Michael E. Moseley

BACKGROUND AND PURPOSE: Predicting long-term clinical outcomes based on early acute ischemic stroke (AIS) information would be useful for many reasons, including patient counseling and clinical trial execution. This study investigates how different regions in brain imaging, including noninfarcted areas, contribute to the accuracy of predicting 90-day stroke outcomes by using deep learning (DL). MATERIALS AND METHODS: We developed and validated DL models in 449 patients with AIS, by using MRI DWI scans from 1–7 days poststroke and 90-day mRS outcome data. These models were trained on various inputs: infarct volumes, full-brain images, infarct masks, intensity-preserved infarct masks, and images in which the infarct region is removed, which we call lesion-neutralized images. Performance was assessed by using accuracy of predicting the specific mRS score, accuracy within ±1 mRS category, mean absolute error (MAE), and the area under the curve (AUC) to predict unfavorable outcome (mRS > 2). RESULTS: The model trained by using only infarct volume size reported the highest (worst) MAE of 1.51 points (95% CI, 1.40–1.61; P < .001), while the model trained with full-brain images achieved the lowest MAE of 1.07 points (95% CI, 0.99–1.16). Models with intermediate amounts of imaging information each improved on the volume-only predictions but did not reach the performance of the full brain images; infarct masks, intensity-preserved infarct masks, and lesion-neutralized images demonstrated MAEs of 1.25 (95% CI, 1.15–1.34; P = .002), 1.21 (95% CI, 1.11–1.30; P = .008), and 1.35 (95% CI, 1.24–1.45; P < .001), respectively. Similar results were seen for other prediction tasks, including AUC to predict unfavorable outcomes, ranging from 0.68 (95% CI, 0.63–0.73) for infarct volume to 0.86 (95% CI, 0.82–0.89) for full brain inputs. CONCLUSIONS: While the best performance came from by using the full brain imaging volume, we demonstrate that the infarct location, its signal characteristics, and importantly, the noninfarcted regions all contribute to the predictions. The noninfarcted areas may be a proxy for overall brain health and resilience, containing important information about potential outcomes.

Background Arterial spin labeling (ASL) MRI is a non-invasive perfusion imaging technique with potential for assessing hemodynamics in children. However, understanding hemodynamic changes in developing brains remains challenging. This study investigates the impact of normal brain development on ASL-derived cerebral blood flow (CBF) and arterial transit time (ATT) in MR-negative children. Methods Thirty-two pediatric subjects (ages 0.7–17.9 years, mean 9.3 ± 5.3y, 19 male) with MR-negative findings were retrospectively included. Four pseudo-continuous ASL (PCASL) scans were acquired: single-delay (PLD 1525 or 2025 ms) and multi-delay (3 or 7 delays). CBF and ATT in supratentorial gray matter (GM), white matter (WM), and total-brain (TB) regions were analyzed using paired t-tests, Cohen’s d, Spearman correlation, and mixed linear regression models. Results Single-delay CBF was significantly higher than 3-delay CBF in GM and WM (p < 0.001 PLD 2025 ms; p = 0.02 PLD 1525 ms). WM and TB CBF correlated negatively with age (rho=-0.56, p < 0.001), whereas GM CBF showed no significant correlation (rho=-0.03, p = 0.87); the trends differed significantly (p = 0.01). GM and TB ATT increased with age (r² > 0.11, p < 0.021). WM and TB CBF correlated with WM and combined WM/GM volumes (rho=-0.42, p = 0.02; rho=-0.46, p = 0.008). Conclusion GM and WM exhibit distinct age-related hemodynamic patterns. WM perfusion declines with age and correlates with WM volume, while GM perfusion remains stable. The progressive increase in GM ATT highlights the need for cautious interpretation of single-delay ASL data in pediatric studies.

Cerebral blood flow (CBF) and transit time are essential biomarkers for assessing brain health. While dynamic susceptibility contrast (DSC) MRI has been widely applied to measure these metrics, it is limited in the pediatric population due to the need for contrast agents. Arterial spin labeling is a non-invasive and quantitative MR modality, and multi-delay ASL can measure CBF and transit time simultaneously. Although multi-delay ASL has been used in adult neuroimaging studies, its application in children requires investigation. Moyamoya disease, a progressive steno-occlusive cerebrovascular disorder, often manifests in childhood. In this work, we present a cohort study that examines multi-delay ASL and DSC MRI to characterize vascular hemodynamics in 22 pediatric patients. We evaluate CBF and transit time in different brain regions before and after revascularization surgeries. Results show that revascularization significantly increased CBF by 24% and 7.6%, respectively, as measured by ASL and DSC; it also significantly decreased transit time by 12% and 15%, indicating improved hemodynamics and metabolism. ASL and DSC results also showed significantly positive correlations in all brain regions. Thus, revascularization improved hemodynamics in pediatric moyamoya patients and shows that multi-delay ASL can effectively characterize CBF and transit time in the pediatric population.

