Mr. Masakazu Matsuura Profile

Masakazu Matsuura

at Canon Medical Research USA Inc

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Proceedings Article | 28 May 2019 Paper

Feature aware deep learning CT image reconstruction

Masakazu Matsuura, Jian Zhou, Naruomi Akino, Zhou Yu

Proceedings Volume 11072, 110721B (2019) https://doi.org/10.1117/12.2534614

KEYWORDS: Tissues, Bone, Lung, Image resolution, Abdomen, CT reconstruction, Spatial resolution, X-ray computed tomography, Neural networks, Reconstruction algorithms

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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.

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