Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT

By Emil Y. Sidky, C. Kao, Xiaochuan Pan. Journal of X-Ray Science and Technology, 2006

1,244 citations recorded by the source corpus.

In practical applications of tomographic imaging, there are often challenges for image reconstruction due to under-sampling and insufficient data. In computed tomography (CT), for example, image reconstruction from few views would enable rapid scanning with a reduced x-ray dose delivered to the patient. Limited-angle problems are also of practical significance in CT. In this work, we develop and investigate an iterative image reconstruction algorithm based on the minimization of the image total variation (TV) that applies to divergent-beam CT. Numerical demonstrations of our TV algorithm are performed with various insufficient data problems in fan-beam CT. The TV algorithm can be generalized to cone-beam CT as well as other tomographic imaging modalities.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/fe0b4136cc39f9125dc9e68425053dad2e3ae65b

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

DART: A Practical Reconstruction Algorithm for Discrete Tomography (2011, background): “The problem of reconstructing images, or more general signals, from a small number of weighted sums of their values has recently attracted considerable interest in the field of Compressed Sensing [12], [13], [27], [28].”
Quantitative X-ray tomography (2014, background): “In such under-sampled cases iterative algorithms can produce substantially better images than FBP methods.(168) For few-phase objects, discrete tomography and compressed sensing algorithms can be very effective at reconstructing images from low numbers of projections.”
Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? (2009, methodology): “Aside from our own CT algorithm, adaptive-steepest-descent (ASD)POCS [62, 63], we know of only one other work that proposes a row-action algorithm for CS application [75].”
Prior image constrained compressed sensing (PICCS) (2008, positive): “Recently, numerical simulations have also been conducted to demonstrate that the norm minimization is a useful method for image reconstruction in x-ray CT (19, 32) and tomosynthesis (21-24).”
A limited-angle CT reconstruction method based on anisotropic TV minimization (2013, methodology): “Exemplary algorithms are POCS with TV minimization (POCS + TV) (Sidky et al 2006, 2009, Bian et al 2010), PICCS (Chen et al 2008, 2009, Tang et al 2009), simultaneous algebraic reconstruction technique (SART) with TV (SART + TV) (Yu and Wang 2009), soft-thresholding algorithms (Yu and Wang 2010, Xu…”
A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution (2018, methodology): “Kim et al. [24] employed reweighted non-local total-variation (R-NLTV) to replace total variation (TV) [11] to preserve image details.”
Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization (2008, methodology): “The algorithm to ﬁnd the solution to the constrained, TV-minimization problem is based on our previous algorithm presented in Sidky et al (2006a).”
Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms (2009, methodology): “Using simple high-contrast digital phantoms such as the high-contrast Shepp–Logan phantom, it has been demonstrated that objects can be accurately reconstructed with only ∼ 20 view angles (Candes et al 2006, Sidky et al 2006, Sidky and Pan 2008).”