Inline AI: Open-source Deep Learning Inference for Cardiac MR

no code implementations • 3 Apr 2024 • Hui Xue, Rhodri H Davies, James Howard, Hunain Shiwani, Azaan Rehman, Iain Pierce, Henry Procter, Marianna Fontana, James C Moon, Eylem Levelt, Peter Kellman

The model was loaded and inference on incoming images were performed while the data acquisition was ongoing, and results were sent back to scanner.

Anatomy

Landmark detection in Cardiac Magnetic Resonance Imaging Using A Convolutional Neural Network

1 code implementation • 14 Aug 2020 • Hui Xue, Jessica Artico, Marianna Fontana, James C. Moon, Rhodri H. Davies, Peter Kellman

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

no code implementations • 2 Nov 2019 • Hui Xue, Rhodri Davies, Louis AE Brown, Kristopher D Knott, Tushar Kotecha, Marianna Fontana, Sven Plein, James C. Moon, Peter Kellman

This solution was integrated on the MR scanner, enabling 'one-click' analysis and reporting of myocardial blood flow.

Segmentation

Automated Detection of Left Ventricle in Arterial Input Function Images for Inline Perfusion Mapping using Deep Learning: A study of 15,000 Patients

no code implementations • 16 Oct 2019 • Hui Xue, Ethan Tseng, Kristopher D Knott, Tushar Kotecha, Louise Brown, Sven Plein, Marianna Fontana, James C. Moon, Peter Kellman

The 3CS model successfully detect LV for 99. 98% of all test cases (1 failed out of 5, 721 cases).

Time Series Analysis