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Sinogram + image domain neural network approach for metal artifact reduction in low-dose cone-beam computed tomography

Sinogram + image domain neural network approach for metal artifact reduction in low-dose... Abstract.Purpose: Cone-beam computed tomography (CBCT) is commonly used in the operating room to evaluate the placement of surgical implants in relation to critical anatomical structures. A particularly problematic setting, however, is the imaging of metallic implants, where strong artifacts can obscure visualization of both the implant and surrounding anatomy. Such artifacts are compounded when combined with low-dose imaging techniques such as sparse-view acquisition.Approach: This work presents a dual convolutional neural network approach, one operating in the sinogram domain and one in the reconstructed image domain, that is specifically designed for the physics and setting of intraoperative CBCT to address the sources of beam hardening and sparse view sampling that contribute to metal artifacts. The networks were trained with images from cadaver scans with simulated metal hardware.Results: The trained networks were tested on images of cadavers with surgically implanted metal hardware, and performance was compared with a method operating in the image domain alone. While both methods removed most image artifacts, superior performance was observed for the dual-convolutional neural network (CNN) approach in which beam-hardening and view sampling effects were addressed in both the sinogram and image domain.Conclusion: The work demonstrates an innovative approach for eliminating metal and sparsity artifacts in CBCT using a dual-CNN framework which does not require a metal segmentation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Imaging SPIE

Sinogram + image domain neural network approach for metal artifact reduction in low-dose cone-beam computed tomography

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Publisher
SPIE
Copyright
© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)
ISSN
2329-4302
eISSN
2329-4310
DOI
10.1117/1.jmi.8.5.052103
Publisher site
See Article on Publisher Site

Abstract

Abstract.Purpose: Cone-beam computed tomography (CBCT) is commonly used in the operating room to evaluate the placement of surgical implants in relation to critical anatomical structures. A particularly problematic setting, however, is the imaging of metallic implants, where strong artifacts can obscure visualization of both the implant and surrounding anatomy. Such artifacts are compounded when combined with low-dose imaging techniques such as sparse-view acquisition.Approach: This work presents a dual convolutional neural network approach, one operating in the sinogram domain and one in the reconstructed image domain, that is specifically designed for the physics and setting of intraoperative CBCT to address the sources of beam hardening and sparse view sampling that contribute to metal artifacts. The networks were trained with images from cadaver scans with simulated metal hardware.Results: The trained networks were tested on images of cadavers with surgically implanted metal hardware, and performance was compared with a method operating in the image domain alone. While both methods removed most image artifacts, superior performance was observed for the dual-convolutional neural network (CNN) approach in which beam-hardening and view sampling effects were addressed in both the sinogram and image domain.Conclusion: The work demonstrates an innovative approach for eliminating metal and sparsity artifacts in CBCT using a dual-CNN framework which does not require a metal segmentation.

Journal

Journal of Medical ImagingSPIE

Published: Sep 1, 2021

Keywords: metal artifact reduction; cone-beam computed tomography; low-dose imaging; image-guided surgery; spine surgery

References