A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging

Kim, Byungjai; Schär, Michael; Park, Hyun Wook; Heo, Hye Young

doi:10.1016/j.neuroimage.2020.117165

Cited by 46 publications

(86 citation statements)

References 56 publications

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“…Generally, quantifying the semisolid MTC exchange rate is challenging because of small signal discrimination between different exchange rates, which is very sensitive to the noise level. 41 Therefore, the optimization of the acquisition schedule is important for high signal discrimination and quantification performance at a given SNR.…”

Section: Discussionmentioning

confidence: 99%

“…The normalized RMSE values of the proposed pseudo‐random schedule were overall lower than those of the schedules used in the previous studies, except for the MTC exchange rate, probably due to a large number of dynamic scans ( N = 40 vs N = 182) or a wide range of RF saturation parameters (Supporting Information Figure ). Generally, quantifying the semisolid MTC exchange rate is challenging because of small signal discrimination between different exchange rates, which is very sensitive to the noise level 41 . Therefore, the optimization of the acquisition schedule is important for high signal discrimination and quantification performance at a given SNR.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the unsupervised learning method was compared with the conventional Bloch fitting method and supervised learning method using fully connected neural networks. The fully connected supervised learning architecture and hyperparameters were the same as those used in our previous study 41 . To address the last question, 3D MTC‐MRF images were synthesized with four different schedules: (1) a pseudo‐random schedule used in this study, (2) an interior‐point (IP) schedule using correlation‐based optimization, 42 (3) a uniform‐random schedule generated by uniform sampling from each scan parameter range, and (4) a linear schedule generated by linearly increasing values between the minimum and maximum points of each scan parameter range.…”

Section: Methodsmentioning

confidence: 99%

“…The fully connected supervised learning architecture and hyperparameters were the same as those used in our previous study. 41 To address the last question, 3D MTC-MRF images were synthesized with four different schedules:…”

Section: Validation Of Mtc-mrf With Synthetic Mrimentioning

confidence: 99%

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Unsupervised learning for magnetization transfer contrast MR fingerprinting: Application to CEST and nuclear Overhauser enhancement imaging

Kang

Kim

Schär

et al. 2020

Magnetic Resonance in Med

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Purpose: To develop a fast, quantitative 3D magnetization transfer contrast (MTC) framework based on an unsupervised learning scheme, which will provide baseline reference signals for CEST and nuclear Overhauser enhancement imaging. Methods: Pseudo-randomized RF saturation parameters and relaxation delay times were applied in an MR fingerprinting framework to generate transient-state signal evolutions for different MTC parameters. Prospectively compressed sensingaccelerated (four-fold) MR fingerprinting images were acquired from 6 healthy volunteers at 3 T. A convolutional neural network framework in an unsupervised fashion was designed to solve an inverse problem of a two-pool MTC Bloch equation, and was compared with a conventional Bloch equation-based fitting approach. The MTC images synthesized by the convolutional neural network architecture were used for amide proton transfer and nuclear Overhauser enhancement imaging as a reference baseline image. Results: The fully unsupervised learning scheme incorporated with the two-pool exchange model learned a set of unique features that can describe the MTC-MR fingerprinting input, and allowed only small amounts of unlabeled data for training. The MTC parameter values estimated by the unsupervised learning method were in excellent agreement with values estimated by the conventional Bloch fitting approach, but dramatically reduced computation time by ~1000-fold. Conclusion: Given the considerable time efficiency compared to conventional Bloch fitting, unsupervised learning-based MTC-MR fingerprinting could be a powerful tool for quantitative MTC and CEST/nuclear Overhauser enhancement imaging.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Validation Of Mtc-mrf With Synthetic Mrimentioning

confidence: 99%

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Unsupervised learning for magnetization transfer contrast MR fingerprinting: Application to CEST and nuclear Overhauser enhancement imaging

Kang

Kim

Schär

et al. 2020

Magnetic Resonance in Med

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“…Recent works that leverage supervised ML for model parameter estimation typically employ one of two training strategies: (1) parameter combinations obtained from traditional model fitting and the corresponding measured qMRI signals [4] [6] [9] [14] [15] [11] [16] [17], or (2) parameters sampled uniformly from the entire plausible parameter space with simulated qMRI signals [5] [18] [19] [20] [21] [22] [23] [24]. While both of these approaches are limited by the model used to estimate parameters or simulate signals, simulations allow considerably more freedom in choosing training data [25] [26] [27].…”

