Search Results (431)

Background/Objectives: The glymphatic system is a recently characterized glial-dependent waste clearance pathway in the brain, which makes use of perivascular spaces for cerebrospinal fluid exchange. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) offers a non-invasive method for estimating perivascular flow, but its biological specificity and susceptibility to methodological variation, particularly head position during MRI acquisition, remain as threats to the validity of this technique. This study aimed to assess the prevalence of current DTI-ALPS practices, evaluate the impact of head orientation on ALPS index calculation, and propose a novel computational approach to improve measurement validity. Methods: We briefly reviewed DTI-ALPS literature to determine the use of head-orientation correction strategies. We then analyzed diffusion MRI data from 172 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to quantify the influence of head orientation on ALPS indices computed using the conventional Unrotated-ALPS, a vecrec-corrected ALPS, and the new LD-ALPS method proposed within. Results: A majority of studies employed Unrotated-ALPS, which does not correct for head orientation. In our sample, Unrotated-ALPS values were significantly associated with absolute head pitch (r₁₆₉ = −0.513, p < 0.001), indicating systematic bias. This relationship was eliminated using either vecreg or LD-ALPS. Additionally, LD-ALPS showed more sensitivity to cognitive status as measured by Mini-Mental State Examination scores. Conclusions: Correcting for head orientation is essential in DTI-ALPS studies. The LD-ALPS method, while computationally more demanding, improves the reliability and sensitivity of perivascular fluid estimates, supporting its use in future research on aging and neurodegeneration. Full article

Deep brain stimulation (DBS) is an established neurosurgical treatment for Parkinson’s disease (PD), mainly targeting motor symptoms resistant to pharmacological therapy. This review examines strategies to optimize DBS using advanced anatomical, functional, and imaging approaches. The subthalamic nucleus (STN) remains the principal target for alleviating bradykinesia and rigidity, while recent evidence highlights the dentato-rubro-thalamic tract (DRTt) as an additional promising target, especially for tremor control. Clinical data demonstrate that co-stimulation of both STN and DRTt via electrode electric fields results in superior motor outcomes, including greater reductions in UPDRS-III scores and lower levodopa requirements. The review highlights the use of high-resolution MRI and diffusion tensor imaging tractography in visualizing STN and DRTt with high precision. These methods support accurate targeting and individualized treatment planning. Electric field modelling is discussed as a tool to quantify stimulation overlap with target structures and predict clinical efficacy. Anatomical variability in DRTt positioning relative to the STN is emphasized, supporting the need for patient-specific DBS approaches. Alternative and emerging DBS targets—including the pedunculopontine nucleus, zona incerta, globus pallidus internus, and nucleus basalis of Meynert—are discussed for their potential in treating axial and cognitive symptoms. The review concludes with a forward-looking discussion on network-based DBS paradigms, the integration of adaptive stimulation technologies, and the potential of multimodal imaging and electrophysiological biomarkers to guide therapy. Together, these advances support a paradigm shift from focal to network-based neuromodulation in PD management. Full article

Current medical informatics systems face critical challenges, including limited scalability across distributed institutions, insufficient real-time AI-driven decision support, and lack of standardized interoperability for heterogeneous medical data exchange. To address these challenges, this paper proposes a novel distributed web system architecture for medical informatics, integrating artificial intelligence techniques and cloud-based services. The system ensures interoperability via HL7 FHIR standards and preserves data privacy and fault tolerance across interconnected medical institutions. A hybrid AI pipeline combining principal component analysis (PCA), K-Means clustering, and convolutional neural networks (CNNs) is applied to diffusion tensor imaging (DTI) data for early detection of neurological anomalies. The architecture leverages containerized microservices orchestrated with Docker Swarm, enabling adaptive resource management and high availability. Experimental validation confirms reduced latency, improved system reliability, and enhanced compliance with medical data exchange protocols. Results demonstrate superior performance with an average latency of 94 ms, a diagnostic accuracy of 91.3%, and enhanced clinical workflow efficiency compared to traditional monolithic architectures. The proposed solution successfully addresses scalability limitations while maintaining data security and regulatory compliance across multi-institutional deployments. This work contributes to the advancement of intelligent, interoperable, and scalable e-health infrastructures aligned with the evolution of digital healthcare ecosystems. Full article

