#5735. Retrospective study of deep learning to reduce noise in non-contrast head CT images

July 2026publication date
Proposal available till 11-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Radiology, Nuclear Medicine and Imaging;
Radiological and Ultrasound Technology;
Computer Graphics and Computer-Aided Design;
Health Informatics;
Computer Vision and Pattern Recognition;
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Abstract:
Purpose: Presented herein is a novel CT denoising method uses a skip residual encoder-decoder framework with group convolutions and a novel loss function to improve the subjective and objective image quality for improved disease detection in patients with acute ischemic stroke (AIS). Materials and methods: In this retrospective study, confirmed AIS patients with full-dose NCCT head scans were randomly selected from a stroke registry between 20XX and 20XX. 325 patients (67 ± 15 years, 176 men) were included. 18 patients each with 4–7 NCCTs performed within 5-day timeframe (83 total scans) were used for model training; 307 patients each with 1–4 NCCTs performed within 5-day timeframe (380 total scans) were used for hold-out testing. In the training group, a mean CT was created from the patients co-registered scans for each input CT to train a rotation-reflection equivariant U-Net with skip and residual connections, as well as a group convolutional neural network (SRED-GCNN) using a custom loss function to remove image noise. Denoising performance was compared to the standard Block-matching and 3D filtering (BM3D) method and RED-CNN quantitatively and visually. Signal-to-noise ratio (SNR) and contrast-to-noise (CNR) were measured in manually drawn regions-of-interest in grey matter (GM), white matter (WM) and deep grey matter (DG). Visual comparison and impact on spatial resolution were assessed through phantom images.
Keywords:
Acute ischemic stroke; CT denoising; Deep learning; Non-contrast head CT

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