Background and purpose: Ultra-high-resolution (UHR) photon-counting-detector (PCD) CT improves image resolution but increases noise, necessitating use of smoother reconstruction kernels that reduce resolution below the system's 0.110 mm maximum spatial resolution. To address this, a denoising convolutional neural network (CNN) was developed to reduce noise in images reconstructed with the available sharpest reconstruction kernel while preserving resolution for enhanced temporal bone visualization.
Materials and methods: With IRB approval, CNN was trained on 6 clinical temporal bone patient cases (1,885 images) and tested on 20 independent cases using a dual-source PCD-CT (NAEOTOM Alpha, Siemens). Images were reconstructed using iterative reconstruction at strength 3 (QIR3) with both clinical routine (Hr84) and the sharpest available head kernel (Hr96). The CNN was applied to images reconstructed with Hr96 and QIR1. Three image series (Hr84-QIR3, Hr96-QIR3, and Hr96-CNN) for each case were randomized for review by two neuroradiologists, assessing overall quality and delineation of the modiolus, stapes footplate, and incudomallear joint.
Results: CNN reduced noise by 80% compared to Hr96-QIR3 and 50% relative to Hr84-QIR3, while maintaining high resolution. When compared to the conventional method at the same kernel (Hr96-QIR3), Hr96-CNN significantly decreased image noise (from 204.63 HU to 47.35 HU) and improved SSIM (from 0.72 to 0.99). Hr96-CNN images ranked higher than Hr84-QIR3 and Hr96-QIR3 in overall quality (p<0.001). Readers preferred Hr96-CNN for all three structures.
Conclusions: The proposed CNN significantly reduced image noise in UHR PCD-CT, enabling the use of sharpest kernel. This combination greatly enhanced diagnostic image quality and anatomical visualization.ABBREVIATIONS: PCD = Photon-counting-detector; UHR = Ultra-high-resolution; IR = Iterative reconstruction; CNN = Convolutional neural network; SSIM: Structural similarity index.
© 2024 by American Journal of Neuroradiology.