Kavli Affiliate: Cheng Peng
| First 5 Authors: Cheng Peng, Haofu Liao, S. Kevin Zhou, Rama Chellappa,
| Summary:
It is a long-standing challenge to reconstruct Cone Beam Computed Tomography
(CBCT) of the lung under respiratory motion. This work takes a step further to
address a challenging setting in reconstructing a multi-phase}4D lung image
from just a single}3D CBCT acquisition. To this end, we introduce
REpiratory-GAted Synthesis of views, or REGAS. REGAS proposes a self-supervised
method to synthesize the undersampled tomographic views and mitigate aliasing
artifacts in reconstructed images. This method allows a much better estimation
of between-phase Deformation Vector Fields (DVFs), which are used to enhance
reconstruction quality from direct observations without synthesis. To address
the large memory cost of deep neural networks on high resolution 4D data, REGAS
introduces a novel Ray Path Transformation (RPT) that allows for distributed,
differentiable forward projections. REGAS require no additional measurements
like prior scans, air-flow volume, or breathing velocity. Our extensive
experiments show that REGAS significantly outperforms comparable methods in
quantitative metrics and visual quality.
| Search Query: ArXiv Query: search_query=au:”Cheng Peng”&id_list=&start=0&max_results=10