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

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Visibility metrics and their applications in visually lossless image compression

Visibility metrics are image metrics that predict the probability that a human observer can detect differences between a pair of images. These metrics can provide localized information in the form of visibility maps, in which each value represents a probability of detection. An important application of the visibility metric is visually lossless image compression that aims at compressing a given image to the lowest fraction of bit per pixel while keeping the compression artifacts invisible at the same time. In previous works, most visibility metrics were modeled based on largely simplified assumptions and mathematical models of human visual systems. This approach generally fits well into experimental data measured with simple stimuli, such as Gabor patches. However, it cannot predict complex non-linear effects, such as contrast masking in natural images, particularly well. To predict visibility of image differences accurately, we collected the largest visibility dataset under fixed viewing conditions for calibrating existing visibility metrics and proposed a deep neural network-based visibility metric. We demonstrated in our experiments that the deep neural network-based visibility metric significantly outperformed existing visibility metrics. However, the deep neural network-based visibility metric cannot predict visibility under varying viewing conditions, such as display brightness and viewing distances that have great impacts on the visibility of distortions. To extend the deep neural network-based visibility metric to varying viewing conditions, we collected the largest visibility dataset under varying display brightness and viewing distances. We proposed incorporating white-box modules, in other words, luminance masking and viewing distance adaptation, into the black-box deep neural network, and we found that the combination of white-box modules and black-box deep neural networks could generalize our proposed visibility metric to varying viewing conditions. To demonstrate the application of our proposed deep neural network-based visibility metric to visually lossless image compression, we collected the visually lossless image compression dataset under fixed viewing conditions and significantly improved the deep neural network-based visibility metric's accuracy of predicting visually lossless image compression threshold by pre-training the visibility metric with a synthetic dataset generated by the state-of-the-art white-box visibility metric---HDR-VDP \cite{Mantiuk2011}. In a large-scale study of 1000 images, we found that with our improved visibility metric, we can save around 60\% to 70\% bits for visually lossless image compression encoding as compared to the default visually lossless quality level of 90. Because predicting image visibility and predicting image quality are closely related research topics, we also proposed a trained perceptually uniform transform for high dynamic range images and videos quality assessments by training a perceptual encoding function on a set of subjective quality assessment datasets. We have shown that when combining the trained perceptual encoding function with standard dynamic range image quality metrics, such as peak-signal-noise-ratio (PSNR), better performance was achieved compared to the untrained version

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

ORGAN MOTION AND IMAGE GUIDANCE IN RADIATION THERAPY

Organ motion and inaccurate patient positioning may compromise radiation therapy outcome. With the aid of image guidance, it is possible to allow for a more accurate organ motion and motion control study, which could lead to the reduction of irradiated healthy tissues and possible dose escalation to the target volume to achieve better treatment results. The studies on the organ motion and image guidance were divided into the following four sections. The first, the interfractional setup uncertainties from day-to-day treatment and intrafractional internal organ motion within the daily treatment from five different anatomic sites were studied with Helical TomoTherapy unit. The pre-treatment mega voltage computed tomography (MVCT) provided the real-time tumor and organ shift coordinates, and can be used to improve the accuracy of patient positioning. The interfractional system errors and random errors were analyzed and the suggested margins for HN, brain, prostate, abdomen and lung were derived. The second, lung stereotactic body radiation therapy using the MIDCO BodyLoc whole body stereotactic localizer combined with TomoTherapy MVCT image guidance were investigated for the possible target and organ motion reduction. The comparison of 3D displacement with and without BodyLoc immobilization showed that, suppression of internal organ motion was improved by using BodyLoc in this study. The third, respiration related tumor motion was accurately studied with the four dimensional computed tomography (4DCT). Deformable registration between different breathing phases was performed to estimate the motion trajectory for lung tumor. Optimization is performed by minimizing the mean squared difference in intensity, and is implemented with a multi-resolution, gradient descent procedure. The fourth, lung tumor mobility and dosimetric benefits were compared with different PTV obtained from 3DCT and 4DCT. The results illustrated that the PTV3D not only included excess normal tissues but also might result in missed target tissue. The normal tissue complication probability (NTCP) from 4D plan was statistically significant smaller than 3D plan for both ipsilateral lung and heart