Search Results (44)

Background/Objectives: Differentiating high-grade glioma recurrence from treatment-related changes remains challenging on conventional MRI. This study evaluated non-contrast arterial spin labeling (ASL) perfusion MRI for this distinction and compared its performance with dynamic susceptibility contrast (DSC) perfusion MRI. Methods: Postoperative follow-up MRI examinations obtained between November 2019 and May 2021 were retrospectively reviewed. The cohort included 63 MRI examinations from 36 adults treated for high-grade glioma. ASL, routine MRI, and DSC images were independently assessed by two neuroradiologists. Final diagnosis was based on histopathology or longitudinal clinical and imaging follow-up. Reader agreement, diagnostic performance, and an exploratory patient-level grouped machine learning analysis using ASL-only, DSC-only, and combined ASL–DSC features were evaluated. Results: ASL- and DSC-derived perfusion parameters were significantly higher in tumor recurrence than in treatment-related changes (all p < 0.001). Both techniques showed high diagnostic performance; DSC achieved the highest accuracy, whereas ASL provided high specificity across readers. Inter-reader agreement ranged from substantial to almost perfect. In grouped cross-validation, ASL-only, DSC-only, and combined models achieved mean AUCs of 0.946, 0.997, and 0.997, respectively. Permutation testing confirmed that combined-model performance exceeded chance expectations (empirical p = 0.002). Conclusions: Non-contrast ASL perfusion showed diagnostic performance comparable to DSC for differentiating high-grade glioma recurrence from treatment effects. ASL may provide a reliable non-invasive alternative for longitudinal surveillance, particularly when gadolinium administration is undesirable or contraindicated. Full article

Background/Objectives: The use of quantitative neuroimaging to establish objective biomarkers in neurodegenerative diseases (NDD) has attracted increasing interest over the last decade. Advanced magnetic resonance imaging (MRI) such as arterial spin labeling (ASL) and diffusion tensor imaging (DTI), as well as [¹⁸F]fluorodeoxyglucose ([¹⁸F]FDG) positron emission tomography (PET), could provide clinically meaningful biomarkers and may support differential diagnosis. The aim of this investigator-initiated, single-center, retrospective comparative study was to implement a framework for multimodal neuroimaging to evaluate cases with rare NDD, using a methodological approach that integrates metabolic, perfusion, and microstructural parameters from simultaneous FDG-PET/MRI, and to investigate its potential to facilitate diagnosis. Methods: Three patients with pathological motor signs (1f/2m; 63, 73, and 52 years) and 19 control subjects with subjective cognitive deficits (SCDs) underwent combined FDG-PET/MRI with pseudo-continuous ASL and DTI. Standardized uptake values (SUVs), relative cerebral blood flow (rCBF), and fractional anisotropy (FA) were calculated to identify pattern alterations in individual patients based on parameterization mapping. The final diagnosis was corticobasal degeneration (CBD, n = 1) or primary lateral sclerosis (PLS, n = 2). Results: At the individual patient level, disease-specific changes in defined brain regions could be demonstrated and quantified compared to control subjects. All three patients showed significantly decreased FA, primarily along parts of the course of the corticospinal tract (CST). In the patient with CBD, asymmetric SUVR and rCBF decreases were observed, mostly overlapping with motor regions. In the two patients with PLS, SUVR revealed mostly unspecific findings (hypothetically due to a slow progression rate or due to potentially early disease stages), while ASL indicated decreased rCBF primarily overlapping within the motor cortex. Changes at the gray matter level were primarily located adjacent to changes in white matter, as indicated by the multimodal analysis approach using simultaneously acquired FDG-PET/MRI data. Conclusions: According to this proof-of-concept study, multimodal neuroimaging by the combination of quantitative MRI and FDG-PET has the potential to guide differential diagnosis in rare NDDs, especially if clinical diagnosis is not straightforward to achieve. Since particularly early diagnosis remains essential for patient counseling, effective treatment, and clinical management, the present framework appears helpful to be developed further until it aligns and integrates with clinical routine. Full article

