Problem
Research questions and friction points this paper is trying to address.
detector response nonuniformity
ring artifacts
photon-counting CT
projection errors
residual stripe amplitudes
TRACE: Two-Stage Detector-Response Estimation With Angular Cosine Expansion for Ring Artifact Correction in Photon-Counting CT
🤖 AI Summary
本文提出TRACE方法，通过两阶段无监督正弦图分解估计并校正光子计数CT中的探测器响应非均匀性引起的环状伪影。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Two-Stage Detector-Response Estimation
Angular Cosine Expansion
Unsupervised Sinogram Decomposition
Discrete Cosine Transform (DCT)
Soft Orthogonality Constraint
🔎 Similar Papers
No similar papers found.
