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Proceedings Paper

The influence of CT dose and reconstruction parameters on automated detection of small pulmonary nodules
Author(s): Robert Ochs; Erin Angel; Kirsten Boedeker; Iva Petkovska; Christoph Panknin; Jonathan Goldin; Denise Aberle; Michael McNitt-Gray; Matthew Brown
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Paper Abstract

The aim of our investigation was to assess the influence of both CT acquisition dose and reconstruction kernel on computer-aided detection (CAD) of pulmonary nodules. Our hypothesis is that the detection of small nodules is affected by the noise characteristics of the image and the signal to noise ratio of the nodule and bronchiovascular anatomy. Knowledge gained from this experiment will assist in developing an advanced CAD system designed to detect smaller and more subtle nodules with minimal false positives. Eleven research subjects were selected from the Lung Image Database Consortium (LIDC) database based on our inclusion criteria of: 1) having at least one nodule and 2) available raw CT projection data for the series that our institution submitted to the LIDC study. Using the original raw projection data, research software simulated raw projection data acquired with a dose reduced 32-40% from the original scan. Projection data for both dose levels was reconstructed with smooth to very sharp kernels (B10f, B30f, B50f, and B70f). The resulting series were used to investigate the influence of dose and reconstruction kernel on CAD performance. A prototype CAD system was used to investigate changes in sensitivity and false positives with varying imaging parameters. In a sub-study, the prototype system was compared to a commercial CAD system. We did not have enough subjects to conclude significance, but the results indicate our research system had a higher sensitivity with the smooth or medium reconstruction kernels than with the sharper kernels. The sensitivity was similar for both dose levels. The false positive rate was higher with the smooth kernels and the lower dose levels.

Paper Details

Date Published: 17 March 2006
PDF: 8 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61445W (17 March 2006); doi: 10.1117/12.658663
Show Author Affiliations
Robert Ochs, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Erin Angel, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Kirsten Boedeker, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Iva Petkovska, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Christoph Panknin, Siemens Medical Solutions (Germany)
Jonathan Goldin, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Denise Aberle, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Michael McNitt-Gray, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Matthew Brown, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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