MATERIALS AND METHODS: Eighteen (18) participants who met the inclusion and exclusion criteria were scanned using a standard non-contrast MRI shoulder protocol including the PDFS pulse sequence and the PROPELLER PDFS pulse sequence using a small flex coil and a dedicated shoulder coil. Two experienced musculoskeletal (MSK) radiologists evaluated and graded the presence of artifacts on the MR images and the SNR and CNR were measured quantitatively.

RESULTS: The non-parametric Wilcoxon Signed Rank test revealed a significant reduction in motion and pulsation artifacts between the PROPELLER PDFS pulse sequence and the standard PDFS pulse sequence. In addition, the nonparametric Mann-Whitney U test revealed that the mean rank of SNR for the standard sequence was statistically significant when compared to the PROPELLER sequence for both coil types. The CNR of the PROPELLER sequence was statistically significant between fat-fluid, bone-fluid, bonetendon, bone-muscle, and muscle-fluid when using SFC and DSC.

MATERIALS AND METHODS: A literature search was carried out to gather eligible studies from the following widely sourced electronic databases such as Scopus, PubMed and Google Scholar using the combination of the following keywords: AD, MRS, brain metabolites, deep learning (DL), machine learning (ML) and artificial intelligence (AI); having the aim of taking the readers through the advancements in the usage of MRS analysis and related AI applications for the detection of AD.

RESULTS: We elaborate on the MRS data acquisition, processing, analysis, and interpretation techniques. Recommendation is made for MRS parameters that can obtain the best quality spectrum for fingerprinting the brain metabolomics composition in AD. Furthermore, we summarise ML and DL techniques that have been utilised to estimate the uncertainty in the machine-predicted metabolite content, as well as streamline the process of displaying results of metabolites derangement that occurs as part of ageing.

MATERIALS AND METHODS: Seventy-five participants underwent MRE as an initial investigation or follow-up for inflammatory bowel disease. A systematic sampling method was used to divide the participants into three different groups: group 1 received 6.7% mannitol concentration, group 2 received 3.3% mannitol concentration and group 3 received pineapple juice as an oral contrast agent during their MRE examination. The degree of bowel distension on MRE images was assessed by a radiologist by measuring the bowel diameter from inner wall to inner wall at specified levels, while qualitative analysis was evaluated based on the presence of artefacts. All patients were asked to score their acceptance of the oral contrast and were asked about side effects such as diarrhoea, abdominal discomfort and vomiting.

RESULTS: All patients were able to completely ingest 1.5L of oral contrast. The mean diameter of bowel distension was 2.1cm in patients who received 6.7% mannitol concentration, 2.0cm in patients who received 3.3% mannitol concentration and 1.6 cm in patients who received pineapple juice. Twothirds of patients who received 6.7% mannitol and 3.3% mannitol solutions had good-quality MRE images, but 68% of patients who received pineapple juice had poor-quality MRE images. Twenty-four patients (96%) who received pineapple juice rated it as slightly acceptable and acceptable but only 12 patients (48%) who received 6.7% mannitol solution rated it as slightly acceptable and acceptable. Eighty-eight percent of patients who received 6.7% mannitol solution experienced at least one form of side effect as compared to 44% of patients who received 3.3% mannitol solution and 18% of patients who received pineapple juice.

METHODS: A systematic search was performed in the PubMed, Scopus, and Web of Science (WoS) databases in June 2022. Patients with head and neck cancer treated with radiotherapy and periodic rs-fMRI assessments were included. A meta-analysis was performed to determine the potential of rs-fMRI for detecting brain changes.

PURPOSE: To demonstrate automatic detection of BM on three MRI datasets using a deep learning-based approach. To improve the performance of the network is iteratively co-trained with datasets from different domains. A systematic approach is proposed to prevent catastrophic forgetting during co-training.

STUDY TYPE: Retrospective.

POPULATION: A total of 156 patients (105 ground truth and 51 pseudo labels) with 1502 BM (BrainMetShare); 121 patients with 722 BM (local); 400 patients with 447 primary gliomas (BrATS). Training/pseudo labels/validation data were distributed 84/51/21 (BrainMetShare). Training/validation data were split: 121/23 (local) and 375/25 (BrATS).

FIELD STRENGTH/SEQUENCE: A 5 T and 3 T/T1 spin-echo postcontrast (T1-gradient echo) (BrainMetShare), 3 T/T1 magnetization prepared rapid acquisition gradient echo postcontrast (T1-MPRAGE) (local), 0.5 T, 1 T, and 1.16 T/T1-weighted-fluid-attenuated inversion recovery (T1-FLAIR) (BrATS).

ASSESSMENT: The ground truth was manually segmented by two (BrainMetShare) and four (BrATS) radiologists and manually annotated by one (local) radiologist. Confidence and volume based domain adaptation (CAVEAT) method of co-training the three datasets on a 3D nonlocal convolutional neural network (CNN) architecture was implemented to detect BM.

STATISTICAL TESTS: The performance was evaluated using sensitivity and false positive rates per patient (FP/patient) and free receiver operating characteristic (FROC) analysis at seven predefined (1/8, 1/4, 1/2, 1, 2, 4, and 8) FPs per scan.

RESULTS: The sensitivity and FP/patient from a held-out set registered 0.811 at 2.952 FP/patient (BrainMetShare), 0.74 at 3.130 (local), and 0.723 at 2.240 (BrATS) using the CAVEAT approach with lesions as small as 1 mm being detected.

DATA CONCLUSION: Improved sensitivities at lower FP can be achieved by co-training datasets via the CAVEAT paradigm to address the problem of data sparsity.

MATERIALS AND METHOD: This work uses two (private and public) datasets. The private dataset consists of 3807 magnetic resonance imaging (MRI) and computer tomography (CT) images belonging to two (normal and AD) classes. The second public (Kaggle AD) dataset contains 6400 MR images. The presented classification model comprises three fundamental phases: feature extraction using an exemplar hybrid feature extractor, neighborhood component analysis-based feature selection, and classification utilizing eight different classifiers. The novelty of this model is feature extraction. Vision transformers inspire this phase, and hence 16 exemplars are generated. Histogram-oriented gradients (HOG), local binary pattern (LBP) and local phase quantization (LPQ) feature extraction functions have been applied to each exemplar/patch and raw brain image. Finally, the created features are merged, and the best features are selected using neighborhood component analysis (NCA). These features are fed to eight classifiers to obtain highest classification performance using our proposed method. The presented image classification model uses exemplar histogram-based features; hence, it is called ExHiF.

RESULTS: We have developed the ExHiF model with a ten-fold cross-validation strategy using two (private and public) datasets with shallow classifiers. We have obtained 100% classification accuracy using cubic support vector machine (CSVM) and fine k nearest neighbor (FkNN) classifiers for both datasets.

METHODS: A literature search was carried out in PubMed and Google Scholar using suitable search terms and reference lists of articles found were searched for further articles.

RESULTS: By the end of February 2023, 82 patients with SARS-CoV-2 associated PRES were recorded. The latency between the onset of COVID-19 and the onset of PRES ranged from 1 day to 70 days. The most common presentations of PRES were mental deterioration (n=47), seizures (n=46) and visual disturbances (n=18). Elevated blood pressure was reported on admission or during hospitalisation in 48 patients. The most common comorbidities were arterial hypertension, diabetes, hyperlipidemia and atherosclerosis. PRES was best diagnosed by multimodal cerebral magnetic resonance imaging (MRI). Complete recovery was reported in 35 patients and partial recovery in 21 patients, while seven patients died.

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).

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.