Assessing cardiac function from total-variation-regularized 4D C-arm CT in the presence of angular undersampling. (14th March 2017)

• Record Type: Journal Article
• Authors: Taubmann, O; Haase, V; Lauritsch, G; Zheng, Y; Krings, G; Hornegger, J; Maier, A
• Is Part Of: Physics in medicine & biology. Volume 62:Number 7(2017:Apr.)
• Page Start: 2762
• Page End: 2777
• Publication Date: 2017-03-14
• Subjects: C-arm computed tomography -- angular undersampling -- cardiac function -- 4D imaging -- total variation -- temporal regularization; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aa6241
• Languages: English
• ISSNs: 0031-9155

Abstract

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 6.9 ± 1.2 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.