Towards Improved Robustness of Low-Dose CT Perfusion Imaging Via Joint Estimation of Structural CT and Functional CBF Images

Viswanath P. Sudarshan, Vartika Sengar, Pavan Kumar Reddy, J. Gubbi, Arpan Pal
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Abstract

Dynamic computed tomography (CT) perfusion is a clinically-established imaging method for estimating cerebral perfusion in conditions such as stroke. Low-dose CT perfusion (CTP) imaging suffers from inherent low signal-to-noise ratio (SNR) that affects the quality and accuracy of the derived perfusion maps. We propose a framework to jointly estimate the structural CT images and the functional CBF map using a generalized sparsity prior suitable for low-dose acquisition schemes. We hypothesize that the joint estimation would improve image quality of both CT images and the CBF maps in comparison to image quality of CBF maps obtained through (i) independent two-stage process and (ii) the direct deconvolution methods with prior information. Through empirical analysis on two different in vivo datasets, we demonstrate the efficacy of our method over the state-of-the-art methods on multiple low-dose settings.
通过结构CT和功能CBF图像联合估计提高低剂量CT灌注成像的鲁棒性
动态计算机断层扫描(CT)灌注是一种临床建立的成像方法,用于估计脑卒中等疾病的脑灌注。低剂量CT灌注成像(CTP)固有的低信噪比(SNR)影响了所得灌注图的质量和准确性。我们提出了一个框架,使用适用于低剂量获取方案的广义稀疏性先验来联合估计结构CT图像和功能CBF图。我们假设,与通过(i)独立的两阶段处理和(ii)具有先验信息的直接反卷积方法获得的CBF图的图像质量相比,联合估计将提高CT图像和CBF图的图像质量。通过对两种不同体内数据集的实证分析,我们证明了我们的方法在多个低剂量设置上优于最先进的方法。
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