Dual-dictionary learning-based iterative image reconstruction for spectral computed tomography application

Zhao, Bo; Ding, Huanjun; Lu, Yang; Wang, Ge; Zhao, Jun; Molloi, Sabee

doi:10.1088/0031-9155/57/24/8217

Cited by 68 publications

(51 citation statements)

References 36 publications

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“…We have previously reported on a dual-dictionary learning based IR CT reconstruction technique. 45 However, the dual-dictionary learning technique requires previously built dictionaries with a reconstruction time of approximately 30 min for each CT slice. The TFIR technique is capable of reconstructing a sequence of related images that are more compressible than one image.…”

Section: Discussionmentioning

confidence: 99%

“…This phantom was also used in a previous study. 45 In this phantom, a cylinder with diameter of 13 cm and length of 2 cm was constructed of resin as the phantom base. The phantom also included an insert with 5 fine Al wires of various diameters (643, 813, 1020, 1290, and 1630 μm) and a cylinder of Delrin with diameter of 2.5 cm and length of 3 cm (Fig.…”

Section: Iic Image Acquisition and Reconstructionmentioning

confidence: 99%

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Tight‐frame based iterative image reconstruction for spectral breast CT

et al. 2013

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Purpose: To investigate tight-frame based iterative reconstruction (TFIR) technique for spectral breast computed tomography (CT) using fewer projections while achieving greater image quality. Methods: The experimental data were acquired with a fan-beam breast CT system based on a cadmium zinc telluride photon-counting detector. The images were reconstructed with a varying number of projections using the TFIR and filtered backprojection (FBP) techniques. The image quality between these two techniques was evaluated. The image's spatial resolution was evaluated using a high-resolution phantom, and the contrast to noise ratio (CNR) was evaluated using a postmortem breast sample. The postmortem breast samples were decomposed into water, lipid, and protein contents based on images reconstructed from TFIR with 204 projections and FBP with 614 projections. The volumetric fractions of water, lipid, and protein from the image-based measurements in both TFIR and FBP were compared to the chemical analysis. Results:The spatial resolution and CNR were comparable for the images reconstructed by TFIR with 204 projections and FBP with 614 projections. Both reconstruction techniques provided accurate quantification of water, lipid, and protein composition of the breast tissue when compared with data from the reference standard chemical analysis. Conclusions: Accurate breast tissue decomposition can be done with three fold fewer projection images by the TFIR technique without any reduction in image spatial resolution and CNR. This can result in a two-third reduction of the patient dose in a multislit and multislice spiral CT system in addition to the reduced scanning time in this system.

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Section: Discussionmentioning

confidence: 99%

Section: Iic Image Acquisition and Reconstructionmentioning

confidence: 99%

Tight‐frame based iterative image reconstruction for spectral breast CT

et al. 2013

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“…There have also been some studies on dual-dictionary methods [60,92] that rely on two dictionaries. The atoms of the two dictionaries are in one-to-one correspondence.…”

Section: Iterative Reconstruction Methodsmentioning

confidence: 99%

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography

Karimi¹,

Ward²

2016

Int J CARS

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“…By incorporating a non-local means algorithm, Yang et al proposed a variational framework in compressed sensing MR reconstruction [23]. Furthermore, Xu et al [24] proposed a patch-based dictionary learning algorithm for low-dose x-ray CT, and Bo et al proposed a patchbased dual dictionary learning algorithm for spectral CT applications [25], in which one dictionary from full dose image as well as another from low dose image were used for iterative reconstructions.…”

Section: Introductionmentioning

confidence: 99%

Sparse-View Spectral CT Reconstruction Using Spectral Patch-Based Low-Rank Penalty

Kim

Worstell³

et al. 2015

IEEE Trans. Med. Imaging

149

114

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Spectral computed tomography (CT) is a promising technique with the potential for improving lesion detection, tissue characterization, and material decomposition. In this paper, we are interested in kVp switching-based spectral CT that alternates distinct kVp X-ray transmissions during gantry rotation. This system can acquire multiple X-ray energy transmissions without additional radiation dose. However, only sparse views are generated for each spectral measurement; and the spectra themselves are limited in number. To address these limitations, we propose a penalized maximum likelihood method using spectral patch-based low-rank penalty, which exploits the self-similarity of patches that are collected at the same position in spectral images. The main advantage is that the relatively small number of materials within each patch allows us to employ the low-rank penalty that is less sensitive to intensity changes while preserving edge directions. In our optimization formulation, the cost function consists of the Poisson log-likelihood for X-ray transmission and the nonconvex patch-based low-rank penalty. Since the original cost function is difficult to minimize directly, we propose an optimization method using separable quadratic surrogate and concave convex procedure algorithms for the log-likelihood and penalty terms, which results in an alternating minimization that provides a computational advantage because each subproblem can be solved independently. We performed computer simulations and a real experiment using a kVp switching-based spectral CT with sparse-view measurements, and compared the proposed method with conventional algorithms. We confirmed that the proposed method improves spectral images both qualitatively and quantitatively. Furthermore, our GPU implementation significantly reduces the computational cost.

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Dual-dictionary learning-based iterative image reconstruction for spectral computed tomography application

Cited by 68 publications

References 36 publications

Tight‐frame based iterative image reconstruction for spectral breast CT

Tight‐frame based iterative image reconstruction for spectral breast CT

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography

Sparse-View Spectral CT Reconstruction Using Spectral Patch-Based Low-Rank Penalty

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