MOdel-Based SyntheTic Data-Driven Learning (MOST-DL): Application in Single-Shot T<sub>2</sub> Mapping With Severe Head Motion Using Overlapping-Echo Acquisition

Yang, Qinqin; Wang, Jiechao; Bao, Jianfeng; Wang, Xiaoyin; Ma, Lu; Yang, Qizhi; Cai, Shuhui; He, Hongjian; Cai, Congbo; Dong, Jiyang; Chen, Zhong; Zhong, Jianhui

doi:10.1109/tmi.2022.3179981

Cited by 19 publications

(21 citation statements)

References 63 publications

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“…Because the acquired MR image (also called overlapping‐echo image) contains multiple

{T}_2^{\ast }

‐weighted signals, it can be used to generate parametric maps (e.g.,

{T}_2^{\ast }

, M 0 , and ∆ B 0 ). In the absence of a 180° pulse and the limitation of echo refocusing, GRE‐MOLED allows higher temporal resolution compared to previous MOLED methods 30–35 . The scan time per slice is reduced from 170 ms (MQMOLED 35 ) to 88 ms (GRE‐MOLED), which facilitates whole‐brain quantitative BOLD FMRI.…”

Section: Methodsmentioning

confidence: 99%

Rapid high‐fidelity T2* mapping using single‐shot overlapping‐echo acquisition and deep learning reconstruction

Yang

Zhou

et al. 2023

Magnetic Resonance in Med

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Purpose To develop and evaluate a single‐shot quantitative MRI technique called GRE‐MOLED (gradient‐echo multiple overlapping‐echo detachment) for rapid T2*$$ {T}_2^{\ast } $$ mapping. Methods In GRE‐MOLED, multiple echoes with different TEs are generated and captured in a single shot of the k‐space through MOLED encoding and EPI readout. A deep neural network, trained by synthetic data, was employed for end‐to‐end parametric mapping from overlapping‐echo signals. GRE‐MOLED uses pure GRE acquisition with a single echo train to deliver T2*$$ {T}_2^{\ast } $$ maps less than 90 ms per slice. The self‐registered B0 information modulated in image phase was utilized for distortion‐corrected parametric mapping. The proposed method was evaluated in phantoms, healthy volunteers, and task‐based FMRI experiments. Results The quantitative results of GRE‐MOLED T2*$$ {T}_2^{\ast } $$ mapping demonstrated good agreement with those obtained from the multi‐echo GRE method (Pearson's correlation coefficient = 0.991 and 0.973 for phantom and in vivo brains, respectively). High intrasubject repeatability (coefficient of variation <1.0%) were also achieved in scan–rescan test. Enabled by deep learning reconstruction, GRE‐MOLED showed excellent robustness to geometric distortion, noise, and random subject motion. Compared to the conventional FMRI approach, GRE‐MOLED also achieved a higher temporal SNR and BOLD sensitivity in task‐based FMRI. Conclusion GRE‐MOLED is a new real‐time technique for T2*$$ {T}_2^{\ast } $$ quantification with high efficiency and quality, and it has the potential to be a better quantitative BOLD detection method.

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“…Because the acquired MR image (also called overlapping‐echo image) contains multiple

{T}_2^{\ast }

‐weighted signals, it can be used to generate parametric maps (e.g.,

{T}_2^{\ast }

Section: Methodsmentioning

confidence: 99%

Rapid high‐fidelity T2* mapping using single‐shot overlapping‐echo acquisition and deep learning reconstruction

Yang

Zhou

et al. 2023

Magnetic Resonance in Med

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“…The synthesis scheme of training samples is depicted in Figure 1. The synthesized ghost‐free EPI image

M( {x,y} )

(named as seed image for its usage of generating multiple training samples) was obtained by the MOST‐DL 32 framework. To make this paper self‐contained, a brief description is provided here.…”

Section: Methodsmentioning

confidence: 99%

“…[27][28][29][30][31][32] Our group recently developed a model-based synthetic data-driven learning framework, termed MOST-DL, to efficiently reconstruct overlapping-echo detachment signal in severe motion scenes. 32 In this framework, Bloch simulation and general MRI models were combined to generate synthetic MRI training samples that are comparable to real-world data. To address the abovementioned EPI limitations, we further extended the MOST-DL framework to the Nyquist ghost correction and parallel imaging reconstruction field.…”

Section: Introductionmentioning

confidence: 99%

“…Training with synthetic data has attracted more and more attention in the field of MRI for addressing the lack of training data 27–32 . Our group recently developed a model‐based synthetic data‐driven learning framework, termed MOST‐DL, to efficiently reconstruct overlapping‐echo detachment signal in severe motion scenes 32 . In this framework, Bloch simulation and general MRI models were combined to generate synthetic MRI training samples that are comparable to real‐world data.…”

Section: Introductionmentioning

confidence: 99%

“…Training with synthetic data has attracted more and more attention in the field of MRI for addressing the lack of training data. [27][28][29][30][31][32] Our group recently developed a model-based synthetic data-driven learning framework, termed MOST-DL, to efficiently reconstruct overlapping-echo detachment signal in severe motion scenes. 32 In this framework, Bloch simulation and general MRI models were combined to generate synthetic MRI training samples that are comparable to real-world data.…”

