Search Results (73)

Background: Stroke and subclinical cerebral ischemia remain important neurological complications of transcatheter aortic valve replacement (TAVR). The Sentinel cerebral embolic protection (CEP) device is designed to capture embolic debris during TAVR, but its impact on clinical and imaging outcomes remains incompletely characterized. Methods: PubMed, Embase, and Cochrane databases were systematically searched for randomized controlled trials (RCTs) comparing Sentinel CEP versus no protection when TAVR was performed. Outcomes of interest included all stroke, disabling stroke, infarct volume by diffusion-weighted MRI in protected and unprotected areas, all-cause mortality, acute kidney injury, and major vascular complications. Risk ratios (RRs) and median differences with 95% confidence intervals (CIs) were calculated using random-effects models and trial sequential analysis (TSA) assessed evidence robustness. Results: Four RCTs including 10,986 patients were analyzed. Sentinel CEP did not significantly reduce clinical stroke (RR 0.88, 95% CI 0.69–1.12) or disabling stroke (RR 0.68, 95% CI 0.41–1.14). Pooled DW-MRI data showed a significant reduction in new ischemic lesion volume within Sentinel CEP-protected territories (difference in medians –75.7 mm³; 95% CI –130.4 to –21.0). Subgroup analyses in elderly, female, and high-surgical-risk patients revealed no benefit with Sentinel CEP. Additionally, TSA indicated that current data are underpowered for definitive conclusions. Conclusions: The Sentinel CEP device during TAVR did not significantly reduce clinical stroke but was associated with lower MRI-detected ischemic lesion volumes compared with no protection. Further adequately powered RCTs integrating clinical and imaging endpoints are needed to define its role in neuroprotection during TAVR. Full article

Background/Objectives: The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by an enriched stroma, hampering the effectiveness of therapy. This co-clinical study aimed to (1) provide insight into early post-treatment changes in the TME using multiparametric magnetic resonance imaging (mpMRI)-derived quantitative imaging biomarkers (QIBs) in a preclinical PDAC model treated with radiotherapy and correlate these QIBs with histology; (2) evaluate the feasibility of obtaining these QIBs in patients with PDAC using clinically approved mpMRI data acquisitions. Methods: Athymic mice (n = 12) at pre- and post-treatment as well as patients with PDAC (n = 11) at pre-treatment underwent mpMRI including diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) data acquisition sequences. DW and DCE data were analyzed using monoexponential and extended Tofts models, respectively. DeepLIIF quantified the total percentage (%) of tumor cells in hematoxylin and eosin (H&E)-stained tissues from athymic mice. Spearman correlation and Wilcoxon signed rank tests were performed for statistical analysis. Results: In the preclinical PDAC model, mean pre- and post-treatment ADC and K^trans values differed significantly (p < 0.01), changing by 20.50% and 20.41%, respectively, and the median total tumor cells quantified by DeepLIIF was 24% (range: 15–53%). Post-treatment ADC values and relative change in v_e (rΔv_e) showed a significant negative correlation with total tumor cells (ρ = −0.77, p < 0.014 for ADC and ρ = −0.77, p = 0.009 for rΔv_e). In patients with PDAC, pre-treatment mean ADC and K^trans values were 1.76 × 10⁻³ (mm²/s) and 0.24 (min⁻¹), respectively. Conclusions: QIBs in both preclinical and clinical settings underscore their potential for future co-clinical research to evaluate emerging drug combinations targeting both tumor and stroma. Full article

