Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial
for expanding the scope of X-ray CT applications. However, they face challenges
due to incomplete data acquisition, resulting in diverse artifacts in the
reconstructed CT images. Emerging implicit neural representation (INR)
techniques, such as NeRF, NeAT, and NeRP, have shown promise in
under-determined CT imaging reconstruction tasks. However, the unsupervised
nature of INR architecture imposes limited constraints on the solution space,
particularly for the highly ill-posed reconstruction task posed by LACT and
ultra-SVCT. In this study, we introduce the Diffusion Prior Driven Neural
Representation (DPER), an advanced unsupervised framework designed to address
the exceptionally ill-posed CT reconstruction inverse problems. DPER adopts the
Half Quadratic Splitting (HQS) algorithm to decompose the inverse problem into
data fidelity and distribution prior sub-problems. The two sub-problems are
respectively addressed by INR reconstruction scheme and pre-trained score-based
diffusion model. This combination initially preserves the implicit image local
consistency prior from INR. Additionally, it effectively augments the
feasibility of the solution space for the inverse problem through the
generative diffusion model, resulting in increased stability and precision in
the solutions. We conduct comprehensive experiments to evaluate the performance
of DPER on LACT and ultra-SVCT reconstruction with two public datasets (AAPM
and LIDC). The results show that our method outperforms the state-of-the-art
reconstruction methods on in-domain datasets, while achieving significant
performance improvements on out-of-domain datasets.

通过采用 Diffusion Prior Driven Neural Representation (DPER) 方法，本研究在解决计算机断层扫描（X-ray CT）重建问题中取得了显著的性能提升，有效应对了限角和稀疏视图场景下数据获取不完整、图像重建过程中出现多样化伪影的挑战。

DPER: 基于扩散先验的限角度和稀疏视图 CT 重建的神经表征

DPER: Diffusion Prior Driven Neural Representation for Limited Angle and  Sparse View CT Reconstruction

Limited-angle computed tomography (CT) is often used in clinical applications
such as C-arm CT for interventional imaging. However, CT images from limited
angles suffers from heavy artifacts due to incomplete projection data. Existing
iterative methods require extensive calculations but can not deliver
satisfactory results. Based on the observation that the artifacts from limited
angles have some directional property and are globally distributed, we propose
a novel multi-scale wavelet domain residual learning architecture, which
compensates for the artifacts. Experiments have shown that the proposed method
effectively eliminates artifacts, thereby preserving edge and global structures
of the image.

利用多尺度小波域残差学习架构结合先前方向性和全局分布性偏差的观察提出的新方法，可以有效消除有限角度下的 CT 图像伪影并保留图像的边缘和全局结构性。