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。