Metal Artifact Reduction in Cone-Beam X-Ray CT via Ray Profile Correction
원문 초록
In computed tomography (CT), metal implants increase the inconsistencies between the measured data and the linear attenuation assumption made by analytic CT reconstruction algorithms. The inconsistencies give rise to dark and bright bands and streaks in the reconstructed image, collectively called metal artifacts. These artifacts make it difficult for radiologists to render correct diagnostic decisions. We describe a data-driven metal artifact reduction (MAR) algorithm for image-guided spine surgery that applies to scenarios in which a prior CT scan of the patient is available. We tested the proposed method with two clinical datasets that were both obtained during spine surgery. Using the proposed method, we were not only able to remove the dark and bright streaks caused by the implanted screws but we also recovered the anatomical structures hidden by these artifacts. This results in an improved capability of surgeons to confirm the correctness of the implanted pedicle screw placements.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Latent Space Consistency for Sparse-View CT Reconstruction
ResPF: Residual Poisson Flow for Efficient and Physically Consistent Sparse-View CT Reconstruction
Physiology-Informed Generative Multi-Task Network for Contrast-Free CT Perfusion
Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification
ResCovNet: A Deep Learning-Based Architecture For COVID-19 Detection From Chest CT Scan Images
SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network
토론
이 논문과 연결된 포럼 글이 아직 없습니다.