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Accelerating multipool CEST MRI of Parkinson's disease using deep learning-based Z-spectral compressed sensing.
Chen, Lin; Xu, Haipeng; Gong, Tao; Jin, Junxian; Lin, Liangjie; Zhou, Yang; Huang, Jianpan; Chen, Zhong.
Afiliación
  • Chen L; Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China.
  • Xu H; Institute of Artificial Intelligence, Xiamen University, Xiamen, China.
  • Gong T; Institute of Artificial Intelligence, Xiamen University, Xiamen, China.
  • Jin J; Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Lin L; Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China.
  • Zhou Y; Clinical & Technical Supports, Philips Healthcare, Beijing, China.
  • Huang J; Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Chen Z; Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China.
Magn Reson Med ; 92(6): 2616-2630, 2024 Dec.
Article en En | MEDLINE | ID: mdl-39044635
ABSTRACT

PURPOSE:

To develop a deep learning-based approach to reduce the scan time of multipool CEST MRI for Parkinson's disease (PD) while maintaining sufficient prediction accuracy.

METHOD:

A deep learning approach based on a modified one-dimensional U-Net, termed Z-spectral compressed sensing (CS), was proposed to recover dense Z-spectra from sparse ones. The neural network was trained using simulated Z-spectra generated by the Bloch equation with various parameter settings. Its feasibility and effectiveness were validated through numerical simulations and in vivo rat brain experiments, compared with commonly used linear, pchip, and Lorentzian interpolation methods. The proposed method was applied to detect metabolism-related changes in the 6-hydroxydopamine PD model with multipool CEST MRI, including APT, CEST@2 ppm, nuclear Overhauser enhancement, direct saturation, and magnetization transfer, and the prediction performance was evaluated by area under the curve.

RESULTS:

The numerical simulations and in vivo rat-brain experiments demonstrated that the proposed method could yield superior fidelity in retrieving dense Z-spectra compared with existing methods. Significant differences were observed in APT, CEST@2 ppm, nuclear Overhauser enhancement, and direct saturation between the striatum regions of wild-type and PD models, whereas magnetization transfer exhibited no significant difference. Receiver operating characteristic analysis demonstrated that multipool CEST achieved better predictive performance compared with individual pools. Combined with Z-spectral CS, the scan time of multipool CEST MRI can be reduced to 33% without distinctly compromising prediction accuracy.

CONCLUSION:

The integration of Z-spectral CS with multipool CEST MRI can enhance the prediction accuracy of PD and maintain the scan time within a reasonable range.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Encéfalo / Imagen por Resonancia Magnética / Aprendizaje Profundo Límite: Animals Idioma: En Revista: Magn Reson Med Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Encéfalo / Imagen por Resonancia Magnética / Aprendizaje Profundo Límite: Animals Idioma: En Revista: Magn Reson Med Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos