The status of MRI databases across the world focused on psychiatric and neurological disorders.

Neuroimaging databases for neuro-psychiatric disorders enable researchers to implement data-driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi-center, multi-disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real-world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention-deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi-center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders.

Recent trends in AI applications for pelvic MRI: a comprehensive review.

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.

Optimizing GPT-4 Turbo Diagnostic Accuracy in Neuroradiology through Prompt Engineering and Confidence Thresholds.

BACKGROUND AND OBJECTIVES: Integrating large language models (LLMs) such as GPT-4 Turbo into diagnostic imaging faces a significant challenge, with current misdiagnosis rates ranging from 30-50%. This study evaluates how prompt engineering and confidence thresholds can improve diagnostic accuracy in neuroradiology. METHODS: We analyze 751 neuroradiology cases from the American Journal of Neuroradiology using GPT-4 Turbo with customized prompts to improve diagnostic precision. RESULTS: Initially, GPT-4 Turbo achieved a baseline diagnostic accuracy of 55.1%. By reformatting responses to list five diagnostic candidates and applying a 90% confidence threshold, the highest precision of the diagnosis increased to 72.9%, with the candidate list providing the correct diagnosis at 85.9%, reducing the misdiagnosis rate to 14.1%. However, this threshold reduced the number of cases that responded. CONCLUSIONS: Strategic prompt engineering and high confidence thresholds significantly reduce misdiagnoses and improve the precision of the LLM diagnostic in neuroradiology. More research is needed to optimize these approaches for broader clinical implementation, balancing accuracy and utility.

Three-dimensional simultaneous T1 and T2* relaxation times and quantitative susceptibility mapping at 3 T: A multicenter validation study.

We aimed to determine the intra-site repeatability and cross-site reproducibility of T1 and T2* relaxation times and quantitative susceptibility (χ) values obtained through quantitative parameter mapping (QPM) at 3 T. This prospective study included three 3-T scanners with the same hardware and software platform at three sites. The brains of twelve healthy volunteers were scanned three times using QPM at three sites. Intra-site repeatability and cross-site reproducibility were evaluated based on voxel-wise and region-of-interest analyses. The within-subject coefficient of variation (wCV), within-subject standard deviation (wSD), linear regression, Bland-Altman plot, and intraclass correlation coefficient (ICC) were used for evaluation. The intra-site repeatability wCV was 11.9 ± 6.86% for T1 and 3.15 ± 0.03% for T2*, and wSD of χ at 3.35 ± 0.10 parts per billion (ppb). Intra-site ICC(1,k) values for T1, T2*, and χ were 0.878-0.904, 0.972-0.976, and 0.966-0.972, respectively, indicating high consistency within the same scanner. Linear regression analysis revealed a strong agreement between measurements from each site and the site-average measurement, with R-squared values ranging from 0.79 to 0.83 for T1, 0.94-0.95 for T2*, and 0.95-0.96 for χ. The cross-site wCV was 13.4 ± 5.47% for T1 and 3.69 ± 2.25% for T2*, and cross-site wSD of χ at 4.08 ± 3.22 ppb. The cross-site ICC(2,1) was 0.707, 0.913, and 0.902 for T1, T2*, and χ, respectively. QPM provides T1, T2*, and χ values with an intra-site repeatability of <12% and cross-site reproducibility of <14%. These findings may contribute to the development of multisite studies.

Deep Learning-based Hierarchical Brain Segmentation with Preliminary Analysis of the Repeatability and Reproducibility.

