Quantification of infarct core signal using CT imaging in acute ischemic stroke.


Journal

NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070

Informations de publication

Date de publication:
2022
Historique:
received: 22 11 2021
revised: 03 03 2022
accepted: 28 03 2022
pubmed: 5 4 2022
medline: 20 5 2022
entrez: 4 4 2022
Statut: ppublish

Résumé

In stroke care, the extent of irreversible brain injury, termed infarct core, plays a key role in determining eligibility for acute treatments, such as intravenous thrombolysis and endovascular reperfusion therapies. Many of the pivotal randomized clinical trials testing those therapies used MRI Diffusion-Weighted Imaging (DWI) or CT Perfusion (CTP) to define infarct core. Unfortunately, these modalities are not available 24/7 outside of large stroke centers. As such, there is a need for accurate infarct core determination using faster and more widely available imaging modalities including Non-Contrast CT (NCCT) and CT Angiography (CTA). Prior studies have suggested that CTA provides improved predictions of infarct core relative to NCCT; however, this assertion has never been numerically quantified by automatic medical image computing pipelines using acquisition protocols not confounded by different scanner manufacturers, or other protocol settings such as exposure times, kilovoltage peak, or imprecision due to contrast bolus delays. In addition, single-phase CTA protocols are at present designed to optimize contrast opacification in the arterial phase. This approach works well to maximize the sensitivity to detect vessel occlusions, however, it may not be the ideal timing to enhance the ischemic infarct core signal (ICS). In this work, we propose an image analysis pipeline on CT-based images of 88 acute ischemic stroke (AIS) patients drawn from a single dynamic acquisition protocol acquired at the acute ischemic phase. We use the first scan at the time of the dynamic acquisition as a proxy for NCCT, and the rest of the scans as a proxy for CTA scans, with bolus imaged at different brain enhancement phases. Thus, we use the terms "NCCT" and "CTA" to refer to them. This pipeline enables us to answer the questions "Does the injection of bolus enhance the infarct core signal?" and "What is the ideal bolus timing to enhance the infarct core signal?" without being influenced by aforementioned factors such as scanner model, acquisition settings, contrast bolus delay, and human reader errors. We use reference MRI DWI images acquired after successful recanalization acting as our gold standard for infarct core. The ICS is quantified by calculating the difference in intensity distribution between the infarct core region and its symmetrical healthy counterpart on the contralateral hemisphere of the brain using a metric derived from information theory, the Kullback-Leibler divergence (KL divergence). We compare the ICS provided by NCCT and CTA and retrieve the optimal timing of CTA bolus to maximize the ICS. In our experiments, we numerically confirm that CTAs provide greater ICS compared to NCCT. Then, we find that, on average, the ideal CTA acquisition time to maximize the ICS is not the current target of standard CTA protocols, i.e., during the peak of arterial enhancement, but a few seconds afterward (median of 3 s; 95% CI [1.5, 3.0]). While there are other studies comparing the prediction potential of ischemic infarct core from NCCT and CTA images, to the best of our knowledge, this analysis is the first to perform a quantitative comparison of the ICS among CT based scans, with and without bolus injection, acquired using the same scanning sequence and a precise characterization of the bolus uptake, hence, reducing potential confounding factors.

Identifiants

pubmed: 35378498
pii: S2213-1582(22)00063-8
doi: 10.1016/j.nicl.2022.102998
pmc: PMC8980621
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

102998

Subventions

Organisme : NINDS NIH HHS
ID : R01 NS121154
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR003167
Pays : United States

Informations de copyright

Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.

Auteurs

Uma Maria Lal-Trehan Estrada (UML)

Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.

Grant Meeks (G)

McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.

Sergio Salazar-Marioni (S)

McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.

Fabien Scalzo (F)

Pepperdine University, Malibu, CA, USA.

Mudassir Farooqui (M)

Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.

Juan Vivanco-Suarez (J)

Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.

Santiago Ortega Gutierrez (SO)

Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.

Sunil A Sheth (SA)

McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.

Luca Giancardo (L)

Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA; Institute for Stroke and Cerebrovascular Diseases, University of Texas Health Science Center at Houston, Houston, TX, USA. Electronic address: luca.giancardo@uth.tmc.edu.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH