The evaluation of the reduction of radiation dose via deep learning-based reconstruction for cadaveric human lung CT images.

ABSTRACT: To compare the quality of CT images of the lung reconstructed using deep learning-based reconstruction (True Fidelity Image: TFI ™; GE Healthcare) to filtered back projection (FBP), and to determine the minimum tube current-time product in TFI without compromising image quality. Four cadaveric human lungs were scanned on CT at 120 kVp and different tube current-time products (10, 25, 50, 75, 100, and 175 mAs) and reconstructed with TFI and FBP. Two image evaluations were performed by three independent radiologists. In the first experiment, using the same tube current-time product, a side-by-side TFI and FBP comparison was performed. Images were evaluated with regard to noise, streak artifacts, and overall image quality. Overall image quality was evaluated in view of whole image quality. In

SUBMITTER: Miyata T 

PROVIDER: S-EPMC9298173 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature