Task-specific deep learning-based denoising for UHR cardiac PCD-CT adaptive to imaging conditions and patient characteristics: Impact on image quality and clinical diagnosis and quantitative assessment.

Abstract

Ultra-high-resolution (UHR) photon-counting detector (PCD) CT offers superior spatial resolution compared to conventional CT, benefiting various clinical areas. However, the UHR resolution also significantly increases image noise, which can limit its clinical adoption in areas such as cardiac CT. In clinical practice, this image noise varies substantially across imaging conditions, such as different diagnostic tasks, patient characteristics (e.g., size), scan protocols, and image reconstruction settings. To address these challenges and provide the full potential of PCD-CT for optimal clinical performance, a convolutional neural network (CNN) denoising algorithm was developed, optimized, and tailored to each specific set of conditions. The algorithm's effectiveness in reducing noise and its impact on coronary artery stenosis quantification across different patient size categories (small: water equivalent diameter <300 mm, medium: 300-320 mm, and large: >320 mm) were objectively assessed. Reconstruction kernels at different sharpness, from Bv60 to Bv76, were investigated to determine optimal settings for each patient size regarding image quality and quantitative assessment of coronary stenosis (in terms of percent diameter stenosis). Our findings indicate that for patients with a water equivalent diameter less than 320 mm, CNN-denoised Bv72 images provide optimal image quality, less blooming artifact, and reduced percent diameter stenosis compared to routine images, while for patients with water equivalent diameter over 320 mm, CNN-denoised Bv60 images are preferable. Quantitatively, the CNN reduces noise-by 85% compared to the input images and 53% compared to commercial iterative reconstructions at strength 4 (QIR4)-while maintaining high spatial resolution and a natural noise texture. Moreover, it enhances stenosis quantification by reducing the percent diameter stenosis measurement by 52% relative to the input and 24% relative to QIR4. These improvements demonstrate the capability of CNN denoising in UHR PCD-CT to enhance image quality and quantitative assessment of coronary artery disease in a manner that is adaptive to patient characteristics and imaging conditions.