ArticleScientific reports2021
Deep learning-Based 3D inpainting of brain MR images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction.Sensors (Basel, Switzerland) · 2024Pooled it
- POWDR: Pathology-Preserving Outpainting with Wavelet Diffusion for 3D MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Contusions bias cortical thickness estimates after traumatic brain injury: A TRACK-TBI study.NeuroImage. Clinical · 2026Article
- PRN: progressive reasoning network and its image completion applications.Scientific reports · 2024Article
- Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method.Nuclear medicine and molecular imaging · 2024Article
- Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network.The British journal of radiology · 2024Article
- Brain morphometry in older adults with and without dementia using extremely rapid structural scans.NeuroImage · 2023Article
- Body composition assessment with limited field-of-view computed tomography: A semantic image extension perspective.Medical image analysis · 2023Article
- Voxel-Based Internal Dosimetry forNuclear medicine and molecular imaging · 2023Article
- nnU-Net Deep Learning Method for Segmenting Parenchyma and Determining Liver Volume From Computed Tomography Images.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2022Article
- Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks.Stem cells international · 2022Article
- Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images.Frontiers in oncology · 2022Article
- Classification of Gliomas and Germinomas of the Basal Ganglia by Transfer Learning.Frontiers in oncology · 2022Article
- Motion correction in MR image for analysis of VSRAD using generative adversarial network.PloS one · 2022Article
- Simultaneous brain structure segmentation in magnetic resonance images using deep convolutional neural networks.Radiological physics and technology · 2021Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The detailed anatomical information of the brain provided by 3D magnetic resonance imaging (MRI) enables various neuroscience research. However, due to the long scan time for 3D MR images, 2D images are mainly obtained in clinical environments. The purpose of this study is to generate 3D images from a sparsely sampled 2D images using an inpainting deep neural network that has a U-net-like structure and DenseNet sub-blocks. To train the network, not only fidelity loss but also perceptual loss based on the VGG network were considered. Various methods were used to assess the overall similarity between the inpainted and original 3D data. In addition, morphological analyzes were performed to investigate whether the inpainted data produced local features similar to the original 3D data. The diagnostic ability using the inpainted data was also evaluated by investigating the pattern of morphological changes in disease groups. Brain anatomy details were efficiently recovered by the proposed neural network. In voxel-based analysis to assess gray matter volume and cortical thickness, differences between the inpainted data and the original 3D data were observed only in small clusters. The proposed method will be useful for utilizing advanced neuroimaging techniques with 2D MRI data.
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