Analysis Of the Performance Of Iodinated Contrast X-Ray Attenuator Under Physiologically Relevant Conditions

X-ray is a radiological tool utilized in healthcare institutions around the world to diagnose abnormalities such as bone fractures or the presence of foreign material within patients. The ability for healthcare providers to properly diagnose a problem is improved with advancements in the quality of radiological images. One way to improve image quality is to optimize the contrast range within a single image created by different attenuating characteristics in various types of tissue. In this study, I used a proof-of-concept prototype model of an x-ray attenuation system and an experimental protocol to examine its capacity to equalize x-ray beam signal values. A scout object consisting of different thicknesses of aluminum with the thickest section representing the most attenuated section and the target for equalization was used as a model of different types of tissue in a patient. The performance of the device and procedure was studied at various x-ray power levels and base acrylic thicknesses to represent anatomically relevant conditions. The different base acrylic thicknesses were used to represent standard attenuation in different sized patients. A statistical analysis was conducted using an unpaired t-test on the data results to identify whether the results are statistically significant and represent an improvement in image quality. The calibration equations developed to calculate the amount of iodinated contrast necessary at certain conditions were tested at intermediate levels to test performance under other conditions. The unpaired t-test was also conducted on these results. The analysis showed the exposure levels in each column were optimized to reduce the dynamic range of signal values

Quantitative analysis of infrared contrast enhancement algorithms

This thesis examines a quantitative analysis of infrared contrast enhancement algorithms found in literature and developed by the author. Four algorithms were studied, three of which were found in literature and one developed by the author: tail-less plateau equalization (TPE), adaptive plateau equalization (APE), the method according to Aare Mallo (MEAM), and infrared multi-scale retinex (IMSR). Engineering code was developed for each algorithm. From this engineering code, a rate of growth analysis was conducted to determine each algorithm’s computational load. From the analysis, it was found that all algorithms with the exception of IMSR have a desirable linear nature. Once the rate of growth analysis was complete, sample infrared imagery was collected. Three scenes were collected for experimentation: a low-to-high thermal variation scene, a low-to-mid thermal variation scene, and a natural scene. After collecting sample imagery and processing it with the engineering code, a paired comparison psychophysical trial was executed using local firefighters, common users of the infrared imaging system. From this trial, two metrics were formed: an average rank and an interval scale. From analysis of both metrics plus an analysis of the rate of growth, MEAM was declared to be the best algorithm overall