PURPOSE: We developed new deep learning-based hierarchical brain segmentation (DLHBS) method that can segment T1-weighted MR images (T1WI) into 107 brain subregions and calculate the volume of each subregion. This study aimed to evaluate the repeatability and reproducibility of volume estimation using DLHBS and compare them with those of representative brain segmentation tools such as statistical parametric mapping (SPM) and FreeSurfer (FS). METHODS: Hierarchical segmentation using multiple deep learning models was employed to segment brain subregions within a clinically feasible processing time. The T1WI and brain mask pairs in 486 subjects were used as training data for training of the deep learning segmentation models. Training data were generated using a multi-atlas registration-based method. The high quality of training data was confirmed through visual evaluation and manual correction by neuroradiologists. The brain 3D-T1WI scan-rescan data of the 11 healthy subjects were obtained using three MRI scanners for evaluating the repeatability and reproducibility. The volumes of the eight ROIs-including gray matter, white matter, cerebrospinal fluid, hippocampus, orbital gyrus, cerebellum posterior lobe, putamen, and thalamus-obtained using DLHBS, SPM 12 with default settings, and FS with the "recon-all" pipeline. These volumes were then used for evaluation of repeatability and reproducibility. RESULTS: In the volume measurements, the bilateral thalamus showed higher repeatability with DLHBS compared with SPM. Furthermore, DLHBS demonstrated higher repeatability than FS in across all eight ROIs. Additionally, higher reproducibility was observed with DLHBS in both hemispheres of six ROIs when compared with SPM and in five ROIs compared with FS. The lower repeatability and reproducibility in DLHBS were not observed in any comparisons. CONCLUSION: Our results showed that the best performance in both repeatability and reproducibility was found in DLHBS compared with SPM and FS.

Preliminary study of substantia nigra analysis by tensorial feature extraction.

PURPOSE: Parkinson disease (PD) is a common progressive neurodegenerative disorder in our ageing society. Early-stage PD biomarkers are desired for timely clinical intervention and understanding of pathophysiology. Since one of the characteristics of PD is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, we propose a feature extraction method for analysing the differences in the substantia nigra between PD and non-PD patients. METHOD: We propose a feature-extraction method for volumetric images based on a rank-1 tensor decomposition. Furthermore, we apply a feature selection method that excludes common features between PD and non-PD. We collect neuromelanin images of 263 patients: 124 PD and 139 non-PD patients and divide them into training and testing datasets for experiments. We then experimentally evaluate the classification accuracy of the substantia nigra between PD and non-PD patients using the proposed feature extraction method and linear discriminant analysis. RESULTS: The proposed method achieves a sensitivity of 0.72 and a specificity of 0.64 for our testing dataset of 66 non-PD and 42 PD patients. Furthermore, we visualise the important patterns in the substantia nigra by a linear combination of rank-1 tensors with selected features. The visualised patterns include the ventrolateral tier, where the severe loss of neurons can be observed in PD. CONCLUSIONS: We develop a new feature-extraction method for the analysis of the substantia nigra towards PD diagnosis. In the experiments, even though the classification accuracy with the proposed feature extraction method and linear discriminant analysis is lower than that of expert physicians, the results suggest the potential of tensorial feature extraction.

Synthesizing 4D Magnetic Resonance Angiography From 3D Time-of-Flight Using Deep Learning: A Feasibility Study.

