Deep learning-based post hoc denoising for 3D volume-rendered cardiac CT in mitral valve prolapse.

We hypothesized that deep learning-based post hoc denoising could improve the quality of cardiac CT for the 3D volume-rendered (VR) imaging of mitral valve (MV) prolapse. We aimed to evaluate the quality of denoised 3D VR images for visualizing MV prolapse and assess their diagnostic performance and efficiency. We retrospectively reviewed the cardiac CTs of consecutive patients who underwent MV repair in 2023. The original images were iteratively reconstructed and denoised with a residual dense network. 3DVR images of the "surgeon's view" were created with blood chamber transparency to display the MV leaflets. We compared the 3DVR image quality between the original and denoised images with a 100-point scoring system. Diagnostic confidence for prolapse was evaluated across eight MV segments: A1-3, P1-3, and the anterior and posterior commissures. Surgical findings were used as the reference to assess diagnostic ability with the area under curve (AUC). The interpretation time for the denoised 3DVR images was compared with that for multiplanar reformat images. For fifty patients (median age 64 years, 30 males), denoising the 3DVR images significantly improved their image quality scores from 50 to 76 (P <.001). The AUC in identifying MV prolapse improved from 0.91 (95% CI 0.87-0.95) to 0.94 (95% CI 0.91-0.98) (P =.009). The denoised 3DVR images were interpreted five-times faster than the multiplanar reformat images (P <.001). Deep learning-based denoising enhanced the quality of 3DVR imaging of the MV, improving the performance and efficiency in detecting MV prolapse on cardiac CT.