Assessing cardiac function from total-variation-regularized 4D C-arm CT in the presence of angular undersampling

Abstract

Time-resolved tomographic cardiac imaging using an angiographic C-arm device may support clinicians during minimally invasive therapy by enabling a thorough analysis of the heart function directly in the catheter laboratory. However, clinically feasible acquisition protocols entail a highly challenging reconstruction problem which suffers from sparse angular sampling of the trajectory. Compressed sensing theory promises that useful images can be recovered despite massive undersampling by means of sparsity-based regularization. For a multitude of reasons-most notably the desired reduction of scan time, dose and contrast agent required-it is of great interest to know just how little data is actually sufficient for a certain task. In this work, we apply a convex optimization approach based on primal-dual splitting to 4D cardiac C-arm computed tomography. We examine how the quality of spatially and temporally total-variation-regularized reconstruction degrades when using as few as [Formula: see text] projection views per heart phase. First, feasible regularization weights are determined in a numerical phantom study, demonstrating the individual benefits of both regularizers. Secondly, a task-based evaluation is performed in eight clinical patients. Semi-automatic segmentation-based volume measurements of the left ventricular blood pool performed on strongly undersampled images show a correlation of close to 99% with measurements obtained from less sparsely sampled data.

3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts

“…To suppress noise and improve edges in a X-CT reconstruction, we employ a TV denoising method where a target function consisting of a data fidelity term and a TV regularizing term is minimized. A computationally efficient implementation for solving such a minimization problem is based on the primal-dual optimization algorithm of Chambolle and Pock [15], and has recently been presented by several authors in the medical X-CT literature [16][17][18].…”

3D Hybrid Imaging for Structural and Congenital Heart Interventions in the Cath Lab

“…These developments include optimizations of the current cone-beam back projection algorithms to acquire 4D reconstructions that include motion estimation and temporal parameterization by acquisition time. [47][48][49] Combinations of 4DRA with quick post-processing to generate 4D reconstructions opens the way for live dynamic 3D roadmaps which could potentially further increase interventional accuracy.…”