Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization

By Emil Y. Sidky, Xiaochuan Pan. Physics in Medicine and Biology, 2008

647 citations recorded by the source corpus.

An iterative algorithm, based on recent work in compressive sensing, is developed for volume image reconstruction from a circular cone-beam scan. The algorithm minimizes the total variation (TV) of the image subject to the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative. The constraints are enforced by the use of projection onto convex sets (POCS) and the TV objective is minimized by steepest descent with an adaptive step-size. The algorithm is referred to as adaptive-steepest-descent-POCS (ASD-POCS). It appears to be robust against cone-beam artifacts, and may be particularly useful when the angular range is limited or when the angular sampling rate is low. The ASD-POCS algorithm is tested with the Defrise disk and jaw computerized phantoms. Some comparisons are performed with the POCS and expectation-maximization (EM) algorithms. Although the algorithm is presented in the context of circular cone-beam image reconstruction, it can also be applied to scanning geometries involving other x-ray source trajectories.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9ba83ac7450fcf2a3c59af0349e95a517a9f07e7

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution (2018, background): “The other strategy is to decrease the number of projections for a given scanning trajectory [11]–[13].”
PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities (2013, neutral): “For materials with a few phases of constant absorption, a classical penalization function f in tomographic reconstruction is the total variation of the image [9, 10, 11, 12, 13, 14], that is a `1 norm of the image gradient.”
Deep Convolutional Neural Network for Inverse Problems in Imaging (2016, background): “The class trivially includes denoising and deconvolution, but also includes MRI [38], X-ray CT [12], [14], and diffraction tomography (DT).”
LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT (2017, neutral): “In practice, with traditional IR methods, such as ASD-POCS, we must adjust the regularization parameter for every task, since the structures of images may be extremely different.”
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network (2018, neutral): “prior information includes non-negativity, maximum CT number, total variation [16], non-local means [17], dictionary learning [18], low rank [19], and their variants.”