alexander-koch/xmodality
Code for the paper "Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based models"
This tool helps radiologists and researchers generate synthetic CTA (Computed Tomography Angiography) images from existing TOF-MRA (Time-of-Flight Magnetic Resonance Angiography) scans. You provide a TOF-MRA image, and the system produces a corresponding CTA image, which can be useful when only MRA data is available or for research into multi-modal medical imaging. Radiologists and medical imaging scientists who work with cerebrovascular imaging would use this.
No commits in the last 6 months.
Use this if you need to create CTA images from TOF-MRA scans for research, training, or when CTA data is unavailable or difficult to acquire.
Not ideal if you need clinical-grade CTA images for patient diagnosis, as synthetic images may not be suitable for primary diagnostic use.
Stars
9
Forks
3
Language
Python
License
AGPL-3.0
Category
Last pushed
Jul 30, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/alexander-koch/xmodality"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
zhangyi-3/IDR
Self-Supervised Image Denoising via Iterative Data Refinement (CVPR2022)
yinboc/liif
Learning Continuous Image Representation with Local Implicit Image Function, in CVPR 2021 (Oral)
XingangPan/deep-generative-prior
Code for deep generative prior (ECCV2020 oral)
mv-lab/swin2sr
[ECCV] Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration. ...
eliahuhorwitz/DeepSIM
Official PyTorch implementation of the paper: "DeepSIM: Image Shape Manipulation from a Single...