Objective and background This study aimed to develop a deep convolutional neural network (DCNN) model capable of generating synthetic 4D magnetic resonance angiography (MRA) from 3D time-of-flight (TOF) images, allowing estimation of temporal changes in arterial flow. TOF MRA provides static information about arterial structures through maximum intensity projection (MIP) processing, but it does not capture the dynamic information of contrast agent circulation, which is lost during MIP processing. Considering the principles of TOF, it is hypothesized that dynamic information about arterial blood flow is latent within TOF signals. Although arterial spin labeling (ASL) can extract dynamic arterial information, ASL MRA has drawbacks, such as longer imaging times and lower spatial resolution than TOF MRA. This study's primary aim is to extend the utility of TOF MRA by training a machine-learning model on paired TOF and ASL data to extract latent dynamic information from TOF signals. Methods A DCNN combining a modified U-Net and a long-short-term memory (LSTM) network was trained on a dataset of 13 subjects (11 men and two women, aged 42-77 years) using paired 3D TOF MRA and 4D ASL MRA images. Subjects had no history of cerebral vessel occlusion or significant stenosis. The dataset was acquired using a 3T MRI system with a 32-channel head coil. Preprocessing involved resampling and intensity normalization of TOF and ASL images, followed by data augmentation and arterial mask generation. The model learned to extract flow information from TOF images and generate 8-phase 4D MRA images. The precision of flow estimation was evaluated using the coefficient of determination (R²) and Bland-Altman analysis. A board-certified neuroradiologist validated the quality of the images and the absence of significant stenosis in the major cerebral arteries. Results The generated 4D MRA images closely resembled the ground-truth ASL MRA data, with R² values of 0.92, 0.85, and 0.84 for the internal carotid artery (ICA), proximal middle cerebral artery (MCA), and distal MCA, respectively. Bland-Altman analysis revealed a systematic error of -0.06, with 95% agreement limits ranging from -0.18 to 0.12. Additionally, the model successfully identified flow abnormalities in a subject with left MCA stenosis, displaying a delayed peak and subsequent flattening distal to the stenosis, indicative of reduced blood flow. Visualization of the predicted arterial flow overlaid on the original TOF MRA images highlighted the spatial progression and dynamics of the flow. Conclusions The DCNN model effectively generated synthetic 4D MRA images from TOF images, demonstrating its potential to estimate temporal changes in arterial flow accurately. This non-invasive technique offers a promising alternative to conventional methods for visualizing and evaluating healthy and pathological flow dynamics. It has significant potential to improve the diagnosis and treatment of cerebrovascular diseases by providing detailed temporal flow information without the need for contrast agents or invasive procedures. The practical implementation of this model could enable the extraction of dynamic cerebral blood flow information from routine brain MRI examinations, contributing to the early diagnosis and management of cerebrovascular disorders.

Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future.

The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized the industry, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. However, the increasing adoption of AI systems also raises concerns about their environmental impact, particularly in the context of climate change. This review explores the intersection of climate change and AI in healthcare, examining the challenges posed by the energy consumption and carbon footprint of AI systems, as well as the potential solutions to mitigate their environmental impact. The review highlights the energy-intensive nature of AI model training and deployment, the contribution of data centers to greenhouse gas emissions, and the generation of electronic waste. To address these challenges, the development of energy-efficient AI models, the adoption of green computing practices, and the integration of renewable energy sources are discussed as potential solutions. The review also emphasizes the role of AI in optimizing healthcare workflows, reducing resource waste, and facilitating sustainable practices such as telemedicine. Furthermore, the importance of policy and governance frameworks, global initiatives, and collaborative efforts in promoting sustainable AI practices in healthcare is explored. The review concludes by outlining best practices for sustainable AI deployment, including eco-design, lifecycle assessment, responsible data management, and continuous monitoring and improvement. As the healthcare industry continues to embrace AI technologies, prioritizing sustainability and environmental responsibility is crucial to ensure that the benefits of AI are realized while actively contributing to the preservation of our planet.

Decreased diffusivity along the perivascular space and cerebral hemodynamic disturbance in adult moyamoya disease.

