Accelerating Magnetic Resonance Fingerprinting Using Hybrid Deep Learning and Iterative Reconstruction

Peng Cao; Di Cui; Yanzhen Ming; Varut Vardhanabhuti; Elaine Lee; Edward Hui

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자료유형: 학술저널

저자정보: Peng Cao (The University of Hong Kong) Di Cui (The University of Hong Kong) Yanzhen Ming (The University of Hong Kong) Varut Vardhanabhuti (The University of Hong Kong) Elaine Lee (The University of Hong Kong) Edward Hui (The Hong Kong Polytechnic University)

저널정보: 대한자기공명의과학회 Investigative Magnetic Resonance Imaging Investigative Magnetic Resonance Imaging 제25권 제4호

발행연도: 2021.12

수록면: 293 - 299 (7page)

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Purpose: To accelerate magnetic resonance fingerprinting (MRF) by developing a flexible deep learning reconstruction method. Materials and Methods: Synthetic data were used to train a deep learning model. The trained model was then applied to MRF for different organs and diseases. Iterative reconstruction was performed outside the deep learning model, allowing a changeable encoding matrix, i.e., with flexibility of choice for image resolution, radiofrequency coil, k-space trajectory, and undersampling mask. In vivo experiments were performed on normal brain and prostate cancer volunteers to demonstrate the model performance and generalizability. Results: In 400-dynamics brain MRF, direct nonuniform Fourier transform caused a slight increase of random fluctuations on the T2 map. These fluctuations were reduced with the proposed method. In prostate MRF, the proposed method suppressed fluctuations on both T1 and T2 maps. Conclusion: The deep learning and iterative MRF reconstruction method described in this study was flexible with different acquisition settings such as radiofrequency coils. It is generalizable for different in vivo applications.

#Deep learning #Iterative reconstruction #MRF reconstruction #MRI reconstruction

참고문헌 (20)

참고문헌 신청

Ma D / 2013 / Magnetic resonance fingerprinting / Nature 495:187~192

Badve C / 2017 / MR fingerprinting of adult brain tumors : initial experience / AJNR Am J Neuroradiol 38:492~499

Chen Y / 2019 / Three-dimensional MR fingerprinting for quantitative breast imaging / Radiology 290:33~40

Yu AC / 2017 / Development of a combined MR fingerprinting and diffusion examination for prostate cancer / Radiology 283:729~738

Liao C / 2017 / 3D MR fingerprinting with accelerated stack-of-spirals and hybrid slidingwindow and GRAPPA reconstruction / Neuroimage 162:13~22

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