Corticoreticular Pathway in Post-Stroke Spasticity: A Diffusion Tensor Imaging Study

One of the pathophysiologies of post-stroke spasticity (PSS) is the imbalance of the reticulospinal tract (RST) caused by injury to the corticoreticular pathway (CRP) after stroke. We investigated the relationship between injuries of the CRP and PSS using MR diffusion tensor imaging (DTI). The subjects were divided into spasticity and control groups. We measured the ipsilesional fractional anisotropy (iFA) and contralesional fractional anisotropy (cFA) values on the reticular formation (RF) of the CRP were on the DTI images. We carried out a retrospective analysis of 70 patients with ischemic stroke. The cFA values of CRP in the spasticity group were lower than those in the control group (p = 0.04). In the sub-ROI analysis of CRP, the iFA values of pontine RF were lower than the cFA values in both groups (p < 0.05). The cFA values of medullary RF in the spasticity group were lower than the iFA values within groups, and also lower than the cFA values in the control group (p < 0.05). This results showed the CRP injury and that imbalance of RST caused by CRP injury was associated with PSS. DTI analysis of CRP could provide imaging evidence for the pathophysiology of PSS.

Multiparametric MRI Texture Analysis in Prediction of Glioma Biomarker Status: Added Value of MR Diffusion

Background Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma. Methods In this retrospective study, patients were included if they 1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53 and PTEN status from surgical pathology and 2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs. conventional + diffusion) was performed. Results From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (p&lt;0.05) increased predictive performance for IDH1, MGMT and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT) and 75% (EGFR). Conclusion Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.

A new framework for MR diffusion tensor distribution

The ability to characterize heterogeneous and anisotropic water diffusion processes within macroscopic MRI voxels non-invasively and in vivo is a desideratum in biology, neuroscience, and medicine. While an MRI voxel may contain approximately a microliter of tissue, our goal is to examine intravoxel diffusion processes on the order of picoliters. Here we propose a new theoretical framework and efficient experimental design to describe and measure such intravoxel structural heterogeneity and anisotropy. We assume that a constrained normal tensor-variate distribution (CNTVD) describes the variability of positive definite diffusion tensors within a voxel which extends its applicability to a wide range of b-values while preserving the richness of diffusion tensor distribution (DTD) paradigm unlike existing models. We introduce a new Monte Carlo (MC) scheme to synthesize realistic 6D DTD numerical phantoms and invert the MR signal. We show that the signal inversion is well-posed and estimate the CNTVD parameters parsimoniously by exploiting the different symmetries of the mean and covariance tensors of CNTVD. The robustness of the estimation pipeline is assessed by adding noise to calculated MR signals and compared with the ground truth. A family of invariant parameters and glyphs which characterize microscopic shape, size and orientation heterogeneity within a voxel are also presented.

Application of First-Order Feature Analysis of DWI-ADC in Rare Malignant Mesenchymal Tumours of the Maxillofacial Region

Objective: To research the first-order features of magnetic resonance (MR) diffusion-weighted imaging (DWI)-apparent diffusion coefficient (ADC) in maxillofacial malignant mesenchymal tumours. Methods: Eight patients that the patients' diagnoses were confirmed by pathology, and the clinical and imaging data were determined to be accurate. The patients were all examined by 1.5T MR imaging (MRI). Results: PyRadiomics were used to extract radiomics imaging features. The ADCmean and ADCmedian of sarcoma tissues were 42.2689 and 42.7275, respectively, significantly higher than those in lymphoma tissues (ADCmean (-61.3343) and ADCmedian (-70.2335)). Conclusion: While the statistical difference is not significant, it is consistent with the outcome of the manual measurement of the ADC mean value of the most significant cross-section of five cases of lymphoma. Development of tumour volume based on the ADC parameter map of DWI demonstrates that the first-order ADC radiomics features analysis can provide new imaging markers for the differentiation of maxillofacial sarcoma and lymphoma. Therefore, first-order ADC features of ADCkurt combined ADCskew may improve the diagnosis level.