IMPULSED model based cytological feature estimation with U‐Net: Application to human brain tumor at 3T. Issue 1 (5th September 2022)

• Record Type: Journal Article
• Authors: Wu, Jian; Kang, Taishan; Lan, Xinli; Chen, Xinran; Wu, Zhigang; Wang, Jiazheng; Lin, Liangjie; Cai, Congbo; Lin, Jianzhong; Ding, Xin; Cai, Shuhui
• Is Part Of: Magnetic resonance in medicine. Volume 89:Issue 1(2023)
• Page Start: 411
• Page End: 422
• Publication Date: 2022-09-05
• Subjects: cell size -- deep neural network -- diffusion‐weighted imaging -- extracellular diffusion coefficient -- IMPULSED -- intracellular volume fraction; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.29429
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: This work introduces and validates a deep‐learning‐based fitting method, which can rapidly provide accurate and robust estimation of cytological features of brain tumor based on the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model fitting with diffusion‐weighted MRI data. Methods: The U‐Net was applied to rapidly quantify extracellular diffusion coefficient ( D ex ), cell size ( d ), and intracellular volume fraction ( v in ) of brain tumor. At the training stage, the image‐based training data, synthesized by randomizing quantifiable microstructural parameters within specific ranges, was used to train U‐Net. At the test stage, the pre‐trained U‐Net was applied to estimate the microstructural parameters from simulated data and the in vivo data acquired on patients at 3T. The U‐Net was compared with conventional non‐linear least‐squares (NLLS) fitting in simulations in terms of estimation accuracy and precision. Results: Our results confirm that the proposed method yields better fidelity in simulations and is more robust to noise than the NLLS fitting. For in vivo data, the U‐Net yields obvious quality improvement in parameter maps, and the estimations of all parameters are in good agreement with the NLLS fitting. Moreover, our method is several orders of magnitude faster than the NLLS fitting (from about 5 min to <1 s). Conclusion: The image‐based training scheme proposed herein helps to improve the quality of the estimated parameters. Our deep‐learning‐based fitting method can estimate the cell microstructural parameters fast and accurately.