Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study

Objectives To assess the impact on image quality and dose reduction of a new deep learning image reconstruction (DLIR) algorithm compared with a hybrid iterative reconstruction (IR) algorithm. Methods Data acquisitions were performed at seven dose levels (CTDIvol : 15/10/7.5/5/2.5/1/0.5 mGy) using a standard phantom designed for image quality assessment. Raw data were reconstructed using the filtered back projection (FBP), two levels of IR (ASiR-V50% (AV50); ASiR-V100% (AV100)), and three levels of DLIR (TrueFidelity (TM) low, medium, high). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index (d ') was computed to model a large mass in the liver, a small calcification, and a small subtle lesion with low contrast. Results NPS peaks were higher with AV50 than with all DLIR levels and only higher with DLIR-H than with AV100. The average NPS spatial frequencies were higher with DLIR than with IR. For all DLIR levels, TTF50% obtained with DLIR was higher than that with IR. d ' was higher with DLIR than with AV50 but lower with DLIR-L and DLIR-M than with AV100. d ' values were higher with DLIR-H than with AV100 for the small low-contrast lesion (10 +/- 4%) and in the same range for the other simulated lesions. Conclusions New DLIR algorithm reduced noise and improved spatial resolution and detectability without changing the noise texture. Images obtained with DLIR seem to indicate a greater potential for dose optimization than those with hybrid IR.