13 citations · 38 across the 30 of their papers we have counts for
30 papers
CMRVision: A Foundation Model for Cardiac MR Image Analysis
Athira J. Jacob, Puneet Sharma, Daniel Rueckert
Cardiac magnetic resonance (CMR) imaging provides complementary information on cardiac anatomy, function, and tissue characterization across multiple sequences and views. In this w…
MR-JEPA: A General Purpose Video Foundation Model for Cardiac MRI
Athira J. Jacob, Puneet Sharma, Dorin Comaniciu +1
Cardiac magnetic resonance imaging (CMR) produces rich sequential data such as temporal cine videos and spatial LGE/mapping stacks, yet most deep learning approaches process indivi…
Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction
Veronika Spieker, Wenqi Huang, Cemre Ariyurek +5
Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan-specific reconstructions usin…
Distortion-Corrected Diffusion MRI Using Rotated-View EPI and Joint Field-Map/Image Estimation with Gaussian Primitives
Wenqi Huang, Zhitao Li, Nan Wang +8
Echo Planar Imaging (EPI) is the standard acquisition technique for diffusion and functional neuroimaging, enabling rapid imaging but suffering from geometric distortions caused by…
Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification
Nikola Cenikj, Özgün Turgut, Alexander Müller +5
Coronary artery stenosis is a common cardiovascular disease, with severe, untreated cases posing significant risks of heart attack. Although coronary (X-ray) angiograms remain the…
LGESynthNet: Controlled Scar Synthesis for Improved Scar Segmentation in Cardiac LGE-MRI Imaging
Athira J. Jacob, Puneet Sharma, Daniel Rueckert
Segmentation of enhancement in LGE cardiac MRI is critical for diagnosing various ischemic and non-ischemic cardiomyopathies. However, creating pixel-level annotations for these im…
Opportunistic Cardiac Health Assessment: Estimating Phenotypes from Localizer MRI through Multi-Modal Representations
Busra Nur Zeybek, Özgün Turgut, Yundi Zhang +5
Cardiovascular diseases are the leading cause of death. Cardiac phenotypes (CPs), e.g., ejection fraction, are the gold standard for assessing cardiac health, but they are derived…
No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space
Yundi Zhang, Sevgi Gokce Kafali, Niklas Bubeck +2
Conventional clinical CMR pipelines rely on a sequential "reconstruct-then-analyze" paradigm, forcing an ill-posed intermediate step that introduces avoidable artifacts and informa…
Gabor Primitives for Accelerated Cardiac Cine MRI Reconstruction
Wenqi Huang, Veronika Spieker, Nil Stolt-Ansó +6
Accelerated cardiac cine MRI requires reconstructing spatiotemporal images from highly undersampled k-space data. Implicit neural representations (INRs) enable scan-specific recons…
Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos
Michelle Espranita Liman, Özgün Turgut, Alexander Müller +3
Electrocardiography (ECG) is a low-cost, widely used modality for diagnosing electrical abnormalities like atrial fibrillation by capturing the heart's electrical activity. However…
Stroke outcome and evolution prediction from CT brain using a spatiotemporal diffusion autoencoder
Adam Marcus, Paul Bentley, Daniel Rueckert
Stroke is a major cause of death and disability worldwide. Accurate outcome and evolution prediction has the potential to revolutionize stroke care by individualizing clinical deci…
Towards a Unified Theoretical Framework for Splitting-based Self-Supervised MRI Reconstruction
Siying Xu, Kerstin Hammernik, Daniel Rueckert +2
The demand for high-resolution, non-invasive imaging continues to drive innovation in magnetic resonance imaging (MRI), but long acquisition times remain a major practical limitati…
Evaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations
Johannes Moll, Markus Graf, Tristan Lemke +7
Vision-language models (VLMs) often produce chain-of-thought (CoT) explanations that sound plausible yet fail to reflect the underlying decision process, undermining trust in high-…
Unsupervised whole-heart function assessment
Yundi Zhang, Daniel Rueckert, Jiazhen Pan
Motivation: CMR is the golden standard for cardiac diagnosis, and medical data annotation is time-consuming. Thus, screening techniques from unlabeled data can help streamline the…
Reconstruction-free segmentation from undersampled k-space using transformers
Yundi Zhang, Nil Stolt-Ansó, Jiazhen Pan +3
Motivation: High acceleration factors place a limit on MRI image reconstruction. This limit is extended to segmentation models when treating these as subsequent independent process…
Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Niklas Bubeck, Suprosanna Shit, Chen Chen +8
Cardiac Magnetic Resonance (CMR) imaging is a critical tool for diagnosing and managing cardiovascular disease, yet its utility is often limited by the sparse acquisition of 2D sho…
Reconstruct or Generate: Exploring the Spectrum of Generative Modeling for Cardiac MRI
Niklas Bubeck, Yundi Zhang, Suprosanna Shit +2
In medical imaging, generative models are increasingly relied upon for two distinct but equally critical tasks: reconstruction, where the goal is to restore medical imaging (usuall…
How Far Have Medical Vision-Language Models Come? A Comprehensive Benchmarking Study
Che Liu, Jiazhen Pan, Weixiang Shen +3
Vision-Language Models (VLMs) trained on web-scale corpora excel at natural image tasks and are increasingly repurposed for healthcare; however, their competence in medical tasks r…
Self-supervised feature learning for cardiac Cine MR image reconstruction
Siying Xu, Marcel Früh, Kerstin Hammernik +6
We propose a self-supervised feature learning assisted reconstruction (SSFL-Recon) framework for MRI reconstruction to address the limitation of existing supervised learning method…
Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL
Che Liu, Haozhe Wang, Jiazhen Pan +6
Improving performance on complex tasks and enabling interpretable decision making in large language models (LLMs), especially for clinical applications, requires effective reasonin…
Physics-Informed Implicit Neural Representations for Joint B0 Estimation and Echo Planar Imaging
Wenqi Huang, Nan Wang, Congyu Liao +4
Echo Planar Imaging (EPI) is widely used for its rapid acquisition but suffers from severe geometric distortions due to B0 inhomogeneities, particularly along the phase encoding di…
PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI
Veronika Spieker, Hannah Eichhorn, Wenqi Huang +9
Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet, reducing acquisition time…
Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging
Wenqi Huang, Veronika Spieker, Siying Xu +6
Conventional cardiac cine MRI methods rely on retrospective gating, which limits temporal resolution and the ability to capture continuous cardiac dynamics, particularly in patient…
Highly efficient non-rigid registration in k-space with application to cardiac Magnetic Resonance Imaging
Aya Ghoul, Kerstin Hammernik, Andreas Lingg +4
In Magnetic Resonance Imaging (MRI), high temporal-resolved motion can be useful for image acquisition and reconstruction, MR-guided radiotherapy, dynamic contrast-enhancement, flo…