Image reconstruction model for limited-angle CT based on prior image induced relative total variation

Gong, Changcheng; Zeng, Li; Wang, Cheng-Xiang

doi:10.1016/j.apm.2019.05.020

Cited by 27 publications

(10 citation statements)

References 39 publications

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“…Other attempts at sparsity promoting variational models for limited-angle tomography rely on dictionary learning [80] or use a regulariser that promotes sparse solutions with respect to wavelets [90,94] or curvelets/shearlets [30,71]. One can further constrain a sparse solution against a given prior image as shown in [18,90,94,34].…”

Section: Variational Models For Reconstructionmentioning

confidence: 99%

Deep Microlocal Reconstruction for Limited-Angle Tomography

Andrade-Loarca¹,

Kutyniok²,

Öktem³

et al. 2021

Preprint

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We present a deep learning-based algorithm to jointly solve a reconstruction problem and a wavefront set extraction problem in tomographic imaging. The algorithm is based on a recently developed digital wavefront set extractor as well as the well-known microlocal canonical relation for the Radon transform. We use the wavefront set information about x-ray data to improve the reconstruction by requiring that the underlying neural networks simultaneously extract the correct ground truth wavefront set and ground truth image. As a necessary theoretical step, we identify the digital microlocal canonical relations for deep convolutional residual neural networks. We find strong numerical evidence for the effectiveness of this approach.

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Section: Variational Models For Reconstructionmentioning

confidence: 99%

Deep Microlocal Reconstruction for Limited-Angle Tomography

Andrade-Loarca¹,

Kutyniok²,

Öktem³

et al. 2021

Preprint

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“…Zhang et al studied an iterative reconstruction method based on the induction and improvement of the similarity between prior image and reconstructed image structures, which used L0 regularization of wavelet compact frame transform coefficients of reconstructed images to deal with limitedangle artifacts [21]. To further suppress noise and limitedangle artifacts, Gong et al proposed a prior image induced relative total variation reconstruction model that used the structure information of the prior image [22]. For incomplete data, high-quality images can be reconstructed using prior image information.…”

Section: Introductionmentioning

confidence: 99%

Guided Image Filtering Reconstruction Based on Total Variation and Prior Image for Limited-Angle CT

Shen

Gong

et al. 2020

IEEE Access

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For limited-angle computed tomography (CT) image reconstruction, the classical total variation (TV) based algorithms suffer from the limited-angle artifacts, because TV only used the gradient information of the image. The priori image constrained compressed sensing (PICCS) based reconstruction algorithms can reduce the limited-angle artifacts by using a priori image consistent with the target image. However, it is difficult to ensure the consistency of priori image and target image in practice. In order to reconstruct high quality image when the prior image is inconsistent with the target image, we proposed a guided image filter reconstruction based on TV and prior image (TVPI-G). In each iteration phase, our algorithm first performs a TV step (include simultaneous algebra reconstruction technique (SART) and TV) to get initiatory reconstruction image; Then, the results of TV iteration are combined with prior images to form an intermediate result; Finally, we use the guided image filter to modify the intermediate results with the TV result as the guide image. Numerical reconstruction results on simulation phantom with different intensities Poisson noise illustrates that our proposed TVPI-G algorithm is better than other comparison algorithms in both qualitative and quantitative aspects, including TV, PICCS, and SART guided image filtering.

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“…Minimizing L 0 norm effectively reduces the shading artifacts and does not penalize large image gradients, thus preserving image structures. For some limited-angle CT applications, some prior images are available for image reconstruction, so these images are incorporated into image reconstruction model to further improve image quality [9], [16]. In fact, using prior images can greatly improve reconstructed image quality, but these reconstruction methods often require accurate image registration that is difficult.…”

Section: Introductionmentioning

confidence: 99%

“…Recently developed relative total variation (RTV) is originally used to extract image structures from regular or irregular background [26]. In addition, RTV and its variant have been applied to different CT reconstruction problems [16], [27]. RTV is defined based on windowed total variation (WTV) and WIV.…”

Section: Introductionmentioning

confidence: 99%

Self-Guided Limited-Angle Computed Tomography Reconstruction Based on Anisotropic Relative Total Variation

Gong

Zeng

2020

IEEE Access

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In some computed tomography (CT) applications, limited-angle projections are used for image reconstruction, and traditional reconstruction methods, such as filtered back-projection (FBP) or simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images without prior knowledge assistance. For limited-angle CT reconstruction, total variation minimization (TVM) method is not conductive to recovering image structures. Image reconstruction methods based on anisotropic total variation (ATV) and reweighted anisotropic total variation (RwATV) can significantly reduce the shading artifacts using prior knowledge of the scanning angular range and image sparsity. However, using the knowledge of image sparsity does not further improve image quality in some applications. In this paper, we propose a new reconstruction method based on anisotropic relative total variation (ARTV) for limited-angle CT reconstruction. In ARTV, the windowed inherent variation (WIV) indicates the strength of structure information and WIV values are adaptively determined by local structure information. In limitedangle CT, particular scanning angular range urges us into exerting different strengths on different directions of ARTV. Experiments on FORBILD HEAD phantom, a thoracic image and real projections of a walnut are performed to test the new method. Experimental results show that the destroyed structures are recovered to some extent, and we acquire higher image quality compared to some existing limited-angle CT reconstruction methods. INDEX TERMS Computed tomography, image reconstruction, limited-angle computed tomography, anisotropic total variation, relative total variation.

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Image reconstruction model for limited-angle CT based on prior image induced relative total variation

Cited by 27 publications

References 39 publications

Deep Microlocal Reconstruction for Limited-Angle Tomography

Deep Microlocal Reconstruction for Limited-Angle Tomography

Guided Image Filtering Reconstruction Based on Total Variation and Prior Image for Limited-Angle CT

Self-Guided Limited-Angle Computed Tomography Reconstruction Based on Anisotropic Relative Total Variation

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