@InProceedings{Chadebec_2025_ICCV,
    author    = {Chadebec, Cl\'ement and Tasar, Onur and Sreetharan, Sanjeev and Aubin, Benjamin},
    title     = {LBM: Latent Bridge Matching for Fast Image-to-Image Translation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2025},
    pages     = {29086-29098}
}

@article{shi2023diffusion,
  title={Diffusion schr{\"o}dinger bridge matching},
  author={Shi, Yuyang and De Bortoli, Valentin and Campbell, Andrew and Doucet, Arnaud},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  pages={62183--62223},
  year={2023}
}

@InProceedings{Li_2023_CVPR,
    author    = {Li, Bo and Xue, Kaitao and Liu, Bin and Lai, Yu-Kun},
    title     = {BBDM: Image-to-Image Translation With Brownian Bridge Diffusion Models},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {1952-1961}
}

@InProceedings{phung2023wavediff,
    author    = {Phung, Hao and Dao, Quan and Tran, Anh},
    title     = {Wavelet Diffusion Models Are Fast and Scalable Image Generators},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {10199-10208}
}

@inproceedings{friedrich2024wdm,
               title={Wdm: 3d wavelet diffusion models for high-resolution medical image synthesis},
               author={Friedrich, Paul and Wolleb, Julia and Bieder, Florentin and Durrer, Alicia and Cattin, Philippe C},
               booktitle={MICCAI Workshop on Deep Generative Models},
               pages={11--21},
               year={2024},
               organization={Springer}}

@article{friedrich2024cwdm,
         title={cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis},
         author={Paul Friedrich and Alicia Durrer and Julia Wolleb and Philippe C. Cattin},
         year={2024},
         journal={arXiv preprint arXiv:2411.17203}}

@Article{Saad2024,
author={Saad, Muhammad Muneeb
and O'Reilly, Ruairi
and Rehmani, Mubashir Husain},
title={A survey on training challenges in generative adversarial networks for biomedical image analysis},
journal={Artificial Intelligence Review},
year={2024},
month={Jan},
day={29},
volume={57},
number={2},
pages={19},
abstract={In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring large image datasets to provide high-level performance. Generative Adversarial Networks (GANs) have been widely utilized to address data limitations through the generation of synthetic biomedical images. GANs consist of two models. The generator, a model that learns how to produce synthetic images based on the feedback it receives. The discriminator, a model that classifies an image as synthetic or real and provides feedback to the generator. Throughout the training process, a GAN can experience several technical challenges that impede the generation of suitable synthetic imagery. First, the mode collapse problem whereby the generator either produces an identical image or produces a uniform image from distinct input features. Second, the non-convergence problem whereby the gradient descent optimizer fails to reach a Nash equilibrium. Thirdly, the vanishing gradient problem whereby unstable training behavior occurs due to the discriminator achieving optimal classification performance resulting in no meaningful feedback being provided to the generator. These problems result in the production of synthetic imagery that is blurry, unrealistic, and less diverse. To date, there has been no survey article outlining the impact of these technical challenges in the context of the biomedical imagery domain. This work presents a review and taxonomy based on solutions to the training problems of GANs in the biomedical imaging domain. This survey highlights important challenges and outlines future research directions about the training of GANs in the domain of biomedical imagery.},
issn={1573-7462},
doi={10.1007/s10462-023-10624-y},
url={https://doi.org/10.1007/s10462-023-10624-y}
}

@article{pix2pix2017,
  title={Image-to-Image Translation with Conditional Adversarial Networks},
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
  journal={CVPR},
  year={2017}
}

@inproceedings{CycleGAN2017,
  title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkss},
  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
  booktitle={Computer Vision (ICCV), 2017 IEEE International Conference on},
  year={2017}
}

@article{dalmaz2022resvit,
  title={ResViT: Residual vision transformers for multimodal medical image synthesis},
  author={Dalmaz, Onat and Yurt, Mahmut and {\c{C}}ukur, Tolga},
  journal={IEEE Transactions on Medical Imaging},
  volume={41},
  number={10},
  pages={2598--2614},
  year={2022},
  publisher={IEEE}
}

