Low-dose CT reconstruction via edge-preserving total variation regularization

Abstract

High radiation dose in CT scans increases a lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with Total Variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, the low contrast structures tend to be smoothed out by the TV regularization, posing a great challenge for the TV method. To solve this problem, in this work we develop an iterative CT reconstruction algorithm with edge-preserving TV regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing an energy consisting of an edge-preserving TV norm and a data fidelity term posed by the x-ray projections. The edge-preserving TV term is proposed to preferentially perform smoothing only on non-edge part of the image in order to better preserve the edges, which is realized by introducing a penalty weight to the original total variation norm. During the reconstruction process, the pixels at edges would be gradually identified and given small penalty weight. Our iterative algorithm is implemented on GPU to improve its speed. We test our reconstruction algorithm on a digital NCAT phantom, a physical chest phantom, and a Catphan phantom. Reconstruction results from a conventional FBP algorithm and a TV regularization method without edge preserving penalty are also presented for comparison purpose. The experimental results illustrate that both TV-based algorithm and our edge-preserving TV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under the low dose context. Our edge-preserving algorithm is superior to the TV-based algorithm in that it can preserve more information of low contrast structures and therefore maintain acceptable spatial resolution.

Medical image processing on the GPU – Past, present and future

“…To reduce the memory requirements, Jia et al (2010) reformulated the functional such that it could be evaluated without the need of large matrix operations. The work was extended by Tian et al (2011b), who developed a GPU-accelerated version of edge-preserving TV in order to minimize unwanted smoothing of edges. The memory problem with large matrices was solved by instead using approaches normally used for FBP.…”

Sparse-view computed tomography image reconstruction via a combination of L1 and SL0 regularization

“…This disadvantage to some extent may lead to smoothed edges and blocky effects. Many efforts have been made to address this issue [15][16][17][18][19][20]. Chang, et al proposed a few-view reweighed sparsity hunting (FRESH) method for CT image reconstruction [15].…”

“…Chang, et al proposed a few-view reweighed sparsity hunting (FRESH) method for CT image reconstruction [15]. Tian, et al proposed an edge preserving TV (EPTV) model [16]. This model can preserve edges by bringing different weights in TV according to edges and homogeneous areas in an image.…”

Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior

“…Due to the assumption of isotropic edge property within TV minimization, the related algorithms often suffer from oversmoothing effects. Hence, the weighted-TV as an extension of the original one were proposed recently to address the aforementioned issue in sparse-view CT image reconstruction [20,23].…”