Abstract
Magnetic resonance imaging (MRI) images acquired as multislice two-dimensional (2D) images present challenges when reformatted in orthogonal planes due to sparser sampling in the through-plane direction. Restoring the "missing" through-plane slices, or regions of an MRI image damaged by acquisition artifacts can be modeled as an image imputation task. In this work, we consider the damaged image data or missing through-plane slices as image masks and proposed an edge-guided generative adversarial network to restore brain MRI images. Inspired by the procedure of image inpainting, our proposed method decouples image repair into two stages: edge connection and contrast completion, both of which used general adversarial networks (GAN). We trained and tested on a dataset from the Human Connectome Project to test the application of our method for thick slice imputation, while we tested the artifact correction on clinical data and simulated datasets. Our Edge-Guided GAN had superior PSNR, SSIM, conspicuity and signal texture compared to traditional imputation tools, the Context Encoder and the Densely Connected Super Resolution Network with GAN (DCSRN-GAN). The proposed network may improve utilization of clinical 2D scans for 3D atlas generation and big-data comparative studies of brain morphometry.
Highlights
Magnetic resonance imaging (MRI), as an indispensable tool for medical diagnosis and imaging research, offers detailed visualization of the human torso, extremities and brain
While a plethora of two-dimensional (2D) MRI scans are acquired in hospitals, retrieving missing information due to image artifacts, or due to large slice thickness is of great importance, especially for downstream meta-analyses
Neither context encoder nor EG-general adversarial networks (GAN) exhibit the stairway effects or broken white matter tracts of the interpolation methods, and both exhibits many of the fine details of the brain anatomy, the result of edge-guided GAN (EG-GAN) appears much more visually plausible, and the method is capable of restoring fine details of small vessels (the second magnified region in Fig.4 (d)) as well as distinguishable and smooth boundaries for the cortex
Summary
Magnetic resonance imaging (MRI), as an indispensable tool for medical diagnosis and imaging research, offers detailed visualization of the human torso, extremities and brain. While a plethora of two-dimensional (2D) MRI scans are acquired in hospitals, retrieving missing information due to image artifacts, or due to large slice thickness is of great importance, especially for downstream meta-analyses. The associate editor coordinating the review of this manuscript and approving it for publication was Junxiu Liu. motion artifact due to respiration or other movement of the imaging subject [4], [5]. Motion artifact due to respiration or other movement of the imaging subject [4], [5] It appears as blurring or coherent ghosting, and in more severe case, it smears the image. Many of them exhibit as voids in the images All of these often leads to the discarding of the affected slice, and a loss of potentially crucial information, especially in cases of pathologic conditions. Correcting the artifact affected slices is of great importance for both clinical and research work
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