@article{ozbey2023unsupervised,
  title={Unsupervised medical image translation with adversarial diffusion models},
  author={{\"O}zbey, Muzaffer and Dalmaz, Onat and Dar, Salman UH and Bedel, Hasan A and {\"O}zturk, {\c{S}}aban and G{\"u}ng{\"o}r, Alper and Cukur, Tolga},
  journal={IEEE Transactions on Medical Imaging},
  volume={42},
  number={12},
  pages={3524--3539},
  year={2023},
  publisher={IEEE}
}

@article{liu20232,
  title={{I$^2$SB}: Image-to-Image {Schr\"odinger} Bridge},
  author={Liu, Guan-Horng and Vahdat, Arash and Huang, De-An and Theodorou, Evangelos A and Nie, Weili and Anandkumar, Anima},
  journal={arXiv preprint arXiv:2302.05872},
  year={2023}
}

@inproceedings{kim2024adaptive,
  title={Adaptive latent diffusion model for 3d medical image to image translation: Multi-modal magnetic resonance imaging study},
  author={Kim, Jonghun and Park, Hyunjin},
  booktitle={Proceedings of the IEEE/CVF Winter conference on applications of computer Vision},
  pages={7604--7613},
  year={2024}
}

@article{baid2021rsna,
  title={The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification},
  author={Baid, Ujjwal and Ghodasara, Satyam and Mohan, Suyash and Bilello, Michel and Calabrese, Evan and Colak, Errol and Farahani, Keyvan and Kalpathy-Cramer, Jayashree and Kitamura, Felipe C and Pati, Sarthak and others},
  journal={arXiv preprint arXiv:2107.02314},
  year={2021}
}

@misc{lipman2023flow,
      title={Flow Matching for Generative Modeling}, 
      author={Yaron Lipman and Ricky T. Q. Chen and Heli Ben-Hamu and Maximilian Nickel and Matt Le},
      year={2023},
      eprint={2210.02747},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2210.02747}, 
}

@article{song2020denoising,
  title={Denoising diffusion implicit models},
  author={Song, Jiaming and Meng, Chenlin and Ermon, Stefano},
  journal={arXiv preprint arXiv:2010.02502},
  year={2020}
}

@misc{salimans2022progressivedistillationfastsampling,
      title={Progressive Distillation for Fast Sampling of Diffusion Models}, 
      author={Tim Salimans and Jonathan Ho},
      year={2022},
      eprint={2202.00512},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2202.00512}, 
}

@inproceedings{song2023consistency,
  title={Consistency models},
  author={Song, Yang and Dhariwal, Prafulla and Chen, Mark and Sutskever, Ilya},
  booktitle={International Conference on Machine Learning},
  pages={32211--32252},
  year={2023}
}

@inproceedings{chang2025controllable,
author = {Chang, Heng and Shang, Yu and Wang, Haifeng and Liang, Yuxia and Wang, Haoyu and Wang, Fan and Niu, Chen and Lian, Chunfeng},
title = {Controllable Flow Matching for 3D Contrast-Enhanced Brain MRI Synthesis from Non-contrast Scans},
year = {2025},
isbn = {978-3-032-05324-4},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-032-05325-1_12},
doi = {10.1007/978-3-032-05325-1_12},
abstract = {Magnetic resonance imaging (MRI) enhanced by the gado-linium-based contrast agents (GBCAs) is crucial in the assessment and management of cancer. However, the use of GBCAs introduces additional costs and raises potential safety concerns, including the risk of gadolinium accumulation in brain. Several generative learning methods based on GANs and diffusion models have been proposed to generate contrast-enhanced MRI from non-contrast-enhanced MRI. However, GANs face challenges such as gradient vanishing and mode collapse. Diffusion models also face several challenges, such as generation instability and long sampling times. In this paper, we propose a controllable flow matching (CFM) model for efficient synthesis of 3D contrast-enhanced brain MRI with fine-grained details of targets of interests. CFM adopts a straight-line generation path, enabling the generation of images in a single step. We design a multi-stage training strategy integrating controllable constraints to ensure that such a single-step sampling generating contrast-enhanced MRI meet specific controllable conditions. Our CFM model has been evaluated on both the BraTS2023 and an in-house datasets. Experimental results demonstrate that CFM led to state-of-the-art image generation and tumor delineation performance with promising generalizability. Our codes can be found at .},
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025: 28th International Conference, Daejeon, South Korea, September 23--27, 2025, Proceedings, Part XVI},
pages = {119--128},
numpages = {10},
keywords = {Contrast-Enhanced Brain MRI Synthesis, Flow Matching, One-Step Generation, Controllable Generation},
location = {Daejeon, Korea (Republic of)}
}

