W. Mullally, M. Betke, M. Albert, and K. Lutchen. "Explaining Clustered Ventilation Defects via a Minimal Number of Airway Closure Locations." Annals of Biomedical Engineering. 37(2):286-300, February 2009. Pdf file. Html open access.
W. Mullally, A. Milutinovic, M. Betke, M. Albert, and K. Lutchen "Personalized Airway Trees from a Generative Model, Lung Atlas, and Hyperpolarized Helium MRI." MICCAI 2006 Workshop "From Statistical Atlases to Personalized Models: Understanding Complex Diseases in Populations and Individuals." Copenhagen, Denmark, October 6, 2006. 4 pp. pdf.
W. Mullally, M. Betke, C. Bellardine, and K. Lutchen. "Locally Switching Between Cost Functions in Iterative Non-Rigid Registration." In Y. Liu, T. Jiang and C. Zhang, editors. Computer Vision for Biomedical Image Applications. First International Workshop, CVBIA 2005, Beijing, China, October 21, 2005. Proceedings. Lecture Notes in Computer Science 3765, pp. 367-377. Springer Verlag. Abstract. pdf.
Computational models of the human lung have been developed to study lung physiology and have been used to identify the airways responsible for mechanical dysfunction in asthmatics. Tgavalekos et al. (2007) used models anatomically consistent with the human lung to link ventilation defects to the heterogeneous closure of small airways. Their approach implicitly assumed a high degree of independence between airway closures as indicated by the low compactness of the airway structures mapped to individual ventilation defects. Venegas et al. (2005), however, have found that significant mutual dependence of airways may play a role in patchy ventilation of asthmatics. This led Mullally et al. (2009) to explore the question to what extent anatomically consistent models can be built which do not implicitly assume high independence of airways but instead allow for the mutual dependence of airways responsible for ventilation defects. They proposed an algorithm for generating subject-specific airway-tree models that minimize the number of airways that must be closed or severely constricted to cause observed ventilation defects. They also proposed novel approaches for measuring the compactness of airway structures. The approach showed that anatomically consistent models which link compact airway structures to ventilation defects can be built. The model also shows that some ventilation defects may be caused by closures of larger airways than previously reported.
Fluoroscopy is currently used in treatment planning for patients undergoing radiation therapy. Radiation oncologists would like to maximize the amount of dose the tumor receives and minimize the amount delivered to the surrounding tissues. During treatment, patients breathe freely and so the tumor location will not be fixed. This makes calculating the amount of dose delivered to the tumor, and verifying that the tumor actually receives that dose, difficult. Betke's group first developed a correlation-based method of tracking the two-dimensional (2D) motion of internal markers (surgical clips) placed around a tumor. The group then developed a method to model the average and maximum three-dimensional (3D) motion of the clips given two orthogonal fluoroscopy videos of the same patients that were taken sequentially. In preliminary experiments, it was shown that both trackers had small errors in estimating marker positions (Brewer et al., MICCAI 2004). If imaging is possible during treatment, such motion models may be used for beam guided radiation.
The approach to correlate tumor motion models to a set of external markers for use in respiratory gating was also examined by the group (Gierga et al. 2005). Although tumor motion generally correlated well with external fiducial marker motion, relatively large underlying tumor motion can occur compared with external-marker motion and variations in the tumor position for a given marker position. Treatment margins should be determined on the basis of a detailed understanding of tumor motion, as opposed to relying only on external-marker information.
M. Betke, J. Ruel, G. C. Sharp, S. B. Jiang, D. P. Gierga, and G. T. Y. Chen. "Tracking and prediction of tumor movement in the abdomen." In A. Fred and A. Lourenço, editors, Pattern Recognition in Information Systems: Proceedings of the 6th International Workshop on Pattern Recogntion in Information Systems - PRIS 2006, pages 27-37, Paphos, Cyprus, May 2006. INSTICC Press. pdf.
D. P. Gierga, J. Brewer, G. C. Sharp, M. Betke, C. G. Willett, G. T. Y. Chen. "The correlation between internal and external markers for abdominal tumors: Implications for respiratory gating." International Journal of Radiation Oncology, Biology, Physics, 61:5, pp. 1551-1558, April 2005, pdf, abstract.
D. P. Gierga, G. T. Y. Chen, J. H. Kung, M. Betke, J. Lombardi, C. G. Willett, "Quantification of Respiration-induced Abdominal Tumor Motion and the Impact on IMRT Dose Distributions." International Journal on Radiation Oncology - Biology - Physics, 58:5, pp. 1584-1595, April 2004. pdf, abstract.
J. Brewer, M. Betke, D. P. Gierga, and G. T. Y. Chen. "Real-time 4D Tumor Tracking and Modeling From Internal and External Fiducials in Fluoroscopy." C. Barillot, D. R. Haynor (editors), Proceedings of the 7th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2004), Part II, LNCS 3217, pp. 594-601, Saint-Malo, France, September 2004, pdf, abstract.
W. Mullally, S. Sclaroff, and M. Betke. Example-Based Image Registration via Boosted Classifiers, Department of Computer Science Technical Report BUCS-2009-007, Boston University. March 11, 2009. Abstract. pdf. ps.
J. Wang, M. Betke, and J. P. Ko, "Pulmonary Fissure Segmentation on CT." Medical Image Analysis, 10(4):530-547, August 2006. PubMed Entry pdf.
M. Betke and J. P. Ko, "Detection of Pulmonary Nodules on CT and Volumetric Assessment over Time." In C. Taylor and A. Colchester, editors, Medical Image Computing and Computer-Assisted Intervention -- MICCAI'99. Second International Conference, Cambridge, UK, September 19-22, 1999, Proceedings. Lecture Notes in Computer Science, Volume 1679/1999, pp. 245-252, Springer-Verlag, Berlin. Abstract. pdf.and received the US patent
J. P. Ko and M. Betke, "Chest CT: Automated Nodule Detection and Assessment of Change over Time-Preliminary Experience." Radiology, 218, 267-273, January 2001. Link to publisher, pdf (restricted access).
"Method and system for the detection, comparison and volumetric quantification of pulmonary nodules on medical computed tomography scans." Inventors Margrit Betke and Jane P. Ko., US Patent 7,206,462, issued on April 17, 2007.In the last decade, Betke and her students at Boston University continued the collaboration with Dr. Ko, who moved to New York University Medical Center, The group developed detection, segmentation, and registration methods for the chest, lungs, fissures, and blood vessels, e.g.,
Additional information on the lung imaging project.
W. Mullally, M. Betke, J. Wang and J. P. Ko, "Segmentation of Nodules on Chest Computed Tomography for Growth Assessment," Medical Physics, 31:4, pp. 839-848, April 2004. pdf.
M. Betke, H. Hong, D. Thomas, C. Prince, J. P. Ko, "Landmark Detection in the Chest and Registration of Lung Surfaces with an Application to Nodule Registration." Medical Image Analysis, 7:3, pp. 265-281, September 2003. Link to publisher and pdf, PubMed Entry.
Margrit Betke, Associate Professor
Computer Science Department
Last updated: August 3, 2010