# Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement

arXiv: https://arxiv.org/abs/2410.10269 · HTML: https://mlsanity.com/paper/2410.10269/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation](https://mlsanity.com/paper/2204.13738/) (2022)
- [Hybrid-Fusion Transformer for Multisequence MRI](https://mlsanity.com/paper/2311.01308/) (2023)

## Built on by (most cited first, 2 of 2)

- [SLaM-DiMM: Shared Latent Modeling for Diffusion Based Missing Modality Synthesis in MRI](https://mlsanity.com/paper/2509.16019/) (2025)
- [Unified Multi-plane Autoregressive Diffusion for 3D Multi-contrast MRI Synthesis](https://mlsanity.com/paper/2610.09376/) (2026)

## Abstract (arXiv)

Despite significant advancements in automatic brain tumor segmentation methods, their performance is not guaranteed when certain MR sequences are missing. Addressing this issue, it is crucial to synthesize the missing MR images that reflect the unique characteristics of the absent modality with precise tumor representation. Typically, MRI synthesis methods generate partial images rather than full-sized volumes due to computational constraints. This limitation can lead to a lack of comprehensive 3D volumetric information and result in image artifacts during the merging process. In this paper, we propose a two-stage approach that first synthesizes MR images from 2D slices using a novel intensity encoding method and then refines the synthesized MRI. The proposed intensity encoding reduces artifacts when synthesizing MRI on a 2D slice basis. Then, the textitRefiner, which leverages complete 3D volume information, further improves the quality of the synthesized images and enhances their applicability to segmentation methods. Experimental results demonstrate that the intensity encoding effectively minimizes artifacts in the synthesized MRI and improves perceptual quality. Furthermore, using the textitRefiner on synthesized MRI significantly improves brain tumor segmentation results, highlighting the potential of our approach in practical applications.