  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).
