Abstract
In computed tomography (CT), there are many situations where reconstruction has to be performed with sparse-view data. In sparse-view CT imaging, strong streak artifacts may appear in conventionally reconstructed images due to limited sampling rate that compromises image quality. Compressed sensing (CS) algorithm has shown potential to accurately recover images from highly undersampled data. In the past few years, total-variation-(TV-) based compressed sensing algorithms have been proposed to suppress the streak artifact in CT image reconstruction. In this paper, we propose an efficient compressed sensing-based algorithm for CT image reconstruction from few-view data where we simultaneously minimize three parameters: the ℓ 1 norm, total variation, and a least squares measure. The main feature of our algorithm is the use of two sparsity transforms—discrete wavelet transform and discrete gradient transform. Experiments have been conducted using simulated phantoms and clinical data to evaluate the performance of the proposed algorithm. The results using the proposed scheme show much smaller streaking artifacts and reconstruction errors than other conventional methods.
Highlights
X-ray computed tomography (CT) is extensively used clinically to evaluate patients with a variety of conditions
We investigated reconstructions from 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, up to 120-view datasets extracted from the full dataset, respectively, representing different levels of data sampling
Streaking artifacts are reduced in algebraic reconstruction technique (ART) and simultaneous ART (SART) reconstructions, we can still see them in smooth regions, as indicated by black arrows in Methods filtered backprojection (FBP) ART [17] SART [18] total variation (TV) [12] Proposed method
Summary
X-ray computed tomography (CT) is extensively used clinically to evaluate patients with a variety of conditions. By its nature, CT scans expose the patients to high X-ray radiation doses which can result in an increased lifetime risk of cancer [1, 2]. The radiation dose to the patients is proportional to the number of X-ray projections. Medical research makes extensive use of CT on the microscopic scale, known as micro-CT. Longitudinal studies on experimental animals such as rats, mice, and rabbits are restricted in resolution and image quality by radiation dose. The defacto standard for reconstruction on the commercial CT scanners is the filtered backprojection (FBP) algorithm, which typically requires a large number (300–1000) of angular views for yielding accurate reconstruction of the image object
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