Evaluation of a commercial Model Based Iterative reconstruction algorithm in computed tomography.

INTRODUCTION

Iterative reconstruction algorithms have been introduced in clinical practice to obtain dose reduction without compromising the diagnostic performance.

PURPOSE

To investigate the commercial Model Based IMR algorithm by means of patient dose and image quality, with standard Fourier and alternative metrics.

MATERIALS AND METHODS

A Catphan phantom, a commercial density phantom and a cylindrical water filled phantom were scanned both varying CTDI and reconstruction thickness. Images were then reconstructed with Filtered Back Projection and both statistical (iDose) and Model Based (IMR) Iterative reconstruction algorithms. Spatial resolution was evaluated with Modulation Transfer Function and Target Transfer Function. Noise reduction was investigated with Standard Deviation. Furthermore, its behaviour was analysed with 3D and 2D Noise Power Spectrum. Blur and Low Contrast Detectability were investigated. Patient dose indexes were collected and analysed.

RESULTS

All results, related to image quality, have been compared to FBP standard reconstructions. Model Based IMR significantly improves Modulation Transfer Function with an increase between 12% and 64%. Target Transfer Function curves confirm this trend for high density objects, while Blur presents a sharpness reduction for low density details. Model Based IMR underlines a noise reduction between 44% and 66% and a variation in noise power spectrum behaviour. Low Contrast Detectability curves underline an averaged improvement of 35-45%; these results are compatible with an achievable reduction of 50% of CTDI. A dose reduction between 25% and 35% is confirmed by median values of CTDI.

CONCLUSION

IMR produces an improvement in image quality and dose reduction.