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

https://doi.org/10.1016/j.compmedimag.2021.101996Get rights and content
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Highlights

  • The proposed SRED-GCNN deep learning model greatly reduced noise in full-dose non-contrast head CTs both quantitatively and visually.

  • Signal-to-noise and contrast-to-noise ratios were boosted 2–3x over the original CT with preserved anatomy.

  • SRED-GCNN significantly outperformed the gold-standard block-matching 3D in denoising performance.

  • SRED-GCNN has equivalent performance in preserving high frequency features as block-matching 3D whereas RED-CNN has significant degradation.

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 2016 and 2020. 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 patient’s 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.

Results

SRED-GCNN reduced the original CT image noise significantly better than BM3D, with SNR improvements in GM, WM, and DG by 2.47x, 2.83x, and 2.64x respectively and CNR improvements in DG/WM and GM/WM by 2.30x and 2.16x respectively. Compared to the proposed SRED-GCNN, RED-CNN reduces noise effectively though the results are visibly blurred. Scans denoised by the SRED-GCNN are shown to be visually clearer with preserved anatomy.

Conclusion

The proposed SRED-GCNN model significantly reduces image noise and improves signal-to-noise and contrast-to-noise ratios in 380 unseen head NCCT cases.

Non-standard abbreviations and acronyms

NCCT
non-contrast CT
SNR
signal-to-noise ratio
CNR
contrast-to-noise ratio
BM3D
block-matching and 3D filtering
GM
grey matter
WM
white matter
DG
deep grey matter
AI
artificial intelligence
RED-CNN
residual encoder-decoder convolutional neural network
SRED-GCNN
skip residual encoder-decoder group convolutional neural network

Keywords

Deep learning
Acute ischemic stroke
Non-contrast head CT
CT denoising

Cited by (0)

1

Contributed equally to this work.