Evaluation of Ultra-low-dose (ULD) Lung Computed Tomography (CT) Using Deep-learning: A Phantom Study

Abstract

As an electromagnetic wave, X-rays are used to acquire diagnostic CT images. The aim of this phantom study was to evaluate the image quality of ultra-low-dose (ULD) lung computed tomography (CT) achieved using a deep-learning based image reconstruction method. The chest phantom was scanned with a tube voltage of 100 kV for various CT dose index (CTDIvol) conditions: 0.4 mGy for ultra-low-dose (ULD), 0.6 mGy for low-dose (LD), 2.7 mGy for standard (SD), and 7.1 mGy for large size (LS). The signal-to-noise ratio (SNR) and noise values in reconstructions produced via filtered back projection (FBP), iterative reconstruction (IR), and deep convolutional neural network (DCNN) were computed for comparison. The quantitative results of both the SNR and noise indicate that the adoption of the DCNN makes the image reconstruction in the ULD setting more stable and robust, achieving a higher image quality when compared with the FBP algorithm in the SD condition. Compared with the conventional FBP and IR, the proposed deep learning-based image reconstruction approach can improve the ULD CT image quality while significantly reducing the patient dose.