Journal article
Joint arterial input function and tracer kinetic parameter estimation from undersampled dynamic contrast‐enhanced MRI using a model consistency constraint
Magnetic resonance in medicine, Vol.79(5), pp.2804-2815
05/2018
DOI: 10.1002/mrm.26904
PMCID: PMC5821580
PMID: 28905411
Abstract
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.
Details
- Title: Subtitle
- Joint arterial input function and tracer kinetic parameter estimation from undersampled dynamic contrast‐enhanced MRI using a model consistency constraint
- Creators
- Yi Guo - University of Southern CaliforniaSajan Goud Lingala - University of Southern CaliforniaYannick Bliesener - University of Southern CaliforniaR. Marc Lebel - GE HealthcareYinghua Zhu - University of Southern CaliforniaKrishna S Nayak - University of Southern California
- Resource Type
- Journal article
- Publication Details
- Magnetic resonance in medicine, Vol.79(5), pp.2804-2815
- DOI
- 10.1002/mrm.26904
- PMID
- 28905411
- PMCID
- PMC5821580
- NLM abbreviation
- Magn Reson Med
- ISSN
- 0740-3194
- eISSN
- 1522-2594
- Number of pages
- 12
- Grant note
- National Institutes of Health (Award Number 5UL1TR000130‐04) National Center for Research Resources (Award Number UL1RR031986) National Center for Advancing Translational Sciences of the National Institutes of Health (Award number: UL1TR000130)
- Language
- English
- Date published
- 05/2018
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984197146702771
Metrics
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