Transactions on Machine Intelligence

Transactions on Machine Intelligence

Evaluation of Grid Performance on Image Quality in Digital Mammography Systems

Document Type : Original Article

Authors
H. Khodajou Choukami 1
B. Vosooghi Vahdat 1
A. Hosseini 2
S. M. Razavi 3
1 PhD, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sharif University of Technology, Tehran, Iran
2 PhD, Faculty of Energy Engineering, Sharif University of Technology, Tehran, Iran
3 MSc, Nik Parto Nuclear Medical Center, Sadeghieh, Tehran, Iran
10.47176/TMI.2018.204
Abstract
Mammography is a crucial imaging technique for early breast cancer detection, where image quality plays a vital role in accurate diagnosis. One of the primary factors degrading image quality is scattered radiation, which reduces contrast and obscures fine details. Anti-scatter grids are widely recognized as the most effective tool for mitigating this issue. Initially developed for screen-film mammography, grids have since been integrated into digital mammography systems. However, despite their widespread adoption, their geometric performance in digital systems has not been thoroughly investigated. This study aims to fill this knowledge gap by evaluating the effectiveness of grids in digital mammography. To achieve this, a comprehensive simulation of a mammography system was conducted based on the international standard IEC 60627:2013, utilizing the latest version of the MCNPX 2.7 Monte Carlo code. The signal-to-noise ratio improvement factor (SNRIF) was used as the key metric to assess the impact of grids on image quality. Various grid parameters, including grid ratio, lead strip thickness, and line density, were analyzed to determine their influence on scattered radiation reduction. The simulation results indicate that grids with thinner lead strips, higher grid ratios, and lower line densities significantly enhance image quality in digital mammography. These findings provide valuable insights for optimizing grid design and improving image contrast, ultimately contributing to more accurate breast cancer diagnosis.
Keywords
Digital Mammography
Grid
Scattered Radiation
MCNPX Code
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Transactions on Machine Intelligence
Volume 1, Issue 4
Autumn 2018
Pages 204-211

  • Receive Date 03 May 2018
  • Revise Date 18 July 2018
  • Accept Date 09 December 2018