Computational Neuroradiology |

Eyelid Tumor Triage using Uncertainty-Aware and Explainable Artificial Intelligence

Fri, 25 Sep 2026 00:00:00 +0000

MRIQT – KI-gestützte Verbesserung der Bildqualität in der neonatalen Ultra-Niedrigfeld-MRT

Mon, 01 Jun 2026 00:00:00 +0000

MRIQT: Physics-Aware Diffusion Model for Image Quality Transfer in Neonatal Ultra-Low-Field MRI

Mon, 17 Nov 2025 00:00:00 +0000

Application of AI and Advanced MRI for a Better Prediction of Disease Progression in Neurological Disorders

Wed, 01 Jan 2025 00:00:00 +0000

TBA

Leveraging Collective Intelligence for Inclusive Global Healthcare

Wed, 01 Jan 2025 00:00:00 +0000

Neonatal Ultra-Low-Field / High-Field MRI Dataset

Wed, 01 Jan 2025 00:00:00 +0000

Curated with the Department of Experimental Neonatology, University Hospital Bonn. Basis of MRIQT, a physics-aware 3D diffusion model for image-quality transfer; expert clinicians rated 85% of generated images as diagnostically useful.

MRIQT: Image-quality transfer from ultra-low-field to high-field neonatal MRI

Mon, 01 Jan 2024 00:00:00 +0000

This project addresses the limited diagnostic usability of portable ultra-low-field MRI (0.064 T) in neonatal neuroimaging, where reduced signal-to-noise ratio constrains clinical interpretation despite bedside accessibility. Together with the clinical partners, we curated a unique in-house cohort comprising 50 paired ultra-low-field and high-field 3D neonatal brain MRI scans, complemented by 230 additional unpaired scans across nine pathologies. Building on this paired–unpaired data structure, we developed MRIQT, a 3D conditional diffusion framework for structure-preserving image quality transfer from ultra-low-field to high-field MRI. The method integrates physics-consistent k-space degradation, classifier-free guided diffusion, and an SNR-weighted volumetric perceptual loss.

MRIQT outperformed CNN- and GAN-based baselines, achieving up to 15.3% improvement in PSNR, while expert clinicians rated 85% of generated images as diagnostically useful with preserved pathology.

Perspective

The project provides a methodological foundation for automated MRI quality enhancement and modality-agnostic quality control. Building on the current results, the work is prepared for translational follow-up and prospective validation, with a planned submission to the Go-Bio Initial funding program.

Joint self-supervised image-volume representation learning with intra-inter contrastive clustering

Sun, 01 Jan 2023 00:00:00 +0000

Lvm-med: Learning large-scale self-supervised vision models for medical imaging via second-order graph matching

Sun, 01 Jan 2023 00:00:00 +0000

Transformer based models for unsupervised anomaly segmentation in brain MR images

Sun, 01 Jan 2023 00:00:00 +0000

Federated disentangled representation learning for unsupervised brain anomaly detection

Sat, 01 Oct 2022 00:00:00 +0000

CTFSH: Full Head CT Anomaly Detection with Unsupervised Learning

Sat, 01 Jan 2022 00:00:00 +0000

ROAM: Random layer mixup for semi-supervised learning in medical images

Sat, 01 Jan 2022 00:00:00 +0000

SPA: Shape-Prior Variational Autoencoders for Unsupervised Brain Pathology Segmentation

Sat, 01 Jan 2022 00:00:00 +0000

Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study