# Complex Swin Transformer for Accelerating Enhanced SMWI Reconstruction

arXiv: https://arxiv.org/abs/2512.22202 · HTML: https://mlsanity.com/paper/2512.22202/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 21 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages](https://mlsanity.com/paper/1812.10411/) (2018)
- [Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection](https://mlsanity.com/paper/1801.08322/) (2018)
- [Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding](https://mlsanity.com/paper/1902.07430/) (2019)
- [Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning](https://mlsanity.com/paper/1912.13335/) (2019)
- [Using Deep Autoencoders for Facial Expression Recognition](https://mlsanity.com/paper/1801.08329/) (2018)
- [Automating Motion Correction in Multishot MRI Using Generative Adversarial Networks](https://mlsanity.com/paper/1811.09750/) (2018)
- [Dual-Stage Deeply Supervised Attention-based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans](https://mlsanity.com/paper/2210.03739/) (2022)
- [Biological Brain Age Estimation using Sex-Aware Adversarial Variational Autoencoder with Multimodal Neuroimages](https://mlsanity.com/paper/2412.05632/) (2024)
- [Anatomy-Guided Representation Learning Using a Transformer-Based Network for Thyroid Nodule Segmentation in Ultrasound Images](https://mlsanity.com/paper/2512.12662/) (2025)

## Built on by (most cited first, 1 of 1)

- [Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI](https://mlsanity.com/paper/2512.20436/) (2025)

## Abstract (arXiv)

Susceptibility Map Weighted Imaging (SMWI) is an advanced magnetic resonance imaging technique used to detect nigral hyperintensity in Parkinsons disease. However, full resolution SMWI acquisition is limited by long scan times. Efficient reconstruction methods are therefore required to generate high quality SMWI from reduced k space data while preserving diagnostic relevance. In this work, we propose a complex valued Swin Transformer based network for super resolution reconstruction of multi echo MRI data. The proposed method reconstructs high quality SMWI images from low resolution k space inputs. Experimental results demonstrate that the method achieves a structural similarity index of 0.9116 and a mean squared error of 0.076 when reconstructing SMWI from 256 by 256 k space data, while maintaining critical diagnostic features. This approach enables high quality SMWI reconstruction from reduced k space sampling, leading to shorter scan times without compromising diagnostic detail. The proposed method has the potential to improve the clinical applicability of SMWI for Parkinsons disease and support faster and more efficient neuroimaging workflows.