Background: Acute focal neurological deficits require rapid differentiation between ischemic stroke and stroke mimics to avoid treatment delays and inappropriate therapy. Seizures, including ictal deficits, status epilepticus, and post-ictal/Todd’s phenomena, are among the most challenging mimics. This review summarizes the role of multimodal neuroimaging in distinguishing acute ischemic stroke from seizure-related deficits. Methods: We performed a focused narrative review of neuroimaging findings in acute stroke mimics, emphasizing non-contrast computed tomography (CT), CT angiography, CT perfusion, magnetic resonance imaging (MRI), including diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC), fluid attenuated inversion recovery (FLAIR), and arterial spin labeling (ASL) sequences. Imaging patterns, diagnostic pitfalls, and practical clues for hyperacute stroke pathways were synthesized. Results: Acute ischemic stroke is typically suggested by vascular-territorial abnormalities, including arterial occlusion or stenosis, territorial hypoperfusion, and congruent DWI/ADC restriction. Seizure-related deficits more often show non-territorial cortical perfusion changes, ictal or status-related hyperperfusion, reversible MRI abnormalities, and absence of arterial occlusion. However, post-ictal hypoperfusion, peri-ictal diffusion restriction, and reperfusion-related hyperperfusion may overlap with ischemic patterns. Conclusions: A multimodal approach integrating vascular imaging, perfusion distribution, DWI/ADC, ASL, clinical timing, and EEG findings can improve diagnostic accuracy in the stroke–seizure differential without delaying treatment in true acute ischemic stroke. Full article

Background/Objectives: Diagnosing hemangioblastomas using magnetic resonance imaging (MRI) is challenging, especially when the tumors appear as solid posterior fossa masses. This study aimed to evaluate the diagnostic performance of perfusion MRI and identify the most useful quantitative features for differentiating hemangioblastomas from other posterior fossa tumors. Methods: Forty-five posterior fossa tumors were analyzed, including 18 hemangioblastomas (HB group) and 27 non-hemangioblastoma tumors (NHB group; 8 metastatic brain tumors, 6 pilocytic astrocytomas, 5 malignant lymphomas, 4 glioblastomas, 2 medulloblastomas, and 2 other tumors). All patients underwent 3.0-T MRI. Arterial spin labeling (ASL) was used to calculate the relative tumor blood flow normalized to the contralateral gray matter. Dynamic susceptibility contrast (DSC) imaging was used to obtain regional cerebral blood flow, regional and corrected cerebral blood volume (CBV), and permeability index (K2) values. Regions of interest (ROIs) were placed within the contrast-enhancing areas. Results: The relative ASL values and corrected CBV were significantly higher in hemangioblastomas than in other tumors (p < 0.001). Relative ASL showed the highest diagnostic performance (sensitivity, 100%; specificity, 93.3%). Conclusions: Non-contrast ASL showed strong diagnostic performance for identifying posterior fossa hemangioblastomas and may serve as a practical alternative to contrast-enhanced DSC, although ROI placement can be challenging in very small mural nodules. Full article

Background and Objectives: Head and neck cancers are heterogeneous malignancies with variable biological behavior and treatment response, contributing to high morbidity and mortality. Conventional imaging techniques are limited in their ability to capture tumor biology, highlighting the need for advanced functional imaging. This review aims to evaluate the role of multiparametric magnetic resonance imaging (MRI) in characterizing the tumor microenvironment. Materials and Methods: A narrative review was conducted based on a targeted literature search of databases, including PubMed and Google Scholar. Studies addressing advanced MRI techniques for assessing tumor cellularity, vascularity, molecular features, and oxygenation were selected and analyzed. Results: Perfusion techniques, such as dynamic contrast-enhanced MRI (DCE-MRI) and arterial spin labeling (ASL), provide a quantitative assessment of tumor vascularity and show value in predicting treatment response. Diffusion-based methods, including diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), enable evaluation of tissue cellularity and heterogeneity. Molecular approaches, such as chemical exchange saturation transfer (CEST) and amide proton transfer (APT), offer insights into protein content and proliferation. Oxygenation-sensitive techniques, such as blood oxygenation level dependent MRI (BOLD MRI) and oxygen-enhanced MRI (OE-MRI), allow non-invasive assessment of tumor hypoxia. Conclusions: Multiparametric MRI provides a comprehensive and biologically relevant evaluation of the tumor microenvironment in head and neck cancer, with potential to improve treatment prediction and support personalized therapeutic strategies. Full article