The current spatial resolution of PET images is 3-4 mm for whole body PET/MR. Anatomical MR images with higher resolution and superior image quality have been used in PET reconstruction to improve the image quality and spatial resolution; however, mismatches between MR priors and actual tracer distribution can hinder accuracy. A novel PET reconstruction with MR priors, Magnetic Resonance-guided Block Sequential Regularized Expectation Maximum (MRgBSREM), that is robust to mismatches between anatomical priors and true activity distribution is proposed. This method is evaluated in diverse clinical settings using various tracers: 18F-florbetaben (FBB) in 373 subjects from a dementia study, 18F-FDG in a patient with chronic ischemic stroke, 18F-NaF in a knee study, and 15Owater in a patient with Moyamoya disease. Reconstruction using MRgBSREM visually improved both spatial resolution and image quality in all studies. In the 18F-florbetaben study, it mitigated white-matter spill-in into gray-matter as well as gray-matter spill over to the adjacent tissues, potentially leading to more accurate measurement of FBB uptake in the gray-matter. Visual assessment suggests that the proposed PET reconstruction enhances spatial resolution, which may contribute to improved diagnostic accuracy, while it displays robustness to mismatches between MR priors and true activity distribution.

A deep learning model fusing noncontrast-enhanced CT and clinical information outperformed an imaging-only model and a clinical information–only model and performed similarly to MRI-based models in predicting 90-day stroke functional outcomes.

Sophie Ostmeier, Justin Xu, Zhihong Chen, Maya Varma, Louis Blankemeier, Christian Bluethgen, Arne Edward Michalson Md, Michael Moseley, Curtis Langlotz, Akshay S Chaudhari, Jean-Benoit Delbrouck. Findings of the Association for Computational Linguistics: EMNLP 2024. 2024.

Phase contrast (PC) magnetic resonance imaging (MRI) is a primary method used to quantify blood flow. Cerebral blood flow (CBF) is an important hemodynamic parameter to characterize cerebrovascular and neurological diseases. However, a critical step in CBF quantification using PC MRI is vessel segmentation, which is largely manual, and thus time-consuming and prone to interrater variability. Here, we present encoder–decoder deep learning models to automate segmentation of neck arteries to accurately quantify CBF. The PC-MRI data were collected from 46 Moyamoya (MM) patients and 107 healthy control (HC) participants. Three segmentation U-Net models (Standard, Nested, and Attention) were compared. The PC MRI images were taken before and 15 min after vasodilation. The models were assessed based on their ability to detect the internal carotid arteries (ICAs), external carotid arteries (ECAs), and vertebral arteries (VAs), using the Dice score coefficient (DSC) of overlap between manual and predicted segmentations and receiver operator characteristic (ROC) metric. Analysis of variance, Wilcoxon rank-sum test, and paired t-test were used for comparisons. The Standard U-NET, Attention U-Net, and Nest U-Net models achieved results of mean DSCs of 0.81 ± 0.21, and 0.85 ± 0.14, and 0.85 ± 0.13, respectively. The ROC curves revealed high area under the curve scores for all methods (≥0.95). While the Nested and Attention U-Net architectures accomplished reliable segmentation performance for HC and MM subsets, Standard U-Net did not perform as well in the subset of MM patients. Blood flow velocities calculated by the models were statistically comparable. In conclusion, optimized deep learning architectures can successfully segment neck arteries in PC MRI images and provide precise quantification of their blood flow.

Background: Cerebrovascular reserve (CVR) reflects the capacity of cerebral blood flow (CBF) to change following a vasodilation challenge. Decreased CVR is associated with a higher stroke risk in patients with cerebrovascular diseases. While revascularization can improve CVR and reduce this risk in adult patients with vasculopathy such as those with Moyamoya disease, its impact on hemodynamics in pediatric patients remains to be elucidated. Arterial spin labeling (ASL) is a quantitative MRI technique that can measure CBF, CVR, and arterial transit time (ATT) non‐invasively.Purpose: To investigate the short‐ and long‐term changes in hemodynamics after bypass surgeries in patients with Moyamoya disease.Study Type: Longitudinal.Population: Forty‐six patients (11 months–18 years, 28 females) with Moyamoya disease.Field Strength/Sequence: 3‐T, single‐ and multi‐delay ASL, T1‐weighted, T2‐FLAIR, 3D MRA.Assessment: Imaging was performed 2 weeks before and 1 week and 6 months after surgical intervention. Acetazolamide was employed to induce vasodilation during the imaging procedure. CBF and ATT were measured by fitting the ASL data to the general kinetic model. CVR was computed as the percentage change in CBF. The mean CBF, ATT, and CVR values were measured in the regions affected by vasculopathy.Statistical Tests: Pre‐ and post‐revascularization CVR, CBF, and ATT were compared for different regions of the brain. P‐values <0.05 were considered statistically significant.Results: ASL‐derived CBF in flow territories affected by vasculopathy significantly increased after bypass by 41 ± 31% within a week. At 6 months, CBF significantly increased by 51 ± 34%, CVR increased by 68 ± 33%, and ATT was significantly reduced by 6.6 ± 2.9%.Data Conclusion: There may be short‐ and long‐term improvement in the hemodynamic parameters of pediatric Moyamoya patients after bypass surgery.Evidence Level: 4Technical Efficacy: Stage 2