Characterization of a computed tomography iterative reconstruction algorithm by image quality evaluations with an anthropomorphic phantom

Objective

This study aims to investigate the consequences on dose and image quality of the choices of different combinations of NI and adaptive statistical iterative reconstruction (ASIR) percentage, the image quality parameters of GE CT equipment.

Methods

An anthropomorphic phantom was used to simulate the chest and upper abdomen of a standard weight patient. Images were acquired with tube current modulation and different values of noise index, in the range 10–22 for a slice thickness of 5 mm and a tube voltage of 120 kV. For each selected noise index, several image series were reconstructed using different percentages of ASIR (0, 40, 50, 60, 70, 100). Quantitative noise was assessed at different phantom locations. Computed tomography dose index (CTDI) and dose length products (DLP) were recorded. Three radiologists reviewed the images in a blinded and randomized manner and assessed the subjective image quality by comparing the image series with the one acquired with the reference protocol (noise index 14, ASIR 40%). The perceived noise, contrast, edge sharpness and overall quality were graded on a scale from −2 (much worse) to +2 (much better).

Results

A repeatable trend of noise reduction versus the percentage of ASIR was observed for different noise levels and phantom locations. The different combinations of noise index and percentage of ASIR to obtain a desired dose reduction were assessed. The subjective image quality evaluation evidenced a possible dose reduction between 24 and 40% as a consequence of an increment of ASIR percentage to 50 or 70%, respectively.

Conclusion

These results highlighted that the same patient dose reduction can be obtained with several combinations of noise index and percentages of ASIR, providing a model with which to choose these acquisition parameters in future optimization studies, with the aim of reducing patient dose by maintaining image quality in diagnostic levels.