The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jun. 16, 2020
Filed:
Dec. 20, 2018
Canon Medical Systems Corporation, Otawara-shi, JP;
University Health Network, Toronto, CA;
Chung Chan, Glenview, IL (US);
Zhou Yu, Wilmette, IL (US);
Jian Zhou, Buffalo Grove, IL (US);
Patrik Rogalla, Toronto, CA;
Bernice Hoppel, Delafield, WI (US);
Kurt Walter Schultz, North Providence, RI (US);
CANON MEDICAL SYSTEMS CORPORATION, Otawara-shi, JP;
UNIVERSITY HEALTH NETWORK, Toronto, CA;
Abstract
A method and apparatus is provided to iteratively reconstruct a computed tomography (CT) image using a spatially-varying content-oriented regularization parameter, thereby achieving uniform statistical properties within respective organs/regions and different statistical properties (e.g., degree of smoothing and noise level) among the respective organs/regions. For example, less smoothing and sharper features/resolution can be applied within a lung region than within a soft-tissue region by using a smaller regularization parameter value in the lung region than in the soft-tissue region. This can be achieved, e.g., using a minimum intensity projection to suppress/eliminate sub-solid nodules in the lung region. The content-oriented regularization parameter can be generated by reconstructing an initial CT image, which is then segmented/classified according to organs and/or tissue type. Segmenting the image and generating the content-oriented regularization parameter can be integrated into one process by applying an HU-to-β mapping to the CT image.