Water–fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network

Goldfarb, James W.; Craft, Jason; Cao, Junli

doi:10.1002/jmri.26658

Cited by 29 publications

(44 citation statements)

References 24 publications

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“…In this work, we adapted the approaches in recent works 13,14 and made

W

F, f

, and

R_{2}^{*}

the target output of the network. A U‐net type network

Ψ (S^{i}; θ)

with network weights

θ

is trained on

M

training pairs

(}{, S^{i}, Ψ_{REF} ()(, S^{i}))

, where

S^{i}

and

{normalΨ}_{REF} (S^{i}) = \{W, F, f, R_{2}^{*}\}

are the input complex GRE signal and the corresponding reference

T_{2}^{*}

‐IDEAL reconstruction (reference), respectively.…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we adapted the approaches in recent works 13,14 and made W, F, f , and R * 2 the target output of the network. A U-net type network Ψ S i ; with network weights is trained on M training pairs…”

Section: Std Water/fat Separationmentioning

confidence: 99%

“…Recently, deep neural network (DNN) has been used to perform water/fat separation using conventional supervised training of DNN with reference data (STD). 13,14 This STD water/fat separation method does not require an initial guess with the potential use of fewer echoes to shorten the scan time, or improve SNR with the same scan time, and lessen dependency on acquisition parameters compared to current standard approaches. 13,14 However, the training of STD requires reference water/fat reconstructions (label), which can be challenging to calculate as discussed above.…”

mentioning

confidence: 99%

“…13,14 This STD water/fat separation method does not require an initial guess with the potential use of fewer echoes to shorten the scan time, or improve SNR with the same scan time, and lessen dependency on acquisition parameters compared to current standard approaches. 13,14 However, the training of STD requires reference water/fat reconstructions (label), which can be challenging to calculate as discussed above. 15 In this work, we investigate an unsupervised training of DNN (UTD) method that uses the physical forward problem in T * 2 -IDEAL as the cost function during conventional training without a need for reference water/fat reconstructions (label free).…”

mentioning

confidence: 99%

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Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training

Jafari

Spincemaille

Zhang

et al. 2020

Magnetic Resonance in Med

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Purpose: To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training. Methods: The current T * 2-IDEAL algorithm for solving water/fat separation is dependent on initialization. Recently, DNN has been proposed to solve water/fat separation without the need for suitable initialization. However, this approach requires supervised training of DNN using the reference water/fat separation images. Here we propose 2 novel DNN water/fat separation methods: 1) unsupervised training of DNN (UTD) using the physical forward problem as the cost function during training, and 2) no training of DNN using physical cost and backpropagation to directly reconstruct a single dataset. The supervised training of DNN, unsupervised training of DNN, and no training of DNN methods were compared with the reference T * 2-IDEAL. Results: All DNN methods generated consistent water/fat separation results that agreed well with T * 2-IDEAL under proper initialization. Conclusion: The water/fat separation problem can be solved using unsupervised deep neural networks.

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“…In this work, we adapted the approaches in recent works 13,14 and made

W

F, f

, and

R_{2}^{*}

the target output of the network. A U‐net type network

Ψ (S^{i}; θ)

with network weights

θ

is trained on

M

training pairs

(}{, S^{i}, Ψ_{REF} ()(, S^{i}))

, where

S^{i}

and

{normalΨ}_{REF} (S^{i}) = \{W, F, f, R_{2}^{*}\}

are the input complex GRE signal and the corresponding reference

T_{2}^{*}

‐IDEAL reconstruction (reference), respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Std Water/fat Separationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training

Jafari

Spincemaille

Zhang

et al. 2020

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

“…DL-based techniques have been recently proposed for automating myocardial T 1 , T 2 , and extra-cellular volume quantification 89,90 and water/fat separation. 91 DL has also been recently used for on-the-fly quality control-driven segmentation of myocardial T 1 mapping. 92 Despite the prognostic value of scar burden, scar quantification in cardiac MR is limited to research studies without standardization, and DL-based scar quantification has shown promise in scar quantification.…”

