Abstract
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.
Highlights
Motivation for This WorkMagnetic resonance imaging (MRI) can provide static anatomical information and capture dynamic processes such as the uptake of an intravenously injected contrast agent with dynamic contrast-enhanced magnetic resonance imaging (MRI) (DCE-MRI)
The regularization weight resulting in preferential image quality metrics (IQMs) score tended to differ between the IQMs at each undersampling factor
We have determined the relationship between 2 IQMs, root mean square error (RMSE) and structural similarity index (SSIM), and their correlation with the recovery accuracy of Ktrans in compressed sensing (CS)-dynamic contrast-enhanced MRI (DCE-MRI)
Summary
Motivation for This WorkMagnetic resonance imaging (MRI) can provide static anatomical information and capture dynamic processes such as the uptake of an intravenously injected contrast agent with dynamic contrast-enhanced MRI (DCE-MRI). Rather than using a fixed temporal resolution for such dynamic scans, emerging methods based on golden-angle sampling such as XD-GRASP (1) or CIRCUS (2) allow retrospective temporal resampling at the time of image reconstruction. This framework allows retrospective control of the temporal “footprint” by varying the number of k-space rays or spokes per undersampled image, without necessarily requiring the use of view sharing, which can introduce temporal blurring of fast dynamics (2, 3). This is true when one considers the large and intertwined joint optimization space associated with these reconstructions, in which the undersampling factor (ie, temporal footprint), the form of regularization enforced, and the degree of regularization must all be simultaneously examined
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