Conference proceeding
Arterial input function and tracer kinetic model-driven network for rapid inference of kinetic maps in Dynamic Contrast-Enhanced MRI (AIF-TK-net)
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020-, pp.1450-1453
04/2020
DOI: 10.1109/ISBI45749.2020.9098349
Abstract
We propose a patient-specific arterial input function (AIF) and tracer kinetic (TK) model-driven network to rapidly estimate the extended Tofts- Kety kinetic model parameters in DCE-MRI. We term our network as AIF-TK-net, which maps an input comprising of an image patch of the DCE-time series and the patient-specific AIF to the output image patch of the TK parameters. We leverage the open-source NEURO-RIDER database of brain tumor DCE-MRI scans to train our network. Once trained, our model rapidly infers the TK maps of unseen DCE-MRI images on the order of a 0.34 sec/slice for a 256x256x65 time series data on a NVIDIA GeForce GTX 1080 Ti GPU. We show its utility on high time resolution DCE-MRI datasets where significant variability in AIFs across patients exists. We demonstrate that the proposed AIF - TK net considerably improves the TK parameter estimation accuracy in comparison to a network, which does not utilize the patient AIF.
Details
- Title: Subtitle
- Arterial input function and tracer kinetic model-driven network for rapid inference of kinetic maps in Dynamic Contrast-Enhanced MRI (AIF-TK-net)
- Creators
- Joseph Kettelkamp - Roy J Carver Department of Biomedical EngineeringSajan Goud Lingala - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020-, pp.1450-1453
- Publisher
- IEEE
- DOI
- 10.1109/ISBI45749.2020.9098349
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Language
- English
- Date published
- 04/2020
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984197125702771
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