Evaluating the effect of noise reduction strategies in CT perfusion imaging for predicting infarct core with deep learning.

ABSTRACT: This study evaluates the efficacy of deep learning models in identifying infarct tissue on computed tomography perfusion (CTP) scans from patients with acute ischemic stroke due to large vessel occlusion, specifically addressing the potential influence of varying noise reduction techniques implemented by different vendors. We analyzed CTP scans from 60 patients who underwent mechanical thrombectomy achieving a modified thrombolysis in cerebral infarction (mTICI) score of 2c or 3, ensuring minimal changes in the infarct core between the initial CTP and follow-up MR imaging. Noise reduction techniques, including principal component analysis (PCA), wavelet, non-local means (NLM), and a no denoising approach, were employed to create hemodynamic parameter maps. Infarct regions identified on fol

SUBMITTER: Crouch JJF 

PROVIDER: S-EPMC11719424 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature