Faculty Bibliography

BACKGROUND AND PURPOSE/OBJECTIVE:Cerebrovascular reactivity (CVR) is a widely studied biomarker of cerebral hemodynamics, commonly used in risk stratification and treatment planning in patients with steno-occlusive disease (SOD). Conventional use relies on normalization of estimates to contralateral hemisphere reference values, which is unsuitable for bilateral or indeterminate distributions of disease. We report upon a custom data-driven approach leveraging random forest classifiers (RFc) to identify candidate voxels for normalization in order to facilitate interrogation outside conditions of known unilateral SOD MATERIALS AND METHODS: We retrospectively analyzed 16 patients with unilateral SOD who underwent acetazolamide-augmented BOLD-MRI and DSC perfusion. Three RFc models were trained using leave-one-out cross-validation (LOOCV) to identify candidate voxels brain-wide whose CVR were within 10% of the normal hemispheric median: i. all voxels; ii. gray matter only; and iii. white matter only. Model input features included time-to-maximum (Tmax), mean transit time (MTT), cerebral blood flow (CBF), and cerebral blood volume (CBV) from contemporaneous DSC. The median model-predicted reference CVR (CVRref) was compared to ground-truth medians in LOOCV, and its impact on threshold-based volumetric classification of CVR reduction assessed. RESULTS:RFc models effectively predicted ground-truth CVR voxels, achieving median absolute percent differences of 12.8% (IQR: 5.0%-18.9%) using all voxels, 11.3% (IQR: 9.3%-16.1%) for gray matter, and 9.8% (IQR: 4.4%-16.9%) for white matter. Volumetric estimates of CVR reduction across thresholds for the models revealed excellent agreement between ground-truth and model estimates without statistically significant differences (p>0.01), excepting lowest white matter CVR thresholds. Model use in a small pilot deployment of bilateral SOD cases demonstrated the potential utility, enabling voxel-wise CVR assessment without reliance on contralateral reference. CONCLUSIONS:We present a novel data-driven approach for normalizing CVR maps in patients with bilateral or indeterminate SOD. Using an RFc, our method provides an individualized, brain-wide reference CVR, expanding the utility of CVR estimates beyond the typical constraints of unilateral disease, and with potential application to other, similarly constrained scenarios such as for SPECT or PET hemodynamic studies. ABBREVIATIONS/BACKGROUND:CVR = cerebrovascular reactivity; RFc = random forest classifier; SOD = steno-occlusive disease.

BACKGROUND:Early prediction of hematoma expansion (HE) following nontraumatic intracerebral hemorrhage (ICH) may inform preemptive therapeutic interventions. We sought to identify how accurately machine learning (ML) radiomics models predict HE compared with expert clinicians using head computed tomography (HCT). METHODS:We used data from 900 study participants with ICH enrolled in the Antihypertensive Treatment of Acute Cerebral Hemorrhage 2 Study. ML models were developed using baseline HCT images, as well as admission clinical data in a training cohort (n = 621), and their performance was evaluated in an independent test cohort (n = 279) to predict HE (defined as HE by 33% or > 6 mL at 24 h). We simultaneously surveyed expert clinicians and asked them to predict HE using the same initial HCT images and clinical data. Area under the receiver operating characteristic curve (AUC) were compared between clinician predictions, ML models using radiomic data only (a random forest classifier and a deep learning imaging model) and ML models using both radiomic and clinical data (three random forest classifier models using different feature combinations). Kappa values comparing interrater reliability among expert clinicians were calculated. The best performing model was compared with clinical predication. RESULTS:The AUC for expert clinician prediction of HE was 0.591, with a kappa of 0.156 for interrater variability, compared with ML models using radiomic data only (a deep learning model using image input, AUC 0.680) and using both radiomic and clinical data (a random forest model, AUC 0.677). The intraclass correlation coefficient for clinical judgment and the best performing ML model was 0.47 (95% confidence interval 0.23-0.75). CONCLUSIONS:We introduced supervised ML algorithms demonstrating that HE prediction may outperform practicing clinicians. Despite overall moderate AUCs, our results set a new relative benchmark for performance in these tasks that even expert clinicians find challenging. These results emphasize the need for continued improvements and further enhanced clinical decision support to optimally manage patients with ICH.

