Application of Response Surface Methodology to systematically optimize image quality in computer tomography: A case study using fresh chestnuts (Castanea spp.)

Abstract

The objective of this study is to describe a method that will systematically and efficiently obtain a model of computerized tomographic (CT) scanning factor levels, which returns optimized high quality CT images. Chestnut (Castanea spp.) two-dimensional CT images were used to describe this optimization procedure, considered to be a critical step in the development of a fast, nondestructive technique, capable of assessing fresh internal quality attributes and components of chestnuts, and other agricultural commodities. Response Surface Methodology (RSM), using a three-factor, three-level Box–Behnken statistical design and digital image processing, were used to optimize the factors affecting image quality, which include X-ray voltage, current, and slice thickness. Response variables representing image quality were digitally and automatically inferred from fresh chestnut image Signal to Noise Ratio, Teflon® cylinder reference volume accuracy, quality assurance (QA) High Contrast Spatial Resolution phantom, and QA Low Contrast Detectability phantom. Second-order RSM prediction models for each response variable reflected a combined maximized CT image quality at a voltage, current, and slice thickness equal to 120kV, 170mA, and 2.5mm respectively. The experiment yielded optimal chestnut CT images that can accurately reflect internal decay of fresh chestnuts with an overall accuracy rate equal to 96%, taking as reference data the of Subjective Quality Rating of five trained chestnut experts.