Evidence Hub

Effect of number of views on cross-sectional Compton imaging: A fundamental study with backprojection

M. Sakai, R. Parajuli, Mikiko Kikuchi, M. Yamaguchi, Y. Nagao, N. Kawachi, K. Arakawa, T. Nakano

Physica medica (Testo stampato), 2018

15 citations

Abstract

INTRODUCTION

We have been developing a medical imaging technique using a Compton camera, which is expected to reconstruct three-dimensional images. If the number of views is not sufficient, star-shaped artifacts (streak artifacts) could arise in cross-sectional images. Therefore, we estimated the point spread function (PSF) of cross-sectional Compton images and the effect of the number of views by Monte Carlo simulations and experimental studies.

MATERIALS AND METHODS

A cross-sectional Compton image was reconstructed using a dataset comprising 719 view directions and PSF was analyzed using a radial distribution. The peak height, full width at half maximum (FWHM), background (BG), and residual sum of squares (RSS) were calculated from the obtained PSF. In addition, RSSs were plotted against the number of views to estimate the required number to suppress star-shaped artifacts.

RESULTS

There was no correlation found between the number of views and both FWHM (16 mm) and peak/BG ratio (∼1 × 104). RSSs were reduced with the number of views and approached the minimum asymptotically. Correlation was observed between the required number of views and the number of Compton events used for image reconstruction.

CONCLUSION

We determined the PSF of cross-sectional Compton images and the effect of the number of views on the images. The required number of views to suppress the star-shaped artifact is related to the square root of the number of Compton events used to reconstruct the image.…

How strong is the evidence?

Evidence Hub reads this as a cross-sectional. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause.

How the five levels of evidence work. The level is assigned automatically from the abstract and can be wrong, so check the paper.

Read the paper

Related studies

Go deeper in Evidence Hub

Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.

Open this study Sign up free