Evaluation of the human airway with multi-detector x-ray-computed tomography and optical imaging.

Defining the healthy human airway is important in enhancing our understanding of pulmonary disease states such as inflammation and cancer. The structure of the human airway, both static and dynamic, can be assessed using multi-detector CT (MDCT) scanning. This modality also allows for the evaluation of structures outside of the airway. The airway wall can be directly visualized using CCD chip high-resolution color optical imaging through endoscopy allowing bronchial wall evaluation by traditional biopsy methods, as well as by newer optically based strategies. We suggest that these two imaging modalities, MDCT and optical imaging, provide complementary information about the normal airway, and the airway in various diseases. Methods for evaluating the human airway using MDCT images are presented facilitating automatic airway segmentation, branchpoint finding and airway dimension analysis. The airway wall color is objectively evaluated as an important surrogate for airway wall inflammation and cancer formation, and the integration of the color endoscopic information into the MDCT scan data set is currently ongoing. The amalgamation of these two digital imaging modalities appears increasingly useful for enabling biopsy techniques, and for relating structure and function of the airway. In addition, these developments may be progressively more useful in understanding the normal airway structure and function, for defining airway diseases patterns and for guiding biopsy and therapeutic procedures.

[1]  E. Hoffman,et al.  Assessment of the pulmonary structure-function relationship and clinical outcomes measures: quantitative volumetric CT of the lung. , 1997, Academic radiology.

[2]  J M Taylor,et al.  Color technology in video endoscopy. , 1994, Journal of clinical engineering.

[3]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[4]  Milan Sonka,et al.  Branchpoint labeling and matching in human airway trees , 2003, SPIE Medical Imaging.

[5]  D. Postma,et al.  Techniques in human airway inflammation: quantity and morphology of bronchial biopsy specimens taken by forceps of three sizes. , 1998, Chest.

[6]  K Knyrim,et al.  Color performance of video endoscopes: quantitative measurement of color reproduction. , 1987, Endoscopy.

[7]  Takayuki Okatani,et al.  Shape Reconstruction from an Endoscope Image by Shape from Shading Technique for a Point Light Source at the Projection Center , 1997, Comput. Vis. Image Underst..

[8]  K. Deguchi Shape reconstruction from endoscope image by its shadings , 1996, 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems (Cat. No.96TH8242).

[9]  Carlos Henrique Quartucci Forster,et al.  Towards 3D reconstruction of endoscope images using shape from shading , 2000, Proceedings 13th Brazilian Symposium on Computer Graphics and Image Processing (Cat. No.PR00878).

[10]  Milan Sonka,et al.  Segmentation, Skeletonization, and Branchpoint Matching - A Fully Automated Quantitative Evaluation of Human Intrathoracic Airway Trees , 2002, MICCAI.

[11]  Edward J. Giorgianni,et al.  Digital Color Management: Encoding Solutions , 1998 .

[12]  E. Hoffman,et al.  Characterization of the interstitial lung diseases via density-based and texture-based analysis of computed tomography images of lung structure and function. , 2003, Academic radiology.

[13]  Takayuki Okatani,et al.  Reconstructing shape from shading with a point light source at the projection center: shape reconstruction from an endoscope image , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[14]  K. Deguchi,et al.  Shape reconstruction from an endoscope image by shape-from-shading technique for a point light source at the projection center , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.