Generating PET Attenuation Maps via Sim2Real Deep Learning–Based Tissue Composition Estimation Combined with MLACF.
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...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 1; pp. 167 - 180 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175966497&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175966497 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2024 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175966497 175966497 175966497 10.1007/s10278-023-00902-0 175966497 ppf: 167 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Generating PET Attenuation Maps via Sim2Real Deep Learning–Based Tissue Composition Estimation Combined with MLACF. aug: au: Kobayashi, Tetsuya Shigeki, Yui Yamakawa, Yoshiyuki Tsutsumida, Yumi Mizuta, Tetsuro Hanaoka, Kohei Watanabe, Shota Morimoto‑Ishikawa, Daisuke Yamada, Takahiro Kaida, Hayato Ishii, Kazunari affil: Technology Research Laboratory, Shimadzu Corporation, 3-9-4, Hikaridai, Seika-cho, Soraku-gun, 619-0237, Kyoto, Japan sug: subj: Positron-Emission Tomography Brain Mapping Deep Learning Methods Image Interpretation, Computer Assisted Tissue Analysis Human Funding Source Tomography, X-Ray Computed ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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