Detecting and removing ring shadows improve photon counting CT images
TRACE: Two-Stage Detector-Response Estimation With Angular Cosine Expansion for Ring Artifact Correction in Photon-Counting CT
Computer Vision and Pattern Recognition
Summary
Ring-shaped artifacts make CT scan images blurry and less clear. The authors created a two-step method called TRACE that estimates and fixes these ring artifacts caused by uneven detector response. TRACE models stripe errors with simple patterns that change slowly as the scanner rotates. It improves image quality without needing example corrected images to learn from. Tests on animal scans show clearer images with details preserved, like soft tissue and fine bone structures.
What this means in practice
- •For medical imaging specialists: Improve quality of photon-counting CT images by reducing ring artifacts and preserving anatomical details using TRACE corrections.
- •For industrial ct operators: Enhance inspection accuracy in photon-counting CT scans by applying TRACE to remove detector-induced ring errors without additional calibration data.
Authors
Jigang Duan, Heran Wang, Ligen Shi, Zheng Sun, Ping Yang, Xing Zhao
Abstract
Detector response nonuniformity introduces systematic projection errors and ring artifacts in photon-counting detector computed tomography (PCD-CT). In measured PCD-CT data, residual stripe amplitudes vary slowly with projection angle, which fixed-bias models cannot adequately capture. We propose TRACE, a two-stage unsupervised sinogram decomposition method for estimating and correcting these response-related errors. TRACE represents stripes as a fixed bias plus low-order discrete cosine transform (DCT) components, using a small number of coefficients to describe angular variations at each detector element. A learnable analysis--synthesis architecture represents the ideal projections, while two-stage optimization separates them from fixed and then dynamic stripes. An angular-gradient soft orthogonality constraint suppresses correlated variations within the shared DCT gradient subspace, reducing the leakage of object structures into the artifact estimate. All parameters are optimized directly on the measured sinogram without paired training data. Experiments on measured QRM mouse phantom and porcine trotter data show that TRACE suppresses ring artifacts and improves image uniformity while preserving edge sharpness, soft-tissue texture, and trabecular detail.