Metin Gurcan PhD, PRESENTER: Nothing to Disclose

Abstract: HTML Purpose: To compare the effects of reconstruction filters, slice thickness and the volume estimation method on the accuracy of nodule volume estimation from helical computed tomography (CT) images. Methods and Materials: A CAD tool for automated assessment of lung nodules (QuickLookTM, CADx Systems, Beavercreek, OH) was used to segment nodules from thoracic helical CT images and report characteristics such as nodule size, volume and average intensity. Nineteen approximately spherical nodules constructed from modeling material were placed in a lung phantom filled with cork chips that simulate lung parenchyma. This phantom was imaged at 1.25 mm collimation, 120 kVp, 100 mA. The CT slices were reconstructed at 1.25 mm, 5 mm, 8 mm, and 10 mm using reconstruction kernels B30, B50, and B70 of a CT scanner (Volume Zoom, Siemens). The in-plane resolution of these studies was kept constant at 0.42 mm. For each of these studies, the nodules were segmented from the helical CT images automatically, and then the volumes were estimated using three 2D and two 3D methods. All of the 2D methods and one of the 3D methods are clinically recognized. Another method was developed to estimate the nodule volumes by taking into account slice overlap information. This 3D method is based on maximum a posteriori (MAP) estimation. The nodule volumes were measured by the equivalent volume of displaced water, and varied between 0.03 ml and 1.12 ml. Each nodule volume was automatically estimated using all five estimation methods for each of the three reconstruction kernels and four slice thicknesses. The average absolute volume estimation error percentages were computed. Results: For 1.25 mm slice thickness and the B50 kernel, the errors were 15.57% (+-11.84), 32.18% (+-18.87), 23.90% (+-18.22) for the 2D methods, and 22.67%(+-11.65) for the standard 3D method. Using the proposed MAP volume estimation method, the error was reduced to 7.69% (+-4.83). For 1.25 mm slice thickness and the MAP estimation method, the errors were 10.41% (+-6.57) and 7.88% (+-5.33) for the B30 and B70 kernels, respectively. For the B50 kernel and the MAP estimation method, the errors were 32.88% (+-15.95), 61.21% (+-49.18), and 77.84% (+-63.56) for 5 mm, 8 mm, and 10 mm slice thickness, respectively. Conclusion: Volumes of automatically segmented nodules can be accurately estimated using a thin-slice study with an appropriate kernel and a 3D volume estimation method. Slice thickness and the volume estimation method have more influence on the accuracy of the volume estimation than the type of reconstruction kernel. (M.G., B.A., J.H. are employees of Dag Wormanns. R.H. is a consultant and S.R. is the President/CEO of CADx Systems, Beavercreek, OH.) Questions about this event email: mgurcan@cadxsystems.com

Gurcan PhD, M, Accurate Nodule Volume Estimation from Helical CT Images: Effect of Reconstruction Filter, Slice Thickness, and Volume Estimation Method.  Radiological Society of North America 2003 Scientific Assembly and Annual Meeting, November 30 - December 5, 2003 ,Chicago IL. http://archive.rsna.org/2003/3104662.html