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Kobayashi T, Shigeki Y, Yamakawa Y, Tsutsumida Y, Mizuta T, Hanaoka K, Watanabe S, Morimoto-Ishikawa D, Yamada T, Kaida H, Ishii K. Generating PET Attenuation Maps via Sim2Real Deep Learning-Based Tissue Composition Estimation Combined with MLACF. J Imaging Inform Med 2024; 37:167-179. [PMID: 38343219 DOI: 10.1007/s10278-023-00902-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/20/2023] [Accepted: 08/10/2023] [Indexed: 03/02/2024]
Abstract
Deep learning (DL) has recently attracted attention for data processing in positron emission tomography (PET). Attenuation correction (AC) without computed tomography (CT) data is one of the interests. Here, we present, to our knowledge, the first attempt to generate an attenuation map of the human head via Sim2Real DL-based tissue composition estimation from model training using only the simulated PET dataset. The DL model accepts a two-dimensional non-attenuation-corrected PET image as input and outputs a four-channel tissue-composition map of soft tissue, bone, cavity, and background. Then, an attenuation map is generated by a linear combination of the tissue composition maps and, finally, used as input for scatter+random estimation and as an initial estimate for attenuation map reconstruction by the maximum likelihood attenuation correction factor (MLACF), i.e., the DL estimate is refined by the MLACF. Preliminary results using clinical brain PET data showed that the proposed DL model tended to estimate anatomical details inaccurately, especially in the neck-side slices. However, it succeeded in estimating overall anatomical structures, and the PET quantitative accuracy with DL-based AC was comparable to that with CT-based AC. Thus, the proposed DL-based approach combined with the MLACF is also a promising CT-less AC approach.
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Affiliation(s)
- Tetsuya Kobayashi
- Technology Research Laboratory, Shimadzu Corporation, 3-9-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0237, Japan.
| | - Yui Shigeki
- Technology Research Laboratory, Shimadzu Corporation, 3-9-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0237, Japan
| | - Yoshiyuki Yamakawa
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan
| | - Yumi Tsutsumida
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan
| | - Tetsuro Mizuta
- Medical Systems Division, Shimadzu Corporation, 1, Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto, 604-8511, Japan
| | - Kohei Hanaoka
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Shota Watanabe
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Daisuke Morimoto-Ishikawa
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Takahiro Yamada
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Hayato Kaida
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
- Department of Radiology, Faculty of Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Kazunari Ishii
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
- Department of Radiology, Faculty of Medicine, Kindai University, 377-2, Onohigashi, Osakasayama, Osaka, 589-8511, Japan
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