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Automated volumetric determination of high R2 * regions in substantia nigra: A feasibility study of quantifying substantia nigra atrophy in progressive supranuclear palsy.
Tessema, Abel Worku; Lee, Hansol; Gong, Yelim; Cho, Hwapyeong; Adem, Hamdia Murad; Lyu, Ilwoo; Lee, Jae-Hyeok; Cho, HyungJoon.
Afiliación
  • Tessema AW; Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea.
  • Lee H; School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
  • Gong Y; Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea.
  • Cho H; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.
  • Adem HM; Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea.
  • Lyu I; Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea.
  • Lee JH; School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
  • Cho H; Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea.
NMR Biomed ; 35(11): e4795, 2022 11.
Article en En | MEDLINE | ID: mdl-35775868
The establishment of an unbiased protocol for the automated volumetric measurement of iron-rich regions in the substantia nigra (SN) is clinically important for diagnosing neurodegenerative diseases exhibiting midbrain atrophy, such as progressive supranuclear palsy (PSP). This study aimed to automatically quantify the volume and surface properties of the iron-rich 3D regions in the SN using the quantitative MRI-R2 * map. Three hundred and sixty-seven slices of R2 * map and susceptibility-weighted imaging (SWI) at 3-T MRI from healthy control (HC) individuals and Parkinson's disease (PD) patients were used to train customized U-net++ convolutional neural network based on expert-segmented masks. Age- and sex-matched participants were selected from HC, PD, and PSP groups to automate the volumetric determination of iron-rich areas in the SN. Dice similarity coefficient values between expert-segmented and detected masks from the proposed network were 0.91 ± 0.07 for R2 * maps and 0.89 ± 0.08 for SWI. Reductions in iron-rich SN volume from the R2 * map (SWI) were observed in PSP with area under the receiver operating characteristic curve values of 0.96 (0.89) and 0.98 (0.92) compared with HC and PD, respectively. The mean curvature of the PSP showed SN deformation along the side closer to the red nucleus. We demonstrated the automated volumetric measurement of iron-rich regions in the SN using deep learning can quantify the SN atrophy in PSP compared with PD and HC.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Parálisis Supranuclear Progresiva Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: NMR Biomed Asunto de la revista: DIAGNOSTICO POR IMAGEM / MEDICINA NUCLEAR Año: 2022 Tipo del documento: Article País de afiliación: Corea del Sur Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Parálisis Supranuclear Progresiva Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: NMR Biomed Asunto de la revista: DIAGNOSTICO POR IMAGEM / MEDICINA NUCLEAR Año: 2022 Tipo del documento: Article País de afiliación: Corea del Sur Pais de publicación: Reino Unido