# TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method

arXiv: https://arxiv.org/abs/2402.11274 · HTML: https://mlsanity.com/paper/2402.11274/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 12 of its 15 references that are in the map.
- First posted on arXiv 17 Feb 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [Score-based diffusion models for accelerated MRI](https://mlsanity.com/paper/2110.05243/) (2021)
- [FreeU: Free Lunch in Diffusion U-Net](https://mlsanity.com/paper/2309.11497/) (2023)
- [Adaptive Diffusion Priors for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2207.05876/) (2022)
- [Towards performant and reliable undersampled MR reconstruction via diffusion model sampling](https://mlsanity.com/paper/2203.04292/) (2022)
- [Over-and-Under Complete Convolutional RNN for MRI Reconstruction](https://mlsanity.com/paper/2106.08886/) (2021)
- [Global Multi-modal 2D/3D Registration via Local Descriptors Learning](https://mlsanity.com/paper/2205.03439/) (2022)
- [Deep De-Aliasing for Fast Compressive Sensing MRI](https://mlsanity.com/paper/1705.07137/) (2017)

## Built on by (most cited first, 5 of 5)

- [No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation](https://mlsanity.com/paper/2509.15017/) (2025)
- [DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous Vehicle Testing](https://mlsanity.com/paper/2411.09451/) (2024)
- [TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scaling](https://mlsanity.com/paper/2408.05705/) (2024)
- [LDPM: Towards undersampled MRI reconstruction with MR-VAE and Latent Diffusion Prior](https://mlsanity.com/paper/2411.02951/) (2024)
- [Variational Network with Wavelet-based UNET in Accelerated MRI Reconstruction from Under Sampled K-space Data](https://mlsanity.com/paper/2606.15167/) (2026)

## Abstract (arXiv)

Recently, diffusion models have gained significant attention as a novel set of deep learning-based generative methods. These models attempt to sample data from a Gaussian distribution that adheres to a target distribution, and have been successfully adapted to the reconstruction of MRI data. However, as an unconditional generative model, the diffusion model typically disrupts image coordination because of the consistent projection of data introduced by conditional bootstrap. This often results in image fragmentation and incoherence. Furthermore, the inherent limitations of the diffusion model often lead to excessive smoothing of the generated images. In the same vein, some deep learning-based models often suffer from poor generalization performance, meaning their effectiveness is greatly affected by different acceleration factors. To address these challenges, we propose a novel diffusion model-based MRI reconstruction method, named TC-DiffRecon, which does not rely on a specific acceleration factor for training. We also suggest the incorporation of the MF-UNet module, designed to enhance the quality of MRI images generated by the model while mitigating the over-smoothing issue to a certain extent. During the image generation sampling process, we employ a novel TCKG module and a Coarse-to-Fine sampling scheme. These additions aim to harmonize image texture, expedite the sampling process, while achieving data consistency. Our source code is available at https://github.com/JustlfC03/TC-DiffRecon.