Dec 7, 2021 · This study modifies the traditional U-net and adds two sinogram feature losses of projection images—namely, continuity and consistency of ...
Dec 7, 2021 · Contemporary methods that use deep networks for completing metal-damaged sinogram data are limited to discontinuity at the boundaries of traces, ...
This study modifies the traditional U-net and adds two sinogram feature losses of projection images—namely, continuity and consistency of projection data at ...
This study modifies the traditional U-net and adds two sinogram feature losses of projection images—namely, continuity and consistency of projection data at ...
Masking the metal traces also ensures the stability and reliability of the unaffected data during metal artifacts reduction. The projection and reconstruction ...
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Dec 29, 2020 · We present a generalizable image and sinogram domain joint learning framework for metal artifact reduction in CT imaging, which integrates the merits of deep ...
Missing: Damaged | Show results with:Damaged
Apr 20, 2023 · A deep learning based metal artifact reduction algorithm (dl-MAR) was developed by simulation of metal implants on CT scans.
Dec 2, 2022 · Several studies have developed deep learning-based algorithms for MAR in the projection or image domain that focused on improving the accuracy ...
Completion of Metal-Damaged Traces Based on Deep Learning in Sinogram Domain for Metal Artifacts Reduction in CT Images · Engineering, Computer Science. Sensors.
Sep 16, 2020 · In this paper, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram ...
Missing: Damaged | Show results with:Damaged