Background/Objectives: We provide a framework for determining how far into the future the spatiotemporal dynamics of tumor growth can be accurately predicted using routinely available magnetic resonance imaging (MRI) data. Our analysis is applied to a coupled set of reaction-diffusion equations describing the spatiotemporal development of tumor cellularity and vascularity, initialized and constrained with diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data, respectively. Methods: Motivated by experimentally acquired murine glioma data, the rat brain serves as the computational domain within which we seed an in silico tumor. We generate a set of 13 virtual tumors defined by different combinations of model parameters. The first parameter combination was selected as it generated a tumor with a necrotic core during our simulated ten-day experiment. We then tested 12 additional parameter combinations to study a range of high and low tumor cell proliferation and diffusion values. Each tumor is grown for ten days via our model system to establish “ground truth” spatiotemporal tumor dynamics with an infinite signal-to-noise ratio (SNR). We then systematically reduce the quality of the imaging data by decreasing the SNR, downsampling the spatial resolution (SR), and decreasing the sampling frequency, our proxy for reduced temporal resolution (TR). With each decrement in image quality, we assess the accuracy of the calibration and subsequent prediction by comparing it to the corresponding ground truth data using the concordance correlation coefficient (CCC) for both tumor and vasculature volume fractions, as well as the Dice similarity coefficient for tumor volume fraction. Results: All tumor CCC and Dice scores for each of the 13 virtual tumors are >0.9 regardless of the SNR/SR/TR combination. Vasculature CCC scores with any SR/TR combination are >0.9 provided the SNR ≥ 80 for all virtual tumors; for the special case of high-proliferating tumors (i.e., proliferation > 0.0263 day⁻¹), any SR/TR combination yields CCC and Dice scores > 0.9 provided the SNR ≥ 40. Conclusions: Our systematic evaluation demonstrates that reaction-diffusion models can maintain acceptable longitudinal prediction accuracy—especially for tumor predictions—despite limitations in the quality and quantity of experimental data. Full article

Background: Despite effective antiretroviral therapy, HIV-associated neurocognitive disorders (HANDs) remain prevalent, highlighting the need for sensitive biomarkers of early brain alterations. Trace-weighted diffusion spectroscopic imaging offers a non-invasive means to assess microstructural changes in brain metabolites in a single shot by measuring apparent diffusion coefficients (ADCs) of total N-acetylaspartate (tNAA), total creatine (tCr), total choline (tCho), and water. Methods: In this study, we used trace-weighted single-shot diffusion-weighted radial echo-planar spectroscopic imaging (DW-RESPI) to investigate metabolite diffusion and relative concentrations in the brains of people living with HIV (PLWH). Using a 3T MRI scanner, we studied 16 PLWH and 15 healthy controls (HCs), and we collected two sets of data with low and high b-values from which metabolite ADCs were computed. Metabolite ratios were derived from the low b-value spectra. A brief neuropsychological assessment evaluated attention, executive function, and memory in a subset of subjects. Cognitive and affective performance was quantified using domain-specific deficit scores, as well as depression and anxiety assessments, offering a comprehensive evaluation of neurobehavioral function. In the male subgroup (N = 15) of PLWH, we calculated the correlations between ADC values and neuropsychological domain scores. Results: tNAA, tCr, tCho, and water ADC values were significantly elevated in multiple gray and white matter regions in PLWH compared to HC, with the most pronounced differences observed in the superior precuneus, anterior cingulate cortex, and corona radiata. Notably, regional ADC values and metabolite ratios showed significant correlations with neuropsychological domain scores. Conclusions: These findings indicate the potential of metabolite and water diffusion metrics as biomarkers for HIV-associated microstructural brain alterations and cognitive impairment. However, the small sample size and preliminary nature of this data warrant further investigation to validate these findings. Full article

Background: Microwave ablation (MWA) is an effective, minimally invasive therapy for benign thyroid nodules; however, the treatment response varies considerably. Identifying imaging biomarkers that can predict volumetric outcomes may optimize patient selection. Diffusion-weighted MRI (DW-MRI) offers a noninvasive assessment of tissue microstructure through apparent diffusion coefficient (ADC) measurements, which may correlate with ablation efficacy. Methods: In this prospective study, 48 patients with 50 cytologically confirmed benign thyroid nodules underwent diffusion-weighted magnetic resonance imaging (DW-MRI) before minimally invasive ablation (MWA). Baseline ADC values were measured, and nodule volumes were assessed by ultrasound at baseline and 1, 3, and 6 months postprocedure. The volume reduction ratio (VRR) was calculated, and associations with baseline variables were analyzed via Pearson correlation and multivariable linear regression. ROC curve analysis was used to evaluate the diagnostic performance of ADC in predicting significant volume reduction (VRR ≥ 50%). Results: Lower baseline ADC values were strongly correlated with greater VRR at 3 months (r = −0.525, p < 0.001) and 6 months (r = −0.564, p < 0.001). Multivariable regression revealed that the baseline ADC was the sole independent predictor of the 6-month VRR (β = −19.52, p = 0.0004). ROC analysis demonstrated excellent discriminative performance (AUC = 0.915; 95% CI: 0.847–0.971), with an ADC cutoff of 2.20 × 10⁻³ mm²/s yielding 90.9% sensitivity and 83.3% specificity for predicting a favorable volumetric response. Conclusions: Baseline ADC values derived from DW-MRI strongly predict volumetric response following microwave ablation of benign thyroid nodules. Incorporating ADC assessment into preprocedural evaluation may enhance patient selection and improve therapeutic outcomes. Full article

