Fast multisliced computerized tomography (Dynamic Spatial Reconstructor or DSR) was used to study the change in total heart volume (content of the pericardial sac) between end diastole (ED) and end systole (ES) with lungs held at 0 and 15 cmH2O airway inflation pressure (Paw). Nine dogs were anesthetized and scanned in the DSR. At 0 cmH2O airway pressure, mean total heart volume changed, on the average, only 2.7 +/- 0.6% (SE) of its ED volume comparing ED with ES. With lungs inflated to 15 cmH2O Paw, total heart volume decreased 12 +/- 0.5%. However, at this new heart volume, the change in total heart volume between ED and ES was only 1.7 +/- 0.5%. The data indicate that the epicardial apex of the heart remains relatively stationary while the atrioventricular groove moves toward the apex during systole and away from the apex during diastole. Thus the atria and ventricles empty and fill reciprocally even when the pericardial contents do not fully distend the pericardial sac. The invariant total heart volume observed in these species would minimize the work of the heart by maximizing the percentage of work expended to move blood and minimizing the work expended to move tissue (i.e., lung).

The Dynamic Spatial Reconstructor (DSR) can be used to determine detailed structure-to-function relationships or organ systems in vivo. A basic index of lung structure (shape and dimensions) is total lung volume. We checked the accuracy with which in vivo lung volumes can be measured by comparing lung volume (air plus tissue) determined by DSR scanning with that determined by excision and water displacement. Six dogs (2.5-26 kg) under morphine-pentobarbital anesthesia were scanned supine or prone at functional residual capacity and/or total lung capacity. With the trachea clamped at the lung volume scanned, a lethal dose of pentobarbital was administered, the lung excised, and its volume determined by water displacement. In vivo scan data were used to reconstruct adjacent 0.9-mm-thick transverse sections over the entire axial extent of the thorax. A three-dimensional surface-detection algorithm was used to generate shaded surface displays of the in situ lungs. The number of voxels (volume picture elements) of known dimensions contained within the three-dimensional image of the lung was summed to estimate total lung volume. Lung volumes calculated from the in vivo images ranged from -3.4 to +2.3% of the lung volume determined in vitro. The mean difference was 1.38 +/- 0.07% (SE). Regression analysis yielded an r value (correlation) of 1.00, a slope of 0.99, and an intercept of -4.35 ml. Multiple lung inflation steps scanned and analyzed in one dog showed similar accuracy. This technique is applicable to subjects with thorax dimensions up to 42 cm in cephalocaudal height and 39 cm in ventrodorsal and transverse diameters.

Three-dimensional (3-D) high-resolution coronary angiograms offer a means for visualizing the entire coronary arterial tree from any orientation and for detecting and quantitating coronary arterial stenoses. Previously, a skilled operator had to perform several hours of tedious manual analysis using an interactive graphical user-interface (GUI) system (Tree Trace) to analyze a 3-D angiogram. The authors have devised an improved GUI system, consisting of three tools for analyzing 3-D angiograms. The Artery Extractor first performs automatic image-analysis operations to extract the central axes of the arterial tree. Next, using the Artery Display tool and results from the Artery Extractor, the operator can visualize structures in the angiogram and compute various measurements. Finally, the aforementioned Tree Trace tool can be used to manually correct irregularities in the automatically generated results of the Artery Extractor. The system greatly reduces operator analysis time, gives exactly reproducible results, uses true 3-D image-processing operations, and provides a comprehensive interface for visualizing and quantifying features of the 3-D coronary arteries.

Many three-dimensional (3-D) medical images have lower resolution in the z direction than in the x or y directions. Before extracting and displaying objects in such images, an interpolated 3-D gray-scale image is usually generated via a technique such as linear interpolation to fill in the missing slices. Unfortunately, when objects are extracted and displayed from the interpolated image, they often exhibit a blocky and generally unsatisfactory appearance, a problem that is particularly acute for thin treelike structures such as the coronary arteries. Two methods for shape-based interpolation that offer an improvement to linear interpolation are presented. In shape-based interpolation, the object of interest is first segmented (extracted) from the initial 3-D image to produce a low-z-resolution binary-valued image, and the segmented image is interpolated to produce a high-resolution binary-valued 3-D image. The first method incorporates geometrical constraints and takes as input a segmented version of the original 3-D image. The second method builds on the first in that it also uses the original gray-scale image as a second input. Tests with 3-D images of the coronary arterial tree demonstrate the efficacy of the methods.

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