Section: Automated Cardiac Mr Image Analysis With DLmentioning

confidence: 99%

Emerging Techniques in Cardiac Magnetic Resonance Imaging

Guo

Weingärtner

Šiurytė

et al. 2021

Magnetic Resonance Imaging

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Cardiovascular disease is the leading cause of death and a significant contributor of health care costs. Noninvasive imaging plays an essential role in the management of patients with cardiovascular disease. Cardiac magnetic resonance (MR) can noninvasively assess heart and vascular abnormalities, including biventricular structure/function, blood hemodynamics, myocardial tissue composition, microstructure, perfusion, metabolism, coronary microvascular function, and aortic distensibility/stiffness. Its ability to characterize myocardial tissue composition is unique among alternative imaging modalities in cardiovascular disease. Significant growth in cardiac MR utilization, particularly in Europe in the last decade, has laid the necessary clinical groundwork to position cardiac MR as an important imaging modality in the workup of patients with cardiovascular disease. Although lack of availability, limited training, physician hesitation, and reimbursement issues have hampered widespread clinical adoption of cardiac MR in the United States, growing clinical evidence will ultimately overcome these challenges. Advances in cardiac MR techniques, particularly faster image acquisition, quantitative myocardial tissue characterization, and image analysis have been critical to its growth. In this review article, we discuss recent advances in established and emerging cardiac MR techniques that are expected to strengthen its capability in managing patients with cardiovascular disease. Level of Evidence: 5 Technical Efficacy: Stage 1

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Deep Learning Staging of Liver Iron Content From Multiecho MR Images

Positano

Meloni

Santarelli

et al. 2022

Magnetic Resonance Imaging

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Background MRI represents the most established liver iron content (LIC) evaluation approach by estimation of liver T2* value, but it is dependent on the choice of the measurement region and the software used for image analysis. Purpose To develop a deep‐learning method for unsupervised classification of LIC from magnitude T2* multiecho MR images. Study Type Retrospective. Population/Subjects A total of 1069 thalassemia major patients enrolled in the core laboratory of the Myocardial Iron Overload in Thalassemia (MIOT) network, which were included in the training (80%) and test (20%) sets. Twenty patients from different MRI vendors included in the external test set. Field Strength/Sequence A5 T, T2* multiecho magnitude images. Assessment Four deep‐learning convolutional neural networks (HippoNet‐2D, HippoNet‐3D, HippoNet‐LSTM, and an ensemble network HippoNet‐Ensemble) were used to achieve unsupervised staging of LIC using five classes (normal, borderline, middle, moderate, severe). The training set was employed to construct the deep‐learning model. The performance of the LIC staging model was evaluated in the test set and in the external test set. The model's performances were assessed by evaluating the accuracy, sensitivity, and specificity with respect to the ground truth labels obtained by T2* measurements and by comparison with operator‐induced variability originating from different region of interest (ROI) placements. Statistical Tests The network's performances were evaluated by single‐class accuracy, specificity, and sensitivity and compared by one‐way repeated measures analysis of variance (ANOVA) and one‐way ANOVA. Results HippoNet‐Ensemble reached an accuracy significantly higher than the other networks, and a sensitivity and specificity higher than HippoNet‐LSTM. Accuracy, sensitivity, and specificity values for the LIC stages were: normal: 0.96/0.93/0.97, borderline: 0.95/0.85/0.98, mild: 0.96/0.88/0.98, moderate: 0.95/0.89/0.97, severe: 0.97/0.95/0.98. Correctly staging of cases was in the range of 85%–95%, depending on the LIC class. Multiclass accuracy was 0.90 against 0.92 for the interobserver variability. Data Conclusion The proposed HippoNet‐Ensemble network can perform unsupervised LIC staging and achieves good prognostic performance. Evidence Level 4 Technical Efficacy Stage 2

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Water–fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network

Cited by 29 publications

References 24 publications

Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training

Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training

Emerging Techniques in Cardiac Magnetic Resonance Imaging

Deep Learning Staging of Liver Iron Content From Multiecho MR Images

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