Apparent diffusion coefficient (ADC) values, derived from diffusion-weighted magnetic resonance imaging (DW-MRI), increase with age, reflecting microstructural changes in white matter integrity. However, factors beyond chronological aging may influence cerebral diffusion characteristics. We investigated whether anticoagulant use is associated with favorable white matter ADC profiles, suggesting preserved microvascular health. ADC values were analyzed in cerebral white matter across four age-defined adult cohorts (20–59 years). Minimum, mean, and maximum ADC values were extracted. Patients at the lowest and highest ends of the ADC spectrum within each group were identified. The prevalence of anticoagulant use was compared between groups, and a logistic regression model adjusted for age was used to assess the independent association between anticoagulant use and lower ADC values. Across all cohorts (n = 892), anticoagulated patients (n = 89) were significantly overrepresented among individuals with low ADC values consistent with younger diffusion profiles. Of the anticoagulated patients, 93.3% had ADC values below the lower cut-off limit. In contrast, only 30% of non-anticoagulated patients exhibited such profiles. Anticoagulant use was independently associated with low ADC values after adjusting for age (OR = 4.89, p < 0.0001). Anticoagulation is strongly associated with lower, more favorable ADC values in cerebral white matter, independent of age. These findings support the potential neuroprotective role of anticoagulants and suggest that diffusion MRI may serve as a surrogate marker for early microvascular brain health. Full article

Background/Objectives: This work prospectively evaluates the vendor-provided Low Variance (LOVA) apparent diffusion coefficient (ADC) gradient nonlinearity correction (GNC) technique for primary tumors, neck nodal metastases, and normal masseter muscles in patients with head and neck cancers (HNCs). Methods: Multiple b-value diffusion-weighted (DW)-MR images were acquired on a 3.0 T scanner using a single-shot echo planar imaging (SS-EPI) and multi-shot (MS)-EPI for diffusion phantom materials (20% and 40% polyvinylpyrrolidone (PVP) in water). Pretreatment DW-MRI acquisitions were performed for sixty HNC patients (n = 60) who underwent chemoradiation therapy. ADC values with and without GNC were calculated offline using a monoexponential diffusion model over all b-values, relative percentage (r%) changes (Δ) in ADC values with and without GNC were calculated, and the ADC histograms were analyzed. Results: Mean ADC values calculated using SS-EPI DW data with and without GNC differed by ≤1% for both PVP20% and PVP40% at the isocenter, whereas off-center differences were ≤19.6% for both concentrations. A similar trend was observed for these materials with MS-EPI. In patients, the mean rΔADC (%) values measured with SS-EPI differed by 4.77%, 3.98%, and 5.68% for primary tumors, metastatic nodes, and masseter muscle. MS-EPI exhibited a similar result with 5.56%, 3.95%, and 4.85%, respectively. Conclusions: This study showed that the GNC method improves the robustness of the ADC measurement, enhancing its value as a quantitative imaging biomarker used in HNC clinical trials. Full article

Background/Objective: The accurate diagnosis of congenital central nervous system abnormalities is critical to pre- and postnatal prognostication and management. When an abnormality is found in the posterior fossa of the fetal brain, parental counseling is challenging because of the wide spectrum of clinical and neurodevelopmental outcomes in patients with Dandy–Walker (DW) spectrum posterior malformations. The objective of this study was to evaluate the utility of biometric measurements obtained from fetal magnetic resonance imaging (MRI) to facilitate the prenatal differentiation of Dandy–Walker (DW) spectrum malformations, including vermian hypoplasia (VH), Blake’s pouch cyst (BPC), and classic Dandy–Walker malformation (DWM). Methods: This retrospective single-center study evaluated 34 maternal–infant dyads referred for fetal MRI evaluation of suspected DW spectrum malformations identified on antenatal ultrasound. Radiologists took posterior fossa measurements, including the vermis anteroposterior (AP) diameter, vermis height (VH), and tegmento–vermian angle (TVA). The posterior fossa, fourth ventricle, and cisterna magna were classified as normal, large, or dilated. The postnatal imaging findings were evaluated for concordance. The acquired values were compared between the groups and with normative data. The genetic testing results are reported when available. Results: A total of 27 DW spectrum fetal MRI cases were identified, including 7 classic DWMs, 14 VHs, and 6 BPCs. The TVA was significantly higher in the DWM group compared with the VH and BPC groups (p < 0.001). All three groups had reduced AP vermis measurements for gestational age compared with normal fetal brains, as well as differences in the means across the groups (p = 0.002). Conclusions: Biometric measurements derived from fetal MRI can effectively facilitate the prenatal differentiation of VH, BPC, and classic DWM when assessing DW spectrum posterior fossa lesions. Standardizing biometric measurements may increase the diagnostic utility of fetal MRI and facilitate improved antenatal counseling and clinical decision-making. Full article

