DOI: 10.1148/rg.253045163
New Tools for Computer Assistance in Thoracic CT Part 2. Therapy Monitoring of Pulmonary Metastases1
Lars Bornemann, Dipl CS,
Jan-Martin Kuhnigk, Dipl CS,
Volker Dicken, PhD,
Stephan Zidowitz, PhD,
Bernd Kuemmerlen, Dipl Phys,
Stefan Krass, PhD,
Heinz-Otto Peitgen, PhD,
Berthold B. Wein, MD,
Henning Schubert, MD,
Hoen-oh Shin, MD and
Dag Wormanns, MD
1 From the MeVis Center for Medical Diagnostic Systems and Visualization, Universitaetsallee 29, 28359 Bremen, Germany (L.B., J.M.K., V.D., S.Z., B.K., S.K., H.O.P.); the Department of Diagnostic Radiology, RWTH Aachen University Hospital, Aachen, Germany (B.B.W., H. Schubert); the Department of Diagnostic Radiology, Medical School Hannover, Germany (H. Shin); and the Institute for Clinical Radiology, University Hospital Muenster, Germany (D.W.). Presented as an infoRAD exhibit at the 2003 RSNA Scientific Assembly. Received April 7, 2004; revision requested June 30 and received August 20; accepted September 10. Supported by grant 01EZ0010 from the German Federal Ministry of Education and Research. All authors have no financial relationships to disclose.
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Figure 1. Baseline scan mode of PulmoTREAT. The user can slice through the image data (left) and identify nodule positions with the mouse. The segmentation of the currently selected nodule can be roughly verified in a zoomed 3D view (right).
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Figure 2. Convenient correction of nodule segmentation in PulmoTREAT. In the "adjust results" panel, the radiologist can obtain a more detailed view of the segmentation in three orthogonal viewers, which show sagittal (left), coronal (center), and axial (right) images. The segmentation can be easily modified by adapting a single shape correction parameter.
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Figure 3. Follow-up mode of PulmoTREAT. The viewer on the left shows the baseline data set including the nodule markers from the corresponding examination. The viewer in the center shows the new data set and the registered marker. This mode is similar to the baseline scan mode in that the user can select nodules and start the segmentation. On the right side, a detailed follow-up report is presented.
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Figure 4. Novel visualization techniques for lung CT data. Left: Filtered distance map of the right lung. Center: Maximum intensity projection view of the right lung shows multiple calcified metastases. Right: Image of reformatted data close to the surface of the right lung.
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Figure 5. Automatically calculated intermediate results for separation of the pleura and nodule. The parenchyma mask is calculated from the original nodule VOI (top left) by using region growing (top center). To reduce calculation time, the parenchymal contour is thinned out (top right). Afterward, the convex hull of the remaining contour voxels is calculated to obtain the "healthy" parenchyma mask (bottom left). This mask is combined with the region growing mask to separate the nodule and pleura (bottom center), resulting in the final nodule segmentation (bottom right).
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Figure 6. Automatically calculated intermediate results for separation of the vasculature and nodule. A mask that includes both vessels and the nodule is computed from the original VOI (top left) by using region growing (top center). Afterward, erosion (top right) and dilation (bottom left) are performed, resulting in an improved approximation of the nodule. Combination with the original nodule mask (bottom center) leads to the final nodule segmentation (bottom right).
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Copyright © 2005 by the Radiological Society of North America.
