Diffusion tensor imaging pipeline measures of cerebral white matter integrity: An overview of recent advances and prospects.

Cerebral small vessel disease (CSVD) is a leading cause of age-related microvascular cognitive decline, resulting in significant morbidity and decreased quality of life. Despite a progress on its key pathophysiological bases and general acceptance of key terms from neuroimaging findings as observed on the magnetic resonance imaging (MRI), key questions on CSVD remain elusive. Enhanced relationships and reliable lesion studies, such as white matter tractography using diffusion-based MRI (dMRI) are necessary in order to improve the assessment of white matter architecture and connectivity in CSVD. Diffusion tensor imaging (DTI) and tractography is an application of dMRI that provides data that can be used to non-invasively appraise the brain white matter connections via fiber tracking and enable visualization of individual patient-specific white matter fiber tracts to reflect the extent of CSVD-associated white matter damage. However, due to a lack of standardization on various sets of software or image pipeline processing utilized in this technique that driven mostly from research setting, interpreting the findings remain contentious, especially to inform an improved diagnosis and/or prognosis of CSVD for routine clinical use. In this minireview, we highlight the advances in DTI pipeline processing and the prospect of this DTI metrics as potential imaging biomarker for CSVD, even for subclinical CSVD in at-risk individuals.

ESTIMATION OF PEDIATRIC DOSE DESCRIPTORS ADAPTED TO INDIVIDUAL SPECIFIC SIZE FROM CT EXAMINATIONS.

Clinical challenges in pediatrics dose estimation by the displayed computed tomography (CT) dose indices may lead to inaccuracy, and thus size-specific dose estimate (SSDE) is introduced for better-personalized dose estimation. This study aims to estimate pediatric dose adapted to specific size. This retrospective study involved pediatric population aged 0-12 y. SSDE was derived from scanner reported volume CT dose index (CTDIvol), based on individual effective diameter (Deff) with corresponding size correction factors. The correlations of Deff with other associated factors such as age, exposure setting, CTDIvol and SSDE were also studied. The average Deff of Malaysian pediatric was smaller than reference phantom size (confidence interval, CI = 0.28, mean = 14.79) and (CI = 0.51, mean = 16.33) for head and abdomen, respectively. These have led to underestimation of pediatric dose as SSDE was higher than displayed CTDIvol. The percentage differences were statistically significant (p < .001) ranged from 0 to 17% and 37 to 60% for head and abdominal CT, respectively. In conclusion, the clinical implementation of SSDE in pediatric CT imaging is highly relevant to reduce radiation risk.

The effectiveness of iron education through a mobile application on donor return after deferral for low hemoglobin.

BACKGROUND: Low hemoglobin level is a common cause of donor deferral and results in a huge loss of the donor pool. This study aimed to evaluate the effectiveness of a mobile application as an educational tool to enhance donor return and improve hemoglobin levels after deferral. MATERIALS AND METHODS: This was an interventional study involving 382 blood donors who were deferred for low hemoglobin. The donors were divided equally into two groups: a control group and the intervention group. The control group received standard management for low hemoglobin deferral, which includes a short counseling session and a 1-month course of oral iron therapy. The intervention group used a mobile application in addition to standard management. The primary endpoint was the number of blood donors who returned during the 7 months of follow-up. The secondary endpoints were the hemoglobin increment at the first visit after the donors' deferral. RESULTS: The return rate was higher in the intervention group, with 81.2% of the donors returning in the 7 months of follow-up compared to 66% of the control group (p<0.001). Male and female donors had mean hemoglobin increments of 1.0 g/dL and 0.7 g/dL, respectively, in the intervention group, compared to decrements of 0.2 g/dL and 0.4 g/dL, respectively, in the control group (p<0.001). Multivariable analysis showed a significant association between intervention method, education level and donation status on donor return (p=0.015, p<0.001, and p<0.001, respectively). DISCUSSION: Higher return rate and greater hemoglobin increase in the interventional group could be attributed to features in the mobile application. Repeat donors had the highest odds of returning to donate, followed by those with a tertiary level of education, and those given the mobile application. This study showed that a mobile application was effective in enhancing donor return and increasing hemoglobin level among deferred blood donors on their first return.

Reduced cerebral vascular fractal dimension among asymptomatic individuals as a potential biomarker for cerebral small vessel disease.

Cerebral small vessel disease is a neurological disease frequently found in the elderly and detected on neuroimaging, often as an incidental finding. White matter hyperintensity is one of the most commonly reported neuroimaging markers of CSVD and is linked with an increased risk of future stroke and vascular dementia. Recent attention has focused on the search of CSVD biomarkers. The objective of this study is to explore the potential of fractal dimension as a vascular neuroimaging marker in asymptomatic CSVD with low WMH burden. Df is an index that measures the complexity of a self-similar and irregular structure such as circle of Willis and its tributaries. This exploratory cross-sectional study involved 22 neurologically asymptomatic adult subjects (42 ± 12 years old; 68% female) with low to moderate 10-year cardiovascular disease risk prediction score (QRISK2 score) who underwent magnetic resonance imaging/angiography (MRI/MRA) brain scan. Based on the MRI findings, subjects were divided into two groups: subjects with low WMH burden and no WMH burden, (WMH+; n = 8) and (WMH-; n = 14) respectively. Maximum intensity projection image was constructed from the 3D time-of-flight (TOF) MRA. The complexity of the CoW and its tributaries observed in the MIP image was characterised using Df. The Df of the CoW and its tributaries, i.e., Df (w) was significantly lower in the WMH+ group (1.5172 ± 0.0248) as compared to WMH- (1.5653 ± 0.0304, p = 0.001). There was a significant inverse relationship between the QRISK2 risk score and Df (w), (rs = - .656, p = 0.001). Df (w) is a promising, non-invasive vascular neuroimaging marker for asymptomatic CSVD with WMH. Further study with multi-centre and long-term follow-up is warranted to explore its potential as a biomarker in CSVD and correlation with clinical sequalae of CSVD.

Brain Image Segmentation in Recent Years: A Narrative Review.

Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted. This has indirectly led to an increased demand for automated brain segmentation solutions to assist medical experts in early diagnosis and treatment interventions. This paper aims to present a critical review of the recent trend in segmentation and classification methods for brain magnetic resonance images. Various segmentation methods ranging from simple intensity-based to high-level segmentation approaches such as machine learning, metaheuristic, deep learning, and hybridization are included in the present review. Common issues, advantages, and disadvantages of brain image segmentation methods are also discussed to provide a better understanding of the strengths and limitations of existing methods. From this review, it is found that deep learning-based and hybrid-based metaheuristic approaches are more efficient for the reliable segmentation of brain tumors. However, these methods fall behind in terms of computation and memory complexity.

Wavelet energy-guided level set-based active contour: a segmentation method to segment highly similar regions.

This paper introduces an approach to perform segmentation of regions in computed tomography (CT) images that exhibit intra-region intensity variations and at the same time have similar intensity distributions with surrounding/adjacent regions. In this work, we adapt a feature computed from wavelet transform called wavelet energy to represent the region information. The wavelet energy is embedded into a level set model to formulate the segmentation model called wavelet energy-guided level set-based active contour (WELSAC). The WELSAC model is evaluated using several synthetic and CT images focusing on tumour cases, which contain regions demonstrating the characteristics of intra-region intensity variations and having high similarity in intensity distributions with the adjacent regions. The obtained results show that the proposed WELSAC model is able to segment regions of interest in close correspondence with the manual delineation provided by the medical experts and to provide a solution for tumour detection.