A Joint Reconstruction and Segmentation Method for Limited-Angle X-Ray Tomography

Wei, Zenghui; Liu, Baodong; Dong, Bin; Wei, Long

doi:10.1109/access.2018.2800719

Cited by 14 publications

(14 citation statements)

References 55 publications

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“…Secondly, given the superior lesion region reconstruction performance demonstrated in the result sections, our framework could also potentially improve the projection data based Computer-Aided Diagnosis (CAD). Recently, there are increasing interests on combining limitedview reconstruction and CAD for a joint reconstruction-CAD network structure, and improved CAD performance is expected with such an end-to-end training strategy [39], [40]. We believe that our CasRedSCAN with high-quality lesion region reconstruction would provide new opportunities for these kinds of studies.…”

Section: Discussionmentioning

confidence: 99%

Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer

Zhou

Duncan

et al. 2021

IEEE Trans. Med. Imaging

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Limited view tomographic reconstruction aims to reconstruct a tomographic image from a limited number of projection views arising from sparse view or limited angle acquisitions that reduce radiation dose or shorten scanning time. However, such a reconstruction suffers from severe artifacts due to the incompleteness of sinogram. To derive quality reconstruction, previous methods use UNet-like neural architectures to directly predict the full view reconstruction from limited view data; but these methods leave the deep network architecture issue largely intact and cannot guarantee the consistency between the sinogram of the reconstructed image and the acquired sinogram, leading to a non-ideal reconstruction. In this work, we propose a cascaded residual dense spatial-channel attention network consisting of residual dense spatial-channel attention networks and projection data fidelity layers. We evaluate our methods on two datasets. Our experimental results on AAPM Low Dose CT Grand Challenge datasets demonstrate that our algorithm achieves a consistent and substantial improvement over the existing neural network methods on both limited angle reconstruction and sparse view reconstruction. In addition, our experimental results on Deep Lesion datasets demonstrate that our method is able to generate high-quality reconstruction for 8 major lesion types.

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Section: Discussionmentioning

confidence: 99%

Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer

Zhou

Duncan

et al. 2021

IEEE Trans. Med. Imaging

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“…Wei et al [ 62 ] proposed a joint reconstruction and segmentation method (JRSM) for limited-angle CT scans, which is directly performed on projection data. In their paper, the primal-dual hybrid gradient approach is modified for nonconvex piecewise constant Mumford-Shah (PCMS) model used for discrete value segmentation.…”

Section: Soft Computingmentioning

confidence: 99%

A Survey of Soft Computing Approaches in Biomedical Imaging

Devi

Singh

Tiwari

et al. 2021

Journal of Healthcare Engineering

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Medical imaging is an essential technique for the diagnosis and treatment of diseases in modern clinics. Soft computing plays a major role in the recent advances in medical imaging. It handles uncertainties and improves the qualities of an image. Until now, various soft computing approaches have been proposed for medical applications. This paper discusses various medical imaging modalities and presents a short review of soft computing approaches such as fuzzy logic, artificial neural network, genetic algorithm, machine learning, and deep learning. We also studied and compared each approach used for other imaging modalities based on the certain parameter used for the system evaluation. Finally, based on comparative analysis, the possible research strategies for further development are proposed. As far as we know, no previous work examined this issue.

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“…In [9] the authors suggested a joint reconstruction and segmentation method in a variational framework. Other techniques include graph cut [10], [11] and convex relaxation techniques [12]. These discrete-valued tomography methods relied on a regular image grid, while DALM does not restrict the solution on the image domain.…”

Section: A Related Workmentioning

confidence: 99%

“…Combining a data fidelity term of reprojection error and smoothness term (12), we aim at finding ν by solving the following minimization problem:…”

Section: B Graph Total Variationmentioning

confidence: 99%

DALM, Deformable Attenuation-Labeled Mesh for Tomographic Reconstruction and Segmentation

Koo

Dahl

2021

IEEE Trans. Comput. Imaging

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Most X-ray tomographic reconstruction methods represent a solution as an image on a regular grid. Such representation may be inefficient for reconstructing homogeneous objects from noisy or incomplete projections. Here, we propose a mesh-based method for reconstruction and segmentation of homogeneous objects directly from sinogram data. The outcome of our proposed method consists of curves outlining the regions of constant attenuation, and this output is represented using a labeled irregular triangle mesh. We find the solution by deforming the mesh to minimize the residual given by the sinogram data. Our method supports multiple materials, and allows for topological changes during deformation. An integral part of our algorithm is an efficient forward projection of the labeled mesh onto the sinogram domain. We initialize our algorithm based on graph total variation, also here taking advantage of the mesh representation. Experimental results on simulated datasets show that our method gives a compact representation of the reconstruction and also accurate segmentation results for challenging data with e.g. large noise, a small number of angles or problems with limited angle. We also demonstrate the result on real fan-beam data. The proposed geometric solution shows a further step towards using alternative representations for tomographic reconstruction.

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A Joint Reconstruction and Segmentation Method for Limited-Angle X-Ray Tomography

Cited by 14 publications

References 55 publications

Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer

Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer

A Survey of Soft Computing Approaches in Biomedical Imaging

DALM, Deformable Attenuation-Labeled Mesh for Tomographic Reconstruction and Segmentation

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