Background: Meningoencephalitis is a complex inflammatory condition of the CNS that can result in significant morbidity and mortality in critically ill adults. Accurate and timely neuromonitoring is essential for guiding management and improving outcomes. This study aimed to descriptively evaluate the prognostic value of early TCCD monitoring, particularly the pulsatility index, and its integration with conventional and perfusion MRI in patients with meningoencephalitis. Methods: We present an observational, retrospective, cohort study involving ten adult patients (median age 56 years, IQR 45.5–68.5; mean 55.9, range 35–76) with neurological syndromes caused by suspected or confirmed infectious meningoencephalitis. Etiologies included bacterial meningitis/meningoencephalitis (50%), viral meningoencephalitis (10%), neurotoxoplasmosis (10%), progressive multifocal leukoencephalopathy (10%), and undetermined origin (20%). Patients underwent TCCD and MRI within 24 h. In five cases, standard MRI sequences were acquired, while in the remaining five, perfusion imaging was performed using Arterial Spin Labelling (ASL). A favorable outcome was defined as survival with neurological recovery (Glasgow Outcome Scale > 5) at ICU discharge. Results: TCCD-derived PI provided valuable information on cerebral hemodynamics. PI values ≤ 1.25 were associated with favorable clinical outcomes and symmetrical MRI findings. Conversely, PI > 1.25 correlated with poor prognosis and often preceded MRI-detectable structural damage. When combined with ASL, PI mirrored the detected perfusion asymmetries and was associated with poor prognosis in fatal cases. Conclusions: Bedside TCCD can offer real-time assessment of cerebrovascular dynamics and, when integrated with conventional and ASL MRI, could enhance the understanding of pathophysiological processes in meningoencephalitis, supporting timely and informed decisions in neurocritical care. Full article

Background: The vestibular schwannoma (VS) is the most common cerebellopontine angle tumor in adults, exhibiting a highly variable natural history, from stability to rapid growth. Accurate, the non-invasive prediction of tumor behavior is essential to guide personalized management and avoid overtreatment or delayed intervention. Objective: To systematically review and synthesize the evidence on MRI-based biomarkers for predicting VS growth and treatment responses. Methods: We conducted a PRISMA-compliant search of PubMed, EMBASE, and Cochrane databases for studies published between 1 January 2000 and 1 January 2025, addressing MRI predictors of VS growth. Cohort studies evaluating texture features, signal intensity ratios, perfusion parameters, and apparent diffusion coefficient (ADC) metrics were included. Study quality was assessed using the NOS (Newcastle–Ottawa Scale) score, GRADE (Grading of Recommendations, Assessment, Development and Evaluation), and ROBIS (Risk of Bias in Systematic reviews) tool. Data on diagnostic performance, including the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and p value, were extracted and descriptively analyzed. Results: Ten cohort studies (five retrospective, five prospective, total n = 525 patients) met the inclusion criteria. Texture analysis metrics, such as kurtosis and gray-level co-occurrence matrix (GLCM) features, yielded AUCs of 0.65–0.99 for predicting volumetric or linear growth thresholds. Signal intensity ratios on gadolinium-enhanced T1-weighted images for tumor/temporalis muscle achieved a 100% sensitivity and 93.75% specificity. Perfusion MRI parameters (K^trans, v_e, ASL, and DSC derived blood-flow metrics) differentiated growing from stable tumors with AUCs up to 0.85. ADC changes post-gamma knife surgery predicted a favorable response, though the baseline ADC had limited value for natural growth prediction. The heterogeneity in growth definitions, MRI protocols, and retrospective designs remains a key limitation. Conclusions: MRI-based biomarkers may provide exploratory signals associated with VS growth and treatment responses. However, substantial heterogeneity in growth definitions and MRI protocols, small single-center cohorts, and the absence of external validation currently limit clinical implementation. Full article