BACKGROUND/UNASSIGNED:Durable occlusion after endovascular coiling can be compromised by recanalization, underscoring the need for accurate cerebral aneurysm assessment. Precise volume measurement not only informs treatment decisions and detects subtle aneurysm growth but also refines calculations of packing density, historically linked to occlusion success. This study compares three volume-measurement approaches-traditional two-dimensional (2D) estimation, a semi-automated three-dimensional (3D) technique, and an artificial intelligence (AI)-based 3D method. METHODS/UNASSIGNED:In this retrospective analysis, 24 aneurysms were assessed using 3D rotational angiography. Manual segmentation by three specialists using ITK-SNAP or mimics served as the reference standard. These results were compared with volumes from a semi-automated 3D platform (Philips Advanced Visualization Workspace), an AI-based tool (RapidAI for Aneurysm), and traditional 2D estimations. Agreement with the reference standard was quantified through Passing-Bablok regression slopes and mean biases. RESULTS/UNASSIGNED:(RapidAI). RapidAI demonstrated the strongest correlation with the reference standard, whereas 2D estimations showed the largest discrepancy. The semi-automated 3D method exhibited intermediate accuracy, potentially influenced by the clinician input required for segmentation. CONCLUSION/UNASSIGNED:All methods underestimated aneurysm volumes compared to the reference standard, suggesting that inaccurate volume measurements may mask early aneurysm growth. Among the techniques assessed, the AI-based approach provided the closest agreement with the reference, indicating that improved volumetric methods-particularly AI-driven ones-can enhance early detection of aneurysm expansion, guide treatment decisions, and help establish more reliable follow-up strategies for both treated and conservatively managed aneurysms.

Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as a low-cost, small form factor, fast, and safe probe for tissue dielectric properties measurements, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence conduction of microwave imaging remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within a human head model. An 8-element ultra-wideband array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mW. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayleigh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for ultra-wideband microwave stroke detection.

Ionic imbalances and sodium channel dysfunction, well-known sequelae of traumatic brain injury (TBI), promote functional impairment in affected subjects. Therefore, non-invasive measurement of sodium concentrations using ²³Na MRI has the potential to detect clinically relevant injury and predict persistent symptoms. Recently, we reported diffusely lower apparent total sodium concentrations (aTSC) in mild TBI patients compared to controls, as well as correlations between lower aTSC and worse clinical outcomes. The main goal of this study was to determine whether these aTSC findings, and their changes over time, predict outcomes at 3- and 12-month from injury. Twenty-seven patients previously studied with ²³Na MRI and outcome measures at 22 ± 10 days (average ± standard deviation) after injury (visit-1, v1) were contacted at 3- (visit-2, v2) and 12-month after injury (visit-3, v3) to complete the Rivermead post-concussion symptoms questionnaire (RPQ), the extended Glasgow outcome scale (GOSE), and the brief test of adult cognition by telephone (BTACT). Follow-up ¹H and ²³Na MRI were additionally scheduled at v2. Linear regression was used to calculate aTSC in global grey and white matters. Six hypotheses were tested in relation to the serial changes in outcome measures and in aTSC, and in relation to the cross-sectional and serial relationships between aTSC and outcome. Twenty patients contributed data at v2 and fifteen at v3. Total RPQ and composite BTACT z-scores differed significantly for v2 and v3 in comparison to v1 (each P < 0.01), reflecting longitudinally reduced symptomatology and improved performance on cognitive testing. No associations between aTSC and outcome were observed at v2. Previously lower grey and white matter aTSC normalized at v2 in comparison to controls, in line with a statistically detectable longitudinal increase in grey matter aTSC between v1 and v2 (P = 0.0004). aTSC values at v1 predicted a subset of future BTACT subtest scores, but not future RPQ scores nor GOSE-defined recovery status. Similarly, aTSC rates of change correlated with BTACT rates of change, but not with those of RPQ. Tissue aTSC, previously shown to be diffusely decreased compared to controls at v1, was no longer reduced by v2, suggesting normalization of the sodium ionic equilibrium. These changes were accompanied by marked improvement in outcome. The results support the notion that early aTSC from ²³Na MRI predicts future BTACT, but not RPQ scores, nor future GOSE status.

