Information-Theoretic Approach for Analyzing Bias and Variance in Lung Nodule Size Estimation With CT: A Phantom Study

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.

[1]  K. Bae,et al.  Automated detection of pulmonary nodules on CT images: effect of section thickness and reconstruction interval--initial results. , 2005, Radiology.

[2]  Anthony P. Reeves,et al.  Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images , 2003, IEEE Transactions on Medical Imaging.

[3]  Kazuo Awai,et al.  Computer-aided volumetry of pulmonary nodules exhibiting ground-glass opacity at MDCT. , 2010, AJR. American journal of roentgenology.

[4]  P. Okunieff,et al.  Lung metastases detection in CT images using 3D template matching. , 2007, Medical physics.

[5]  L. Washington,et al.  Inherent variability of CT lung nodule measurements in vivo using semiautomated volumetric measurements. , 2006, AJR. American journal of roentgenology.

[6]  Berkman Sahiner,et al.  Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: a phantom study. , 2008, Physics in medicine and biology.

[7]  Ugo Pastorino,et al.  Pulmonary nodules: volume repeatability at multidetector CT lung cancer screening. , 2009, Radiology.

[8]  Dorin Comaniciu,et al.  Robust anisotropic Gaussian fitting for volumetric characterization of Pulmonary nodules in multislice CT , 2005, IEEE Transactions on Medical Imaging.

[9]  Marcos Salganicoff,et al.  Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners , 2007, European Radiology.

[10]  Michael F. McNitt-Gray,et al.  Patient-specific models for lung nodule detection and surveillance in CT images , 2001, IEEE Transactions on Medical Imaging.

[11]  Mathias Prokop,et al.  Pulmonary nodules: Interscan variability of semiautomated volume measurements with multisection CT-- influence of inspiration level, nodule size, and segmentation performance. , 2007, Radiology.

[12]  Hiroshi Fujita,et al.  Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique , 2001, IEEE Transactions on Medical Imaging.

[13]  L. Schwartz,et al.  Lung cancer: computerized quantification of tumor response--initial results. , 2006, Radiology.

[14]  Leon Kaufman,et al.  Characterization of Small Nodules by Automatic Segmentation of X-ray Computed Tomography Images , 2004, Journal of computer assisted tomography.

[15]  G. Chatellier,et al.  Pulmonary nodules: preliminary experience with three-dimensional evaluation. , 2004, Radiology.

[16]  Kyle J. Myers,et al.  A resource for the assessment of lung nodule size estimation methods: database of thoracic CT scans of an anthropomorphic phantom◊ , 2010, Optics express.

[17]  M. L. R. D. Christenson,et al.  Pulmonary Nodules Detected at Lung Cancer Screening: Interobserver Variability of Semiautomated Volume Measurements , 2007 .

[18]  Bidyut Baran Chaudhuri,et al.  Elliptic fit of objects in two and three dimensions by moment of inertia optimization , 1991, Pattern Recognit. Lett..

[19]  Kyle J. Myers,et al.  Approximations of noise structures in helical multi-detector CT scans: application to lung nodule volume estimation , 2010, Medical Imaging.

[20]  M. Prokop,et al.  Lung nodule volumetry: segmentation algorithms within the same software package cannot be used interchangeably , 2010, European Radiology.

[21]  Jiang Hsieh,et al.  Computed Tomography: Principles, Design, Artifacts, and Recent Advances, Fourth Edition , 2022 .

[22]  Gilles Chatellier,et al.  Software volumetric evaluation of doubling times for differentiating benign versus malignant pulmonary nodules. , 2006, AJR. American journal of roentgenology.

[23]  B. Ginneken,et al.  A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: What is the minimum increase in size to detect growth in repeated CT examinations , 2009, European Radiology.

[24]  Peng Wang,et al.  Volume change determination of metastatic lung tumors in CT images using 3-D template matching , 2009, Medical Imaging.

[25]  Anthony P. Reeves,et al.  THREE-DIMENSIONAL MULTICRITERION AUTOMATIC SEGMENTATION OF PULMONARY NODULES OF HELICAL COMPUTED TOMOGRAPHY IMAGES , 1999 .

[26]  Margrit Betke,et al.  Small pulmonary nodules: volume measurement at chest CT--phantom study. , 2003, Radiology.

[27]  Antoni B. Chan,et al.  On measuring the change in size of pulmonary nodules , 2006, IEEE Transactions on Medical Imaging.

[28]  Kyle J. Myers,et al.  Volume error analysis for lung nodules attached to pulmonary vessels in an anthropomorphic thoracic phantom , 2008, SPIE Medical Imaging.

[29]  Du-Ming Tsai,et al.  Fast normalized cross correlation for defect detection , 2003, Pattern Recognit. Lett..

[30]  Paulo R. S. Mendonça,et al.  Model-based detection of lung nodules in computed tomography exams1 , 2004 .

[31]  Richard C. Pais,et al.  Evaluation of Lung MDCT Nodule Annotation Across Radiologists and Methods 1 , 2006 .

[32]  Kyle J Myers,et al.  Noncalcified lung nodules: volumetric assessment with thoracic CT. , 2009, Radiology.

[33]  H Hu,et al.  Multi-slice helical CT: scan and reconstruction. , 1999, Medical physics.

[34]  A. Aisen,et al.  Effect of varying CT section width on volumetric measurement of lung tumors and application of compensatory equations. , 2003, Radiology.

[35]  O. Miettinen,et al.  CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. , 2002, AJR. American journal of roentgenology.

[36]  R. Truyen,et al.  Aspects of computer-aided detection (CAD) and volumetry of pulmonary nodules using multislice CT. , 2005, The British journal of radiology.

[37]  Heinz-Otto Peitgen,et al.  Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans , 2006, IEEE Transactions on Medical Imaging.

[38]  Rafael Wiemker,et al.  Computer aided segmentation of pulmonary nodules: automated vasculature cutoff in thin- and thickslice CT , 2003, CARS.

[39]  Avinash C. Kak,et al.  Principles of computerized tomographic imaging , 2001, Classics in applied mathematics.

[40]  Simina C. Fluture,et al.  Small pulmonary nodules: reproducibility of three-dimensional volumetric measurement and estimation of time to follow-up CT. , 2004, Radiology.