@ARTICLE{chartsias2017multimodal,
  author={Chartsias, Agisilaos and Joyce, Thomas and Giuffrida, Mario Valerio and Tsaftaris, Sotirios A.},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Multimodal MR Synthesis via Modality-Invariant Latent Representation}, 
  year={2018},
  volume={37},
  number={3},
  pages={803-814},
  keywords={Image segmentation;Robustness;Decoding;Biological neural networks;Neural network;multi-modality fusion;magnetic resonance imaging (MRI);machine learning;brain},
  doi={10.1109/TMI.2017.2764326}}

@article{haca3paper,
title = {HACA3: A unified approach for multi-site MR image harmonization},
journal = {Computerized Medical Imaging and Graphics},
volume = {109},
pages = {102285},
year = {2023},
issn = {0895-6111},
doi = {https://doi.org/10.1016/j.compmedimag.2023.102285},
url = {https://www.sciencedirect.com/science/article/pii/S0895611123001039},
author = {Lianrui Zuo and Yihao Liu and Yuan Xue and Blake E. Dewey and Samuel W. Remedios and Savannah P. Hays and Murat Bilgel and Ellen M. Mowry and Scott D. Newsome and Peter A. Calabresi and Susan M. Resnick and Jerry L. Prince and Aaron Carass},
keywords = {MRI, Harmonization, Standardization, Disentanglement, Attention, Contrastive learning, Synthesis},
abstract = {The lack of standardization and consistency of acquisition is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in hardware and acquisition parameters. In recent years, image synthesis-based MR harmonization with disentanglement has been proposed to compensate for the undesired contrast variations. The general idea is to disentangle anatomy and contrast information from MR images to achieve cross-site harmonization. Despite the success of existing methods, we argue that major improvements can be made from three aspects. First, most existing methods are built upon the assumption that multi-contrast MR images of the same subject share the same anatomy. This assumption is questionable, since different MR contrasts are specialized to highlight different anatomical features. Second, these methods often require a fixed set of MR contrasts for training (e.g., both T1-weighted and T2-weighted images), limiting their applicability. Lastly, existing methods are generally sensitive to imaging artifacts. In this paper, we present Harmonization with Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), a novel approach to address these three issues. HACA3 incorporates an anatomy fusion module that accounts for the inherent anatomical differences between MR contrasts. Furthermore, HACA3 can be trained and applied to any combination of MR contrasts and is robust to imaging artifacts. HACA3 is developed and evaluated on diverse MR datasets acquired from 21 sites with varying field strengths, scanner platforms, and acquisition protocols. Experiments show that HACA3 achieves state-of-the-art harmonization performance under multiple image quality metrics. We also demonstrate the versatility and potential clinical impact of HACA3 on downstream tasks including white matter lesion segmentation for people with multiple sclerosis and longitudinal volumetric analyses for normal aging subjects. Code is available at https://github.com/lianruizuo/haca3.}
}

@misc{wang2024generaltextguidedimagesynthesis,
      title={Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI Generation}, 
      author={Yulin Wang and Honglin Xiong and Kaicong Sun and Shuwei Bai and Ling Dai and Zhongxiang Ding and Jiameng Liu and Qian Wang and Qian Liu and Dinggang Shen},
      year={2024},
      eprint={2409.16818},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2409.16818}, 
}