Background/Objectives: Germ Cell Neoplasia In Situ (GCNIS) is considered the precursor lesion for the majority of testicular germ cell tumors (TGCTs). The aim of this study was to evaluate whether first-order radiomics features derived from volumetric diffusion tensor imaging (DTI) metrics—specifically apparent diffusion coefficient (ADC) and fractional anisotropy (FA) histogram parameters—can detect GCNIS. Methods: This study included 15 men with TGCTs and 10 controls. All participants underwent scrotal MRI, including DTI. Volumetric ADC and FA histogram metrics were calculated for the following tissues: group 1, TGCT; group 2: testicular parenchyma adjacent to tumor, histologically positive for GCNIS; and group 3, normal testis. Non-parametric statistics were used to assess differences in ADC and FA histogram parameters among the three groups. Pearson’s correlation analysis was followed by ordinal regression analysis to identify key predictive histogram parameters. Results: Widespread distributional differences (p < 0.05) were observed for many ADC and FA variables, with both TGCTs and GCNIS showing significant divergence from normal testes. Among the ADC statistics, the 10th percentile and skewness (p = 0.042), range (p = 0.023), interquartile range (p = 0.021), total energy (p = 0.033), entropy and kurtosis (p = 0.027) proved the most significant predictors for tissue classification. FA_energy (p = 0.039) was the most significant fingerprint of the carcinogenesis among the FA metrics. These parameters correctly characterized 88.8% of TGCTs, 87.5% of GCNIS tissues and 100% of normal testes. Conclusion: Radiomics features derived from volumetric ADC and FA histograms have promising potential to differentiate TGCTs, GCNIS, and normal testicular tissue, aiding early detection and characterization of pre-cancerous lesions. Full article

Objectives: This study investigated fractional anisotropy (FA) differences within key white matter tracts across patient groups stratified by Montreal Cognitive Assessment (MoCA) scores, aiming to evaluate FA’s potential as a biomarker for cognitive impairment. Methods: Seventy participants (aged 57–96 years) were categorized into high (HP, MoCA ≥ 26), moderate (MP, MoCA 18–25), and low (LP, MoCA < 18) cognitive performance groups. Diffusion Tensor Imaging (DTI) was used to obtain FA values in corticospinal tracts, superior longitudinal fasciculus, inferior fronto-occipital fasciculus, and cingulum. Statistical analyses included ANOVA and post-hoc tests. Results: Significant differences in FA values and normative percentiles were observed across cognitive groups in several tracts. Notably, the MP group exhibited significantly higher FA values in the Left Superior Longitudinal Fasciculus—Arcuate (mean FA 0.329 vs. LP 0.306, p = 0.033) and Right Superior Longitudinal Fasciculus—Arcuate (mean FA 0.329 vs. LP 0.306, p = 0.009), Left Inferior Fronto-Occipital Fasciculus (mean FA 0.308 vs. LP 0.283, p = 0.021), and Right Inferior Fronto-Occipital Fasciculus (mean FA 0.289 vs. LP 0.266, p = 0.017) compared to the LP group. Conclusions: Our findings reveal significant FA alterations across MoCA-defined cognitive groups, with moderate impairment showing higher FA than low performance. This suggests FA may reflect complex microstructural changes in early cognitive decline. While our modest sample size, particularly in the low-performance group, limits definitive conclusions, these results highlight the need for larger, multimodal studies to validate FA’s role as a sensitive, albeit complex, biomarker for cognitive impairment. Full article

