Influence of model based iterative reconstruction algorithm on image quality of multiplanar reformations in reduced dose chest CT

Abstract

BACKGROUND Model-based iterative reconstruction (MBIR) reduces image noise and improves image quality (IQ) but its influence on post-processing tools including maximal intensity projection (MIP) and minimal intensity projection (mIP) remains unknown. PURPOSE To evaluate the influence on IQ of MBIR on native, mIP, MIP axial and coronal reformats of reduced dose computed tomography (RD-CT) chest acquisition. MATERIAL AND METHODS Raw data of 50 patients, who underwent a standard dose CT (SD-CT) and a follow-up RD-CT with a CT dose index (CTDI) of 2-3 mGy, were reconstructed by MBIR and FBP. Native slices, 4-mm-thick MIP, and 3-mm-thick mIP axial and coronal reformats were generated. The relative IQ, subjective IQ, image noise, and number of artifacts were determined in order to compare different reconstructions of RD-CT with reference SD-CT. RESULTS The lowest noise was observed with MBIR. RD-CT reconstructed by MBIR exhibited the best relative and subjective IQ on coronal view regardless of the post-processing tool. MBIR generated the lowest rate of artefacts on coronal mIP/MIP reformats and the highest one on axial reformats, mainly represented by distortions and stairsteps artifacts. CONCLUSION The MBIR algorithm reduces image noise but generates more artifacts than FBP on axial mIP and MIP reformats of RD-CT. Conversely, it significantly improves IQ on coronal views, without increasing artifacts, regardless of the post-processing technique.