Discussion and Conclusions

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Arterial tree morphometry is an important application of image processing and analysis in clinical practice and the biomedical sciences. The severity of coronary artery disease is routinely assessed in the clinic with the aid of sophisticated image processing software to quantify stenoses. Presurgical planning for vascular abnormalities such as cerebral aneurysms is facilitated by segmentation and visualization of the intracerebral vasculature. Clinical studies provide information about arterial morphology on a macro scale. On the other end of the scale continuum, histological and electron microscopic methods have a long history of providing valuable insights into the cellular makeup and ultrastructure of vessel walls, and the many forms of medial hypertrophy. Micro-CT techniques such as those developed in our laboratory and others [ 120,121] and micro-MR methods under development have the potential to shed further light on the mechanisms implicated in diseases such as pulmonary and systemic hypertension by providing mesoscale images. Clinical imaging modalities cannot capture dimensional changes in the small vessels most likely involved in elevating the resistance of the arterial tree, nor can they localize microemboli in isolated vessels less than about 500 microns in diameter. Histology and vascular casting methods do not allow an appreciation for where the observed pathological conditions such as hypertrophy or obliteration occur in the branching tree hierarchy as an intact structure. We and others are trying to bridge the gap between micro- and macroscopic imaging methods.

The methods we have developed for arterial tree morpho-metry to date are capable of obtaining 3D image data rapidly

while the organ is maintained in a near physiological state and have demonstrated sensitivity to pulmonary hypertension in a rat model of the disease. The image analysis methods are still rather labor intensive, and future efforts will include the design of algorithms and software to speed the 3D image analysis by automating it insofar as possible. It may also prove fruitful to try other methods of extracting statistical measures of structure from the reconstructed volumes, perhaps related to those utilized in the well-established science of stereology [122-126]. It is likely that for diffuse diseases such as hypertension it may not be necessary or even desirable to analyze the tree structure in a brute-force classical way, from top down, as we have been attempting so far. The precise appearance on images of a disease such as emphysema or diffuse vascular disorders, including diabetes and hypertension, is likely quite different from one animal to the next in its specifics, suggesting the potential merits of a search for sensitive statistics of a higher order than the simple lengths, diameters, and angles of classical morphometry.

It is clear that new imaging technologies and increased computational, storage, and transmission capacity will continue to provide more and better imagery at ever-increasing rates. The major challenge in avoiding information overload and data opacity certainly lies in the area of devising image processing algorithms and data analysis methods that will yield the highest discriminatory power and the keenest biological insights into pathological vascular remodeling mechanisms and thus provide the highest value, first for the animals and time invested, and eventually in the form of improved therapeutic and preventive strategies.

FIGURE 16 Plot of vessel segment diameter vs distance from the pulmonary arterial inlet for the principal pathway of a normal rat lung imaged at relatively high and at low pressure.

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