Background/Objectives: The ability to drive is closely linked to participation in daily activities and quality of life in people living with neurological disorders. Cognitive deficits in people with multiple sclerosis (pwMS) are known to hinder this ability, yet concrete fitness-to-drive criteria remain elusive and assessment guidelines lack uniformity. A plethora of cognitive tests have provided associations with various aspects of driving performance and on-road behavior; however, several studies reveal limitations and inconsistencies in most tests’ sensitivity and predictive effect. Novel and resurfaced modalities for cognitive assessment, in the form of advanced imaging techniques and electrophysiological studies, may offer improved sensitivity in driving-related abilities in earlier and milder stages. Their application in addition to evaluations in driving simulators may aid future research and enhance the quality of evidence to inform decision-making. Methods: We searched for the relevant literature in the PubMed database and synthesized the available findings for the applications of currently clinically used cognitive tests, markers derived from functional magnetic resonance imaging (fMRI) and diffuse tensor imaging (DTI), as well as event-related potentials (ERP). Results: Advanced imaging modalities and ERP studies may better capture neurobiological changes that lead to driving impairment in pwMS, and they may also be applied to detect cognitive alterations earlier and with greater precision, helping to predict driving difficulties in this population. Conclusions: Novel tools and driving simulator settings could improve our understanding of the relation between cognition and driving in pwMS, enhance protocol homogeneity in driving studies, and aid in the formation of guidelines. The evidence in this review supports an increase in their application in future studies. Full article

Purpose: Focal cortical dysplasia (FCD) is a common developmental malformation disease of the cerebral cortex. Although mounting evidence has suggested that FCD lesions have variable locations and topographies throughout the cortex, few studies have explored consistencies in structural connectivity among different lesion types. In this study, we analyzed microscopic structural changes via lesion analysis and explored structural changes in nonlesion regions across the brain. Methods: Diffusion tensor imaging (DTI) and magnetization transfer imaging were used to compare FCD lesions and contralateral normal appearing gray/white matter (cNAG/WM). Voxel-based morphometry was calculated for 28 children with FCD and 34 sex- and age-matched healthy participants. DTI indices of the FCD and healthy control groups were analyzed via the tract-based spatial statistic method to evaluate the microstructure abnormalities of WM fiber tracts in individuals with FCD. Results: In terms of FCD lesions, compared with those of the cNAG, the fractional anisotropy (FA) values were decreased, and the mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) values were increased; the magnetization transfer ratios were also decreased. In terms of whole-brain changes due to FCD, compared with the healthy control group, the FCD group showed a decrease in the volume of the right hippocampus and left anterior cingulate cortex. FCD patients had lower FA values, higher MD values, lower AD values, and mainly increased RD values in relation to WM microstructure. Conclusions: Microstructural abnormalities outside lesion regions may be related to injury to the epileptic network, and the identification of such abnormalities may complement diagnoses of FCD in pediatric patients. Full article

Astrocyte-derived extracellular vesicles (ADEVs) have emerged as promising neuroprotective agents for ischemic stroke. In this study, we evaluated the therapeutic potential of hypoxia-conditioned ADEVs (HxEVs) administered intracerebroventricularly in a rat model of transient middle cerebral artery occlusion (tMCAO). Serial magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) was performed at 1, 7, 14, and 21 days post-stroke. HxEV treatment produced a significant reduction in infarct volume from day 1, sustained through day 21, and was accompanied by improvements in motor and sensory recovery. DTI analyses showed progressive normalization of fractional anisotropy (FA) and radial diffusivity (RD), particularly in the corpus callosum and striatum, reflecting microstructural repair. In contrast, mean diffusivity (MD) was less sensitive to these treatment effects. Regional differences in therapeutic response were evident, with earlier and more sustained recovery in the corpus callosum than in other brain regions. Histological findings confirmed greater preservation of dendrites and axons in HxEV-treated animals, supporting the role of these vesicles in accelerating post-stroke neurorepair. Together, these results demonstrate that hypoxia-conditioned ADEVs promote both structural and functional recovery after ischemic stroke. They also highlight the value of DTI-derived biomarkers as non-invasive tools to monitor neurorepair. The identification of region-specific therapeutic effects and the validation of reliable imaging markers provide a strong foundation for future research and development. Full article

