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We aimed to compare the ability of diffusion tensor imaging and multi‐compartment spherical mean technique to detect focal tissue damage and in distinguishing between different connectivity patterns associated with varying clinical outcomes in multiple sclerosis (MS). Seventy‐six people diagnosed with MS were scanned using a SIEMENS Prisma Fit 3T magnetic resonance imaging (MRI), employing both conventional (T1w and fluid‐attenuated inversion recovery) and advanced diffusion MRI sequences from which fractional anisotropy (FA) and microscopic FA (μFA) maps were generated. Using automated fiber quantification (AFQ), we assessed diffusion profiles across multiple white matter (WM) pathways to measure the sensitivity of anisotropy diffusion metrics in detecting localized tissue damage. In parallel, we analyzed structural brain connectivity in a specific patient cohort to fully grasp its relationships with cognitive and physical clinical outcomes. This evaluation comprehensively considered different patient categories, including cognitively preserved (CP), mild cognitive deficits (MCD), and cognitively impaired (CI) for cognitive assessment, as well as groups distinguished by physical impact: those with mild disability (Expanded Disability Status Scale [EDSS] <=3) and those with moderate–severe disability (EDSS >3). In our initial objective, we employed Ridge regression to forecast the presence of focal MS lesions, comparing the performance of μFA and FA. μFA exhibited a stronger association with tissue damage and a higher predictive precision for focal MS lesions across the tracts, achieving an R‐squared value of.57, significantly outperforming the R‐squared value of.24 for FA (p‐value <.001). In structural connectivity, μFA exhibited more pronounced differences than FA in response to alteration in both cognitive and physical clinical scores in terms of effect size and number of connections. Regarding cognitive groups, FA differences between CP and MCD groups were limited to 0.5% of connections, mainly around the thalamus, while μFA revealed changes in 2.5% of connections. In the CP and CI group comparison, which have noticeable cognitive differences, the disparity was 5.6% for FA values and 32.5% for μFA. Similarly, μFA outperformed FA in detecting WM changes between the MCD and CI groups, with 5% versus 0.3% of connections, respectively. When analyzing structural connectivity between physical disability groups, μFA still demonstrated superior performance over FA, disclosing a 2.1% difference in connectivity between regions closely associated with physical disability in MS. In contrast, FA spotted a few regions, comprising only 0.6% of total connections. In summary, μFA emerged as a more effective tool than FA in predicting MS lesions and identifying structural changes across patients with different degrees of cognitive and global disability, offering deeper insights into the complexities of MS‐related impairments. [ABSTRACT FROM AUTHOR]

Introduction: Small vessel disease (SVD) causes most spontaneous intracerebral haemorrhage (ICH) and is associated with widespread microstructural brain tissue disruption, which can be quantified via diffusion tensor imaging (DTI) metrics: mean diffusivity (MD) and fractional anisotropy (FA). Little is known about the impact of whole-brain microstructural alterations after SVD-related ICH. We aimed to investigate: (1) association between whole-brain DTI metrics and functional outcome after ICH; and (2) predictive ability of these metrics compared to the pre-existing ICH score. Methods: Sixty-eight patients (38.2% lobar) were retrospectively included. We assessed whole-brain DTI metrics (obtained within 5 days after ICH) in cortical and deep grey matter and white matter. We used univariable logistic regression to assess the associations between DTI and clinical-radiological variables and poor outcome (modified Rankin Scale > 2). We determined the optimal predictive variables (via LASSO estimation) in: model 1 (DTI variables only), model 2 (DTI plus non-DTI variables), model 3 (DTI plus ICH score). Optimism-adjusted C-statistics were calculated for each model and compared (likelihood ratio test) against the ICH score. Results: Deep grey matter MD (OR 1.04 [95% CI 1.01–1.07], p = 0.010) and white matter MD (OR 1.11 [95% CI 1.01–1.23], p = 0.044) were associated (univariate analysis) with poor outcome. Discrimination values for model 1 (0.67 [95% CI 0.52–0.83]), model 2 (0.71 [95% CI 0.57–0.85) and model 3 (0.66 [95% CI 0.52–0.82]) were all significantly higher than the ICH score (0.62 [95% CI 0.49–0.75]). Conclusion: Our exploratory study suggests that whole-brain microstructural disruption measured by DTI is associated with poor 6-month functional outcome after SVD-related ICH. Whole-brain DTI metrics performed better at predicting recovery than the existing ICH score. [ABSTRACT FROM AUTHOR]

