qTICI: Quantitative assessment of brain tissue reperfusion on digital subtraction angiograms of acute ischemic stroke patients

Assessment of automated TICI scoring during endovascular treatment in patients with an ischemic stroke

BACKGROUND

The extended Thrombolysis in Cerebral Infarction (eTICI) score is used in digital subtraction angiography (DSA) to quantify reperfusion grade in patients with an ischemic stroke who undergo endovascular thrombectomy (EVT). A previously developed automatic TICI score (autoTICI), which quantifies the ratio of reperfused pixels after EVT, demonstrates good correlation with eTICI.

OBJECTIVE

To evaluate the autoTICI model in a large multicenter registry of patients with an ischemic stroke, investigate the association with visual eTICI, and compare prediction of functional outcome between autoTICI and eTICI.

METHODS

Patients in the MR CLEAN Registry with an internal carotid artery, M1, and M2 occlusion were selected if both anteroposterior and lateral views were present in pre- and post-EVT DSA scans. The autoTICI score was compared with eTICI in predicting favorable functional outcome (modified Rankin Scale score 0-2), using area under the receiver operating characteristics curve (AUC) with a multivariable logistic regression model including known prognostic characteristics.

RESULTS

In total 421 of 3637 patients were included. AutoTICI was significantly associated with eTICI non-linearly (below 70% cOR=2.3 (95% CI 2.1 to 2.5), above 70% cOR=1.6 (95% CI 1.6 to 1.7) per 10% increment). The AUC of the model predicting favorable functional outcome was similar for autoTICI and eTICI (0.86, 95% CI 0.82 to 0.92 vs 0.86, 95% CI 0.83 to 0.90, P=0.73) and was higher than for a model with prognostic patient characteristics alone (0.86 vs 0.84, P=0.01).

CONCLUSION

Automatic quantitative assessment of reperfusion after EVT is associated with eTICI, and prediction of functional outcome is similar to that with visual eTICI. Therefore, autoTICI could be used as an alternative or additional review for visual reperfusion assessment to facilitate reproducible and uniform reporting.

Automated image registration of cerebral digital subtraction angiography

PURPOSE

Our aim is to automatically align digital subtraction angiography (DSA) series, recorded before and after endovascular thrombectomy. Such alignment may enable quantification of procedural success.

METHODS

Firstly, we examine the inherent limitations for image registration, caused by the projective characteristics of DSA imaging, in a representative set of image pairs from thrombectomy procedures. Secondly, we develop and assess various image registration methods (SIFT, ORB). We assess these methods using manually annotated point correspondences for thrombectomy image pairs.

RESULTS

Linear transformations that account for scale differences are effective in aligning DSA sequences. Two anatomical landmarks can be reliably identified for registration using a U-net. Point-based registration using SIFT and ORB proves to be most effective for DSA registration and are applicable to recordings for all patient sub-types. Image-based techniques are less effective and did not refine the results of the best point-based registration method.

CONCLUSION

We developed and assessed an automated image registration approach for cerebral DSA sequences, recorded before and after endovascular thrombectomy. Accurate results were obtained for approximately 85% of our image pairs.

Priorities for Advancements in Neuroimaging in the Diagnostic Workup of Acute Stroke

STAIR XII (12th Stroke Treatment Academy Industry Roundtable) included a workshop to discuss the priorities for advancements in neuroimaging in the diagnostic workup of acute ischemic stroke. The workshop brought together representatives from academia, industry, and government. The participants identified 10 critical areas of priority for the advancement of acute stroke imaging. These include enhancing imaging capabilities at primary and comprehensive stroke centers, refining the analysis and characterization of clots, establishing imaging criteria that can predict the response to reperfusion, optimizing the Thrombolysis in Cerebral Infarction scale, predicting first-pass reperfusion outcomes, improving imaging techniques post-reperfusion therapy, detecting early ischemia on noncontrast computed tomography, enhancing cone beam computed tomography, advancing mobile stroke units, and leveraging high-resolution vessel wall imaging to gain deeper insights into pathology. Imaging in acute ischemic stroke treatment has advanced significantly, but important challenges remain that need to be addressed. A combined effort from academic investigators, industry, and regulators is needed to improve imaging technologies and, ultimately, patient outcomes.

No-reflow phenomenon following stroke recanalization therapy: Clinical assessment advances: A narrative review

The no-reflow phenomenon (NRP) after successful vascular recanalization in acute ischemic stroke (AIS) has become a major cause of poor clinical prognosis and ineffective recanalization. However, there is currently no clear definition or unified clinical assessment method for the NRP. Therefore, it is urgent to clarify the clinical evaluation criteria for the NRP and develop new no-reflow evaluation techniques so that remedial treatment can be applied to AIS patients suffering from the NRP. In this brief review, a variety of NRP assessment methods and defining criteria for clinical practice are presented.

Eloquence-based reperfusion scoring and its ability to predict post-thrombectomy disability and functional status

BACKGROUND

Angiographic reperfusion after endovascular thrombectomy in acute ischemic stroke is commonly graded using volume-based reperfusion scores such as the modified thrombolysis in cerebral infarct score. The location of non-reperfused regions is not included in modified thrombolysis in cerebral infarct score. We studied the predictive ability of an eloquence-based reperfusion score.

METHODS

Consecutive cases of endovascular thrombectomy for anterior circulation strokes performed between January 2018 and April 2020 were included. Digital subtraction angiograms were reviewed by two blinded neurointerventionalist operators. Incomplete reperfusion was further classified by lobar regions lacking reperfusion to create various cohorts. Outcomes were graded four to seven days post-procedure with the National Institute of Health Stroke Scale (NIHSS) and 90 days post-procedure with the modified Rankin Scale.

