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Found 6 papers, 1 papers with code
Inline AI: Open-source Deep Learning Inference for Cardiac MR
The model was loaded and inference on incoming images were performed while the data acquisition was ongoing, and results were sent back to scanner.
Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy
The ability to recover MRI signal from noise is key to achieve fast acquisition, accurate quantification, and high image quality.
Cut out the annotator, keep the cutout: better segmentation with weak supervision
We propose a framework that fuses limited label learning and weak supervision for segmentation tasks, enabling users to train high-performing segmentation CNNs with very few hand-labeled training points.
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).
