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Found 7 papers, 2 papers with code
Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR << 1 Imaging
Purpose: To propose a flexible and scalable imaging transformer (IT) architecture with three attention modules for multi-dimensional imaging data and apply it to MRI denoising with very low input SNR.
SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation
Out-of-distribution tests were conducted on cardiac real-time cine, first-pass cardiac perfusion, and neuro and spine MRI, all acquired at 1. 5T, to test model generalization across imaging sequences, dynamically changing contrast, different anatomies, and field strengths.
A 3D deep learning classifier and its explainability when assessing coronary artery disease
Furthermore, the current deep learning methods do not provide exact explainability and limit the usefulness of these methods to be deployed in clinical settings.
Landmark detection in Cardiac Magnetic Resonance Imaging Using A Convolutional Neural Network
Conclusions: This study developed, validated and deployed a CNN solution for robust landmark detection in both long and short-axis CMR images for cine, LGE and T1 mapping sequences, with the accuracy comparable to the inter-operator variation.
Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning
This solution was integrated on the MR scanner, enabling 'one-click' analysis and reporting of myocardial blood flow.
Automated Detection of Left Ventricle in Arterial Input Function Images for Inline Perfusion Mapping using Deep Learning: A study of 15,000 Patients
The 3CS model successfully detect LV for 99. 98% of all test cases (1 failed out of 5, 721 cases).
Improving the generalizability of convolutional neural network-based segmentation on CMR images
We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy.
