PURPOSE: To evaluate the effect of IE on the marrow fat content and fat unsaturation levels in the proximal femur using 1 H-MRS.

STUDY TYPE: Prospective.

SUBJECTS: Twenty-three subjects were included in this study, seven control and 16 β-thalassemia subjects.

FIELD STRENGTH/SEQUENCE: 3.0T; stimulated echo acquisition Mode (STEAM); magnetic resonance spectroscopy (MRS) sequence.

ASSESSMENT: Multiecho MRS scans were performed in four regions of the proximal left femur of each subject, that is, diaphysis, femoral neck, femoral head, and greater trochanter. The examined regions were grouped into red (diaphysis and femoral neck) and yellow marrow regions (femoral head and greater trochanter).

PURPOSE: To investigate the relationship between marrow fat and cortical bone thickness in β-thalassemia and to identify key determinants influencing these variables.

STUDY TYPE: Prospective.

SUBJECTS: Thirty-five subjects in four subject groups of increasing disease severity: 6 healthy control (25.0 ± 5.3 years, 2 male), 4 β-thalassemia minor, 13 intermedia, and 12 major (29.1 ± 6.4 years, 15 male).

FIELD STRENGTH/SEQUENCE: 3.0 T, 3D fast low angle shot sequence and T1-weighted turbo spin echo.

ASSESSMENT: Analyses on proton density fat fraction (PDFF) and R2* values in femur subregions (femoral head, greater trochanter, intertrochanteric, diaphysis, distal) and cortical thickness (CBI) of the subjects' left femur. Clinical data such as age, sex, body mass index (BMI), and disease severity were also included.

STATISTICAL TESTS: One-way analysis of variance (ANOVA), mixed ANOVA, Pearson correlation and multiple regression. P-values <0.05 were considered significant.

RESULTS: Bone marrow PDFF significantly varied between the femur subregions, F(2.89,89.63) = 44.185 and disease severity, F(1,3) = 12.357. A significant interaction between subject groups and femur subregions on bone marrow PDFF was observed, F(8.67,89.63) = 3.723. Notably, a moderate positive correlation was observed between PDFF and CBI (r = 0.33-0.45). Multiple regression models for both PDFF (R2 = 0.476, F(13,151) = 10.547) and CBI (R2 = 0.477, F(13,151) = 10.580) were significant. Significant predictors for PDFF were disease severity (βTMi = 0.36, βTMa = 0.17), CBI (β = 0.24), R2* (β = -0.32), and height (β = -0.29) while for CBI, the significant determinants were sex (β = -0.27), BMI (β = 0.55), disease severity (βTMi = 2.15), and PDFF (β = 0.25).

DATA CONCLUSION: This study revealed a positive correlation between bone marrow fat fraction and cortical bone thickness in β-thalassemia with varying disease severity, potentially indicating a complex interplay between bone health and marrow composition.

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: We retrospectively analyzed the clinical and imaging data of cervical cancer patients diagnosed pathologically at our hospital from January 2021 to June 2024. All patients underwent routine magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and APT imaging before treatment. Apparent diffusion coefficient (ADC) and APT values were measured. Based on the pathological results, patients were categorized into LVSI (+) and LVSI (-) groups, and PMI (+) and PMI (-) groups. Independent sample t-tests were used to compare the ADC and APT values between these groups. Receiver operating characteristic (ROC) curves were used to assess the sensitivity, specificity, and area under the curve (AUC) of ADC, APT, and ADC + APT in predicting PMI and LVSI. The Delong test was employed to compare the diagnostic performance among these measures.

METHODS: This protocol describes a non-interventional case control study. The AD and MCI patients and the healthy elderly controls will undergo multi-parametric MRI. The protocol consists of sMRI, fMRI, DTI, and single-voxel proton MRS sequences. An eco-planar imaging (EPI) will be used to perform resting-state fMRI sequence. The structural images will be analysed using Computational Anatomy Toolbox-12, functional images will be analysed using Statistical Parametric Mapping-12, DPABI (Data Processing & Analysis for Brain Imaging), and Conn software, while DTI and 1H-MRS will be analysed using the FSL (FMRIB's Software Library) and Tarquin respectively. Correlation of the MRI results and the data acquired from the APOE genotyping, neuropsychological evaluations (i.e. Montreal Cognitive Assessment [MoCA], and Mini-Mental State Examination [MMSE] scores) will be performed. The imaging results will also be correlated with the sociodemographic factors. The diagnosis of AD and MCI will be standardized and based on the DSM-5 criteria and the neuropsychological scores.