Moyamoya disease (MMD) causes cerebral arterial stenosis and hemodynamic disturbance, the latter of which may disrupt glymphatic system activity, the waste clearance system. We evaluated 46 adult patients with MMD and 33 age- and sex-matched controls using diffusivity along the perivascular space (ALPS) measured with diffusion tensor imaging (ALPS index), which may partly reflect glymphatic system activity, and multishell diffusion MRI to generate freewater maps. Twenty-three patients were also evaluated via 15O-gas positron emission tomography (PET), and all patients underwent cognitive tests. Compared to controls, patients (38.4 (13.2) years old, 35 females) had lower ALPS indices in the left and right hemispheres (1.94 (0.27) vs. 1.65 (0.25) and 1.94 (0.22) vs. 1.65 (0.19), P < 0.001). While the right ALPS index showed no correlation, the left ALPS index was correlated with parenchymal freewater (ρ = -0.47, P < 0.001); perfusion measured with PET (cerebral blood flow, ρ = 0.70, P < 0.001; mean transit time, ρ = -0.60, P = 0.003; and oxygen extraction fraction, ρ = -0.52, P = 0.003); and cognitive tests (trail making test part B for executive function; ρ = -0.37, P = 0.01). Adult patients with MMD may exhibit decreased glymphatic system activity, which is correlated with the degree of hemodynamic disturbance, increased interstitial freewater, and cognitive dysfunction, but further investigation is needed.

The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.

The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.

Assessing interstitial fluid dynamics in type 2 diabetes mellitus and prediabetes cases through diffusion tensor imaging analysis along the perivascular space.

Background and purpose: Glymphatic system in type 2 diabetes mellitus (T2DM) but not in the prodrome, prediabetes (Pre-DM) was investigated using diffusion tensor image analysis along the perivascular space (DTI-ALPS). Association between glymphatic system and insulin resistance of prominent characteristic in T2DM and Pre-DM between is yet elucidated. Therefore, this study delves into the interstitial fluid dynamics using the DTI-ALPS in both Pre-DM and T2DM and association with insulin resistance. Materials and methods: In our cross-sectional study, we assessed 70 elderly individuals from the Bunkyo Health Study, which included 22 with Pre-DM, 18 with T2DM, and 33 healthy controls with normal glucose metabolism (NGM). We utilized the general linear model (GLM) to evaluate the ALPS index based on DTI-ALPS across these groups, considering variables like sex, age, intracranial volume, years of education, anamnesis of hypertension and hyperlipidemia, and the total Fazekas scale. Furthermore, we have explored the relationship between the ALPS index and insulin resistance, as measured by the homeostasis model assessment of insulin resistance (HOMA-IR) using GLM and the same set of covariates. Results: In the T2DM group, the ALPS index demonstrated a reduction compared with the NGM group [family-wise error (FWE)-corrected p < 0.001; Cohen's d = -1.32]. Similarly, the Pre-DM group had a lower ALPS index than the NGM group (FWE-corrected p < 0.001; Cohen's d = -1.04). However, there was no significant disparity between the T2DM and Pre-DM groups (FWE-corrected p = 1.00; Cohen's d = -0.63). A negative correlation was observed between the ALPS index and HOMA-IR in the combined T2DM and Pre-DM groups (partial correlation coefficient r = -0.35, p < 0.005). Conclusion: The ALPS index significantly decreased in both the pre-DM and T2DM groups and showed a correlated with insulin resistance. This indicated that changes in interstitial fluid dynamics are associated with insulin resistance.

Association between mammillary body atrophy and memory impairment in retired athletes with a history of repetitive mild traumatic brain injury.

Cognitive dysfunction, especially memory impairment, is a typical clinical feature of long-term symptoms caused by repetitive mild traumatic brain injury (rmTBI). The current study aims to investigate the relationship between regional brain atrophy and cognitive impairments in retired athletes with a long history of rmTBI. Overall, 27 retired athletes with a history of rmTBI (18 boxers, 3 kickboxers, 2 wrestlers, and 4 others; rmTBI group) and 23 age/sex-matched healthy participants (control group) were enrolled. MPRAGE on 3 T MRI was acquired and segmented. The TBV and TBV-adjusted regional brain volumes were compared between groups, and the relationship between the neuropsychological test scores and the regional brain volumes were evaluated. Total brain volume (TBV) and regional brain volumes of the mammillary bodies (MBs), hippocampi, amygdalae, thalami, caudate nuclei, and corpus callosum (CC) were estimated using the SPM12 and ITK-SNAP tools. In the rmTBI group, the regional brain volume/TBV ratio (rmTBI vs. control group, Mann-Whitney U test, p < 0.05) underwent partial correlation analysis, adjusting for age and sex, to assess its connection with neuropsychological test results. Compared with the control group, the rmTBI group showed significantly lower the MBs volume/TBV ratio (0.13 ± 0.05 vs. 0.19 ± 0.03 × 10-3, p < 0.001). The MBs volume/TBV ratio correlated with visual memory, as assessed, respectively, by the Rey-Osterrieth Complex Figure test delayed recall (ρ = 0.62, p < 0.001). In conclusion, retired athletes with rmTBI have MB atrophy, potentially contributing to memory impairment linked to the Papez circuit disconnection.

