# QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field

arXiv: https://arxiv.org/abs/1905.03356 · HTML: https://mlsanity.com/paper/1905.03356/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2019.

## Built on by (most cited first, 10 of 10)

- [MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mapping](https://mlsanity.com/paper/2101.08413/) (2021)
- [3D Brain and Heart Volume Generative Models: A Survey](https://mlsanity.com/paper/2210.05952/) (2022)
- [Quantitative Susceptibility Mapping through Model-based Deep Image Prior (MoDIP)](https://mlsanity.com/paper/2308.09467/) (2023)
- [Improved Padding in CNNs for Quantitative Susceptibility Mapping](https://mlsanity.com/paper/2106.15331/) (2021)
- [Affine Transformation Edited and Refined Deep Neural Network for Quantitative Susceptibility Mapping](https://mlsanity.com/paper/2211.13942/) (2022)
- [DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging](https://mlsanity.com/paper/2209.04504/) (2022)
- [NeXtQSM -- A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data](https://mlsanity.com/paper/2107.07752/) (2021)
- [Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders](https://mlsanity.com/paper/2203.10645/) (2022)
- [Quantitative Susceptibility Mapping in Cognitive Decline: A Review of Technical Aspects and Applications](https://mlsanity.com/paper/2211.04764/) (2022)
- [IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules](https://mlsanity.com/paper/2406.12300/) (2024)

## Abstract (arXiv)

Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We propose QSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Net architecture with increased receptive field of the input phase compared to the output and further refined the network using the WGAN with gradient penalty training strategy. Our method generates accurate QSM maps from single orientation phase maps efficiently and performs significantly better than traditional non-learning-based dipole inversion algorithms. The generalization capability was verified by applying the algorithm to an unseen pathology--brain tumor patients with radiation-induced cerebral microbleeds.