Background: The epidermal growth factor receptor (EGFR) is an important factor in the behavior of diffuse glioma, serving as a potential biomarker for tumor aggressiveness and a therapeutic target. Diffusion tensor imaging (DTI) provides insights into the microstructural integrity of brain tissues, allowing for detailed visualization of tumor-induced changes in white matter tracts. This imaging technique can complement molecular pathology by correlating imaging findings with molecular markers and genetic profiles, potentially enhancing the understanding of tumor behavior and aiding in the formulation of targeted therapeutic strategies. The present study aimed to investigate the molecular properties of diffuse glioma based on DTI sequences. Methods: A total of 27 patients with diffuse glioma (in accordance with the WHO 2021 classification) were investigated using preoperative DTI sequences. The study was conducted using the tractography software DSI Studio (Hou versions 2025.04.16). Following the preprocessing of the raw data, volumes of the arcuate fasciculus (AF), frontal aslant tract (FAT), inferior fronto-occipital fasciculus (IFOF), superior longitudinal fasciculus (SLF), and uncinate fasciculus (UF) were reconstructed, and fractional anisotropy (FA) was derived. Molecular pathological examination was conducted to assess the presence of EGFR amplifications. Results: The mean age of patients was 56 ± 13 years, with 33% females. EGFR amplification was observed in 8/27 (29.6%) of cases. Following correction for multiple comparisons, FA in the left AF (p = 0.025) and in the left FAT (p = 0.020) was found to be significantly lowered in EGFR amplified glioma. In the right language network, however, no statistically significant changes were observed. Conclusions: EGFR amplification may be associated with lower white matter integrity of left hemispheric language tracts, possibly impairing neurological function and impacting surgical outcomes. The underlying molecular and cellular mechanisms driving this association require further investigation. Full article

Background: Brachial plexopathies comprise a diverse array of illnesses with multifactorial etiologies, including trauma, inflammation, neoplasia, and iatrogenic damage, frequently manifesting with nonspecific clinical symptoms. Precise and prompt imaging evaluation is essential for diagnosis, treatment planning, and monitoring. Objective: To equip radiologists with interpretative tools for a systematic assessment of the brachial plexus utilizing advanced imaging modalities, specifically ultrasound (US) and magnetic resonance imaging (MRI), while emphasizing techniques, indications, limitations, and critical radiologic signs for differential diagnosis. Imaging Techniques: This narrative review concentrates on US and MRI. High-frequency linear probes with multiplanar dynamic scans provide US visualization of trunks, cords, and terminal branches in superficial areas. Specialized MRI procedures (T1, T2, STIR, DWI, contrast-enhanced) provide comprehensive evaluation of spinal roots and deep tissues, differentiating preganglionic from postganglionic lesions. A combined US–MRI methodology can enhance diagnostic efficacy. Findings: Ultrasound is excellent for superficial and dynamic assessment, especially in post-traumatic and iatrogenic lesions, while MRI is the gold standard for deep structures and complex disorders. The integration of two modalities enhances lesion identification and treatment direction. Emerging methodologies further enhance diagnostic and prognostic capabilities. Conclusions: The synergistic application of US and MRI, emphasizing nerve injury patterns and muscle denervation indicators, facilitates precise and prompt diagnosis of brachial plexopathies. Standardizing imaging standards and incorporating modern techniques are essential for interdisciplinary, customized patient care. Full article