Background: Tuberculous meningitis (TBM) is a severe central nervous system infection that can lead to cerebral vasculitis and infarction. This study aimed to evaluate changes in cerebral perfusion and vasculitis on magnetic resonance imaging (MRI) before and after anti-tuberculosis treatment, focusing on both infarcted and non-infarcted brain regions and comparing them with age-matched controls. Methods: Quantitative arterial spin labeling (ASL) perfusion and black-blood vessel wall MRI were performed at diagnosis and after 3–6 months of treatment in TBM patients and healthy controls. Regions of interest included infarcted areas, the contralateral normal brain, and TBM-affected regions without infarction. Cerebral blood flow (CBF), perfusion grading, and vasculitis were assessed and correlated with clinical stage and disease severity. Results: In total, 73 TBM patients and 26 controls were included. Among the patients, 26 (35.6%) had acute infarctions, mainly in the basal ganglia and corona radiata, and 65 (89.0%) exhibited vasculitis predominantly involving anterior circulation. Pretreatment MRI showed significantly reduced CBF in infarcted regions compared with contralateral brain and controls (p < 0.05), and both contralateral and non-infarcted TBM regions also showed lower CBF than controls (p < 0.05). After treatment, CBF increased significantly in non-infarcted regions (p < 0.05), and post-treatment perfusion grade correlated with TBM stage and vasculitis severity. Conclusions: TBM-related infarcts demonstrated marked hypoperfusion, while non-infarcted regions exhibited reversible ischemic changes. ASL and vessel wall imaging can quantitatively monitor treatment response and vascular inflammation, as well as predict late infarction in TBM patients. Full article

Background/Objectives: Diaschisis, reduced neural activity, perfusion, and metabolism in structurally intact but anatomically connected regions, is a network-level consequence of focal brain injury. Despite the extensive literature, its prevalence across imaging modalities and diaschisis subtypes has not been systematically synthesized. This review aims to identify convergent evidence for diaschisis after ischemic stroke and clarify how its detection relates to neuroanatomical disconnection, clinical factors, and imaging methods. (PROSPERO: CRD420251017909). Methods: PubMed and Embase were searched through February 2025 for studies reporting quantitative measures of diaschisis using perfusion, metabolic, or functional imaging. Pooled prevalence and modality-specific estimates were calculated. Subgroup analyses examined diaschisis subtypes, stroke severity, age, and study quality. Results: Sixty-six studies (3021 patients) were included. Overall pooled prevalence was 53% (95% CI: 47–58%). Crossed cerebellar diaschisis was most frequently studied (49%), while thalamic and other remote patterns showed comparable or higher effect sizes. Detection varied primarily by imaging modality: ASL MRI (67%) and PET (58%) showed the highest sensitivity; SPECT (53%) and CTP (49%) were intermediate; DSC-PWI had the lowest (28%). In contrast, age had no measurable effect and stroke severity only modestly increased detection, suggesting that diaschisis is driven predominantly by neuroanatomical disconnection rather than demographic or clinical variables. Egger’s tests indicated minimal publication bias. Conclusions: Diaschisis is a common manifestation of network vulnerability after ischemic stroke, determined chiefly by lesion topology and long-range anatomical connectivity. Detection depends more on imaging physiology than patient characteristics. Standardized definitions and longitudinal multimodal studies are needed to clarify its temporal evolution and clinical significance. Full article

Mild traumatic brain injury (mTBI) frequently causes subtle brain changes that are difficult to detect with conventional diagnostic approaches. In this exploratory pilot study, we combined tri-exponential intravoxel incoherent motion (IVIM) and pseudocontinuous arterial spin labeling (pCASL) MRI with Multimodal Canonical Correlation Analysis and joint independent component analysis (mCCA+jICA) to identify imaging signatures distinguishing mTBI patients from healthy controls (HCs) and their associations with clinical function. Cerebral blood flow (CBF) and IVIM-derived metrics were extracted from 90 brain regions in 19 mTBI patients and 24 HCs, and multivariate components were identified using mCCA+jICA. Two independent components (IC2, IC15) showed group differences at the uncorrected level (p < 0.05) but did not survive false discovery rate (FDR) correction. IC2 correlated positively with CBF and perfusion fraction (F_p) and negatively with tissue diffusion fraction (F_s), consistent with reduced vascular integrity in mTBI, while IC15 showed similar trends. One component correlated with Glasgow Outcome Scale–Extended (GOS-E) scores (uncorrected p = 0.046). Although this study is preliminary and limited by a small sample size, our findings suggest that mTBI is associated with perfusion and microstructural alterations, particularly in subcortical regions, and demonstrate the potential value of combining IVIM and ASL within multivariate fusion frameworks to reveal patterns not captured by single-modality approaches. Full article