A fundamental limitation of fMRI based on the BOLD effect is its limited spatial specificity. This is because the BOLD signal reflects neurovascular coupling, leading to macrovascular changes that are not strictly limited to areas of increased neural activity. However, neuronal activation also induces microstructural changes within the brain parenchyma by modifying the diffusion of extracellular biological water. Therefore, diffusion-weighted imaging (DWI) has been applied in fMRI to overcome BOLD limits and better explain the mechanisms of functional activation, but the results obtained so far are not clear. This is because a DWI signal depends on many experimental variables: instrumental, physiological, and microstructural. Here, we hypothesize that the γ parameter of the fractional diffusion representation could be of particular interest for DW-fMRI applications, due to its proven dependence on local magnetic susceptibility and diffusion multi-compartmentalization. BOLD fMRI and DW-fMRI experiments were performed at 3T using an exemplar application to task-based activation of the human visual cortex. The results, corroborated by simulation, highlight that γ provides complementary information to conventional diffusion fMRI and γ can quantify cellular morphology changes and neurovascular regulation during neuronal activation with higher sensitivity and specificity than conventional BOLD fMRI and DW-fMRI. Full article

This study aims to evaluate the role of MRI-based radiomic analysis and machine learning using both DWI with multiple B-values and dynamic contrast-enhanced T1-weighted sequences to differentiate benign (B) and malignant (M) parotid tumors. Patients underwent DCE- and DW-MRI. An expert radiologist performed the manual selection of 3D ROIs. Classification of malignant vs. benign parotid tumors was based on radiomic features extracted from DCE-based and DW-based parametric maps. Care was taken in robustness evaluation and the no-bias selection of features. Several classifiers were employed. Sensitivity and specificity ranged from 0.6 to 0.8. The combination of LASSO + neural networks achieved the highest performance (0.76 sensitivity and 0.75 specificity). Our study identified a few robust DCE-based radiomic features with respect to ROI selection that can effectively be adopted in classifying malignant vs. benign parotid tumors. Full article

Neurological symptoms such as impaired smell and taste have been recognized as hallmark manifestations of severe acute respiratory syndrome coronavirus (SARS-CoV-2) infection. This study investigates and quantifies microstructural changes in the white matter of the olfactory bulb and taste-related brain regions (frontal operculum, insular cortex and parietal operculum) using diffusion-weighted magnetic resonance imaging (DW-MRI). Apparent diffusion coefficient (ADC) values were measured in patients with confirmed coronavirus disease of 2019 (COVID-19) at the onset of anosmia and ageusia (24 patients, scanned between March and December 2020), 1 month post-infection (20 subjects) and 36 months post-infection (20 participants). ADC values were analyzed over time and compared to normal white matter ADC ranges (calculated retrospectively from 979 pre-pandemic patients) and to those from patients infected with the 2024 strain of SARS-CoV-2 (27 patients). The results revealed significantly elevated ADC values in the white matter of the targeted brain regions, with a peak at the time of infection, followed by a decline 1 month post-infection, and a return to near-normal levels 3 years later. In contrast, the 2024 COVID-19 variant demonstrated reduced virus-related alterations in brain microstructure compared to the 2020 strain. These findings highlight the potential of DWI as a non-invasive tool for elucidating the molecular mechanisms underlying olfactory and taste dysfunction in COVID-19 patients. Full article

