# Learning Implicit Brain MRI Manifolds with Deep Learning

arXiv: https://arxiv.org/abs/1801.01847 · HTML: https://mlsanity.com/paper/1801.01847/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2018.

## Built on by (most cited first, 20 of 20)

- [GANs for Medical Image Synthesis: An Empirical Study](https://mlsanity.com/paper/2105.05318/) (2021)
- [MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation](https://mlsanity.com/paper/2304.04106/) (2023)
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- [Translating Simulation Images to X-ray Images via Multi-Scale Semantic Matching](https://mlsanity.com/paper/2304.07693/) (2023)
- [MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks](https://mlsanity.com/paper/2401.09624/) (2024)
- [Integrating Anatomical Priors into a Causal Diffusion Model](https://mlsanity.com/paper/2509.09054/) (2025)
- [Self-supervised training of deep denoisers in multi-coil MRI considering noise correlations](https://mlsanity.com/paper/2208.07552/) (2022)
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- [Method and Software Tool for Generating Artificial Databases of Biomedical Images Based on Deep Neural Networks](https://mlsanity.com/paper/2405.16119/) (2024)

## Abstract (arXiv)

An important task in image processing and neuroimaging is to extract quantitative information from the acquired images in order to make observations about the presence of disease or markers of development in populations. Having a lowdimensional manifold of an image allows for easier statistical comparisons between groups and the synthesis of group representatives. Previous studies have sought to identify the best mapping of brain MRI to a low-dimensional manifold, but have been limited by assumptions of explicit similarity measures. In this work, we use deep learning techniques to investigate implicit manifolds of normal brains and generate new, high-quality images. We explore implicit manifolds by addressing the problems of image synthesis and image denoising as important tools in manifold learning. First, we propose the unsupervised synthesis of T1-weighted brain MRI using a Generative Adversarial Network (GAN) by learning from 528 examples of 2D axial slices of brain MRI. Synthesized images were first shown to be unique by performing a crosscorrelation with the training set. Real and synthesized images were then assessed in a blinded manner by two imaging experts providing an image quality score of 1-5. The quality score of the synthetic image showed substantial overlap with that of the real images. Moreover, we use an autoencoder with skip connections for image denoising, showing that the proposed method results in higher PSNR than FSL SUSAN after denoising. This work shows the power of artificial networks to synthesize realistic imaging data, which can be used to improve image processing techniques and provide a quantitative framework to structural changes in the brain.