Purpose: The American College of Radiology (ACR) requires MR personnel not to work alone due to the increased risk of safety issues such as projectiles, aggressive patients, and technologist fatigue. As a result, we intend to assess the current safety of lone-working MRI technologists in MRI departments in Saudi Arabia. Materials and Methods: A cross-sectional study using a self-report questionnaire was conducted in 88 Saudi hospitals. Results: A response rate of 64% (174/270) was obtained among the 270 MRI technologists which were identified. The study discovered that 86% of MRI technologists had prior experience working alone. In terms of MRI safety training, 63% of MRI technologists received such training. A question about lone MRI workers’ awareness of the ACR’s recommendations revealed that 38% were unaware of such recommendations. Furthermore, 22% were misinformed, believing that working alone in an MRI unit is optional or depends on the individual’s desire to work alone. Working alone has the primary consequence of being statistically significantly associated with projectile/object-related accidents/mistakes (p = 0.03). Conclusion: Saudi Arabian MRI technologists have extensive experience working alone without supervision. Most MRI technologists are unaware of lone working regulations, which has raised concerns about accidents/mistakes. There is a need for MRI safety training and adequate practical experience to raise awareness of MRI safety regulations and policies related to lone working among departments and MRI workers.

Background: Multiple sclerosis (MS) is a chronic and heterogeneous disease characterized by demyelination and axonal loss and damage. Magnetic resonance imaging (MRI) has been employed to distinguish these changes in various types of MS lesions. Objectives: We aimed to evaluate intravoxel incoherent motion (IVIM) diffusion and perfusion MRI metrics across different brain regions in healthy individuals and various types of MS lesions, including enhanced, non-enhanced, and black hole lesions. Methods: A prospective study included 237 patients with MS (65 males and 172 females) and 29 healthy control participants (25 males and 4 females). The field strength was 1.5 Tesla. The imaging sequences included three-dimensional (3D) T₁, 3D fluid-attenuated inversion recovery, two-dimensional (2D) T₁, T₂-weighted imaging, and 2D diffusion-weighted imaging (DWI) sequences. IVIM-derived parameters—apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion (D*), and perfusion fraction (f)—were quantified for commonly observed lesion types (2506 lesions from 224 patients with MS, excluding 13 patients due to MRI artifacts or not meeting the diagnostic criteria for RR-MS) and for corresponding brain regions in 29 healthy control participants. A one-way analysis of variance, followed by post-hoc analysis (Tukey’s test), was performed to compare mean values between the healthy and MS groups. Receiver operating characteristic curve analyses, including area under the curve, sensitivity, and specificity, were conducted to determine the cutoff values of IVIM parameters for distinguishing between the groups. A p-value of ≤0.05 and 95% confidence intervals were used to report statistical significance and precision, respectively. Results: All IVIM parametric maps in this study discriminated among most MS lesion types. ADC, D, and D* values for MS black hole lesions were significantly higher (p < 0.0001) than those for other MS lesions and healthy controls. ADC, D, and D* maps demonstrated high sensitivity and specificity, whereas f maps exhibited low sensitivity but high specificity. Conclusions: IVIM parameters provide valuable diagnostic and clinical insights by demonstrating high sensitivity and specificity in evaluating different categories of MS lesions.

Background/Objectives: Multiple sclerosis (MS) is an autoimmune disorder affecting the central nervous system, characterised by the degradation of myelin, which results in various neurological symptoms. This study aims to utilise radiomics features to evaluate the predictive value of IVIM diffusion parameters, namely, the true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f), in relation to disability severity, assessed using the Expanded Disability Status Scale (EDSS), and mobility in patients with relapsing–remitting MS. Methods: This retrospective cross-sectional study analysed MRI data from 197 patients diagnosed with multiple sclerosis (MS). Quantitative intravoxel incoherent motion (IVIM) parameters were obtained using a 1.5 Tesla MRI scanner. Clinical information collected included age, disease duration, number of relapses, status of disease-modifying therapy (DMT), and the need for mobility assistance. Machine learning (ML) techniques, such as XGB, Random Forest, and ANN, were employed to explore the relationships between radiomic IVIM parameters and these clinical variables. Results: IVIM radiomics achieved high accuracy in lesion phenotyping. Random Forest distinguished enhancements from non-enhancing lesions with 96% accuracy and AUC = 0.99 with IVIM-f and D* maps. CNN also reached ~92% accuracy (AUC 0.97) with IVIM-f. For disability prediction, IVIM-D and D* radiomics strongly correlated with EDSS: Random Forest achieved 89% accuracy (AUC = 0.90), while CNN achieved 90% accuracy (AUC = 0.95). Mobility impairment was predicted with the highest performance—RNN achieved 96% accuracy (AUC = 0.99) across IVIM-f features. In contrast, relapse history, disease duration, and treatment status were poorly predicted (<75% accuracy). Conclusions: ML analyses of IVIM metrics provided independent predictors of functional impairment and disability in MS. Our novel approach may be used to improve diagnostic accuracy and develop personalised treatment strategies for MS patients.

