Cone beam computed tomography (CBCT) is an important imaging technology
widely used in medical scenarios, such as diagnosis and preoperative planning.
Using fewer projection views to reconstruct CT, also known as sparse-view
reconstruction, can reduce ionizing radiation and further benefit
interventional radiology. Compared with sparse-view reconstruction for
traditional parallel/fan-beam CT, CBCT reconstruction is more challenging due
to the increased dimensionality caused by the measurement process based on
cone-shaped X-ray beams. As a 2D-to-3D reconstruction problem, although
implicit neural representations have been introduced to enable efficient
training, only local features are considered and different views are processed
equally in previous works, resulting in spatial inconsistency and poor
performance on complicated anatomies. To this end, we propose C^2RV by
leveraging explicit multi-scale volumetric representations to enable
cross-regional learning in the 3D space. Additionally, the scale-view
cross-attention module is introduced to adaptively aggregate multi-scale and
multi-view features. Extensive experiments demonstrate that our C^2RV achieves
consistent and significant improvement over previous state-of-the-art methods
on datasets with diverse anatomy.

通过利用显式多尺度体素表示来实现三维空间中的区域间学习，并引入比例 - 视图交叉注意模块以自适应地聚合多尺度和多视图特征，我们提出了 C^2RV 方法，通过广泛实验证明其在具有多样化解剖结构的数据集上相比先前的领先方法实现了一致并显著的改进。

C^2RV：稀疏视图 CBCT 重建的跨地域和跨视图学习

C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT  Reconstruction

Cone Beam CT (CBCT) is an essential imaging modality nowadays, but the image
quality of CBCT still lags behind the high quality standards established by the
conventional Computed Tomography. We propose LIRE+, a learned iterative scheme
for fast and memory-efficient CBCT reconstruction, which is a substantially
faster and more parameter-efficient alternative to the recently proposed LIRE
method. LIRE+ is a rotationally-equivariant multiscale learned invertible
primal-dual iterative scheme for CBCT reconstruction. Memory usage is optimized
by relying on simple reversible residual networks in primal/dual cells and
patch-wise computations inside the cells during forward and backward passes,
while increased inference speed is achieved by making the primal-dual scheme
multiscale so that the reconstruction process starts at low resolution and with
low resolution primal/dual latent vectors. A LIRE+ model was trained and
validated on a set of 260 + 22 thorax CT scans and tested using a set of 142
thorax CT scans with additional evaluation with and without finetuning on an
out-of-distribution set of 79 Head and Neck (HN) CT scans. Our method surpasses
classical and deep learning baselines, including LIRE, on the thorax test set.
For a similar inference time and with only 37 % of the parameter budget, LIRE+
achieves a +0.2 dB PSNR improvement over LIRE, while being able to match the
performance of LIRE in 45 % less inference time and with 28 % of the parameter
budget. Rotational equivariance ensures robustness of LIRE+ to patient
orientation, while LIRE and other deep learning baselines suffer from
substantial performance degradation when patient orientation is unusual. On the
HN dataset in the absence of finetuning, LIRE+ is generally comparable to LIRE
in performance apart from a few outlier cases, whereas after identical
finetuning LIRE+ demonstates a +1.02 dB PSNR improvement over LIRE.

CBCT 重建中提出了一种快速、高效的学习迭代方案 LIRE+，在参数使用效率和推理速度上分别优于 LIRE 方法，通过旋转等变性确保对于异常患者方向的稳健性。

用于锥束 CT 的等变多尺度学习可逆重建

Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam  CT

Cone beam computed tomography (CBCT) is an emerging medical imaging technique
to visualize the internal anatomical structures of patients. During a CBCT
scan, several projection images of different angles or views are collectively
utilized to reconstruct a tomographic image. However, reducing the number of
projections in a CBCT scan while preserving the quality of a reconstructed
image is challenging due to the nature of an ill-posed inverse problem.
Recently, a neural attenuation field (NAF) method was proposed by adopting a
neural radiance field algorithm as a new way for CBCT reconstruction,
demonstrating fast and promising results using only 50 views. However,
decreasing the number of projections is still preferable to reduce potential
radiation exposure, and a faster reconstruction time is required considering a
typical scan time. In this work, we propose a fast and accurate sparse-view
CBCT reconstruction (FACT) method to provide better reconstruction quality and
faster optimization speed in the minimal number of view acquisitions ($<$ 50
views). In the FACT method, we meta-trained a neural network and a hash-encoder
using a few scans (= 15), and a new regularization technique is utilized to
reconstruct the details of an anatomical structure. In conclusion, we have
shown that the FACT method produced better, and faster reconstruction results
over the other conventional algorithms based on CBCT scans of different body
parts (chest, head, and abdomen) and CT vendors (Siemens, Phillips, and GE).

通过提出一种快速准确的稀疏视角锥形束计算机断层成像（CBCT）重建方法（FACT），我们发现该方法相对于传统算法在不同身体部位（胸部、头部和腹部）和计算机断层成像（CT）供应商（西门子、飞利浦和 GE）的 CBCT 扫描中产生了更好且更快的重建结果。