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The implementation of a method for improving the quality (by reducing artifacts) of images reconstructed from small numbers of projections is described. The technique is based on an algorithm originally proposed by McKinnon and Bates [1] and includes enhancements suggested by Heffernan and Robb [2]. Emphasis is placed on practical aspects of algorithm implementation, including important noise-suppressing refinements not previously incorporated into the algorithm. The efficacy of the method is demonstrated using actual X-ray projection data of the intact beating heart recorded with the Mayo Clinic's dynamic spatial reconstructor. The implications of improved "stop-action" reconstruction of the heart are considered in the context of additional objectives, including automatic object recognition and definition, and improved four-dimensional reconstruction of the beating heart.

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We examined the consistency of apnoea recognition between three human experts. The hypothesis was that computer detection of apnoea could emulate human expert apnoea recognition. The aim was to detect apnoeas with the highest possible accuracy from a single breathing signal, by both human experts and computer. Three human experts independently examined recordings of breathing wave-form from overnight sleep studies from 10 infants aged 3-17 weeks. All apnoeas of 5 s or more were identified and reviewed. However, there still remained 10% disagreement. A computer apnoea detector was implemented. An algorithm analysed statistical properties of the signal to find breathing pauses. Optimal performance was 1% missed apnoeas (compared with the agreed apnoeas identified by the three experts) and 29% false detections. This computer algorithm reliably identified most apnoeas but did not replace the human expert.