Background: This study aimed to explore the differences in quantitative diffusion-weighted (DW) MRI parameters in oropharyngeal squamous cell carcinoma (OPC) based on Human Papillomavirus (HPV) status before and during radiotherapy (RT). Methods: Echo planar DW sequences acquired before and during (chemo)radiotherapy (CRT) of 178 patients with histologically proven OPC were prospectively analyzed. The volumetric region of interest (ROI) was manually drawn on the apparent diffusion coefficient (ADC) map, and 105 DW-MRI radiomic parameters were extracted. Change in ADC values (Δ ADC) was calculated as the difference between baseline and during RT at week 4, normalized by the baseline values. Results: Pre-treatment first-order 10th percentile ADC and Gray Level co-occurrence matrix (GLCM)-correlation were significantly lower in HPV-positive compared with HPV-negative tumors (82.4 × 10⁻⁵ mm²/s vs. 90.3 × 10⁻⁵ mm²/s, p = 0.03 and 0.18 vs. 0.30, p < 0.01). In the fourth week of RT, all first-order ADC values were significantly higher in HPV-positive tumors (p < 0.01). Δ ADC mean was significantly higher for the HPV-positive compared with the HPV-negative OPC group (95% vs. 55%, p < 0.01). A predictive model for HPV status based on smoking status, alcohol consumption, GLCM correlation, and mean ADC and 10th percentile ADC values yielded an area under the curve of 0.77 (95% CI 0.70–0.84). Conclusions: Our results highlight the potential of DW-MR imaging as a non-invasive biomarker for the prediction of HPV status, although its current role remains supplementary to pathological confirmation. Full article

Purpose: To determine the diagnostic accuracy of quantitative diffusion-weighted (DW) MRI apparent diffusion coefficient (ADC) and tumour volumes to differentiate between malignant (neuroblastoma (NB)) and benign types of neuroblastic tumours (ganglioneuroma (GN) and ganglioneuroblastoma (GNB)) using different region-of-interest (ROI) sizes. Materials and Methods: This single-centre retrospective study included malignant and benign paediatric neuroblastic tumours that had undergone DW MRI at diagnosis. The outcome was diagnostic accuracy of the tumour volume from structural and ADC DW MRI, in comparison to histopathology (reference standard). Results: Data from 40 patients (NB, n = 24; GNB, n = 6; GN, n = 10), 18 (45%) females and 22 (55%) males, with a median age at diagnosis of 21 months (NB), 64 months (GNB), and 133 months (GN), respectively, ranging from 0 to 193 months, were evaluated. The area under the receiver operating characteristic (AUROC) curve for ADC for discriminating between neuroblastic tumours’ histopathology for a small ROI was 0.86 (95% CI: 0.75–0.98), and for a large ROI, 0.83 (95% CI: 0.71–0.96). An ADC cut-off value of 1.06 × 10⁻³ mm²/s was able to distinguish malignant from benign tumours with 83% (68–98%) sensitivity and 75% (95% CI: 54–98%) specificity. Tumour volume was not indicative of malignant vs. benign tumour diagnosis. Conclusions: In this study, both small and large ROIs used to derive ADC DW MRI metrics demonstrated high accuracy to differentiate malignant from benign neuroblastic tumours, with the ADC AUROC for the averaged multiple small ROIs being slightly greater than that of large ROIs, but with overlapping 95% CIs. This should be taken into consideration for standardisation of ROI-related data analysis by international initiatives. Full article

Polyethylene terephthalate (PET) is one of the most produced plastic materials in the world. The emergence of microplastics and nanoplastics (MPs/NPs) as a significant environmental contaminant has become a matter of increasing concern. While the toxicological effects of PET NPs have been widely researched, there is a lack of methodologies for studying their accumulation. The present study introduces a novel method to monitor the distribution of PET NPs in germinating wheat (Triticum aestivum L.) seeds. This involves the functionalization of superparamagnetic iron oxide nanoparticles (SPIONs) with PET NPs (PET–fSPIONs) coupled with magnetic resonance microimaging (µMRI) to provide insight into their distribution within the seed. The present study has demonstrated that PET–fSPIONs accumulate in specific regions of germinating wheat seeds, including the shoot apical meristem, the radicle, the coleoptile, the plumule, and the scutellum. Furthermore, the accumulation of PET–fSPIONs has been shown to exert a discernible effect on spin–spin relaxation (T₂), as observed via MRI and quantitative T₂ relaxation time analysis. The accumulation of PET NPs in embryo regions was also confirmed by SEM. Diffusion-weighted magnetic resonance imaging (DW-MRI) and non-invasive chemical shift imaging analyses demonstrated that PET NPs resulted in restricted diffusion within the highlighted areas, as well as an impact on lipid content. Our study reveals that using µMRI with fSPIONs provides a non-invasive method to monitor the biodistribution of PET nanoparticles in wheat seeds. Additionally, it offers valuable insights into the microstructural interactions of PET. Full article