Generation of deep learning based virtual contrast-enhanced CT images from noncontrast CT images for target volume delineation in cervical cancer radiotherapy

Le résumé fourni par la source

Purpose: This study sought to develop an nnTransUNet model for synthesizing virtual contrast-enhanced CT from non-contrast CT, and to evaluate its feasibility for target volume delineation in cervical cancer radiotherapy by comparison with conventional non-contrast and contrast-enhanced CT. Methods: A total of 210 patients with cervical cancer who underwent noncontrast CT and contrast-enhanced CT scans before and after intravenous administration of iodine contrast agent were selected. The "nnTransUNet" network architecture was used to convert noncontrast CT images into virtual contrast-enhanced CT images. Noncontrast, enhanced and virtual contrast-enhanced CT images were designed for cervical cancer radiotherapy, and their image similarity measurements, supervisor image quality evaluations, CT value distributions and dosimetric evaluations were compared. Results: The virtual contrast-enhanced CT images achieved scores of 0.958, 48.332, 0.997, and 0.976 in terms of mean squared error (MSE), peak signal-to-noise ratio (PSNR), universal quality index (UQI), and structural similarity index (SSIM), respectively. The Dice similarity coefficient (DSC) and Hausdorff distance (HD) for tumor delineation were 0.95 and<7.1 mm. In the subjective evaluation, the virtual contrast-enhanced CT images achieved a score of 4 points in terms of artefacts, noise, image structural integrity and image distortion, which was consistent with the scores for contrast-enhanced CT images. In terms of anatomical structure clarity, the score was slightly lower than that of the contrast-enhanced CT image (3.7 points vs. 4 points). The CT values of the virtual contrast-enhanced CT images were close to those of the contrast-enhanced CT images, and the CT values of the blood vessels and bone marrow were much greater than those of the noncontrast CT images. Compared with that of contrast-enhanced CT, the dose matching between virtual contrast-enhanced CT and noncontrast CT images was closer, and the relative dose difference in the target area was less than 2%. No significant difference in the organs at risk (OARs) dose distribution between the virtual contrast-enhanced CT images and noncontrast CT images. Conclusions: We developed a deep learning model based on the nnTransUNet architecture for generating virtual contrast-enhanced CT images from non-contrast CT scans, and validated its feasibility in terms of image quality assessment, radiotherapy dose calculation, and target volume delineation for cervical cancer.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.