title: Information-theoretic approach for analyzing bias and variance in lung nodule size estimation with CT: a phantom study.
abstract: This work is a part of our more general effort to probe the interrelated factors impacting the accuracy and precision of lung nodule measurement tasks. For such a task a low-bias size estimator is needed so that the true effect of factors such as acquisition and reconstruction parameters, nodule characteristics and others can be assessed. Towards this goal, we have developed a matched filter based on an adaptive model of the object acquisition and reconstruction process. Our model derives simulated reconstructed data of nodule objects (templates) which are then matched to computed tomography data produced from imaging the actual nodule in a phantom study using corresponding imaging parameters. This approach incorporates the properties of the imaging system and their effect on the discrete 3-D representation of the object of interest. Using a sum of absolute differences cost function, the derived matched filter demonstrated low bias and variance in the volume estimation of spherical synthetic nodules ranging in density from -630 to +100 HU and in size from 5 to 10 mm. This work could potentially lead to better understanding of sources of error in the task of lung nodule size measurements and may lead to new techniques to account for those errors.
authors.affiliation: Division of Imaging and Applied Mathematics (DIAM), Office of Science and Engineering Laboratories (OSEL), Center for Devices and Radiological Health (CDRH), U.S. Food and Drug Administration (FDA), Silver Spring, MD 20993, USA. marios.gavrielides@fda.hhs.gov
authors.forename: Marios A
authors.initials: MA
authors.lastname: Gavrielides
authors.forename: Rongping
authors.initials: R
authors.lastname: Zeng
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Kinnard
authors.forename: Kyle J
authors.initials: KJ
authors.lastname: Myers
authors.forename: Nicholas
authors.initials: N
authors.lastname: Petrick
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D006801:Humans; D007257:Information Theory; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*