Clinical feasibility and image quality assessment of deep learning-based virtual contrast enhancement from photon-counting computed tomography.

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Abstract

The purpose of this study was to evaluate image quality and diagnostic performance of deep learning-based virtual contrast-enhanced (VCE) images generated from photon-counting CT (PCCT) datasets. Forty consecutive patients who underwent contrast-enhanced PCCT were retrospectively included and divided into training (<i>n</i> = 20) and testing (<i>n</i> = 20) cohorts. A supervised U-Net-based convolutional neural network was trained to generate VCE images from virtual non-contrast inputs, using true contrast-enhanced (TC) images as the reference. Quantitative similarity was assessed using the structural similarity index measure (SSIM), mean absolute error (MAE), mean squared error (MSE), and peak signal-to-noise ratio (PSNR). Qualitative evaluation was performed independently by two radiologists using a 5-point Likert scale across thoracic and abdominal structures, with assessment of exact, ±1-point, and diagnostic agreement. Quantitative analysis demonstrated high similarity between VCE and TC images, with very high SSIM and PSNR values and low MAE, while MSE showed a right-skewed distribution. Qualitatively, VCE images were consistently rated lower than TC images. However, central thoracic vascular structures, cardiac chambers, and mediastinal lymph nodes were frequently rated as diagnostic (Likert ≥3) with high diagnostic agreement. In contrast, abdominal solid organs and vessels were often judged non-diagnostic, mainly due to heterogeneous enhancement and apparent contrast defects. The synthesis of VCE images from PCCT datasets is feasible and shows excellent quantitative similarity to TC images, with promising results for thoracic applications but limited reliability for abdominal evaluation at this stage.

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