Thin-Slice Brain CT Image Quality and Lesion Detection Evaluation in Deep Learning Reconstruction Algorithm.

Authors

Sun J,Yao H,Han T,Wang Y,Yang L,Hao X,Wu S

Affiliations (4)

  • Department of Radiology, Beijing Water Conservancy Hospital, No. 19 Yuyuantan South Road, Beijing, Haidian District, China.
  • Philips CT Clinical Science Global, Philips Health Technology Co. Ltd, 258 Zhong Yuan Road, Suzhou Industrial Park, Suzhou, China.
  • Philips CT Clinical Support, Great China, Philips Healthcare, Floor 7, Building 2, World Profit Center, No. 16 Tianze Road, Beijing, Chaoyang District, China.
  • Department of Radiology, Beijing Water Conservancy Hospital, No. 19 Yuyuantan South Road, Beijing, Haidian District, China. [email protected].

Abstract

Clinical evaluation of Artificial Intelligence (AI)-based Precise Image (PI) algorithm in brain imaging remains limited. PI is a deep-learning reconstruction (DLR) technique that reduces image noise while maintaining a familiar Filtered Back Projection (FBP)-like appearance at low doses. This study aims to compare PI, Iterative Reconstruction (IR), and FBP-in improving image quality and enhancing lesion detection in 1.0 mm thin-slice brain computed tomography (CT) images. A retrospective analysis was conducted on brain non-contrast CT scans from August to September 2024 at our institution. Each scan was reconstructed using four methods: routine 5.0 mm FBP (Group A), thin-slice 1.0 mm FBP (Group B), thin-slice 1.0 mm IR (Group C), and thin-slice 1.0 mm PI (Group D). Subjective image quality was assessed by two radiologists using a 4- or 5‑point Likert scale. Objective metrics included contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and image noise across designated regions of interest (ROIs). 60 patients (65.47 years ± 18.40; 29 males and 31 females) were included. Among these, 39 patients had lesions, primarily low-density lacunar infarcts. Thin-slice PI images demonstrated the lowest image noise and artifacts, alongside the highest CNR and SNR values (p < 0.001) compared to Groups A, B, and C. Subjective assessments revealed that both PI and IR provided significantly improved image quality over routine FBP (p < 0.05). Specifically, Group D (PI) achieved superior lesion conspicuity and diagnostic confidence, with a 100% detection rate for lacunar lesions, outperforming Groups B and A. PI reconstruction significantly enhances image quality and lesion detectability in thin-slice brain CT scans compared to IR and FBP, suggesting its potential as a new clinical standard.

Topics

Journal Article

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.