A computational model of soft tissue mechanics and air flow has been developed with the aim of linking computed tomography measures of ventilation distribution to subject-specific predictions in image-based geometric (finite element) models of the lung and airway tree. Computational techniques that can deal with anatomical detail and spatially-distributed non-linear material properties have been used to couple solution of parenchymal soft tissue mechanics in an anatomically-based model of the ovine lung to predictions of flow and pressure in an embedded model of the ovine airway tree. The lung is modeled as a homogeneous, compressible, non-linear elastic body. Using equations for large deformation mechanics, the change in geometry of the lung is simulated at static inflation pressures from 25 to 0 cmH<sub>2</sub>O. Multi-detector row computed tomography imaging has been used to define the model geometry (lung and airway), to define the movement of the model lung surface during inflation, and for measurements of internal material point displacements for comparison with the predicted internal displacements of the model. This preliminary model predicts airway bifurcation point displacements that are generally in agreement with imaged displacements (total RMS error for all bifurcation points is < 4 mm from 25 to 0 cm H<sub>2</sub>O). Further development of the model will provide a predictive link between subject-specific anatomical and functional information.
The accurate segmentation of the human airway tree from volumetric CT images builds an important corner stone in pulmonary image processing. It is the basis for many consecutive processing steps like branch-point labeling and matching, virtual bronchoscopy, and more. Previously reported airway tree segmentation methods often suffer from "leaking" into the surrounding lung tissue, caused by the anatomically thin airway wall combined with the occurrence of partial volume effect and noise. Another common problem with previously proposed airway segmentation algorithms is their difficulties with segmenting low dose scans and scans of
heavily diseased lungs. We present a new airway tree segmentation method that works in 3D, avoids leaks, and automatically adapts to different types of scans without the need for the user to iteratively adjust any parameters.
Proc. SPIE. 5032, Medical Imaging 2003: Image Processing
KEYWORDS: Magnetic resonance imaging, Image segmentation, Image processing, Quantitative analysis, Medical imaging, Computed tomography, In vivo imaging, Algorithm development, Binary data, 3D image processing
Quantitative assessment of tree structures is very important for evaluation of airway or vascular tree morphology and its associated function. Our skeletonization and branch-point identification method provides a basis for tree quantification or tree matching, tree-branch diameter measurement in any orientation, and labeling individual branch segments. All main components of our method were specifically developed to deal with imaging artifacts typically present in volumetric medical image data. The proposed method has been tested in a computer phantom subjected to changes of its orientation as well as in a repeatedly CT-scanned rigid plastic phantom. In all cases, our method produced reliable and well positioned centerlines and branch-points.
The functional understanding of the pulmonary anatomy as well as the
tracking of the natural course of respiratory diseases are critically
dependent on our ability to repeatedly evaluate the same region of the lungs time after time and perform accurate and reliable positionally corresponding measurements. We present a method for accurate labeling of airway branchpoints with their anatomical names as well as an approach for accurate matching of airway tree branchpoints beyond those with anatomical names. An intra-subject tree-matching as well as matching across subjects is achieved. The labeling process is based on matching against a population average. This population average incorporates the anatomical variability that is typically observed across the population. The matching algorithm is based on an association graph method. The computing time is drastically reduced by introducing a hierarchical splitting and only matching two sub-trees at a time. Both steps well tolerate possible false branches. Validation against an independent standard provided by human experts shows a high degree of accuracy (> 90%) for both labeling and matching. The average error compares well to the inter-observer variability among human experts.
A novel method for a fully automated determination of maximum blood velocity curves in Doppler ultrasound flow diagrams is reported. The method bases on VCR-recorded image sequences and hence uses an image processing scheme. Time-sequences of flow diagrams are evaluated and a chronological sequence of cardiac cycles is extracted. The cardiac cycles are numerically evaluated and the results in form of the peak velocity and the velocity-time-integral are reported.
This paper presents a new approach for the evaluation of Doppler flow velocity diagrams, obtained during brachial artery flow mediated dilatation (FMD) studies. The velocity diagrams are stored as image sequences on VCR tape. For this reason standard signal processing methods can not be used. A method for determination of blood velocity envelopes from image data is reported that uses Doppler-data specific heuristic to achieve high accuracy and robustness. The approach was tested in 40 Doppler blood flow images. Comparisons with manually defined independent standards demonstrated a very good correlation in determined peak velocity values (r equals 0.993) and flow envelope areas (r equals 0.996). The method is currently tested in a large volume clinical study.