Leveraging Self-Supervised Learning for Non-Invasive Intra-Cardiac Magnetic Resonance Oximetry Assessment
Chen, J.; Pham, T.-H.; Zhang, P.; Varghese, J.
Show abstract
Accurate measurement of intra-cardiac blood oxygen (O2) saturation is essential for cardiovascular assessment, yet current methods require invasive catheterization. T2-based cardiac magnetic resonance imaging (CMRI) enables non-invasive O2 quantification, but deep learning automation is constrained by scarce annotated data. We propose a unified self-supervised learning (SSL) framework integrating cine CMRI and T2 oximetry CMRI to learn generalizable representations without labels. Our approach pre-trains ResNet and vision transformer encoders using contrastive learning and masked image modeling on over 48,000 cardiac images. Pre-trained encoders are fine-tuned for O2 saturation regression with uncertainty quantification to enhance clinical trustworthiness. Our SSL framework significantly outperforms traditional radiomics and supervised baselines, with SimCLR pre-trained ResNet achieving a mean absolute error of 3.70, representing over 15\% improvement. These findings demonstrate SSL's potential to address annotation bottlenecks in non-invasive cardiac diagnostics.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- SpinFlowSim: a blood flow simulation framework for histology-informed diffusion MRI microvasculature mapping in cancer 95%
- dStripe: slice artefact correction in diffusion MRI via constrained neural network 94%
- Automated 3D reconstruction of the fetal thorax in the standard atlas space from motion-corrupted MRI stacks for 21-36 weeks GA range 93%
Similar papers in this journal
- Deep Learning Based Cardiac Cine Segmentation – Transfer Learning Application to 7T Ultrahigh-Field MRI 95%
- Image- vs. histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images 94%
- Spherical Echo-Planar Time-resolved Imaging (sEPTI) for rapid 3D quantitative T2* and Susceptibility imaging 94%
Similar papers in this journal
Similar papers in this journal
- An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids 94%
- Probabilistic Brain MR Image Transformation Using Generative Models 94%
- Dual Adversarial Deconfounding Autoencoder for joint batch-effects removal from multi-center and multi-scanner radiomics data 93%
Similar papers in this journal
- Weakly supervised classification of rare aortic valve malformations using unlabeled cardiac MRI sequences 94%
- EchoFM: A Pre-training and Fine-tuning Framework for Echocardiogram Videos Vision Foundation Model 94%
- Lowering the Thermal Noise Barrier in Functional Brain Mapping with Magnetic Resonance Imaging 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.