Background/Objectives: Glioblastoma (GBM) is an aggressive primary brain tumor marked by diffuse infiltration into surrounding brain tissue. The peritumoral zone often appears normal on imaging yet harbors microscopic invasion. While perfusion-based studies, such as arterial spin labeling (ASL), have profiled this region, longitudinal radiomic monitoring remains limited. This study investigates delta radiomics using multiparametric MRI (mpMRI) in a GBM mouse model to track subtle peritumoral changes over time. Methods: A G7 GBM xenograft model was established in nine nude mice, imaged at 9- and 12 weeks post-implantation using MRI (T1W, T2W, T2 mapping, DWI-ADC, FA, and ASL) and co-registered histopathology (H&E, HLA staining). Tumor and peritumoral regions were manually segmented, and 107 radiomic features (shape, first-order, texture) were extracted per sequence and histology. The delta features were calculated and compared between timepoints. Results: The robust T2W texture and T2 map first-order features demonstrated the greatest sensitivity and reproducibility in capturing temporal peritumoral brain zone changes, distinguishing between time points used by K-mean. Conclusions: Delta radiomics offers added value over static analysis for early monitoring of peritumoral brain zone changes. The first-order and texture features of radiomics could serve as robust biomarkers of peritumoral invasion. These findings highlight the potential of longitudinal MRI-based radiomics to characterize glioblastoma progression and inform translational research. Full article

Accurately predicting survival within patients diagnosed with diffuse glioma remains one of the most difficult issues in neuro-oncology. While most prior research has focused on multimodal fusion or clinical data, we introduce a modality-specific deep learning framework that employs preoperative MRI only to predict mortality outcomes using patient MRI scans. Using the UCSF-PDGM dataset containing structural, diffusion, and perfusion imaging of 495 glioma patients, we trained VGG16 models on every MRI modality individually, including T1, T2, FLAIR, SWI, DWI, ASL, HARDI-derived metrics, and segmentation maps. Our findings revealed that segmentation-based and diffusion-derived features, particularly FA or tensor eigenvalues, possessed the greatest predictive strength, surpassing those obtained from standard structural MRI in binary survival classifications. This approach of modality-specific model training allows for clearer explanations of the prediction process compared to fused approaches and is more practical in scenarios where not all types of MRI are performed on patients. This approach demonstrates the strong predictive power of individual MRI sequences for mortality in glioma cases, providing a modular, adaptable, and clinically actionable deep-learning framework. Additional enhancements can incorporate volumetric models, longitudinal imaging, and non-imaging datasets, including genomic and clinical information. Full article

Quantitative magnetic resonance imaging (qMRI) denotes MRI methods that estimate physical tissue parameters in units, rather than relative signal. Typical readouts include T1/T2 relaxation (ms; or R1/R2 in s⁻¹), proton density (%), diffusion metrics (e.g., ADC in mm²/s, FA), magnetic susceptibility (χ, ppm), perfusion (e.g., CBF in mL/100 g/min; rCBV; K^trans), and regional brain volumes (cm³; cortical thickness). This review synthesizes brain qMRI across T1/T2 relaxometry, myelin/MT (MWF, MTR/MTsat/qMT), diffusion (DWI/DTI/DKI/IVIM), susceptibility imaging (SWI/QSM), perfusion (DSC/DCE/ASL), and volumetry using a unified framework: physics and signal model, acquisition and key parameters, outputs and units, validation/repeatability, clinical applications, limitations, and future directions. Our scope is the adult brain in neurodegenerative, neuro-inflammatory, neuro-oncologic, and cerebrovascular disease. Representative utilities include tracking demyelination and repair (T1, MWF/MTsat), grading and therapy monitoring in gliomas (rCBV, K^trans), penumbra and tissue-at-risk assessment (DWI/DKI/ASL), iron-related pathology (QSM), and early dementia diagnosis with normative volumetry. Persistent barriers to routine adoption are protocol standardization, vendor-neutral post-processing/QA, phantom-based and multicenter repeatability, and clinically validated cut-offs. We highlight consensus efforts and AI-assisted pipelines, and outline opportunities for multiparametric integration of complementary qMRI biomarkers. As methodological convergence and clinical validation mature, qMRI is poised to complement conventional MRI as a cornerstone of precision neuroimaging. Full article

