Impact of Knowledge-Based Iterative Model Reconstruction on Image Quality and Hemodynamic Parameters in Dynamic Myocardial Computed Tomography Perfusion Using Low-Tube-Voltage Scan: A Feasibility Study

摘要

Objective Knowledge-based iterative model reconstruction (IMR) yields diagnostically acceptable image quality in low-dose static computed tomography (CT). We aimed to evaluate the feasibility of IMR in dynamic myocardial computed tomography perfusion (CTP). Methods We enrolled 24 patients who underwent stress dynamic CTP using a 256-slice CT. Images were reconstructed using filtered back projection (FBP), hybrid IR, and IMR. Image quality and hemodynamic parameters were compared among three algorithms. Results Qualitative image quality and contrast-to-noise ratio were significantly higher by IMR than by FBP or hybrid IR (visual score: 4.1 vs. 3.0 and 3.5; contrast-to-noise ratio: 12.4 vs. 6.6 and 8.4; P < 0.05). No significant difference was observed among algorithms in CTP-derived myocardial blood flow (1.68 vs. 1.73 and 1.70 mL/g/min). Conclusions The use of knowledge-based iterative model reconstruction improves image quality without altering hemodynamic parameters in low-dose dynamic CTP, compared with FBP or hybrid IR.