materials and Methods
DatasetElectrocardiographically gated end-systolic and enddiastolic cardiac CT angiographic studies from 100 patients obtained between
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.