Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning

Autor: Chuang Niu, Jay T Rubinstein, Ge Wang, Wenxiang Cong, James Bennett, Josef Uher, Zheng Fang, Mengzhou Li, Weiwen Wu
Rok vydání: 2020
Předmět:
Signal Processing (eess.SP)
General Computer Science
robotic arms
Computer science
FOS: Physical sciences
Image registration
Computed tomography
interior tomography
Article
photon-counting detector
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
FOS: Electrical engineering
electronic engineering
information engineering
medicine
General Materials Science
Computer vision
Electrical Engineering and Systems Science - Signal Processing
Image resolution
X-ray computed tomography
temporal bone imaging
medicine.diagnostic_test
business.industry
Detector
Radiation dose
high-resolution imaging
General Engineering
deep learning
Isocenter
Reconstruction algorithm
Clinical micro-CT
Physics - Medical Physics
030220 oncology & carcinogenesis
lcsh:Electrical engineering. Electronics. Nuclear engineering
Medical Physics (physics.med-ph)
Tomography
Artificial intelligence
business
lcsh:TK1-9971
Zdroj: IEEE Access, Vol 8, Pp 229018-229032 (2020)
IEEE access : practical innovations, open solutions
ISSN: 2169-3536
Popis: While micro-CT systems are instrumental in preclinical research, clinical micro-CT imaging has long been desired with cochlear implantation as a primary example. The structural details of the cochlear implant and the temporal bone require a significantly higher image resolution than that (about 0.2 mm) provided by current medical CT scanners. In this paper, we propose a clinical micro-CT (CMCT) system design integrating conventional spiral cone-beam CT, contemporary interior tomography, deep learning techniques, and technologies of micro-focus X-ray source, photon-counting detector (PCD), and robotic arms for ultrahigh resolution localized tomography of a freely-selected volume of interest (VOI) at a minimized radiation dose level. The whole system consists of a standard CT scanner for a clinical CT exam and VOI specification, and a robotic-arm based micro-CT scanner for a local scan at much higher spatial and spectral resolution as well as much reduced radiation dose. The prior information from global scan is also fully utilized for background compensation to improve interior tomography from local data for accurate and stable VOI reconstruction. Our results and analysis show that the proposed hybrid reconstruction algorithm delivers superior local reconstruction, being insensitive to the misalignment of the isocenter position and initial view angle in the data/image registration while the attenuation error caused by scale mismatch can be effectively addressed with bias correction. These findings demonstrate the feasibility of our system design. We envision that deep learning techniques can be leveraged for optimized imaging performance. With high resolution imaging, high dose efficiency and low system cost synergistically, our proposed CMCT system has great potentials in temporal bone imaging as well as various other clinical applications.
Comment: 10 pages, 13 figures, 3 tables
Databáze: OpenAIRE
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