Distortion correction of single-shot EPI enabled by deep-learning

Hu, Zhangxuan; Wang, Yishi; Zhang, Zhe; Zhang, Jieying; Zhang, Huimao; Guo, Chunjie; Sun, Yuejiao; Guo, Hua

doi:10.1016/j.neuroimage.2020.117170

Cited by 33 publications

(48 citation statements)

References 33 publications

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“…Last, we note as shown in Table . Table . 4 that Voss performs significantly better in the subjective evaluation than TOPUP. However, TOPUP method is recognized as the major method for the postprocessing distortion correction of diffusion MRI data [31,32].…”

Section: Discussionmentioning

confidence: 99%

Geometric Evaluation of Distortion Correction Methods in Diffusion MRI of the Spinal Cord

Snoussi

Caruyer

Cohen‐Adad

et al. 2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

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Acquiring and processing Diffusion MRI in spinal cord present inherent challenges. Differences in magnetic susceptibility between soft tissues, air and bone make the magnetic field of spinal cord non-uniform and inhomogeneous. In this context various procedures were proposed for correcting this distortion. In this work, we propose novel geometric statistics to measure the alignment of the reconstructed diffusion model with the apparent centerline of the spine. In parallel of the correlation with an anatomical T2-weighted image, we show the utility of these statistics to study and evaluate the impact of distortion correction by comparing 3 distortion correction methods from reversed gradient polarity strategy.

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Section: Discussionmentioning

confidence: 99%

Geometric Evaluation of Distortion Correction Methods in Diffusion MRI of the Spinal Cord

Snoussi

Caruyer

Cohen‐Adad

et al. 2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

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“…First, although the method for echo plane imaging-distortion correction (i.e. non-linear registration) used in the current study is common in the field, state-of-the-art distortion correction methods like file-mapping [80], topup-based approach [81][82][83], or machine learning approaches [84,85] are encouraged to be used in future studies. Second, currently there is no widely accepted optimal approach to defining nodes and edges.…”

Section: Discussionmentioning

confidence: 99%

Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis

Wei

Hou

et al. 2021

Transl Neurodegener

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Objective There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate. Methods Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms. Results Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient Cp (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small‐worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%. Conclusion Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.

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“…However, it may fail when the field inhomogeneity patterns are different. Hu et al [19] proposed a Unet-based method for SS-EPI distortion correction, where high-quality D distortion-free PSF-EPI images were used as references to correct distorted SS-EPI images. This method outperformed the field-mapping and top-up methods.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Super-Resolution and Distortion Correction for Single-shot EPI DWI using Deep Learning

Wang

et al. 2021

Preprint

Self Cite

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Single-shot echo planer imaging (SS-EPI) is widely used for clinical Diffusion-weighted magnetic resonance imaging (DWI) acquisitions. However, due to the limited bandwidth along the phase encoding direction, the obtained images suffer from distortion and blurring, which limits its clinical value for diagnosis. Here we proposed a deep learning-based image-quality-transfer method with a novel loss function with improved network structure to simultaneously increase the resolution and correct distortions for SS-EPI. We proposed a modified network structure based on Generative Adversarial Networks (GAN). A dense net with gradient map guidance and a multi-level fusion block was employed as the generator to suppress the over-smoothing effect. We also proposed a fractional anisotropy (FA) loss to exploit the intrinsic signal relations in DWI. In-vivo brain DWI data were used to test the proposed method. The results showed that the distortion-corrected high-resolution DWI images with restored anatomical details can be obtained from low-resolution SS-EPI images by taking the advantage of high-resolution anatomical images. Additionally, the proposed FA loss can improve the image quality and quantitative accuracy of diffusion metrics by utilizing the intrinsic relations among different diffusion directions.

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Distortion correction of single-shot EPI enabled by deep-learning

Cited by 33 publications

References 33 publications

Geometric Evaluation of Distortion Correction Methods in Diffusion MRI of the Spinal Cord

Geometric Evaluation of Distortion Correction Methods in Diffusion MRI of the Spinal Cord

Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis

Simultaneous Super-Resolution and Distortion Correction for Single-shot EPI DWI using Deep Learning

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