Use of a convolutional neural network to identify infarct core using computed tomography perfusion parameters

Identification of infarct core and ischemic penumbra using computed tomography perfusion and deep learning

Purpose

The size and location of infarct and penumbra are key to decision-making for acute ischemic stroke (AIS) management. CT perfusion (CTP) software estimate infarct and penumbra volume using contralateral hemisphere relative thresholding. This approach is not robust and widely contested by the scientific community. In this study, we investigate the use of deep learning-based algorithms to efficiently locate infarct and penumbra tissue on CTP hemodynamic maps.

Approach

CTP scans were retrospectively collected for 60 and 59 patients in the infarct only and infarct + penumbra substudies respectively. Commercial CTP software was used to generate cerebral blood flow, cerebral blood volume, mean transit time, time to peak, and delay time maps. U-Net-shaped architectures were trained to segment infarct or infarct + penumbra. Test-time-augmentation, ensembling, and watershed segmentation were used as postprocessing techniques. Segmentation performance was evaluated using Dice coefficients (DC) and mean absolute volume errors (MAVE).

Results

The algorithm segmented infarct tissue resulted in DC of 0.64 ± 0.03 (0.63, 0.65), and MAVE of 4.91 ± 0.94 (4.5, 5.32) mL. In comparison, the commercial software predicted infarct with a DC of 0.31 ± 0.17 (0.26, 0.36) and MAVE of 9.77 ± 8.35 (7.12, 12.42) mL. The algorithm was able to segment infarct + penumbra with a DC of 0.61 ± 0.04 (0.6, 0.63), and MAVE of 6.51 ± 1.37 (5.91, 7.11) mL. In comparison, the commercial software predicted infarct + penumbra with a DC of 0.3 ± 0.19 (0.25, 0.35) and MAVE of 9.18 ± 7.55 (7.25, 11.11) mL.

Conclusions

Use of deep learning algorithms to assess severity of AIS in terms of infarct and penumbra volume is precise and outperforms current relative thresholding methods. Such an algorithm would enhance the reliability of CTP in guiding treatment decisions.

Agreement between estimated computed tomography perfusion ischemic core and follow-up infarct on diffusion-weighted imaging

BACKGROUND

Computed tomography perfusion (CTP) is frequently performed during the diagnostic workup of acute ischemic stroke patients. Yet, ischemic core estimates vary widely between different commercially available software packages. We assessed the volumetric and spatial agreement of the ischemic core on CTP with the follow-up infarct on diffusion-weighted imaging (DWI) using an automated software.

METHODS

We included successfully reperfused patients who underwent endovascular treatment (EVT) with CTP and follow-up DWI between November 2017 and September 2020. CTP data were processed with a fully automated software using relative cerebral blood flow (rCBF) < 30% to estimate the ischemic core. The follow-up infarct was segmented on DWI imaging data, which were acquired at approximately 24 h. Ischemic core on CTP was compared with the follow-up infarct lesion on DWI using intraclass correlation coefficient (ICC) and Dice similarity coefficient (Dice).

RESULTS

In 59 patients, the median estimated core volume on CTP was 16 (IQR 8-47) mL. The follow-up infarct volume on DWI was 11 (IQR 6-42) mL. ICC was 0.60 (95% CI 0.33-0.76), indicating moderate volumetric agreement. Median Dice was 0.20 (IQR 0.01-0.35). The median positive predictive value was 0.24 (IQR 0.05-0.57), and the median sensitivity was 0.3 (IQR 0.13-0.47). Severe core overestimation on computed tomography perfusion > 50 mL occurred in 4/59 (7%) of the cases.

CONCLUSIONS

In patients with successful reperfusion after EVT, CTP-estimated ischemic core showed moderate volumetric and spatial agreement with the follow-up infarct lesion on DWI, similar to the most used commercially available CTP software packages. Severe ischemic core overestimation was relatively uncommon.

Basis and current state of computed tomography perfusion imaging: a review

Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.

Validation of an artificial intelligence-driven large vessel occlusion detection algorithm for acute ischemic stroke patients

Rapid and accurate diagnosis of large vessel occlusions (LVOs) in acute ischemic stroke (AIS) patients using automated software could improve clinical workflow in determining thrombectomy in eligible patients. Artificial intelligence-based methods could accomplish this; however, their performance in various clinical scenarios, relative to clinical experts, must be thoroughly investigated. We aimed to assess the ability of Canon's ^AUTOStroke Solution LVO application in properly detecting and locating LVOs in AIS patients. Data from 202 LVO and 101 non-LVO AIS patients who presented with stroke-like symptoms between March 2019 and February 2020 were collected retrospectively. LVO patients had either an internal carotid artery (ICA) (n = 59), M1 middle cerebral artery (MCA) (n = 82) or M2 MCA (n = 61) occlusion. Computed tomography angiography (CTA) scans from each patient were pushed to the automation platform and analyzed. The algorithm's ability to detect LVOs was assessed using accuracy, sensitivity and Matthews correlation coefficients (MCCs) for each occlusion type. The following results were calculated for each occlusion type in the study (accuracy, sensitivity, MCC): ICA = (0.95, 0.90, 0.89), M1 MCA = (0.89, 0.77, 0.78) and M2 MCA = (0.80, 0.51, 0.59). For the non-LVO cohort, 98% (99/101) of cases were correctly predicted as LVO negative. Processing time for each case was 69.8 ± 1.1 seconds (95% confidence interval). Canon's ^AUTOStroke Solution LVO application was able to accurately identify ICA and M1 MCA occlusions in addition to almost perfectly assessing when an LVO was not present. M2 MCA occlusion detection needs further improvement based on the sensitivity results displayed by the LVO detection algorithm.