An HPC Pipeline for Calcium Quantification of Aortic Root From Contrast-Enhanced CCT Scans

Precise assessment of calcification lesions in the Aortic Root (AR) is relevant for the success of the Transcatheter Aortic Valve Implantation (TAVI) procedure. To this end, the radiologists analyze the Cardiac Computed Tomography (CCT) scans of patients, and detect the position and extent of the calcium deposits. In this contribution, we develop a computationally efficient High-Performance Computing (HPC) system to detect, segment, and quantify volumes of calcium in contrast-enhanced CCTs, embedding in a three-step pipeline two 3D Convolutional Neural Networks (CNN) and a threshold adaptive filter. The first step crops the images to a bounding-box around the AR keeping the original resolution, the second builds the segmentation, and the third detects and measures the volume of the calcium lesions. Our system is trained on high-resolution contrast-CCTs routinely planned for the TAVI manually annotated by expert radiologists, and evaluated on a test-set of patients with different levels of calcifications. The accuracy achieved in segmenting the AR is approximately 92% for the test-set, while the average difference of calcium lesion volumes with respect to the radiologists measurements is about 0.49 mm3. Running on a 4X NVIDIA-V100 and an 8X NVIDIA-A100 GPU systems, we achieve a remarkable inference throughput of 17 and 70 CCT/sec respectively, and a linear scaling of computing performance. Our contribution provides an HPC system suitable for hospital premises installation and is able to aid radiologists in assessing the calcification level of patients undergoing the TAVI, making this process automated, fast and more reliable.