MATERIALS AND METHODS: Sixteen children with GDD underwent magnetic resonance imaging (MRI) and cross-sectional DTI. Formal developmental assessment of all GDD patients was performed using the Mullen Scales of Early Learning. An automated processing pipeline for the WMT assessment was implemented. The DTI-derived metrics of the children with GDD were compared to healthy children with normal development (ND).
RESULTS: Only two out of the 17 WMT demonstrated significant differences (p<0.05) in DTI parameters between the GDD and ND group. In the uncinate fasciculus (UF), the GDD group had lower mean values for fractional anisotropy (FA; 0.40 versus 0.44), higher values for mean diffusivity (0.96 versus 0.91×10-3 mm2/s) and radial diffusivity (0.75 versus 0.68×10-3 mm2/s) compared to the ND group. In the superior cerebellar peduncle (SCP), mean FA values were lower for the GDD group (0.38 versus 0.40). Normal myelination pattern of DTI parameters was deviated against age for GDD group for UF and SCP.
CONCLUSION: The UF and SCP WMT showed microstructural changes suggestive of compromised white matter maturation in children with GDD. The DTI metrics have potential as imaging markers for inadequate white matter maturation in GDD children.
MATERIALS AND METHODS: 18F-FDG PET/CT images of 14 healthy control (HC) subjects (MoCA score > 26 (mean+SD~ 26.93+0.92) with no clinical evidence of cognitive deficits or neurological disease) and 16 AD patients (MoCA ≤22 (mean+SD~18.6+9.28)) were pre-processed in SPM12 while using our developed Malaysian healthy control brain template. The AD patients were assessed for disease severity using ADAS-Cog neuropsychological test. KNE96 template was used for registration-induced deformation in comparison with the ICBM templates. All deformation fields were corrected using the Malaysian healthy control template. The images were then nonlinearly modified by DARTEL to segment grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) to produce group-specific templates. Age, intracranial volume, MoCA score, and ADASCog score were used as variables in two sample t test between groups. The inference of our brain analysis was based on a corrected threshold of p<0.001 using Z-score threshold of 2.0, with a positive value above it as hypometabolic. The relationship between regional atrophy in GM and WM atrophy were analysed by comparing the means of cortical thinning between normal control and three AD stages in 15 clusters of ROI based on Z-score less than 2.0 as atrophied.
RESULTS: One-way ANOVA indicated that the means were equal for TIV, F(2,11) = 1.310, p=0.309, GMV, F(2,11) = 0.923, p=0.426, WMV, F(2,11) = 0.158, p=0.856 and CSF, F(2,11) = 1.495 p=0.266. Pearson correlations of GM, WM and CSF volume between HC and AD groups indicated the presence of brain atrophy in GM (p=-0.610, p<0.0001), WM (p=-0.178, p=0.034) and TIV (p=-0.374, p=0.042) but showed increased CSF volume (p=0.602, p<0.0001). Voxels analysis of the 18FFDG PET template revealed that GM atrophy differs significantly between healthy control and AD (p<0.0001). Zscore comparisons in the region of GM & WM were shown to distinguish AD patients from healthy controls at the prefrontal cortex and parahippocampal gyrus. The atrophy rate within each ROI is significantly different between groups (c2=35.9021, df=3, p<0.0001), Wilcoxon method test showed statistically significant differences were observed between Moderate vs. Mild AD (p<0.0001), Moderate AD vs. healthy control (p=0.0005), Mild AD vs. HC (p=0.0372) and Severe AD vs. Moderate AD (p<0.0001). The highest atrophy rate within each ROI between the median values ranked as follows severe AD vs. HC (p<0.0001) > mild AD vs. HC (p=0.0091) > severe AD vs. moderate AD (p=0.0143).
CONCLUSION: We recommend a reliable method in measuring the brain atrophy and locating the patterns of hypometabolism using a group-specific template registered to a quantitatively validated KNE96 group-specific template. The studied regions together with neuropsychological test approach is an effective method for the determination of AD severity in a Malaysian population.
METHODS: Sixty-six women who underwent HIFU treatment were divided into groups A (NPVr ≥90%; n = 26) and B (NPVr <90%, n = 40). Multivariate logistic regression analyses of MRI features were conducted to identify the potential predictors of an NPVr ≥90%.
RESULTS: Generalized estimating equation (GEE) analysis was used to model the prediction of an NPVr ≥90% with four significant predictors from multivariate analyses: the thickness of the subcutaneous fat layer, adenomyosis volume, T2 signal intensity (SI) ratio of adenomyosis to myometrium, and the Ktrans ratio of adenomyosis to myometrium. Clinical efficacy was significantly greater in group A than in group B. The findings showed no serious AEs and no significant differences between AMH concentrations before and 6 months after treatment.
CONCLUSIONS: The present retrospective study demonstrated that achievement of NPVr ≥90% as a measure of clinical treatment success in MRI-guided HIFU treatment of adenomyosis using multivariate analyses and a prediction model is clinically possible without compromising the safety of patients.
METHODS: 50 POAG patients and 50 normal subjects were recruited and an MRI brain with T1-magnetization-prepared rapid gradient-echo was performed. Medial temporal lobe and parietal lobe atrophy were by MTA and PCA/Koedam scoring. The score of the PCA and MTA were compared between the POAG group and the controls.
RESULTS: There was a significant statistical difference between PCA score in POAG and the healthy control group (p-value = 0.026). There is no statistical difference between MTA score in POAG compared to the healthy control group (p-value = 0.58).
CONCLUSION: This study suggests a correlation between POAG and PCA score. Potential application of this scoring method in clinical diagnosis and monitoring of POAG patients.
ADVANCES IN KNOWLEDGE: The scoring method used in AD may also be applied in the diagnosis and monitoring of POAGMRI brain, specifically rapid volumetric T1 spoiled gradient echo sequence, may be applied in POAG assessment.
DISCUSSION: It is a set of various methodologies which are used to capture internal or external images of the human body and organs for clinical and diagnosis needs to examine human form for various kind of ailments. Computationally intelligent machine learning techniques and their application in medical imaging can play a significant role in expediting the diagnosis process and making it more precise.
CONCLUSION: This review presents an up-to-date coverage about research topics which include recent literature in the areas of MRI imaging, comparison with other modalities, noise in MRI and machine learning techniques to remove the noise.
OBJECTIVES: The aims of this study is to investigate the capability of random walks as knee cartilage segmentation method.
METHODS: Experts would scribble on knee cartilage image to initialize random walks segmentation. Then, reproducibility of the method is assessed against manual segmentation by using Dice Similarity Index. The evaluation consists of normal cartilage and diseased cartilage sections which is divided into whole and single cartilage categories.
RESULTS: A total of 15 normal images and 10 osteoarthritic images were included. The results showed that random walks method has demonstrated high reproducibility in both normal cartilage (observer 1: 0.83±0.028 and observer 2: 0.82±0.026) and osteoarthritic cartilage (observer 1: 0.80±0.069 and observer 2: 0.83±0.029). Besides, results from both experts were found to be consistent with each other, suggesting the inter-observer variation is insignificant (Normal: P=0.21; Diseased: P=0.15).
CONCLUSION: The proposed segmentation model has overcame technical problems reported by existing semi-automated techniques and demonstrated highly reproducible and consistent results against manual segmentation method.