Paper
28 March 2007 Methodology for determining dose reduction for chest tomosynthesis
Author Affiliations +
Abstract
Digital tomosynthesis is an imaging technique that reconstructs tomographic planes in an object from a set of projection images taken over a fixed angle1. Preliminary results show that this technique increases the detectability of lung nodules2. Current settings acquire images with approximately the same exposure as a screen-film lateral. However, due to the increased detectability of lung nodules from the removal of overlying structures, patient dose may be reduced while still maintaining increased sensitivity and specificity over conventional chest radiographs. This study describes a simulation method that provides realistic reduced dose images by adding noise to digital chest tomosynthesis images in order to simulate lower exposure settings for the purpose of dose optimization. Tomosynthesis projections of human subjects were taken at dose levels which were specified based on either patient thickness or a photo-timed digital chest radiograph acquired prior to tomosynthesis acquisition. For the purposes of this study, subtle nodules of varying size were simulated in the image for demonstration purposes before the noise simulation in order to have a known truth for nodule location and to evaluate the effect of additive noise on tumor detection. Noise was subsequently added in order to simulate 3/4, 1/2, and 1/4 of the original exposure in each projection. The projections were then processed with the MITS algorithm to produce slice images. This method will be applied to a study of dose reduction in the future using human subject cases.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christina M. Li and James T. Dobbins III "Methodology for determining dose reduction for chest tomosynthesis", Proc. SPIE 6510, Medical Imaging 2007: Physics of Medical Imaging, 65102D (28 March 2007); https://doi.org/10.1117/12.713554
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Cited by 6 scholarly publications.
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KEYWORDS
Signal to noise ratio

Lung

Chest

Image filtering

Chest imaging

Computer simulations

Sensors

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