Free water in gray matter linked to gut microbiota changes with decreased butyrate producers in Alzheimer's disease and mild cognitive impairment.

Neuroinflammation contributes to the pathology and progression of Alzheimer's disease (AD), and it can be observed even with mild cognitive impairment (MCI), a prodromal phase of AD. Free water (FW) imaging estimates the extracellular water content and has been used to study neuroinflammation across several neurological diseases including AD. Recently, the role of gut microbiota has been implicated in the pathogenesis of AD. The relationship between FW imaging and gut microbiota was examined in patients with AD and MCI. Fifty-six participants underwent neuropsychological assessments, FW imaging, and gut microbiota analysis targeting the bacterial 16S rRNA gene. They were categorized into the cognitively normal control (NC) (n = 19), MCI (n = 19), and AD (n = 18) groups according to the neuropsychological assessments. The correlations of FW values, neuropsychological assessment scores, and the relative abundance of gut microbiota were analyzed. FW was higher in several white matter tracts and in gray matter regions, predominantly the frontal, temporal, limbic and paralimbic regions in the AD/MCI group than in the NC group. In the AD/MCI group, higher FW values in the temporal (superior temporal and temporal pole), limbic and paralimbic (insula, hippocampus and amygdala) regions were the most associated with worse neuropsychological assessment scores. In the AD/MCI group, FW values in these regions were negatively correlated with the relative abundances of butyrate-producing genera Anaerostipes, Lachnospiraceae UCG-004, and [Ruminococcus] gnavus group, which showed a significant decreasing trend in the order of the NC, MCI, and AD groups. The present study showed that increased FW in the gray matter regions related to cognitive impairment was associated with low abundances of butyrate producers in the AD/MCI group. These findings suggest an association between neuroinflammation and decreased levels of the short-chain fatty acid butyrate that is one of the major gut microbial metabolites having a potentially beneficial role in brain homeostasis.

Reduced Diffusivity along Perivascular Spaces on MR Imaging Associated with Younger Age of First Use and Cognitive Impairment in Recreational Marijuana Users.

BACKGROUND AND PURPOSE: The impairment of the glymphatic system, a perivascular network crucial for brain waste clearance, has been linked to cognitive impairment, potentially attributed to the accumulation of brain waste. Although marijuana use has been associated with poorer cognitive performance, particularly in adolescents, its influence on the glymphatic system remains unexplored. This study evaluated the influence of the age of first marijuana use and the total number of lifetime uses on the glymphatic system, measured using the index of DTI along the perivascular space (DTI-ALPS). Furthermore, we explored the correlation between glymphatic clearance and cognitive performance among marijuana users. MATERIALS AND METHODS: In this study, 125 individuals who reported using marijuana at least once in their lifetime (43 men; mean age, 28.60 [SD, 3.84] years) and 125 individuals with zero lifetime cannabis use (nonusers; 44 men; mean age, 28.82 [SD, 3.56] years) were assessed. ALPS indices of all study participants were calculated using 3T diffusion MR imaging data (b = 1000 s/mm2). RESULTS: After we adjusted for age, sex, education years, Pittsburgh Sleep Quality Index, alcohol use, tobacco use, and intracranial volume, our analysis using a univariate General Linear Model revealed no significant difference in the ALPS index among nonusers and marijuana users with different ages of first use or various frequencies of lifetime usage. However, in marijuana users, multiple linear regression analyses showed associations between a lower ALPS index and earlier age of first marijuana use (standardized ß, -0.20; P = .041), lower accuracy in the working memory 0-back task (standardized ß, 0.20; P = .042), and fewer correct responses in the Fluid Intelligence Test (standardized ß, 0.19; P = .045). CONCLUSIONS: This study shows the potential use of DTI-ALPS as a noninvasive indirect indicator of the glymphatic clearance in young adults. Our findings show novel adverse effects of younger age at first use of marijuana on the glymphatic system function, which is associated with impaired working memory and fluid intelligence. Gaining insight into the alterations in glymphatic function following marijuana use could initiate novel strategies to reduce the risk of cognitive impairment.