Background/Objectives: Cerebral small vessel disease (CSVD) often manifests as enlarged perivascular spaces (ePVS), which are linked to reduced processing speed even in asymptomatic individuals. Glymphatic dysfunction (or glymphopathy) has been proposed as a mechanism underlying ePVS, with the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index serving as a potential non-invasive surrogate marker. This study aimed to examine the association between DTI-ALPS index, ePVS burden, and processing speed in community-dwelling adults without overt neurological symptoms, stratified by QRISK3 cardio-cerebrovascular risk prediction score. Methods: Sixty young-to-middle-aged adults (aged 25–65 years), classified as low-to-moderate QRISK3 scores, underwent brain 3T diffusion magnetic resonance imaging (MRI) to evaluate ePVS burden and calculate DTI-ALPS indices. Processing speed index (PSI) was assessed using the Wechsler Adult Intelligence Scale—Version IV (WAIS-IV). Results: Approximately 43% of subjects reported having ePVS with significantly lower DTI-ALPS indices and PSI compared to those without ePVS. The DTI-ALPS index was inversely correlated with ePVS burden and positively correlated with PSI. Mediation analysis showed that the lower DTI-ALPS partially mediated the association between ePVS burden and slower processing speed. Conclusions: Visible ePVS in our cohort may reflect early glymphopathy and subtle cognitive slowing, while the DTI-ALPS index may serve as an early biomarker for preclinical CSVD-related cognitive vulnerability, supporting targeted prevention strategies. Full article

Background/Objectives: Spinal diffusion tensor imaging (sDTI) remains a challenging method for the selective evaluation of key anatomical structures, like pyramidal tracts (PTs) and dorsal columns (DCs), and for reliably quantifying diffusion metrics such as fractional anisotropy (FA), radial diffusivity (RD), mean diffusivity (MD), and axial diffusivity (AD). This prospective, single-center study aimed to assess the reproducibility, robustness, and reliability of an optimized axial sDTI protocol, specifically intended for long fiber tracts. Methods: We developed an optimized Stejskal–Tanner sequence for high-resolution, axial sDTI of the cervical spinal cord at 3.0 T. Using advanced standardized evaluation and post-processing methods, we estimated DTI values for PTs, DCs, and AHs at the level of the second cervical vertebra. Reliability was evaluated through repeated measurements in 16 healthy volunteers and by comparing results from two 3.0 T scanners (Magnetom Skyra and Magnetom Prisma, Siemens Healthineers, Erlangen, Germany). Reproducibility was assessed using paired t-tests, intraclass correlation coefficients (ICCs), Bland–Altman analysis, and coefficients of variation (CVs). Results: The optimized sDTI protocol demonstrated high consistency for FA between test–retest sessions and across scanners. For the Skyra, the DC region showed the highest reliability (average ICC = 0.858) followed by the PT region (average ICC = 0.789). On the Prisma, the PT region reached an average ICC of 0.854, with the DC region at 0.758. Pooled inter-scanner data indicated good-to-excellent agreement, particularly in the PT region (average ICC = 0.860). FA CVs remained low (<10%) across all regions and scanners. RD showed good-to-excellent ICC values for PTs and DCs (average ICC for Skyra 0.642 and 0.769 and 0.926 and 0.830 for Prisma, respectively) but showed a higher CV between 14.6 and 19.4% for these two scanners. Conclusions: Improved sDTI offers highly reproducible FA measurements for all metrics with scanner independence, supporting its potential as a robust tool for detecting and monitoring spinal cord pathologies. Full article

This paper presents a novel multi-modal segment anything model (MSAM) for glioma tumor segmentation using structural MRI images and diffusion tensor imaging data. We designed an effective multimodal feature fusion block to effectively integrate features from different modalities of data, thereby improving the accuracy of brain tumor segmentation. We have designed an effective missing modality training method to address the issue of missing modalities in actual clinical scenarios. To evaluate the effectiveness of MSAM, a series of experiments were conducted comparing its performance with U-Net across various modality combinations. The results demonstrate that MSAM consistently outperforms U-Net in terms of both Dice Similarity Coefficient and 95% Hausdorff Distance, particularly when structural modality data are used alone. Through feature visualization and the use of missing modality training, we show that MSAM can effectively adapt to missing data, providing robust segmentation even when key modalities are absent. Additionally, segmentation accuracy is influenced by tumor region size, with smaller regions presenting more challenges. These findings underscore the potential of MSAM in clinical applications where incomplete data or varying tumor sizes are prevalent. Full article