Introduction: Diffusion tensor imaging (DTI) can assess the structural integrity of the corticospinal tract (CST) in vivo. We aimed to investigate whether CST DTI metrics after intracerebral haemorrhage (ICH) are associated with 6-month functional outcome and can improve the predictive performance of the existing ICH score. Methods: We retrospectively included 42 patients with DTI performed within 5 days after deep supratentorial spontaneous ICH. Ipsilesional-to-contralesional ratios were calculated for fractional anisotropy (rFA) and mean diffusivity (rMD) in the pontine segment (PS) of the CST. We determined the most predictive variables for poor 6-month functional outcome [modified Rankin Scale (mRS) > 2] using the least absolute shrinkage and selection operator (LASSO) method. We calculated discrimination using optimism-adjusted estimation of the area under the curve (AUC). Results: Patients with 6-month mRS > 2 had lower rFA (0.945 [± 0.139] vs 1.045 [± 0.130]; OR 0.004 [95% CI 0.00–0.77]; p = 0.04) and higher rMD (1.233 [± 0.418] vs 0.963 [± 0.211]; OR 22.5 [95% CI 1.46–519.68]; p = 0.02). Discrimination (AUC) values were: 0.76 (95% CI 0.61–0.91) for the ICH score, 0.71 (95% CI 0.54–0.89) for rFA, and 0.72 (95% CI 0.61–0.91) for rMD. Combined models with DTI and non-DTI variables offer an improvement in discrimination: for the best model, the AUC was 0.82 ([95% CI 0.68–0.95]; p = 0.15). Conclusion: In our exploratory study, PS-CST rFA and rMD had comparable predictive ability to the ICH score for 6-month functional outcome. Adding DTI metrics to clinical-radiological scores might improve discrimination, but this needs to be investigated in larger studies. [ABSTRACT FROM AUTHOR]

In this paper, tetrahedral background cells are used in nodal integration of radial point interpolation method (RPIM). The nodal integration is based on Taylor series terms and it is originally applied for the solutions of 2D problems in literature. Therefore, in this study, it is attempted that the tetrahedral integration cells are used in the solution of 3D elasto-static problems. The accuracy is seriously affected by order of Taylor series terms and it is investigated up to fifth order. A methodology is developed for prevention of negative volumes and calculation problems in subdivision of integration cells for each node. Three different case studies are solved with different support domain sizes and shape parameters. The best accuracy is achieved with fourth-order Taylor terms in nodal integration radial point interpolation method (NI-RPIM). -value of 3.00 and value of 1.03 in radial basis functions give good results in all cases. [ABSTRACT FROM AUTHOR]

The purpose of this study was to obtain quantitative measurements of the apparent diffusion coefficient (ADC1), flow insensitive apparent diffusion coefficient (ADC2) and perfusion fraction (F) of colorectal hepatic metastases using DWI and to compare these measurements with those obtained in liver parenchyma. Forty patients with 66 hepatic metastases from colorectal carcinoma were prospectively evaluated using DWI with three b values. Quantitative maps of the ADC1 (using b = 0, 150, 500 s/mm2 images), ADC2 (using b = 150, 500 s/mm2 images) and fractional variation (F) between ADC1 and ADC2, which reflects perfusion fraction, were calculated. The ADC1, ADC2 and F derived from metastases and liver parenchyma were compared. The mean ADC1 values of liver parenchyma and metastases were significantly higher than the mean ADC2 values (P < 0.0001, paired t-test). Colorectal metastases were found to have higher mean ADC1 and ADC2 values compared with liver (P < 0.0001, Mann-Whitney test). However, the estimated F was found to be lower in metastases compared to liver (P = 0.03, Mann-Whitney test). Colorectal hepatic metastases were characterised by higher ADC1 and ADC2 values, but lower F values compared to liver. [ABSTRACT FROM AUTHOR]