Ultra-High-Resolution Photon-Counting-Detector CT with a Dedicated Denoising Convolutional Neural Network for Enhanced Temporal Bone Imaging.

Abstract

Background and purpose: Ultra-high-resolution (UHR) photon-counting-detector (PCD) CT improves image resolution but increases noise, necessitating the use of smoother reconstruction kernels that reduce resolution below the 0.125-mm maximum spatial resolution. 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 to address this issue.

Materials and methods: With institutional review board approval, the CNN was trained on 6 patient cases of clinical temporal bone imaging (1885 images) and tested on 20 independent cases using a dual-source PCD-CT (NAEOTOM Alpha). Images were reconstructed using quantum iterative reconstruction at strength 3 (QIR3) with both a clinical routine kernel (Hr84) and the sharpest available head kernel (Hr96). The CNN was applied to images reconstructed with Hr96 and QIR1 kernel. For each case, three series of images (Hr84-QIR3, Hr96-QIR3, and Hr96-CNN) were randomized for review by 2 neuroradiologists assessing the overall quality and delineating the modiolus, stapes footplate, and incudomallear joint.

Results: The CNN reduced noise by 80% compared with Hr96-QIR3 and by 50% relative to Hr84-QIR3, while maintaining high resolution. Compared with the conventional method at the same kernel (Hr96-QIR3), Hr96-CNN significantly decreased image noise (from 204.63 to 47.35 HU) and improved its structural similarity index (from 0.72 to 0.99). Hr96-CNN images ranked higher than Hr84-QIR3 and Hr96-QIR3 in overall quality (P < .001). Readers preferred Hr96-CNN for all 3 structures.

Conclusions: The proposed CNN significantly reduced image noise in UHR PCD-CT, enabling the use of the sharpest kernel. This combination greatly enhanced diagnostic image quality and anatomic visualization.