Diurnal Variation of Brain Activity in the Human Suprachiasmatic Nucleus.

The suprachiasmatic nucleus (SCN) is the central clock for circadian rhythms. Animal studies have revealed daily rhythms in the neuronal activity in the SCN. However, the circadian activity of the human SCN has remained elusive. In this study, to reveal the diurnal variation of the SCN activity in humans, we localized the SCN by employing an areal boundary mapping technique to resting-state functional images and investigated the SCN activity using perfusion imaging. In the first experiment (n = 27, including both sexes), we scanned each participant four times a day, every 6 h. Higher activity was observed at noon, while lower activity was recorded in the early morning. In the second experiment (n = 20, including both sexes), the SCN activity was measured every 30 min for 6 h from midnight to dawn. The results showed that the SCN activity gradually decreased and was not associated with the electroencephalography. Furthermore, the SCN activity was compatible with the rodent SCN activity after switching off the lights. These results suggest that the diurnal variation of the human SCN follows the zeitgeber cycles of nocturnal and diurnal mammals and is modulated by physical lights rather than the local time.

The glymphatic system as a potential biomarker and therapeutic target in secondary progressive multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is a refractory immune-mediated inflammatory disease of the central nervous system, and some cases of the major subtype, relapsing-remitting (RR), transition to secondary progressive (SP). However, the detailed pathogenesis, biomarkers, and effective treatment strategies for secondary progressive multiple sclerosis have not been established. The glymphatic system, which is responsible for waste clearance in the brain, is an intriguing avenue for investigation and is primarily studied through diffusion tensor image analysis along the perivascular space (DTI-ALPS). This study aimed to compare DTI-ALPS indices between patients with RRMS and SPMS to uncover potential differences in their pathologies and evaluate the utility of the glymphatic system as a possible biomarker. METHODS: A cohort of 26 patients with MS (13 RRMS and 13 SPMS) who met specific criteria were enrolled in this prospective study. Magnetic resonance imaging (MRI), including diffusion MRI, 3D T1-weighted imaging, and relaxation time quantification, was conducted. The ALPS index, a measure of glymphatic function, was calculated using diffusion-weighted imaging data. Demographic variables, MRI metrics, and ALPS indices were compared between patients with RRMS and those with SPMS. RESULTS: The ALPS index was significantly lower in the SPMS group. Patients with SPMS exhibited longer disease duration and higher Expanded Disability Status Scale (EDSS) scores than those with RRMS. Despite these differences, the correlations between the EDSS score, disease duration, and ALPS index were minimal, suggesting that the impact of these clinical variables on ALPS index variations was negligible. DISCUSSION: Our study revealed the potential microstructural and functional differences between RRMS and SPMS related to glymphatic system impairment. Although disease severity and duration vary among subtypes, their influence on ALPS index differences appears to be limited. This highlights the stronger association between SP conversion and changes in the ALPS index. These findings align with those of previous research, indicating the involvement of the glymphatic system in the progression of MS. CONCLUSION: Although the causality remains uncertain, our study suggests that a reduced ALPS index, reflecting glymphatic system dysfunction, may contribute to MS progression, particularly in SPMS. This suggests the potential of the ALPS index as a diagnostic biomarker for SPMS and underscores the potential of the glymphatic system as a therapeutic target to mitigate MS progression. Future studies with larger cohorts and pathological validation are necessary to confirm these findings. This study provides new insights into the pathogenesis of SPMS and the potential for innovative therapeutic strategies.

