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Feature aware deep learning CT image reconstruction
Author(s): Masakazu Matsuura; Jian Zhou; Naruomi Akino; Zhou Yu
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Paper Abstract

In conventional CT, it is difficult to generate consistent organ specific noise and resolution with a single reconstruction kernel. Therefore, it is necessary in principle to reconstruct a single scan multiple times using different kernels in order to obtain clinical diagnosis information for different anatomies. In this paper, we provide a deep learning solution which can obtain organ specific noise and resolution balance with one single reconstruction. We propose image reconstruction using a deep convolution neural network (DCNN) trained by a specific feature aware reconstruction target. It integrates desirable features from multiple reconstructions each of which provides optimal noise and resolution tradeoff for one specific anatomy. The performance of our proposed method has been verified with actual clinical data. The results show that our method can outperform standard model based iterative reconstruction (MBIR) by offering consistent noise and resolution properties across different organs using only one single image reconstruction.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110721B (28 May 2019); doi: 10.1117/12.2534614
Show Author Affiliations
Masakazu Matsuura, Canon Medical Research USA, Inc. (United States)
Jian Zhou, Canon Medical Research USA, Inc. (United States)
Naruomi Akino, Canon Medical Systems Corp. (Japan)
Zhou Yu, Canon Medical Research USA, Inc. (United States)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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