Grant: Discovery Grants Program - Individual

Incomplete problems are encountered in scientific and engineering investigations when available measurable indications are not sufficient, or are not fully credible, to enable the determination of the state of a studied system. In radiological imaging, such incompleteness is compensated for by expert judgement and knowledge of the nature of the examined object or subject, or by comparison to previous or other independent indications. Although such measures are often effective, they sometimes fail, as evident by the medical imaging misdiagnosis reported in Canada and elsewhere within the past few years. Given available computing power, in terms of memory and speed, and the wide use of digital imaging, it is not inconceivable to numerically analyze such images in terms of the quality of data, the quality of the image and even the credibility of diagnosis. This can be accomplished by incorporating expert knowledge as probabilistic estimates with a certain degree of influence, or by accommodating other indications as constraints and utilizing previous trends to confine the range of assessment. Information from such exercise can also be utilized as feedback to the imaging process itself to improve its quality. This proposal addresses these incomplete problems in the particular area of non-destructive computed imaging with atomic/nuclear radiation for industrial, security and nuclear safeguard applications. The three fronts of (1) the quality of input data, (2) the image reconstruction process and (3) the quality of the produced image will be tackled. Methods for feedback from one front to the other will be developed. The goal is to benefit from expert knowledge and other available information, while liberating the process from subjective judgement. The developed processes can subsequently be adopted in medical imaging.