Christopher P. Hess

BACKGROUND: Microvascular hemodynamic deterioration is a proposed mechanism underlying the no-reflow phenomenon in acute ischemic stroke. Experimental studies suggested that relative heterogeneity of capillary transit time (RTH) is a potential imaging biomarker of microvascular impairment. We investigated the relationship between peri-infarct RTH and infarct growth, measured on immediate post-thrombectomy (PT) perfusion magnetic resonance imaging (MRI), and functional outcomes in patients with acute ischemic stroke with successful reperfusion. METHODS: In this retrospective case-control study from the University of California, Los Angeles, patients with acute ischemic stroke from 2015 to 2020 were included if they had excellent reperfusion (modified Thrombolysis in Cerebral Infarction score ≥2c) and diffusion- and perfusion-weighted MRIs at baseline, immediately PT, and 24 to 48 hours after thrombectomy. Peri-infarct regions were defined as time to maximum ≥6-second regions on baseline MRI that were not infarcted on immediate PT MRI. Following image coregistration, quantitative values of perfusion parameters, including RTH, were obtained from peri-infarct regions on immediate PT MRI. Two primary outcomes were assessed: (1) infarct growth ≥10 mL and (2) poor functional outcome (90-day modified Rankin Scale score >2). Associations between clinical and imaging parameters and outcomes were evaluated using univariate analysis. Age, sex, and covariates that were significant in univariate analysis were included in logistic regression analyses. RESULTS: Among 47 patients included in the analysis (62% women; mean age, 72±13 years; median National Institutes of Health Stroke Scale score, 14), 20 (43%) patients had substantial infarct growth and poor functional outcome. Peri-infarct RTH was significantly ( P =0.005) higher in patients with infarct growth >10 mL than those without and remained as an independent predictor (odds ratio, 1.4 per 10% increase [95% CI, 1.0–2.0]; P =0.04). Similarly, the peri-infarct RTH was significantly higher ( P P =0.03). CONCLUSIONS: Increased peri-infarct RTH on immediate PT MRI predicts substantial infarct growth and poor functional outcomes in successfully reperfused patients with acute ischemic stroke. Results highlight the role of microvascular dysfunction depicted by elevated RTH as a potential marker of no-reflow phenomenon despite excellent macrovascular reperfusion.

Radiology workforce sustainability requires coordinated strategies that align imaging demand, workforce capacity, workflow redesign, and the work environment.

A combination of MRI perfusion and metabolic biomarkers significantly improved diffusion reversal prediction performance and refined ischemic core volume estimation in patients with acute ischemic stroke for whom reperfusion was successful.

This consensus statement from the International Society for Strategic Studies in Radiology calls for artificial intelligence–driven strategies to streamline workflows and align radiology with broader health system and payer priorities amid rapid digital innovation.

Proprietary and open-source large language models (LLMs) generated radiologically relevant indications from clinical notes that were more comprehensive and factual than clinician indications, and when generated by the proprietary LLM, were ranked most useful in protocoling and imaging interpretation by radiologists.

Artificial intelligence (AI) is rapidly transforming medical imaging, yet its integration into neuroradiology remains uneven. This survey-based study assesses AI usage, tools, applications, barriers, and future expectations among U.S. neuroradiology departments. This cross-sectional survey questionnaire comprised 19 items, blending multiple-choice, multi-select, and open-ended formats. Descriptive statistics were used to identify patterns in data. Most departments (81%) reported AI use, primarily for stroke-related applications, with smaller numbers using tools for report generation, segmentation, and image quality enhancement. Most clinical tools were FDA-approved. AI had minimal perceived impact on workload, and performance was viewed as variable, with concerns about accuracy and false positives. Cost, integration challenges, and limited efficacy evidence were the main barriers to adoption. Despite these limitations, most respondents anticipated increased AI use over the next five years. Findings underscore the need for clinician-vendor collaboration to realize AI's potential in reducing workload and improving outcomes.

Artificial intelligence (AI) models have shown promise in neuroradiology, yet their real-world generalizability remains uncertain, partly due to variability in imaging acquisition and protocols. We aimed to evaluate the impact of data source, scanner manufacturer, scan mode, slice thickness, and the AI models developed by participating teams on AI performance in this secondary analysis of the 2019 American Society of Functional Neuroradiology (ASFNR) AI Competition. We included 1,177 anonymized noncontrast head CT scans from five institutions. Four teams participated, developing models to detect acute ischemic stroke, intracranial hemorrhage, mass effect, and to assess age-appropriate normality. Generalized estimating equations (GEE) were used to evaluate the effects of the aforementioned variables on model performance, and collinearity diagnostics were applied to exclude redundant variables. Due to collinearity with scanner manufacturer, data source was excluded from the model. Across all tasks, the AI model employed significantly influenced performance. Scanner manufacturer was significantly associated with accuracy in detecting intracranial hemorrhage and acute ischemic stroke but not mass effect or age-based normality. Slice thickness significantly associated with detection of intracranial hemorrhage and mass effect, with thinner slices yielding higher accuracy, but showed no effect on ischemic stroke or normality assessments. Scan mode did not significantly influence performance for any task. This secondary analysis demonstrates that imaging acquisition and protocol variability may significantly affect AI model performance. Scanner manufacturer, slice thickness, and the developed AI model were significantly associated with model accuracy, whereas scan mode had no significant impact. Among these, the developed AI model consistently proved most influential, reflecting the importance of training data, model architecture, and preprocessing methods.

BACKGROUND: In patients with acute ischemic stroke, infarct growth occurs despite successful reperfusion. Oxygen extraction fraction (OEF) has shown promising results in evaluating ischemic tissue viability and can now be quantified from routinely performed dynamic susceptibility contrast perfusion. We aimed to determine the association of OEF alterations within the ischemic tissue on pretreatment magnetic resonance imaging and infarct growth in patients who underwent successful reperfusion. METHODS: In this retrospective cohort study from the University of California, Los Angeles, between 2015 and 2020, patients were included if they had anterior circulation large vessel occlusion, achieved successful reperfusion (Thrombolysis in Cerebral Infarction ≥2b), had pretreatment dynamic susceptibility contrast perfusion and posttreatment magnetic resonance imaging within 48 hours from reperfusion. Dynamic susceptibility contrast-derived OEF values were quantified from the segmented ischemic core (apparent diffusion coefficient ≤620×10 ⁻⁶ mm ² /s) and penumbra tissue (time-to-maximum [Tmax] >6 s) on pretreatment magnetic resonance imaging and normalized to contralateral hemisphere (relative oxygen extraction fraction [OEF _r ]). Primary outcome was substantial infarct growth ≥10 mL, and secondary outcomes were continuous measures of infarct growth volume and penumbra-to-infarct conversion ratio. The associations between baseline clinical and imaging variables, including OEF _r and outcome measures, were tested by multivariate and regression analysis. RESULTS: Among 89 patients who met inclusion criteria, 33 (37%) patients had infarct growth ≥10 mL. Patients with infarct growth had significantly ( P r values compared with those without infarct growth. There was significant association between penumbra OEF _r and infarct growth (β=−2.9 [95% CI, −5.0 to −0.8]; P =0.007) and similarly for penumbra-to-infarct conversion ratio (β=−10.4 [95% CI, −19.6 to −1.2]; P =0.028). CONCLUSIONS: Our results showed penumbra-OEF _r is a promising imaging biomarker for predicting infarct growth in acute ischemic stroke following successful reperfusion. Although elevation of penumbra-OEF _r is protective, patients with lower penumbra-OEF _r values sustained further ischemic injury and infarct growth.

Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.