RESULTS

One hundred patients were identified. Via multivariate analysis, we found that frontal lobe non-reperfusion (mean difference (MD) = -1.60, p = 0.002) and occipital lobe non-reperfusion (MD = -1.68, p = 0.001) were associated with worse mental status improvement while left-sided stroke (MD = 2.02, p < 0.001) featured better improvement post-thrombectomy. Occipital lobe non-reperfusion (MD = -0.734, p = 0.009) was associated with the worse improvement of visual fields. The non-reperfusion of the frontal lobe was associated with a 1.732-worse NIHSS hemibody strength score (95% confidence interval (95%CI) = -3.39 to -0.072, p = 0.041). Worse improvement in NIHSS scores was found to be associated with frontal lobe non-reperfusion (MD = -5.34, 95%CI = -9.52 to -1.18, p = 0.013) and occipital lobe non-reperfusion (MD = -6.35, 95%CI = -10.4 to -2.31, p = 0.002). Odds of achieving modified Rankin Scale of 0-2 at 90 days were decreased with frontal lobe non-reperfusion (odds ratio (OR) = 0.279, 95%CI = 0.090-0.869, p = 0.028) and left laterality (OR = 0.376, 95%CI = 0.153-0.922, p = 0.033).

CONCLUSIONS

Eloquence-based reperfusion assessment is an important predictor for functional outcomes after thrombectomy.

Clinical Effect of Digital Subtraction Angiography Combined with Neurointerventional Thrombolysis for Acute Ischemic Cerebrovascular Disease and Its Influence on Vascular Endothelial Function and Oxidative Stress

Objective

Ischemic cerebrovascular disease is a commonly seen vascular disorder in clinical practice. Given the difficulty of drug therapy to achieve ideal curative effects, interventional therapy has gradually become the preferred treatment for the disease. This research primarily discusses the short-term efficacy of digital subtraction angiography- (DSA-) guided neurointerventional thrombolysis for acute ischemic cerebrovascular disease (AICVD) and its influence on vascular endothelial function (VEF) and oxidative stress (OS).

Methods

All the clinical data of 162 patients diagnosed with AICVD and treated between June 2019 and December 2021 were collected and analyzed retrospectively. They were assigned to two cohorts according to the difference in interventional methods: a conventional group (CG) given recombinant tissue plasminogen activator (rt-PA) therapy and an observation group (OG) intervened by DSA-guided neurointerventional thrombolysis. The two groups were compared with respect to short-term treatment efficacy, the National Institutes of Health Stroke Scale (NIHSS) score, cerebral hemodynamics, and VEF and OS indexes.

Results

The short-term efficacy was better in OG (93.98%) than in CG (82.28%). After treatment, the NIHSS score decreased in both cohorts with obvious differences within the group at different time points, and the posttreatment NIHSS score was lower in OG as compared to CG. OG had higher Q m and V m while lower W v, Z cv, and R v than CG. Higher endothelial-dependent flow-mediated dilatation (FMD) was observed in OG, as well as lower ankle-brachial index (ABI) and pulse wave velocity (PWV). And the posttreatment MDA was lower while SOD, GSH-Px, and TAC were higher in OG compared with those on CG. All the above differences were of statistical significance (P < 0.05).

Conclusions

DSA-guided neurointerventional thrombolysis is highly effective in the treatment of AICVD, which can not only effectively improve patients' neurological function and cerebral hemodynamics but also mitigate VEF injury and help to alleviate patients' OS.

Deep Learning-Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study

Background and Purpose

Mechanical thrombectomy is an established procedure for treatment of acute ischemic stroke. Mechanical thrombectomy success is commonly assessed by the Thrombolysis in Cerebral Infarction (TICI) score, assigned by visual inspection of X-ray digital subtraction angiography data. However, expert-based TICI scoring is highly observer-dependent. This represents a major obstacle for mechanical thrombectomy outcome comparison in, for instance, multicentric clinical studies. Focusing on occlusions of the M1 segment of the middle cerebral artery, the present study aimed to develop a deep learning (DL) solution to automated and, therefore, objective TICI scoring, to evaluate the agreement of DL- and expert-based scoring, and to compare corresponding numbers to published scoring variability of clinical experts.

Methods

The study comprises 2 independent datasets. For DL system training and initial evaluation, an in-house dataset of 491 digital subtraction angiography series and modified TICI scores of 236 patients with M1 occlusions was collected. To test the model generalization capability, an independent external dataset with 95 digital subtraction angiography series was analyzed. Characteristics of the DL system were modeling TICI scoring as ordinal regression, explicit consideration of the temporal image information, integration of physiological knowledge, and modeling of inherent TICI scoring uncertainties.

Results

For the in-house dataset, the DL system yields Cohen’s kappa, overall accuracy, and specific agreement values of 0.61, 71%, and 63% to 84%, respectively, compared with the gold standard: the expert rating. Values slightly drop to 0.52/64%/43% to 87% when the model is, without changes, applied to the external dataset. After model updating, they increase to 0.65/74%/60% to 90%. Literature Cohen’s kappa values for expert-based TICI scoring agreement are in the order of 0.6.

Conclusions

The agreement of DL- and expert-based modified TICI scores in the range of published interobserver variability of clinical experts highlights the potential of the proposed DL solution to automated TICI scoring.

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) acquisition 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.