The 2021 WHO classification of brain tumours revolutionised the oncological field by emphasising the role of molecular, genetic and pathogenetic advances in classifying brain tumours. In this context, incidental gliomas have been increasingly identified due to the widespread performance of standard and advanced MRI sequences and represent a diagnostic and therapeutic challenge. The impactful decision to perform a surgical procedure deeply relies on the non-invasive identification of features or parameters that may correlate with brain tumour genetic profile and grading. Therefore, it is paramount to reach an early and proper diagnosis through neuroradiological techniques, such as MRI. Standard MRI sequences are the cornerstone of diagnosis, while consolidated and emerging roles have been awarded to advanced sequences such as Diffusion-Weighted Imaging/Apparent Diffusion Coefficient (DWI/ADC), Perfusion-Weighted Imaging (PWI), Magnetic Resonance Spectroscopy (MRS), Diffusion Tensor Imaging (DTI) and functional MRI (fMRI). The current novelty relies on the application of AI in brain neuro-oncology, mainly based on radiomics and radiogenomics models, which enhance standard and advanced MRI sequences in predicting glioma genetic status by identifying the mutation of multiple key biomarkers deeply impacting patients’ diagnosis, prognosis and treatment, such as IDH, EGFR, TERT, MGMT promoter, p53, H3-K27M, ATRX, Ki67 and 1p19. AI-driven models demonstrated high accuracy in glioma detection, grading, prognostication, and pre-surgical planning and appear to be a promising frontier in the neuroradiological field. On the other hand, standardisation challenges in image acquisition, segmentation and feature extraction variability, data scarcity and single-omics analysis, model reproducibility and generalizability, the black box nature and interpretability concerns, as well as ethical and privacy challenges remain key issues to address. Future directions, rooted in enhanced standardisation and multi-institutional validation, advancements in multi-omics integration, and explainable AI and federated learning, may effectively overcome these challenges and promote efficient AI-based models in glioma management. The aims of our multidisciplinary review are to: (1) extensively present the role of standard and advanced MRI sequences in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (2) give an overview of the current and main applications of AI tools in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (3) show the role of MRI, radiomics and radiogenomics in unravelling glioma genetic profiles. Standard and advanced MRI, radiomics and radiogenomics are key to unveiling the grading and genetic profile of gliomas and supporting the pre-operative planning, with significant impact on patients’ differential diagnosis, prognosis prediction and treatment strategies. Today, neuroradiologists are called to efficiently use AI tools for the in vivo, non-invasive, and comprehensive assessment of gliomas in the path towards patients’ personalised medicine. Full article

Objectives: To synthesize evidence on structural and functional neuroplasticity in patients after anterior cruciate ligament reconstruction (ACLR) and its clinical implications. Methods: Adhering to the PRISMA guidelines for systematic reviews and meta-analyses, a literature search was conducted using PubMed, Embase, Web of Science, Scopus, and Cochrane CENTRAL (2018–2025) using specific keyword combinations, screening the results based on predetermined inclusion and exclusion criteria. Results: Among the 27 included studies were the following: (1) sensory cortex reorganization with compensatory visual dependence (5 EEG/fMRI studies); (2) reduced motor cortex efficiency evidenced by elevated AMT (TMS, 8 studies) and decreased γ-CMC (EEG, 3 studies); (3) progressive corticospinal tract degeneration (increased radial diffusivity correlating with postoperative duration); (4) enhanced sensory-visual integration correlated with functional recovery. Conclusions: This review provides a novel synthesis of evidence from transcranial magnetic stimulation (TMS), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI) studies. It delineates characteristic patterns of post-ACLR structural and functional neural reorganization. Targeting visual–cognitive integration and corticospinal facilitation may optimize rehabilitation. Full article