使用带微分同胚正则化的后向变形隐式神经表示进行非周期动态CT重建（BIRD）
Nonperiodic dynamic CT reconstruction using backward-warping INR with regularization of diffeomorphism (BIRD)
AI总结：
本文提出基于INR的BIRD框架，通过后向变形、微分同胚正则化、运动补偿解析重建和降维坐标编码，解决非周期动态CT重建中的运动伪影与计算效率问题，并验证其在心脏成像等场景中的有效性。
摘要：
动态计算机断层扫描（CT）重建在解决运动伪影方面面临重大挑战，尤其是对于非周期快速运动，例如心率较快的心脏成像。传统方法难以应对非周期情形中固有的极端有限角度问题。深度学习方法虽然提高了性能，但面临泛化挑战。最近的隐式神经表示（INR）技术通过自监督深度学习展现出前景，但存在关键局限：由于前向变形建模导致的计算效率低下、难以在变形向量场（DVF）复杂度与解剖学合理性之间取得平衡，以及在不额外进行患者特异性预扫描的情况下难以保留细节。本文提出了一种新颖的基于INR的框架BIRD，用于非周期动态CT重建。它通过四项关键贡献应对这些挑战：（1）后向变形，能够直接计算每个动态体素，显著降低计算成本；（2）基于微分同胚的DVF正则化，在保持表示能力的同时确保解剖学上合理的变形；（3）运动补偿解析重建，无需额外预扫描即可增强细节；（4）用于高效四维坐标编码的降维设计。通过各种仿真和实际研究，包括数字体模、物理体模和回顾性患者数据，我们证明了该方法在非周期动态CT重建中的有效性，能够增强细节并减少运动伪影。所提出的框架能够实现更准确的动态CT重建，并具有潜在的临床应用价值，例如单次心跳心脏重建、用于功能成像的电影图像序列以及常规CT扫描中的运动伪影减少。
英文摘要：
Dynamic computed tomography (CT) reconstruction faces significant challenges in addressing motion artifacts, particularly for nonperiodic rapid movements such as cardiac imaging with fast heart rates. Traditional methods struggle with the extreme limited-angle problems inherent in nonperiodic cases. Deep learning methods have improved performance but face generalization challenges. Recent implicit neural representation (INR) techniques show promise through self-supervised deep learning, but have critical limitations: computational inefficiency due to forward-warping modeling, difficulty balancing DVF complexity with anatomical plausibility, and challenges in preserving fine details without additional patient-specific pre-scans. This paper presents a novel INR-based framework, BIRD, for nonperiodic dynamic CT reconstruction. It addresses these challenges through four key contributions: (1) backward-warping deformation that enables direct computation of each dynamic voxel with significantly reduced computational cost, (2) diffeomorphism-based DVF regularization that ensures anatomically plausible deformations while maintaining representational capacity, (3) motion-compensated analytical reconstruction that enhances fine details without requiring additional pre-scans, and (4) dimensional-reduction design for efficient 4D coordinate encoding. Through various simulations and practical studies, including digital and physical phantoms and retrospective patient data, we demonstrate the effectiveness of our approach for nonperiodic dynamic CT reconstruction with enhanced details and reduced motion artifacts. The proposed framework enables more accurate dynamic CT reconstruction with potential clinical applications, such as one-beat cardiac reconstruction, cinematic image sequences for functional imaging, and motion artifact reduction in conventional CT scans.
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