Spatio-Temporal U-Net for Cerebral Artery and Vein Segmentation in Digital Subtraction Angiography

X-ray digital subtraction angiography (DSA) is widely used for vessel and/or flow visualization and interventional guidance during endovascular treatment of patients with a stroke or aneurysm. To assist in peri-operative decision making as well as post-operative prognosis, automatic DSA analysis algorithms are being developed to obtain relevant image-based information. Such analyses include detection of vascular disease, evaluation of perfusion based on time intensity curves (TIC), and quantitative biomarker extraction for automated treatment evaluation in endovascular thrombectomy. Methodologically, such vessel-based analysis tasks may be facilitated by automatic and accurate artery-vein segmentation algorithms. The present work describes to the best of our knowledge the first study that addresses automatic artery-vein segmentation in DSA using deep learning. We propose a novel spatio-temporal U-Net (ST U-Net) architecture which integrates convolutional gated recurrent units (ConvGRU) in the contracting branch of U-Net. The network encodes a 2D+t DSA series of variable length and decodes it into a 2D segmentation image. On a multi-center routinely acquired dataset, the proposed method significantly outperformed U-Net (P<0.001) and traditional Frangi-based K-means clustering (P$<$0.001). Particularly in artery-vein segmentation, ST U-Net achieved a Dice coefficient of 0.794, surpassing the existing state-of-the-art methods by a margin of 12\%-20\%. Code will be made publicly available upon acceptance

autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter- and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) sequence is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.Comment: 10 pages; submitted to IEEE TM

Improving quality of stroke care through benchmarking center performance:why focusing on outcomes is not enough.

Background: Between-center variation in outcome may offer opportunities to identify variation in quality of care. By intervening on these quality differences, patient outcomes may be improved. However, whether observed differences in outcome reflect the true quality improvement potential is not known for many diseases. Therefore, we aimed to analyze the effect of differences in performance on structure and processes of care, and case-mix on between-center differences in outcome after endovascular treatment (EVT) for ischemic stroke. Methods: In this observational cohort study, ischemic stroke patients who received EVT between 2014 and 2017 in all 17 Dutch EVT-centers were included. Primary outcome was the modified Rankin Scale, ranging from 0 (no symptoms) to 6 (death), at 90 days. We used random effect proportional odds regression modelling, to analyze the effect of differences in structure indicators (center volume and year of admission), process indicators (time to treatment and use of general anesthesia) and case-mix, by tracking changes in tau2, which represents the amount of between-center variation in outcome. Results: Three thousand two hundred seventy-nine patients were included. Performance on structure and process indicators varied significantly between EVT-centers (P < 0.001). Predicted probability of good functional outcome (modified Rankin Scale 0–2 at 90 days), which can be interpreted as an overall measure of a center’s case-mix, varied significantly between 17 and 50% across centers. The amount of between-center variation (tau2) was estimated at 0.040 in a model only accounting for random variation. This estimate more than doubled after adding case-mix variables (tau2: 0.086) to the model, while a small amount of between-center variation was explained by variation in performance on structure and process indicators (tau2: 0.081 and 0.089, respectively). This indicates that variation in case-mix affects the differences in outcome to a much larger extent. Conclusions: Between-center variation in outcome of ischemic stroke patients mostly reflects differences in case-mix, rather than differences in structure or process of care. Since the latter two capture the real quality improvement potential, these should be used as indicators for comparing center performance. Especially when a strong association exists between those indicators and outcome, as is the case for time to treatment in ischemic stroke

Hospital Variation in Time to Endovascular Treatment for Ischemic Stroke:What Is the Optimal Target for Improvement?

Background Time to reperfusion in patients with ischemic stroke is strongly associated with functional outcome and may differ between hospitals and between patients within hospitals. Improvement in time to reperfusion can be guided by between-hospital and within-hospital comparisons and requires insight in specific targets for improvement. We aimed to quantify the variation in door-to-reperfusion time between and within Dutch intervention hospitals and to assess the contribution of different time intervals to this variation. Methods and Results We used data from the MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) Registry. The door-to-reperfusion time was subdivided into time intervals, separately for direct patients (door-to-computed tomography, computed tomography-to-computed tomography angiography [CTA], CTA-to-groin, and groin-to-reperfusion times) and for transferred patients (door-to-groin and groin-to-reperfusion times). We used linear mixed models to distinguish the variation in door-to-reperfusion time between hospitals and between patients. The proportional change in variance was used to estimate the amount of variance explained by each time interval. We included 2855 patients of 17 hospitals providing endovascular treatment. Of these patients, 44% arrived directly at an endovascular treatment hospital. The between-hospital variation in door-to-reperfusion time was 9%, and the within-hospital variation was 91%. The contribution of case-mix variables on the variation in door-to-reperfusion time was marginal (2%-7%). Of the between-hospital variation, CTA-to-groin time explained 83%, whereas groin-to-reperfusion time explained 15%. Within-hospital variation was mostly explained by CTA-to-groin time (33%) and groin-to-reperfusion time (42%). Similar results were found for transferred patients. Conclusions Door-to-reperfusion time varies between, but even more within, hospitals providing endovascular treatment for ischemic stroke. Quality of stroke care improvements should not only be guided by between-hospital comparisons, but also aim to reduce variation between patients within a hospital, and should specifically focus on CTA-to-groin time and groin-to-reperfusion time

