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Open AccessArticle

Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study

1
Department of Physics, Carleton University, Ottawa, ON, Canada
2
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada
3
Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada
4
BIOmedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada
*
Author to whom correspondence should be addressed.
Tomography 2020, 6(4), 362-372; https://doi.org/10.18383/j.tom.2020.00045
Received: 10 September 2020 / Revised: 6 October 2020 / Accepted: 4 November 2020 / Published: 1 December 2020
We aim to extend the use of image quality metrics (IQMs) from static magnetic resonance imaging (MRI) applications to dynamic MRI studies. We assessed the use of 2 IQMs, the root mean square error and structural similarity index, in evaluating the reconstruction of quantitative dynamic contrast-enhanced (DCE) MRI data acquired using golden-angle sampling and compressed sensing (CS). To address the difficulty of obtaining ground-truth knowledge of parameters describing dynamics in real patient data, we developed a Matlab simulation framework to assess quantitative CS-DCE-MRI. We began by validating the response of each IQM to the CS-MRI reconstruction process using static data and the performance of our simulation framework with simple dynamic data. We then extended the simulations to the more realistic extended Tofts model. When assessing the Tofts model, we tested 4 different methods of selecting a reference image for the IQMs. Results from the retrospective static CS-MRI reconstructions showed that each IQM is responsive to the CS-MRI reconstruction process. Simulations of a simple contrast evolution model validated the performance of our framework. Despite the complexity of the Tofts model, both IQM scores correlated well with the recovery accuracy of a central model parameter for all reference cases studied. This finding may form the basis of algorithms for automated selection of image reconstruction aspects, such as temporal resolution, in golden-angle-sampled CS-DCE-MRI. These further suggest that objective measures of image quality may find use in general dynamic MRI applications.
Keywords: compressive sensing; golden-angle sampling; root mean square error; structural similarity index; image quality metric compressive sensing; golden-angle sampling; root mean square error; structural similarity index; image quality metric
MDPI and ACS Style

Murtha, N.; Mason, A.; Bowen, C.; Clarke, S.; Rioux, J.; Beyea, S. Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study. Tomography 2020, 6, 362-372. https://doi.org/10.18383/j.tom.2020.00045

AMA Style

Murtha N, Mason A, Bowen C, Clarke S, Rioux J, Beyea S. Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study. Tomography. 2020; 6(4):362-372. https://doi.org/10.18383/j.tom.2020.00045

Chicago/Turabian Style

Murtha, Nathan; Mason, Allister; Bowen, Chris; Clarke, Sharon; Rioux, James; Beyea, Steven. 2020. "Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study" Tomography 6, no. 4: 362-372. https://doi.org/10.18383/j.tom.2020.00045

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