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Abstract: This paper presents super resolution (SR) of 3D MR images which is effective for brain segmentation as practical application.
In this paper, we study deep three-dimensional convolutional neural networks for the super-resolution of brain magnetic resonance imaging data.
High-Resolution 3D MRI With Deep Generative Networks via Novel ...
www.ncbi.nlm.nih.gov › PMC10501177
Aug 22, 2023 · The SPTSR framework enables the application of deep learning super-resolution to a single stack of multi-slice 2D TSE MRI to achieve 3D ...
Apr 16, 2022 · Deep learning has been applied to the problem of SISR in MRI before. Pham et al. applied a 3D version of the SRCNN network to MR brain images.
Abstract: The objective of this work is to apply 3D super resolution (SR) techniques to brain magnetic resonance (MR) image restoration.
This article is part of the Research Topic Advances in Machine Learning Methods Facilitating Collaborative Image-based Decision Making for Neuroscience
In this article, we propose a methodology and a software solution for carrying out simultaneously high-resolution reconstruction and segmentation of brain MRI ...
Mar 2, 2023 · Conclusions: Our novel segmentation algorithm obtained excellent results on MR images of fetuses with severe brain abnormalities. Analysis of ...
Super‐resolution of brain tumor MRI images based on deep learning
aapm.onlinelibrary.wiley.com › acm2
Sep 15, 2022 · MRBT-SR-GAN exhibited great potential in the early detection and accurate evaluation of the recurrence and prognosis of brain tumors.
Most of the recent leading multiple magnetic resonance imaging (MRI) super-resolution techniques for brain are limited to rigid motion. In this study, the ...