Inline AI: Open-source Deep Learning Inference for Cardiac MR
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909159396474880 |
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| author | Xue, Hui Davies, Rhodri H Howard, James Shiwani, Hunain Rehman, Azaan Pierce, Iain Procter, Henry Fontana, Marianna Moon, James C Levelt, Eylem Kellman, Peter |
| author_facet | Xue, Hui Davies, Rhodri H Howard, James Shiwani, Hunain Rehman, Azaan Pierce, Iain Procter, Henry Fontana, Marianna Moon, James C Levelt, Eylem Kellman, Peter |
| contents | Cardiac Magnetic Resonance (CMR) is established as a non-invasive imaging technique for evaluation of heart function, anatomy, and myocardial tissue characterization. Quantitative biomarkers are central for diagnosis and management of heart disease. Deep learning (DL) is playing an ever more important role in extracting these quantitative measures from CMR images. While many researchers have reported promising results in training and evaluating models, model deployment into the imaging workflow is less explored.
A new imaging AI framework, the InlineAI, was developed and open-sourced. The main innovation is to enable the model inference inline as a part of imaging computation, instead of as an offline post-processing step and to allow users to plug in their models. We demonstrate the system capability on three applications: long-axis CMR cine landmark detection, short-axis CMR cine analysis of function and anatomy, and quantitative perfusion mapping.
The InlineAI allowed models to be deployed into imaging workflow in a streaming manner directly on the scanner. The model was loaded and inference on incoming images were performed while the data acquisition was ongoing, and results were sent back to scanner. Several biomarkers were extracted from model outputs in the demonstrated applications and reported as curves and tabular values. All processes are full automated. the model inference was completed within 6-45s after the end of imaging data acquisition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02384 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Inline AI: Open-source Deep Learning Inference for Cardiac MR Xue, Hui Davies, Rhodri H Howard, James Shiwani, Hunain Rehman, Azaan Pierce, Iain Procter, Henry Fontana, Marianna Moon, James C Levelt, Eylem Kellman, Peter Image and Video Processing Cardiac Magnetic Resonance (CMR) is established as a non-invasive imaging technique for evaluation of heart function, anatomy, and myocardial tissue characterization. Quantitative biomarkers are central for diagnosis and management of heart disease. Deep learning (DL) is playing an ever more important role in extracting these quantitative measures from CMR images. While many researchers have reported promising results in training and evaluating models, model deployment into the imaging workflow is less explored. A new imaging AI framework, the InlineAI, was developed and open-sourced. The main innovation is to enable the model inference inline as a part of imaging computation, instead of as an offline post-processing step and to allow users to plug in their models. We demonstrate the system capability on three applications: long-axis CMR cine landmark detection, short-axis CMR cine analysis of function and anatomy, and quantitative perfusion mapping. The InlineAI allowed models to be deployed into imaging workflow in a streaming manner directly on the scanner. The model was loaded and inference on incoming images were performed while the data acquisition was ongoing, and results were sent back to scanner. Several biomarkers were extracted from model outputs in the demonstrated applications and reported as curves and tabular values. All processes are full automated. the model inference was completed within 6-45s after the end of imaging data acquisition. |
| title | Inline AI: Open-source Deep Learning Inference for Cardiac MR |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2404.02384 |