[논문 리뷰] Continuity-driven Synergistic Diffusion with Neural Priors for Ultra-Sparse-View CBCT Reconstruction

Junlin Wang, Jiancheng Fang외 3명|arXiv (Cornell University)|2026. 02. 08.

Medical Imaging Techniques and Applications인용 수 0

한 줄 요약

연속성 구동 확산 프레임워크와 신경 사전(CSDN)을 사용하여 초-희소 각도 뷰에서 고품질 CBCT를 재구성하고, sinograms와 inter-slice consistency를 시너지로 정제한 뒤 coherent 3D 볼륨 재구성을 위한 적응형 융합을 수행한다.

ABSTRACT

The clinical application of cone-beam computed tomography (CBCT) is constrained by the inherent trade-off between radiation exposure and image quality. Ultra-sparse angular sampling, employed to reduce dose, introduces severe undersampling artifacts and inter-slice inconsistencies, compromising diagnostic reliability. Existing reconstruction methods often struggle to balance angular continuity with spatial detail fidelity. To address these challenges, we propose a Continuity-driven Synergistic Diffusion with Neural priors (CSDN) for ultra-sparse-view CBCT reconstruction. Neural priors are introduced as a structural foundation to encode a continuous threedimensional attenuation representation, enabling the synthesis of physically consistent dense projections from ultra-sparse measurements. Building upon this neural-prior-based initialization, a synergistic diffusion strategy is developed, consisting of two collaborative refinement paths: a Sinogram Refinement Diffusion (Sino-RD) process that restores angular continuity and a Digital Radiography Refinement Diffusion (DR-RD) process that enforces inter-slice consistency from the projection image perspective. The outputs of the two diffusion paths are adaptively fused by the Dual-Projection Reconstruction Fusion (DPRF) module to achieve coherent volumetric reconstruction. Extensive experiments demonstrate that the proposed CSDN effectively suppresses artifacts and recovers fine textures under ultra-sparse-view conditions, outperforming existing state-of-the-art techniques.

연구 동기 및 목표

• 진단 영상 품질을 유지하면서 방사선량을 낮춘 초-희소 뷰 CBCT 재구성의 동기를 부여한다.
• 초-희소 데이터에서 Dense projection 합성을 위한 연속적 3D 감쇠 표현을 인코딩하기 위해 신경 사전을 도입한다.
• 각도 연속성과 슬라이스 간 일관성을 강제하는 이중 확산 정제 프레임워크를 개발한다.

제안 방법

• Use neural priors to establish a continuous 3D attenuation representation for synthesizing dense projections from ultra-sparse measurements.
• Develop Sinogram Refinement Diffusion (Sino-RD) to restore angular continuity in projection data.
• Develop Digital Radiography Refinement Diffusion (DR-RD) to enforce inter-slice consistency from projection images.
• Introduce Dual-Projection Reconstruction Fusion (DPRF) to adaptively fuse outputs from Sino-RD and DR-RD for coherent volumetric reconstruction.
• Base the approach on a diffusion-based refinement pipeline that iteratively improves the reconstructed volume under ultra-sparse-view conditions.

실험 결과

연구 질문

• RQ1 Can neural priors provide a robust continuous 3D attenuation representation to synthesize dense projections from ultra-sparse CBCT measurements?
• RQ2 Can a dual diffusion refinement strategy jointly restore angular continuity and inter-slice consistency to improve ultra-sparse-view CBCT reconstructions?
• RQ3 Does adaptive fusion of Sino-RD and DR-RD outputs yield more coherent 3D reconstructions than single-path refinements?
• RQ4 How does the proposed method perform relative to state-of-the-art ultra-sparse-view CBCT reconstruction techniques in suppressing artifacts and preserving textures?

주요 결과

• The proposed CSDN method effectively suppresses artifacts under ultra-sparse-view CBCT conditions.
• Neural priors enable synthesis of physically consistent dense projections from sparse measurements.
• Two collaboration diffusion paths (Sino-RD and DR-RD) restore angular continuity and inter-slice consistency, respectively.
• Adaptive fusion via the DPRF module produces coherent volumetric reconstructions by combining the two refinement paths.