Biplane correlation imaging: a feasibility study based on phantom and human data.

Journal Article (Journal Article)

The objective of this study was to implement and evaluate the performance of a biplane correlation imaging (BCI) technique aimed to reduce the effect of anatomic noise and improve the detection of lung nodules in chest radiographs. Seventy-one low-dose posterior-anterior images were acquired from an anthropomorphic chest phantom with 0.28° angular separations over a range of ±10° along the vertical axis within an 11 s interval. Similar data were acquired from 19 human subjects with institutional review board approval and informed consent. The data were incorporated into a computer-aided detection (CAD) algorithm in which suspect lesions were identified by examining the geometrical correlation of the detected signals that remained relatively constant against variable anatomic backgrounds. The data were analyzed to determine the effect of angular separation, and the overall sensitivity and false-positives for lung nodule detection. The best performance was achieved for angular separations of the projection pairs greater than 5°. Within that range, the technique provided an order of magnitude decrease in the number of false-positive reports when compared with CAD analysis of single-view images. Overall, the technique yielded ~1.1 false-positive per patient with an average sensitivity of 75%. The results indicated that the incorporation of angular information can offer a reduction in the number of false-positives without a notable reduction in sensitivity. The findings suggest that the BCI technique has the potential for clinical implementation as a cost-effective technique to improve the detection of subtle lung nodules with lowered rate of false-positives.

Full Text

Duke Authors

Cited Authors

  • Samei, E; Majdi-Nasab, N; Dobbins, JT; McAdams, HP

Published Date

  • February 2012

Published In

Volume / Issue

  • 25 / 1

Start / End Page

  • 137 - 147

PubMed ID

  • 21618054

Pubmed Central ID

  • PMC3264726

Electronic International Standard Serial Number (EISSN)

  • 1618-727X

Digital Object Identifier (DOI)

  • 10.1007/s10278-011-9392-z


  • eng

Conference Location

  • United States