Section: Introductionmentioning

confidence: 99%

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Learning from synthetic data for reference‐free Nyquist ghost correction and parallel imaging reconstruction of echo planar imaging

et al. 2022

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Background: Echo planar imaging (EPI) suffers from Nyquist ghost caused by eddy currents and other non-ideal factors. Deep learning has received interest for EPI ghost correction.However,large datasets with qualified labels are usually unavailable, especially for the under-sampled EPI data due to the imperfection of traditional ghost correction algorithms. Purpose: To develop a multi-coil synthetic-data-based deep learning method for the Nyquist ghost correction and reconstruction of under-sampled EPI. Methods: Our network is trained purely with synthetic data. The labels of the training samples are generated by combining a public magnetic resonance imaging dataset and a few pre-collected coil sensitivity maps. The input is synthesized by under-sampling (for the accelerated case) and adding phase errors between the even and odd echoes of the label. To bridge the gap between synthetic data and data from real acquisition, linear and non-linear 2D phase errors are considered during the training data generation. Results:The proposed method outperformed the existing mainstream approaches in several experiments. The average ghost-to-signal ratios of our/3-line navigator-based methods were 0.51%/5.36% and 0.42%/8.64% in fully-sampled and under-sampled in vivo experiments, respectively. In the sagittal experiments, our method successfully corrected higher-order and 2D phase errors. Our method also outperformed other reference-based methods on motion-corrupted data. In the simulation experiments, the peak signal-to-noise ratios were 37.6/38.3 dB for 2D linear/non-linear simulated phase errors, indicating that our method was consistently reliable for different kinds of phase errors. Conclusion: Our method achieves superb ghost correction and parallel imaging reconstruction without any calibration information, and can be readily adapted to other EPI-based applications.

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Preoperative Subtyping of WHO Grade 1 Meningiomas Using a Single‐Shot Ultrafast MR T2 Mapping

Li,

Zhang,

Wang

et al. 2023

Magnetic Resonance Imaging

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BackgroundMeningioma subtype is crucial in treatment planning and prognosis delineation, for grade 1 meningiomas. T2 relaxometry could provide detailed microscopic information but is often limited by long scanning times.PurposeTo investigate the potential of T2 maps derived from multiple overlapping‐echo detachment imaging (MOLED) for predicting meningioma subtypes and Ki‐67 index, and to compare the diagnostic efficiency of two different region‐of‐interest (ROI) placements (whole‐tumor and contrast‐enhanced, respectively).Study TypeProspective.Phantom/SubjectsA phantom containing 11 tubes of MnCl2 at different concentrations, eight healthy volunteers, and 75 patients with grade 1 meningioma.Field Strength/Sequence3 T scanner. MOLED, T2‐weighted spin‐echo sequence, T2‐dark‐fluid sequence, and postcontrast T1‐weighted gradient echo sequence.AssessmentTwo ROIs were delineated: the whole‐tumor area (ROI1) and contrast‐enhanced area (ROI2). Histogram parameters were extracted from T2 maps. Meningioma subtypes and Ki‐67 index were reviewed by a neuropathologist according to the 2021 classification criteria.Statistical TestsLinear regression, Bland–Altman analysis, Pearson's correlation analysis, independent t test, Mann–Whitney U test, Kruskal–Wallis test with Bonferroni correction, and multivariate logistic regression analysis with the P‐value significance level of 0.05.ResultsThe MOLED T2 sequence demonstrated excellent accuracy for phantoms and volunteers (Meandiff = −1.29%, SDdiff = 1.25% and Meandiff = 0.36%, SDdiff = 2.70%, respectively), and good repeatability for volunteers (average coefficient of variance = 1.13%; intraclass correlation coefficient = 0.877). For both ROI1 and ROI2, T2 variance had the highest area under the curves (area under the ROC curve = 0.768 and 0.761, respectively) for meningioma subtyping. There was no significant difference between the two ROIs (P = 0.875). Significant correlations were observed between T2 parameters and Ki‐67 index (r = 0.237–0.374).Data ConclusionMOLED T2 maps can effectively differentiate between meningothelial, fibrous, and transitional meningiomas. Moreover, T2 histogram parameters were significantly correlated with the Ki‐67 index.Level of Evidence1Technical EfficacyStage 2

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MOdel-Based SyntheTic Data-Driven Learning (MOST-DL): Application in Single-Shot T₂ Mapping With Severe Head Motion Using Overlapping-Echo Acquisition

Cited by 19 publications

References 63 publications

Rapid high‐fidelity T2* mapping using single‐shot overlapping‐echo acquisition and deep learning reconstruction

Rapid high‐fidelity T2* mapping using single‐shot overlapping‐echo acquisition and deep learning reconstruction

Learning from synthetic data for reference‐free Nyquist ghost correction and parallel imaging reconstruction of echo planar imaging

Preoperative Subtyping of WHO Grade 1 Meningiomas Using a Single‐Shot Ultrafast MR T2 Mapping

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MOdel-Based SyntheTic Data-Driven Learning (MOST-DL): Application in Single-Shot T2 Mapping With Severe Head Motion Using Overlapping-Echo Acquisition

Cited by 19 publications

References 63 publications

Rapid high‐fidelity T2* mapping using single‐shot overlapping‐echo acquisition and deep learning reconstruction

Rapid high‐fidelity T2* mapping using single‐shot overlapping‐echo acquisition and deep learning reconstruction

Learning from synthetic data for reference‐free Nyquist ghost correction and parallel imaging reconstruction of echo planar imaging

Preoperative Subtyping of WHO Grade 1 Meningiomas Using a Single‐Shot Ultrafast MR T2 Mapping

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MOdel-Based SyntheTic Data-Driven Learning (MOST-DL): Application in Single-Shot T₂ Mapping With Severe Head Motion Using Overlapping-Echo Acquisition