Joint arterial input function and tracer kinetic parameter estimation from undersampled dynamic contrast‐enhanced MRI using a model consistency constraint

Purpose To develop and evaluate a model‐based reconstruction framework for joint arterial input function (AIF) and kinetic parameter estimation from undersampled brain tumor dynamic contrast‐enhanced MRI (DCE‐MRI) data. Methods The proposed method poses the tracer‐kinetic (TK) model as a model consi...

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Published in:Magnetic resonance in medicine Vol. 79; no. 5; pp. 2804 - 2815
Main Authors: Guo, Yi, Lingala, Sajan Goud, Bliesener, Yannick, Lebel, R. Marc, Zhu, Yinghua, Nayak, Krishna S.
Format: Journal Article
Language:English
Published: United States Wiley Subscription Services, Inc 01.05.2018
Subjects:
Brain
Brain cancer
Brain mapping
Brain tumors
compressed sensing
Consistency
Constraint modelling
Data processing
DCE‐MRI
Image reconstruction
kinetic modeling
Magnetic resonance imaging
model‐based reconstruction
Parameter estimation
Solvers
DCE-MRI
compressed sensing
kinetic modeling
model-based reconstruction
ISSN:0740-3194, 1522-2594, 1522-2594
Online Access:Get full text
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Summary:Purpose To develop and evaluate a model‐based reconstruction framework for joint arterial input function (AIF) and kinetic parameter estimation from undersampled brain tumor dynamic contrast‐enhanced MRI (DCE‐MRI) data. Methods The proposed method poses the tracer‐kinetic (TK) model as a model consistency constraint, enabling the flexible inclusion of different TK models and TK solvers, and the joint estimation of the AIF. The proposed method is evaluated using an anatomic realistic digital reference object (DRO), and nine retrospectively down‐sampled brain tumor DCE‐MRI datasets. We also demonstrate application to 30‐fold prospectively undersampled brain tumor DCE‐MRI. Results In DRO studies with up to 60‐fold undersampling, the proposed method provided TK maps with low error that were comparable to fully sampled data and were demonstrated to be compatible with a third‐party TK solver. In retrospective undersampling studies, this method provided patient‐specific AIF with normalized root mean‐squared‐error (normalized by the 90th percentile value) less than 8% at up to 100‐fold undersampling. In the 30‐fold undersampled prospective study, the proposed method provided high‐resolution whole‐brain TK maps and patient‐specific AIF. Conclusion The proposed model‐based DCE‐MRI reconstruction enables the use of different TK solvers with a model consistency constraint and enables joint estimation of patient‐specific AIF. TK maps and patient‐specific AIF with high fidelity can be reconstructed at up to 100‐fold undersampling in k,t‐space. Magn Reson Med 79:2804–2815, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
Bibliography:The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Correction added after online publication 13 October 2017. The authors updated the title to correct “Tracker” to “Tracer.”
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ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.26904