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confidence: 99%

Training Data Distribution Significantly Impacts the Estimation of Tissue Microstructure with Machine Learning

Gyori

Palombo

Clark

et al. 2021

Preprint

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Purpose: Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify the effect of different training strategies on the accuracy and precision of parameter estimates when supervised ML is used for fitting. Methods: We fit a two-compartment biophysical model to diffusion measurements from in-vivo human brain, as well as simulated diffusion data, using both traditional model fitting and supervised ML. For supervised ML, we train several artificial neural networks, as well as random forest regressors, on different distributions of ground truth parameters. We compare the accuracy and precision of parameter estimates obtained from the different estimation approaches using synthetic test data. Results: When the distribution of parameter combinations in the training set matches those observed in similar data sets, we observe high precision, but inaccurate estimates for atypical parameter combinations. In contrast, when training data is sampled uniformly from the entire plausible parameter space, estimates tend to be more accurate for atypical parameter combinations but may have lower precision for typical parameter combinations. Conclusion: This work highlights the need to consider the choice of training data when deploying supervised ML for estimating microstructural metrics, as performance depends strongly on the training-set distribution. We show that high precision obtained using ML may mask strong bias, and visual assessment of the parameter maps is not sufficient for evaluating the quality of the estimates.

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Improving Sensitivity of Arterial Spin Labeling Perfusion MRI in Alzheimer's Disease Using Transfer Learning of Deep Learning‐Based ASL Denoising

Zhang

Xie

et al. 2021

Magnetic Resonance Imaging

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Background Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) denoising through deep learning (DL) often faces insufficient training data from patients. One solution is to train DL models using healthy subjects' data which are more widely available and transfer them to patients' data. Purpose To evaluate the transferability of a DL‐based ASL MRI denoising method (DLASL). Study Type Retrospective. Subjects Four hundred and twenty‐eight subjects (189 females) from three cohorts. Field Strength/Sequence 3 T two‐dimensional (2D) echo‐planar imaging (EPI)‐based pseudo‐continuous ASL (PCASL) and 2D EPI‐based pulsed ASL (PASL) sequences. Assessment DLASL was trained using young healthy adults' PCASL data (Dataset 1: 250/30 subjects as training/validation set) and was directly transferred (DTF) to PCASL data from Dataset 2 (45 subjects test set) of normal controls (NC) and Alzheimer's disease (AD) groups. DLASL was fine‐tuned (DLASLFT) and tested on PASL data from Dataset 3 (103 subjects test set) of NC and AD. An existing non‐DL method (NonDL) was used for comparison. Cerebral blood flow (CBF) images from ASL MRI were compared between NC and AD to assess characteristic hypoperfusion (lower CBF) patterns in AD. CBF image quality and CBF map sensitivity for detecting hypoperfusion using peak t‐value and suprathreshold cluster size are outcome measures. Statistical Tests Paired t‐test, two‐sample t‐test, one‐way analysis of variance, and Tukey honestly significant difference, and linear mixed‐effects models were used. P < 0.05 was considered statistically significant. Results Mean contrast‐to‐noise ratio (CNR) of Dataset 2 showed that DTF outperformed NonDL (AD: 3.38 vs. 2.64, NC: 3.80 vs. 3.36). On Dataset 3, DLASLFT outperformed NonDL measured by mean CNR (AD: 2.45 vs. 1.87, NC: 2.54 vs. 2.17) and mean radiologic score (2.86 vs. 2.44). Image quality improvement was significant on both test sets. DTF and DLASLFT improved sensitivity for detecting AD‐related hypoperfusion patterns compared with NonDL. Data Conclusion We demonstrated the DLASL's transferability across different ASL sequences and different populations. Level of Evidence 3 Technical Efficacy Stage 2

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A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging

Cited by 46 publications

References 56 publications

Unsupervised learning for magnetization transfer contrast MR fingerprinting: Application to CEST and nuclear Overhauser enhancement imaging

Unsupervised learning for magnetization transfer contrast MR fingerprinting: Application to CEST and nuclear Overhauser enhancement imaging

Training Data Distribution Significantly Impacts the Estimation of Tissue Microstructure with Machine Learning

Improving Sensitivity of Arterial Spin Labeling Perfusion MRI in Alzheimer's Disease Using Transfer Learning of Deep Learning‐Based ASL Denoising

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