Direct parametric reconstruction from undersampled (k,t)-space data in dynamic contrast enhanced MRI
Open accessAbstract
The Magnetic Resonance Imaging (MRI) signal can be made sensitive to functional parameters that provide information about tissues. In dynamic contrast enhanced (DCE) MRI these functional parameters are related to the microvasculature environment and the concentration changes that occur rapidly after the injection of a contrast agent. Typically DCE images are reconstructed individually and kinetic parameters are estimated by fitting a pharmacokinetic model to the time-enhancement response; these methods can be denoted as "indirect". If undersampling is present to accelerate the acquisition, techniques such as kt-FOCUSS can be employed in the reconstruction step to avoid image degradation. This paper suggests a Bayesian inference framework to estimate functional parameters directly from the measurements at high temporal resolution. The current implementation estimates pharmacokinetic parameters (related to the extended Tofts model) from undersampled (k, t)-space DCE MRI. The proposed scheme is evaluated on a simulated abdominal DCE phantom and prostate DCE data, for fully sampled, 4 and 8-fold undersampled (k, t)-space data. Direct kinetic parameters demonstrate better correspondence (up to 70% higher mutual information) to the ground truth kinetic parameters (of the simulated abdominal DCE phantom) than the ones derived from the indirect methods. For the prostate DCE data, direct kinetic parameters depict the morphology of the tumour better. To examine the impact on cancer diagnosis, a peripheral zone prostate cancer diagnostic model was employed to calculate a probability map for each method.
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- Authors
- Dikaios, N., Arridge, S., Hamy, V., Punwani, S., & Atkinson, D.
- Affiliations
- University College London (GB)
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 18
- Issue
- 7
- Pages
- 989-1001
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2084824769
- Cited by
- 40
- References
- 44
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 4 · 2022: 5 · 2021: 7 · 2020: 6 · 2019: 6 · 2018: 3 · 2017: 3 · 2016: 2 · 2015: 1
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Medical Image Analysis (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- UK EPSRC (EP/I018700/1, EP/H046410/1)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- dynamic contrast-enhanced MRI, parametric image reconstruction, Bayesian inference, extended Tofts model, pharmacokinetic parameters, prostate cancer, peripheral zone, microcirculation
- MeSH terms
- Algorithms, Bayes Theorem, Computer Simulation, Contrast Media, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Prostatic Neoplasms, Sensitivity and Specificity, Reproducibility of Results, Respiratory Mechanics, Phantoms, Imaging
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