KEYWORDS: Magnetic resonance imaging, Diffusion tensor imaging, Principal component analysis, Shape analysis, Surgery, Tissues, Anisotropy, Signal to noise ratio
Quantitative MRI (qMRI) has been shown to be crucial for assessing organ dysfunction in the body. Usually, in qMRI approaches, a few metrics are extracted to distinguish normal and abnormal tissues. In this study, we coupled four MRI protocols (mDIXON T1, T1 and T2 mapping and DTI) to obtain 34 complementary metrics including 20 shape metrics, 2 texture metrics and 12 water diffusivity metrics for thigh muscle analysis. These metrics were calculated on both thighs to detect a pathological difference between a pair of right and left muscles. The method is based on a dimension reduction method and a projection of shape and diffusivity metrics into a three-dimensional linear latent space, along with two texture metrics. 5 healthy individuals (10 thighs, each thigh 7 muscles, i.e., 4 flexors and 3 extensors) were scanned to provide the reference scores. The developed pipeline was used to analyse the thighs of 4 patients in order to suggest a specific muscle therapy before total knee arthroplasty (TKA) and for each of the 7 muscles studied. Preliminary results from the analysis of thigh muscle texture, shape and diffusivity showed that this qMRI protocol can help to suggest a targeted, patient-specific exercise plan to improve muscle recovery after TKA surgery. More healthy and pathological subjects are needed to confirm these encouraging results.
Purpose: Brain image volumetric measurements (BVM) methods have been used to quantify brain tissue volumes using magnetic resonance imaging (MRI) when investigating abnormalities. Although BVM methods are widely used, they need to be evaluated to quantify their reliability. Currently, the gold-standard reference to evaluate a BVM is usually manual labeling measurement. Manual volume labeling is a time-consuming and expensive task, but the confidence level ascribed to this method is not absolute. We describe and evaluate a biomimetic brain phantom as an alternative for the manual validation of BVM.
Methods: We printed a three-dimensional (3D) brain mold using an MRI of a three-year-old boy diagnosed with Sturge-Weber syndrome. Then we prepared three different mixtures of styrene-ethylene/butylene-styrene gel and paraffin to mimic white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The mold was filled by these three mixtures with known volumes. We scanned the brain phantom using two MRI scanners, 1.5 and 3.0 Tesla. Our suggestion is a new challenging model to evaluate the BVM which includes the measured volumes of the phantom compartments and its MRI. We investigated the performance of an automatic BVM, i.e., the expectation–maximization (EM) method, to estimate its accuracy in BVM.
Results: The automatic BVM results using the EM method showed a relative error (regarding the phantom volume) of 0.08, 0.03, and 0.13 (±0.03 uncertainty) percentages of the GM, CSF, and WM volume, respectively, which was in good agreement with the results reported using manual segmentation.
Conclusions: The phantom can be a potential quantifier for a wide range of segmentation methods.
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