INTRODUCTION/BACKGROUND:The prompt detection of intracranial hemorrhage (ICH) on a non-contrast head CT (NCCT) is critical for the appropriate triage of patients, particularly in high volume/high acuity settings. Several automated ICH detection tools have been introduced; however, at present, most suffer from suboptimal specificity leading to false-positive notifications. METHODS:NCCT scans from 4 large databases were evaluated for the presence of an ICH (IPH, IVH, SAH or SDH) of >0.4 ml using fully-automated RAPID ICH 3.0 as compared to consensus detection from at least two neuroradiology experts. Scans were excluded for (1) severe CT artifacts, (2) prior neurosurgical procedures, or (3) recent intravenous contrast. ICH detection accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratios by were determined. RESULTS:A total of 881 studies were included. The automated software correctly identified 453/463 ICH-positive cases and 416/418 ICH-negative cases, resulting in a sensitivity of 97.84% and specificity 99.52%, positive predictive value 99.56%, and negative predictive value 97.65% for ICH detection. The positive and negative likelihood ratios for ICH detection were similarly favorable at 204.49 and 0.02 respectively. Mean processing time was <40 seconds. CONCLUSIONS:In this large data set of nearly 900 patients, the automated software demonstrated high sensitivity and specificity for ICH detection, with rare false-positives.

BACKGROUND:) using dynamic CVR analysis, offering a fully dynamic characterization of CVR to hemodynamic stimuli. PURPOSE:estimation. STUDY TYPE:Retrospective. POPULATION:A total of 23 patients (median age: 51 years, 10 females) with unilateral chronic steno-occlusive cerebrovascular disease, without prior knowledge of CCD status. FIELD STRENGTH/SEQUENCE:A 3-T, T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) and acetazolamide-augmented BOLD imaging performed with a gradient-echo echo-planar imaging (EPI) sequence. ASSESSMENT:were calculated for bilateral cerebral and cerebellar hemispheres. Three independent observers evaluated all data for the presence of CCD. STATISTICAL TESTS:Pearson correlations for comparing CVR across hemispheres, two-proportion Z-tests for comparing CCD prevalence, and Wilcoxon signed-rank tests for comparing median CVR. The level of statistical significance was set at P ≤ 0.05. RESULTS:(r = 0.705). DATA CONCLUSION:may underestimate CVR and could exaggerate CCD. EVIDENCE LEVEL:4. TECHNICAL EFFICACY:Stage 3.

Temperature is a hallmark parameter influencing almost all magnetic resonance properties (e.g., T₁ , T₂ , proton density, and diffusion). In the preclinical setting, temperature has a large influence on animal physiology (e.g., respiration rate, heart rate, metabolism, and oxidative stress) and needs to be carefully regulated, especially when the animal is under anesthesia and thermoregulation is disrupted. We present an open-source heating and cooling system capable of regulating the temperature of the animal. The system was designed using Peltier modules capable of heating or cooling a circulating water bath with active temperature feedback. Feedback was obtained using a commercial thermistor, placed in the animal rectum, and a proportional-integral-derivative controller was used to modulate the temperature. Its operation was demonstrated in a phantom as well as in mouse and rat animal models, where the standard deviation of the temperature of the animal upon convergence was less than a 10th of a degree. An application where brain temperature of a mouse was modulated was demonstrated using an invasive optical probe and noninvasive magnetic resonance spectroscopic thermometry measurements.