Background/Objectives: Quantitative intravoxel incoherent motion (IVIM) imaging, incorporating both diffusion- and perfusion-derived metrics, offers a promising non-invasive approach for assessing tissue microstructure and clinical disability in multiple sclerosis (MS). This study aimed to investigate the correlation and predictive values of the IVIM apparent diffusion coefficient (ADC), true diffusion coefficient (D), and perfusion-derived pseudo-diffusion coefficient (D*) and perfusion fraction (f) parameters with disability status, measured using the Expanded Disability Status Scale (EDSS), in relapsing–remitting MS patients. Methods: This cross-sectional study retrospectively analyzed MRI data from 197 MS patients. Quantitative IVIM parameters were extracted from scans obtained using a 1.5 T MRI scanner. Clinical data were also obtained, including age, disease duration, number of relapses, disease-modifying therapy (DMT) status, and need for mobility assistance. Bivariate analyses were conducted to compare mean values across subgroups. Pearson correlation was used to examine associations between EDSS score and imaging/clinical variables. Multiple linear regression was applied to identify independent predictors of EDSS score. Results: The bivariate analyses revealed that ADC, D, D*, and EDSS values were higher in patients over 50 years old, those with a longer disease duration, and those who required mobility assistance. f was higher in females and DMT-treated patients, but it had no effect on EDSS score. Patients with longer disease duration and limited mobility had a higher number of MS lesions and relapses. EDSS score exhibited positive Pearson correlations with ADC, D, D*, the number of MS lesions, and the number of relapses (p-value < 0.001). In the multivariate regression analysis, only the number of MS lesions and relapses emerged as independent predictors of EDSS score (p-value < 0.001). Other variables, including ADC, D, D*, f, age, and disease duration, were not independently associated with EDSS score (p-value > 0.05). Conclusions: This study demonstrates the utility of IVIM parameters in detecting microstructural alterations associated with MS impairment. Despite relapse frequency and lesion count being the strongest predictors of EDSS score, IVIM metrics showed meaningful clinical correlations. The findings support combining IVIM biomarkers with clinical data for better disability assessment.

Gross morphological differences exist among different brands of pediatric supraglottic devices (SGDs). The aim of this study is to compare the spatial relationship of i-gel® and Ambu® AuraOnce (AO)TM on pediatric airway based on three dimensional (3-D) magnetic resonance imaging (MRI) measurements. Sixty patients up to 12 years of age were enrolled and assigned in two groups, i-gel® or Ambu® AOTM. After confirmation of proper placement of these SGDs, 3-D MRI scans of head and neck were performed. Another native scan was also obtained after removal of the SGD for comparison. i-gel® produced significant degree of compression of the tongue (P<0.001) while Ambu® AOTM significantly reduced the axial diameter of glottis (P=0.033) compared to their native values. Both i-gel® and Ambu® AOTM significantly reduced the area of the glottic opening (P<0.001 for each device) and the distance between the arytenoids (P<0.001 and P=0.007 respectively); and increased the distance between the hyoid bone and cervical spine (P<0.001 and P=0.001 respectively) in comparison to their corresponding native values. Bowl of i-gel® produced greater dilation of the upper esophageal sphincter at all levels of measurement- upper (P<0.001), middle (P=0.001) and lower (P=0.015) in comparison to Ambu® AOTM. Based on 3-D MRI measurements done on living patients, both SGDs distorted the anatomy of pediatric airway compared to their respective native values to variable extent. The relevance of these effects needs further studies on larger patient group in order to reduce morbidity on pediatric airway.