Published April 2018 | Version v1
Journal article

Feasibility study on low-dosage digital tomosynthesis (DTS) using a multislit collimation technique

  • 1. Department of Radiation Convergence Engineering, Yonsei University, Wonju, 26493, South (Korea, Republic of)

Description

Highlights: • We investigated on an effective low-dose digital tomosynthesis (DTS). • It is performed for a simulation of the proposed DTS with multislit collimators. • We considered an iterative algorithm based on compressed-sensing (CS). • The proposed DTS can contribute to reducing patient dose. In this study, we investigated an effective low-dose digital tomosynthesis (DTS) where a multislit collimator placed between the X-ray tube and the patient oscillates during projection data acquisition, partially blocking the X-ray beam to the patient thereby reducing the radiation dosage. We performed a simulation using the proposed DTS with two sets of multislit collimators both having a 50% duty cycle and investigated the image characteristics to demonstrate the feasibility of this proposed approach. In the simulation, all projections were taken at a tomographic angle of θ=±50° and an angle step of Δθ=2°. We utilized an iterative algorithm based on a compressed-sensing (CS) scheme for more accurate DTS reconstruction. Using the proposed DTS, we successfully obtained CS-reconstructed DTS images with no bright-band artifacts around the multislit edges of the collimator, thus maintaining the image quality. Therefore, the use of multislit collimation in current real-world DTS systems can reduce the radiation dosage to patients.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.nima.2018.01.069

Additional details

Identifiers

DOI
10.1016/j.nima.2018.01.069;
PII
S0168900218300986;

Publishing Information

Journal Title
Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
Journal Volume
888
Journal Page Range
p. 132-137
ISSN
0168-9002
CODEN
NIMAER

Optional Information

Copyright
Copyright (c) 2018 Elsevier B.V. All rights reserved.