Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network

Shaojie Chang; Jeffrey F. Marsh Jr.; Emily K. Koons; Hao Gong; Cynthia H. McCollough; Shuai Leng

doi:10.1117/1.JMI.11.S1.S12804

24 May 2024 Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network

Shaojie Chang, Jeffrey F. Marsh Jr., Emily K. Koons, Hao Gong, Cynthia H. McCollough, Shuai Leng

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue S1, S12804 (May 2024). https://doi.org/10.1117/1.JMI.11.S1.S12804

Abstract

Purpose

We aim to reduce image noise in high-resolution (HR) virtual monoenergetic images (VMIs) from photon-counting detector (PCD) CT scans by developing a prior knowledge-aware iterative denoising neural network (PKAID-Net) that efficiently exploits the unique noise characteristics of VMIs at different energy (keV) levels.

Approach

PKAID-Net offers two major features: first, it utilizes a lower-noise VMI (e.g., 70 keV) as a prior input; second, it iteratively constructs a refined training dataset to improve the neural network’s denoising performance. In each iteration, the denoised image from the previous module serves as an updated target image, which is included in the dataset for the subsequent training iteration. Our study includes 10 patient coronary CT angiography exams acquired on a clinical dual-source PCD-CT (NAEOTOM Alpha, Siemens Healthineers). The HR VMIs were reconstructed at 50, 70, and 100 keV, using a sharp vascular kernel (Bv68) and thin (0.6 mm) slice thickness (0.3 mm increment). PKAID-Net’s performance was evaluated in terms of image noise, spatial detail preservation, and quantitative accuracy.

Results

PKAID-Net achieved a noise reduction of 96% compared to filtered back projection and 65% relative to iterative reconstruction, all while preserving spatial and spectral fidelity and maintaining a natural noise texture. The iterative refinement of PCD-CT data during the training process substantially enhanced the robustness of deep learning-based denoising compared to the original method, which resulted in some spatial detail loss.

Conclusions

The PKAID-Net provides substantial noise reduction while maintaining spatial and spectral fidelity of the HR VMIs from PCD-CT.

Citation Download Citation

Shaojie Chang, Jeffrey F. Marsh Jr., Emily K. Koons, Hao Gong, Cynthia H. McCollough, and Shuai Leng "Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network," Journal of Medical Imaging 11(S1), S12804 (24 May 2024). https://doi.org/10.1117/1.JMI.11.S1.S12804

Received: 2 February 2024; Accepted: 14 May 2024; Published: 24 May 2024

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KEYWORDS

Denoising

Education and training

Infrared imaging

Computed tomography

Photodetectors

Sensors

Neural networks

Purpose

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