Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization

Abstract

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. ‘Time is brain’ is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in-vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions.

Low-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations

“…However, excessive quantum noise in low-mAs projection data acquisition would unavoidably lead to degraded images and hemodynamic parameter maps. To address this ill-posed problem, many approaches have been reported, including projection and image filtering techniques [5, 15, 16, 17], sequential-images iterative reconstruction [6], and parameter maps estimation by an iterative scheme with a strong regularization [18, 19, 20]. For example, Ma et al presented an iterative image reconstruction method based on maximum a posterior with an pre-contrast scan induced edge-preserving prior [6].…”

“…For example, Ma et al presented an iterative image reconstruction method based on maximum a posterior with an pre-contrast scan induced edge-preserving prior [6]. Fang et al presented a robust low-dose CT perfusion deconvolution method via tensor total-variation regularization [19]. Meanwhile, a major drawback of sequential-image or parameter map iterative reconstruction methods is the computational load caused by multiple re- and back-projection operations in image or parameter map domains.…”

Denoising magnetic resonance images using collaborative non-local means

“…This approach is not always practical due to the long acquisition time. The other is the software approach [2, 3, 4, 5, 6, 7, 8, 9], which uses computer algorithms to extract the true signals from noisy measurements. In this work, we focus on the second approach because it can be applied to existing data without requiring expensive equipment upgrades.…”

Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization: a preclinical study

“…Recently, Fang et al (2015) introduced a tensor total variation (TTV) regularization for low-dose cerebral perfusion CT (CPCT) deconvolution. Although the TTV regularization based deconvolution algorithm can achieve noticeable gains in cerebral perfusion hemodynamic maps estimation, the TTV regularization as an extension of conventional total variation (TV) regularization may suffer from loss of fine structures and contrast and may produce staircasing artifacts due to the assumption of isotropic edge property of TTV regularization (Liu et al 2012).…”