# Cross Modality Image Translation In Medical Imaging Using Generative Frameworks arXiv: https://arxiv.org/abs/2605.13686 ยท HTML: https://mlsanity.com/paper/2605.13686/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. - Builds on 35 of its 96 references that are in the map. - First posted on arXiv 13 May 2026. ## Placed in - Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/) ## Builds on (most cited first, 30 of 35) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022) - [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023) - [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016) - [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020) - [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021) - [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021) - [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022) - [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://mlsanity.com/paper/2205.07680/) (2022) - [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018) - [Deep MR to CT Synthesis using Unpaired Data](https://mlsanity.com/paper/1708.01155/) (2017) - [GANs for Medical Image Analysis](https://mlsanity.com/paper/1809.06222/) (2018) - [Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Study](https://mlsanity.com/paper/2311.00265/) (2023) - [SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy](https://mlsanity.com/paper/2303.16320/) (2023) - [The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma](https://mlsanity.com/paper/2305.07642/) (2023) - [Explicitly Minimizing the Blur Error of Variational Autoencoders](https://mlsanity.com/paper/2304.05939/) (2023) - [Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model](https://mlsanity.com/paper/2305.19467/) (2023) - [Deep learning-based synthetic-CT generation in radiotherapy and PET: a review](https://mlsanity.com/paper/2102.02734/) (2021) - [SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy](https://mlsanity.com/paper/2502.17609/) (2025) - [Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality](https://mlsanity.com/paper/2503.00266/) (2025) - [Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray images](https://mlsanity.com/paper/2305.07644/) (2023) - [Similarity and Quality Metrics for MR Image-To-Image Translation](https://mlsanity.com/paper/2405.08431/) (2024) - [A study of why we need to reassess full reference image quality assessment with medical images](https://mlsanity.com/paper/2405.19097/) (2024) - [Multimodal Explainability via Latent Shift applied to COVID-19 stratification](https://mlsanity.com/paper/2212.14084/) (2022) - [Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Model](https://mlsanity.com/paper/2407.05059/) (2024) - [Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation](https://mlsanity.com/paper/2308.09345/) (2023) - [A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT Denoising](https://mlsanity.com/paper/2304.05359/) (2023) - [Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin](https://mlsanity.com/paper/2503.15555/) (2025) ## Built on by (most cited first, 1 of 1) - [Compositional Cross-Modality Translation via Whole-Volume Multitask Latent Flow Matching](https://mlsanity.com/paper/2608.08135/) (2026) ## Abstract (arXiv) Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) provide complementary information about tissues. Medical image-to-image (I2I) translation enables virtual scanning by synthesizing a target modality from a source one without requiring an additional acquisition. Despite growing interest, many methods operate on 2D slices, are evaluated on isolated tasks under different experimental settings, and lack clinically oriented assessment. This work presents a reproducible benchmark for 3D I2I translation in oncological imaging that compares seven generative models: three Generative Adversarial Networks (Pix2Pix, CycleGAN, and SRGAN) and four latent models (Latent Diffusion Model, Latent Diffusion Model+ControlNet, Brownian Bridge, and Flow Matching). The benchmark comprises 77 experiments across eleven configurations drawn from five datasets, covering three anatomical regions (head/neck, lung, and pelvis) and four translation directions (cone-beam CT to CT, MRI to CT, CT to PET, and T2-weighted MRI to T2-FLAIR). Under the evaluated configurations, SRGAN achieves the highest quantitative image fidelity across all tasks, while latent models perform less well, due to information loss introduced by the variational autoencoder. A tumor-level analysis reveals that all models struggle with small lesions and that, in CT to PET synthesis, models reproduce tumor shape more reliably than tracer uptake values. A Visual Turing test involving 17 physicians, including 15 radiologists, shows near-chance classification accuracy (56.7%), suggesting that experts struggle to distinguish real from synthetic volumes under the viewing conditions of the study. Expert preferences do not follow quantitative rankings, exposing a dissociation between quantitative metrics and clinical preference.