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Medical X-ray dose reduction including adaptive image processing.

Podd, Frank J.W. (1997) Medical X-ray dose reduction including adaptive image processing. Doctoral thesis, University of Surrey (United Kingdom)..

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This thesis investigates possible methods for dose reduction for one of the main contributors to medical x-ray dose, that of fluoroscopic examinations. Background information is provided on the subjects of radiation interaction mechanisms, radiation dose measures, and the health risk from medical x-rays. This illuminates a running theme of the thesis, namely the compromise between image quality and low patient dose. Possible dose reduction methods using both spatial and temporal image processing techniques are investigated. Edge detection is one of the most important sub-components of the spatial image processing system. The commonly used edge detectors are investigated from a theoretical viewpoint and their performances under Poisson noise conditions are compared using receiver operating characteristic analysis. A new metric is suggested for the quantitative comparison of the edge operators under high detection and low false alarm probability conditions. An adaptive pulse dropping control system is created in order to use the image processing sub-systems with low-dose examinations. This control system determines the best x-ray tube pulse-rate based on the amount of movement present in the image. A method of distributing the dose so that areas of high clinical importance have a higher image quality than less important regions is discussed. This method uses a wedge-shaped x-ray beam filter. The problem of varying pixel intensity due to the differing filter thickness is countered by rescaling the image. The various image processing techniques are combined to create a low-dose imaging system. This system achieves a dose reduction of an order of magnitude.

Item Type: Thesis (Doctoral)
Divisions : Theses
Authors :
Podd, Frank J.W.
Date : 1997
Contributors :
Depositing User : EPrints Services
Date Deposited : 09 Nov 2017 12:11
Last Modified : 16 Mar 2018 19:39

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