1,544 research outputs found

    Measuring Dose to Small Animals in Micro-CT

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    An important application of William Röntgen’s discovery of x-rays is computed tomography (CT). First developed in the 1970’s, CT scanners of today are able to provide a detailed image of a patient’s body with minimal risk to patient and a very short turnaround time from scan to reconstructed image. This powerful tool provides physicians another way to diagnose patients while simultaneously allowing for researchers to learn about the human body. Scientists soon became interested in using the technology on small animals but practical issues plagued the widespread use of CT in preclinical research. The scale of the scanners was simply too large to provide useful images of the animals, mainly mice and rats. As a direct result of this problem, the field of micro-CT was developed. Micro-CT scanners can be used to generate images of small animals in while the platform itself has been used to develop advancements applied to clinical CT. In February 2006, the Syracuse Medical Imaging Research Group (SMIRG) acquired a Siemens Micro CAT II scanner. At the time, only theoretical predictions of dose to small animals existed and they were based in part on computer models. It became necessary to perform a dosimetry study of the micro-CT scanner in order to empirically determine the dose to small animals during a scan. Utilizing materials and the method outlined by the SUNY Upstate Medical University Department of Radiation Safety, the study was successfully completed in May 2008. In order to measure exposure, thermoluminescent dosimeters (TLDs) were calibrated using an ionization chamber and then exposed in-air to obtain conversion factors. The TLDs were then exposed inside of a phantom. Post phantom exposure measurements were converted into kerma measurements which were finally converted into dose estimates using the f-factor for mice. Mathematical equations which can predict dose estimates to small animals during scanning were developed. The equations, one for each of three filter thicknesses, allow the researcher to input their scan’s technique (peak voltage and current applied to the tube, and exposure time) and obtain an empirically-derived prediction of dose to the subject. This project provided a powerful tool to researchers and proved that dose to animals during micro-CT scanning, while small, is not insignificant. In addition, it also validated earlier dose predictions which were developed using computer models

    Toolbox for in vivo imaging of host-parasite interactions at multiple scales

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    Animal models have for long been pivotal for parasitology research. Over the last few years, techniques such as intravital, optoacoustic and magnetic resonance imaging, optical projection tomography, and selective plane illumination microscopy developed promising potential for gaining insights into host-pathogen interactions by allowing different visualization forms in vivo and ex vivo. Advances including increased resolution, penetration depth, and acquisition speed, together with more complex image analysis methods, facilitate tackling biological problems previously impossible to study and/or quantify. Here we discuss advances and challenges in the in vivo imaging toolbox, which hold promising potential for the field of parasitology

    Two dimensional angular domain optical imaging in biological tissues

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    Optical imaging is a modality that can detect optical contrast within a biological sample that is not detectable with other conventional imaging techniques. Optical trans-illumination images of tissue samples are degraded by optical scatter. Angular Domain Imaging (ADI) is an optical imaging technique that filters scattered photons based on the trajectory of the photons. Previous angular filters were limited to one dimensional arrays, greatly limiting the imaging capability of the system. We have developed a 2D Angular Filter Array (AFA) that is capable of acquiring two dimensional projection images of a sample. The AFA was constructed using rapid prototyping techniques. The contrast and the resolution of the AFA was evaluated. The results suggest that a 2D AFA can be used to acquire two dimensional projection images of a sample with a reduced acquisition time compared to a scanning 1D AFA

    Towards ultrasound full-waveform inversion in medical imaging

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    Ultrasound imaging is a front-line clinical modality with a wide range of applications. However, there are limitations to conventional methods for some medical imaging problems, including the imaging of the intact brain. The goal of this thesis is to explore and build on recent technological advances in ultrasonics and related areas such as geophysics, including the ultrasound data parallel acquisition hardware, advanced computational techniques for field modelling and for inverse problem solving. With the significant increase in the computational power now available, a particular focus will be put on exploring the potential of full-waveform inversion (FWI), a high-resolution image reconstruction technique which has shown significant success in seismic exploration, for medical imaging applications. In this thesis a range of technologies and systems have been developed in order to improve ultrasound imaging by taking advantage of these recent advances. In the first part of this thesis the application of dual frequency ultrasound for contrast enhanced imaging of neurovasculature in the mouse brain is investigated. Here we demonstrated a significant improvement in the contrast-to-tissue ratio that could be achieved by using a multi-probe, dual frequency imaging system when compared to a conventional approach using a single high frequency probe. However, without a sufficiently accurate calibration method to determine the positioning of these probes the image resolution was found to be significantly reduced. To mitigate the impact of these positioning errors, a second study was carried out to develop a sophisticated dual probe ultrasound tomography acquisition system with a robust methodology for the calibration of transducer positions. This led to a greater focus on the development of ultrasound tomography applications in medical imaging using FWI. A 2.5D brain phantom was designed that consisted of a soft tissue brain model surrounded by a hard skull mimicking material to simulate a transcranial imaging problem. This was used to demonstrate for the first time, as far as we are aware, the experimental feasibility of imaging the brain through skull using FWI. Furthermore, to address the lack of broadband sensors available for medical FWI reconstruction applications, a deep learning neural network was proposed for the bandwidth extension of observed narrowband data. A demonstration of this proposed technique was then carried out by improving the FWI image reconstruction of experimentally acquired breast phantom imaging data. Finally, the FWI imaging method was expanded for3D neuroimaging applications and an in silico feasibility of reconstructing the mouse brain with commercial transducers is demonstrated.Open Acces

    Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications

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    Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes, fundamental to nuclear fusion energy, advanced manufacturing, green transportation and others, often involve one mole or more atoms, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: a.) Detectors; b.) U-RadIT modalities; c.) Data and algorithms; and d.) Applications. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT makes increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.Comment: 51 pages, 31 figures; Overview of ultrafast radiographic imaging and tracking as a part of ULITIMA 2023 conference, Mar. 13-16,2023, Menlo Park, CA, US

    Biennial Scientific Report 2007-2008 : Volume 2: Cancer Research

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