Cross-vendor multiparametric mapping of the human brain using 3D-QALAS: A multicenter and multivendor study.

PURPOSE: To evaluate a vendor-agnostic multiparametric mapping scheme based on 3D quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) for whole-brain T1, T2, and proton density (PD) mapping. METHODS: This prospective, multi-institutional study was conducted between September 2021 and February 2022 using five different 3T systems from four prominent MRI vendors. The accuracy of this technique was evaluated using a standardized MRI system phantom. Intra-scanner repeatability and inter-vendor reproducibility of T1, T2, and PD values were evaluated in 10 healthy volunteers (6 men; mean age ± SD, 28.0 ± 5.6 y) who underwent scan-rescan sessions on each scanner (total scans = 100). To evaluate the feasibility of 3D-QALAS, nine patients with multiple sclerosis (nine women; mean age ± SD, 48.2 ± 11.5 y) underwent imaging examination on two 3T MRI systems from different manufacturers. RESULTS: Quantitative maps obtained with 3D-QALAS showed high linearity (R2 = 0.998 and 0.998 for T1 and T2, respectively) with respect to reference measurements. The mean intra-scanner coefficients of variation for each scanner and structure ranged from 0.4% to 2.6%. The mean structure-wise test-retest repeatabilities were 1.6%, 1.1%, and 0.7% for T1, T2, and PD, respectively. Overall, high inter-vendor reproducibility was observed for all parameter maps and all structure measurements, including white matter lesions in patients with multiple sclerosis. CONCLUSION: The vendor-agnostic multiparametric mapping technique 3D-QALAS provided reproducible measurements of T1, T2, and PD for human tissues within a typical physiological range using 3T scanners from four different MRI manufacturers.

Free-water diffusion magnetic resonance imaging under selegiline treatment in Parkinson's disease.

INTRODUCTION: Monoamine oxidase type B inhibitors, including selegiline, are established as anti-Parkinsonian Drugs. Inhibition of monoamine oxidase type B enzymes might suppress the inflammation because of inhibition to generate reactive oxygen species. However, its effect on brain microstructure remains unclear. The aim of this study is to elucidate white matter and substantia nigra (SN) microstructural differences between Patients with Parkinson's disease with and without selegiline treatment by two independently recruited cohorts. METHODS: Diffusion tensor imaging and free water imaging indices of WM and SN were compared among 22/15 Patients with Parkinson's disease with selegiline (PDselegiline(+)), 33/23 Patients with Parkinson's disease without selegiline (PDselegiline(-)), and 25/20 controls, in the first/second cohorts. Two cohorts were analyzed with different MRI protocols. RESULTS: Diffusion tensor imaging and free-water indices of major white matter tracts were significantly differed between the PDselegiline(-) and controls in both cohorts, although not between the PDselegiline(+) and controls except for restricted areas. Compared with the PDselegiline(+), free-water was significantly higher in the PDselegiline(-) in the inferior fronto-occipital fasciculus, superior longitudinal fasciculus, and superior and posterior corona radiata (first cohort) and the forceps major and splenium of the corpus callosum (second cohort). There were no significant differences in free-water of anterior or posterior substantia nigra between PDselegiline(+) and PDselegiline(-). CONCLUSIONS: Selegiline treatment might reduce the white matter microstructural abnormalities detected by free-water imaging in Parkinson's disease.