Effect of first pass reperfusion on outcome in patients with posterior circulation ischemic stroke

BACKGROUND: First pass reperfusion (FPR), that is, excellent reperfusion (expanded treatment in cerebral ischemia (eTICI) 2C-3) in one pass, after endovascular treatment (EVT) of an occluded artery in the anterior circulation, is associated with favorable clinical outcome, even when compared with multiple pass excellent reperfusion (MPR). In patients with posterior circulation ischemic stroke (PCS), the same association is expected, but currently unknown. We aimed to assess characteristics associated with FPR and the influence of FPR versus MPR on outcomes in patients with PCS. METHODS: We used data from the MR CLEAN Registry, a prospective observational study. The effect of FPR on 24-hour National Institutes of Health Stroke Scale (NIHSS) score, as percentage reduction, and on modified Rankin Scale (mRS) scores at 3 months, was tested with linear and ordinal logistic regression models. RESULTS: Of 224 patients with PCS, 45 patients had FPR, 47 had MPR, and 90 had no excellent reperfusion (eTICI <2C). We did not find an association between any of the patient, imaging, or treatment characteristics and FPR. FPR was associated with better NIHSS (-45% (95% CI: -65% to -12%)) and better mRS scores (adjusted common odds ratio (acOR): 2.16 (95% CI: 1.23 to 3.79)) compared with no FPR. Outcomes after FPR were also more favorable compared with MPR, but the effect was smaller and not statistically significant (NIHSS: -14% (95% CI: -51% to 49%), mRS acOR: 1.50 (95% CI: 0.75 to 3.00)). CONCLUSIONS: FPR in patients with PCS is associated with favorable clinical outcome in comparison with no FPR. In comparison with MPR, the effect of FPR was no longer statistically significant. Nevertheless, our data support the notion that FPR should be the treatment target to pursue in every patient treated with EVT

Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography

BACKGROUND: Machine learning algorithms hold the potential to contribute to fast and accurate detection of large vessel occlusion (LVO) in patients with suspected acute ischemic stroke. We assessed the diagnostic performance of an automated LVO detection algorithm on CT angiography (CTA). METHODS: Data from the MR CLEAN Registry and PRESTO were used including patients with and without LVO. CTA data were analyzed by the algorithm for detection and localization of LVO (intracranial internal carotid artery (ICA)/ICA terminus (ICA-T), M1, or M2). Assessments done by expert neuroradiologists were used as reference. Diagnostic performance was assessed for detection of LVO and per occlusion location by means of sensitivity, specificity, and area under the curve (AUC). RESULTS: We analyzed CTAs of 1110 patients from the MR CLEAN Registry (median age (IQR) 71 years (60-80); 584 men; 1110 with LVO) and of 646 patients from PRESTO (median age (IQR) 73 years (62-82); 358 men; 141 with and 505 without LVO). For detection of LVO, the algorithm yielded a sensitivity of 89% in the MR CLEAN Registry and a sensitivity of 72%, specificity of 78%, and AUC of 0.75 in PRESTO. Sensitivity per occlusion location was 88% for ICA/ICA-T, 94% for M1, and 72% for M2 occlusion in the MR CLEAN Registry, and 80% for ICA/ICA-T, 95% for M1, and 49% for M2 occlusion in PRESTO. CONCLUSION: The algorithm provided a high detection rate for proximal LVO, but performance varied significantly by occlusion location. Detection of M2 occlusion needs further improvement

The Efficacy of Coil Embolization to Obtain Intrahepatic Redistribution in Radioembolization: Qualitative and Quantitative Analyses

Purpose: To evaluate the efficacy of coil embolization to obtain intrahepatic redistribution in patients undergoing radioembolization. Materials and Method: All patients treated with radioembolization at our institute were retrospectively analyzed, and all cases in which a tumor-feeding vessel was coil-embolized were selected. Two nuclear medicine physicians visually assessed the effect of redistribution. Furthermore, the redistribution of microspheres was measured by quantifying the activity distributed to the coil-embolized (dependent) segment relative to the other (non-dependent) segments and to the tumor(s) in that segment. Quantitative analysis was performed on post-treatment 90Y-PET and 166Ho-SPECT using Simplicit90Y software. Lesion response was measured according to RECIST 1.1 criteria at 3 months post-treatment. Results: Out of 37 cases, 32 were suitable for quantitative analysis and 37 for qualitative analysis. In the qualitative analysis, redistribution was deemed successful in 69% of cases. The quantitative analysis showed that the median ratio of the activity to the dependent embolized segments and the non-dependent segments was 0.88 (range 0.26–2.05) and 0.80 (range 0.19–1.62) for tumors in dependent segments compared with tumors in non-dependent segments. Using a cutoff ratio of 0.7 (30% lower activity concentration in comparison with the rest of the liver), 57% of cases were successful. At 3 months post-treatment, 6% of dependent tumors had partial response, 20% progressive disease, and 74% stable disease. In non-dependent tumors, this was, respectively, 16%, 20%, and 64%. Conclusion: Coil embolization of hepatic arteries to induce redistribution of microspheres has a limited success rate. Qualitative assessment tends to overrate redistribution