| Project/Area Number |
20K16758
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 52040:Radiological sciences-related
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| Research Institution | Kobe University |
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Principal Investigator |
曽 菲比 神戸大学, 医学部附属病院, 医員 (50837680)
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| Project Period (FY) |
2020-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2023: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2022: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2021: ¥130,000 (Direct Cost: ¥100,000、Indirect Cost: ¥30,000)
Fiscal Year 2020: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | PET/MRI / 減弱補正 / 深層学習 / in-phase Zero echo-time / 吸収補正 |
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| Outline of Research at the Start |
本研究の目的は，胸部を対象としたPET/MRIにおいて，MR画像から深層学習を行いてγ線吸収補正マップを作成することにより，定量精度の高いPETをPET/MRI装置を用いて得ることである.従来法およびZTE法によるMRI画像から，深層学習を用いて骨の情報を抽出することで，骨を含めた5つの組織のMRI吸収補正マップが作成可能となると考えた．また胸部領域では，複雑な形態の骨が存在するとともに，呼吸運動や心臓の運動による画像の劣化が想定されるため，MRIは呼吸同期法を用いて撮像するとともに，中心周波数を調整して撮像したipZTE法を用いる.
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| Outline of Annual Research Achievements |
In our previous study, pseudo-CT (pCT) with bone components from 360 ZTE-MRI training data set using the 2-dimensional (2D) deep learning (DL) method including unsupervised generative adversarial networks (GAN) yielded μmaps with bone components (μmapbone-2D) in combination with conventional bone-lacking μmaps (μmapno-bone) in the chest. We aimed to assess the feasibility of pCT and μmap (μmapbone-2.5D) from ZTE using 2.5 dimensional (2.5D) DL method and compare the quantitative values of the normal tissues and lesions between μmapno-bone-, μmapbone-2D-, and μmapbone-2.5D-besed reconstruction.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
μmapbone-2.5D was significantly larger than μmapbone-2D in the normal bone and the liver (p=0.0009, p< 0.0001). The SUVmax by μmapbone-2.5D was significantly larger than μmapbone-2D in all regions (p= 0.0176). The correlation coefficients of the histogram for μmapbone-2.5D and μmapbone-2D were significantly higher than μmapno-bone in the bone (p<0.0001). The image-quality scores of pCT bone-2.5D for bone delineation and bone continuity were significantly higher than pCTbone-2D in both readers (p= 0.0106, p< 0.0001). μmapbone-2.5D are assumed to yield smaller differences in SUV with μmapCT than μmapno-bone and μmapbone-2D in normal tissues and lesions. The result supports the feasibility of using the 2.5D method for the generation of bone components from ZTE with DL.
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| Strategy for Future Research Activity |
Attenuation correction (AC) of bone components in the chest on positron emission tomography-magnetic resonance images (PET/MRI) is challenging due to the complicated anatomical structures and limited delineation of the bone on MRI. A deep learning approach using unsupervised generative adversarial networks (GANs) with adaptive layer-instance normalization for image-to-image translation (U-GAT-IT) in combination with a modality-independent neighborhood descriptor (MIND) performed in our previous study yielded a pseudo-CT (pCT) generation with bone components from Zero echo-time (ZTE) MRI for AC in the chest. The purpose of this study was the external validation of the deep learning approach.
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