The influence of model iterative reconstruction on the image quality in standard and low-dose computer tomography of the chest. Experimental study

Author:

Silin Антон Yu.ORCID,Gruzdev Ivan S.ORCID,Morozov Sergey P.ORCID

Abstract

Background. One of the ways to reduce the radiation dose in CT is to the image reconstruction algorithms. The latest offer from CT scanner manufacturers is Model Iterative Reconstruction (MIR). Aims: to compare the quality of visualization of the structures of the chest organs and to prove the effectiveness of the low-dose protocol with iterative model reconstruction. Methods. A calibration phantom with a spatial resolution module and an anthropomorphic phantom of the upper body of an adult with nodules in the lungs were scanned using two CT scanners of different manufacturers. Two protocols were applied: the standard dose protocol (SDCT) with the algorithms of hybrid iterative reconstruction (HIR) of images and MIR and a low-dose protocol (LDCT) with the MIRalgorithm. The quality of the obtained images was evaluated by the following parameters: noise (SD), the contrast-to-noise ratio (CNR), spatial resolution and visualization of pulmonary nodules. The radiation dose was calculated according to the scanner data, the data of individual dosimeters placed on the anthropomorphic phantom, and using a dosimetric phantom. Results. The average SD was 11.5; 24.4 and 21.6; CNR 85.47; 40.6 and 45.6; spatial resolution 2 mm; 2 mm and 3 mm for SDCT with MIR, SDCT with HIR and LDCT with MIR respectively. Visualization of the pulmonary lesions remained excellent in all cases. The radiation dose in case of SDCT was 2.7, and in case of LDCT 0.67 mSv. The dose reduction was confirmed by the dosimeter data. Similar results were obtained by repeating the experiment with a second scanner. Conclusions. The model iterative reconstruction application will allow reducing the irradiatin dose during CT scanning of the chest organs without deterioration of the visualization quality.

Publisher

ECO-Vector LLC

Subject

General Medicine

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