Background: Regional cerebral blood flow (rCBF) is a putative biomarker for neuropsychiatric disorders, including major depressive disorder (MDD). Methods: Here, we show that rCBF can be predicted from resting-state functional MRI (rsfMRI) at the voxel level while correcting for partial volume averaging (PVA) artifacts. Cortical patterns of MDD-related CBF differences decoded from rsfMRI using a PVA-corrected approach showed excellent agreement with CBF measured using single-photon emission computed tomography (SPECT) and arterial spin labeling (ASL). A support vector machine algorithm was trained to decode cortical voxel-wise CBF from temporal and power-spectral features of voxel-level rsfMRI time series while accounting for PVA. Three datasets, Amish Connectome Project (N = 300; 179 M/121 F, both rsfMRI and ASL data), UK Biobank (N = 8396; 3097 M/5319 F, rsfMRI data), and Amen Clinics Inc. datasets (N = 372: N = 183 M/189 F, SPECT data), were used. Results: PVA-corrected CBF values predicted from rsfMRI showed significant correlation with the whole-brain (r = 0.54, p = 2 × 10⁻⁵) and 31 out of 34 regional (r = 0.33 to 0.59, p < 1.1 × 10⁻³) rCBF measures from 3D ASL. PVA-corrected rCBF values showed significant regional deficits in the UKBB MDD group (Cohen’s d = −0.30 to −0.56, p < 10⁻²⁸), with the strongest effect sizes observed in the frontal and cingulate areas. The regional deficit pattern of MDD-related hypoperfusion showed excellent agreement with CBF deficits observed in the SPECT data (r = 0.74, p = 4.9 × 10⁻⁷). Consistent with previous findings, this new method suggests that perfusion signals can be predicted using voxel-wise rsfMRI signals. Conclusions: CBF values computed from widely available rsfMRI can be used to study the impact of neuropsychiatric disorders such as MDD on cerebral neurophysiology. Full article

Background: Traumatic brain injury (TBI) remains a significant contributor to global mortality and long-term neurological disability. Accurate prognostic biomarkers are crucial for enhancing prognostic accuracy and guiding personalized clinical management. Objective: This review assesses the prognostic value of arterial spin labeling (ASL), a non-invasive MRI technique, in adult and pediatric TBI, with a focus on quantitative cerebral blood flow (CBF) and arterial transit time (ATT) measures. A comprehensive literature search was conducted across PubMed, Embase, Scopus, and IEEE databases, including observational studies and clinical trials that applied ASL techniques (pCASL, PASL, VSASL, multi-PLD) in TBI patients with functional or cognitive outcomes, with outcome assessments conducted at least 3 months post-injury. Results: ASL-derived CBF and ATT parameters demonstrate potential as prognostic indicators across both acute and chronic stages of TBI. Hypoperfusion patterns correlate with worse neurocognitive outcomes, while region-specific perfusion alterations are associated with affective symptoms. Multi-delay and velocity-selective ASL sequences enhance diagnostic sensitivity in TBI with heterogeneous perfusion dynamics. Compared to conventional perfusion imaging, ASL provides absolute quantification without contrast agents, making it suitable for repeated monitoring in vulnerable populations. ASL emerges as a promising prognostic biomarker for clinical use in TBI. Conclusion: Integrating ASL into multiparametric models may improve risk stratification and guide individualized therapeutic strategies. Full article