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(1 - 3 of 3)
- Title
- Simulation, design and applications of a table top analyzer-based phase contrast mammography system
- Creator
- Caudevilla Torras, Oriol
- Date
- 2019
- Description
-
Analyzer-based Imaging is a promising phase contrast technology with huge potential for soft tissue imaging. Unlike absorption-contrast...
Show moreAnalyzer-based Imaging is a promising phase contrast technology with huge potential for soft tissue imaging. Unlike absorption-contrast methods, phase-contrast modalities measure refraction and scatter properties of the tissue. Such images are particularly suitable for applications such as mammography.The potential advantages of the Analyzer-Based Imaging technology are three fold. First, it shows exceptional contrast when imaging soft tissue, which produces extremely sharp images of the breast compared to absorption images. Second, it provides additional insights about the breast. In particular, the density and scatter images of breast micro-calcifications can help assessing their malignancy better than common mammograms. Third, it has shown potential to reduce the radiation dose deposited in the breast tissue by an order of magnitude compared to common mammography procedures.In the past, Analyzer-Based Imaging has been mainly developed with synchrotron light sources and focused on obtaining micro-resolution images. For such applications, quasi-monoenergetic beams are required. Nevertheless, monochromatic radiation can be easily obtained in synchrotron setups by filtering the source’s spectrum with crystal optics. Since synchrotrons are very brilliant sources, most of their radiation can be filtered out and still obtain low noise phase contrast images. Nowadays, there is a lot of interest in transitioning the technology to a table-top system using compact X-ray sources for mammography. However, compact sources are several orders of magnitude less brilliant, which causes extremely long exposure times. Additionally, the trade-off between exposure time (throughput) and resolution in compact analyzer-based imaging systems is yet to be completely understood.In this thesis, we lay down the principles to develop compact analyzer-based imaging systems capable of imaging a full-sized breast under ten seconds, while ensuring a resolution under 100 microns. This represents a major breakthrough towards obtaining a clinical analyzer-based mammography system. Additionally, we explore a unique application of the analyzer-based technology for breast diagnosis consisting on the assessment of the chemical composition of micro-calcifications. In conjunction with ABI’s unparalleled image quality, determining the chemical composition of micro- calcifications can help to mitigate the high false positive rate in common mammography.
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- Title
- Fast mesh based reconstruction for cardiac-gated SPECT and methodology for medical image quality assessment
- Creator
- Massanes Basi, Francesc
- Date
- 2018
- Description
-
In this work, we are studying two different subjects that are intricately connected. For the first subject we are considering tools to...
Show moreIn this work, we are studying two different subjects that are intricately connected. For the first subject we are considering tools to improve the quality of single photon emission computed tomography (SPECT) imaging. Currently, SPECT images assist physicians to evaluate perfusion levels within the myocardium, aide in the diagnosis of various types of carcinomas, and measure pulmonary function. The SPECT technique relies on injecting a radioactive material into the patient's body and then detecting the emitted radiation by means of a gamma camera. However, the amount of radioactive material that can be given to a patient is limited by the negative effects that the radiation will have on the patient's health. This causes SPECT images to be highly corrupted by noise. We will focus our work on cardiac SPECT, which adds the challenge of the heart's continuous motion during the acquisition process. First, we describe the methodology used in SPECT imaging and reconstruction. Our methodology uses a content adaptive model, which uses more samples on the regions of the body that we want to be reconstructed more accurately and less in other areas. Then we describe our algorithm and our novel implementation that lets us use the content adaptive model to perform the reconstruction. In this work, we show that our implementation outperforms the reconstruction method used for clinical applications. In the second subject we are evaluating tools to measure image quality in the context of medical diagnosis. In signal processing, accuracy is typically measured as the amount of similarity between an original signal and its reconstruction. This similarity is traditionally a numeric metric that does not take into account the intended purpose of the reconstructed images. In the field of medical imaging, a reconstructed image is meant to aid a physician to perform a diagnostic task. Therefore, the quality of the reconstruction should be measured by how much it helps to perform the diagnostic task. A model observer is a computer tool that aims to mimic the performance of human observer, usually a radiologist, at a relevant diagnosis task. In this work we present our linear model observer designed to automatically select the features needed to model a human observer response. This is a novelty from the model observers currently being used in the medical imaging field, which instead usually have ad-hoc chosen features. Our model observer dependents only on the resolution of the image, not the type of imaging technique used to acquire the image.
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- Title
- STRATEGIES TO MAXIMIZE DOSE REDUCTION IN SPECT MYOCARDIAL PERFUSION IMAGING
- Creator
- Juan Ramon, Albert
- Date
- 2019
- Description
-
Radiation exposure in medical imaging has become a topic of major concern, gaining intense attention within the clinical and research...
Show moreRadiation exposure in medical imaging has become a topic of major concern, gaining intense attention within the clinical and research communities. In 2009, the National Council on Radiation Protection and Measurements (NCRP) announced radiation exposure of patients via medical imaging increased more than sixfold between the 1980s and 2006, with cardiac nuclear medicine, specifically myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT) being the second biggest culprit. The goal of this work is to evaluate several strategies to enable radiation dose to be minimized while maintaining current levels of diagnostic accuracy in the clinic. We achieve dose reduction through optimization of advanced image reconstruction strategies, to obtain higher-quality images at a given dose (noise) level, through a machine learning approach to predict the optimal dose for each patient, and through advanced deep learning (DL) algorithms to improve the quality of reconstructed images. Our ultimate objective is to provide the nuclear cardiology field with a new set of algorithms and guidelines for selecting administered activity levels and image reconstruction procedures in the clinic. The project is based on a clinical study in which imaging and various other data are being collected for a set of patients. The project has the following components. First, we investigate a global dose-reduction approach (i.e., reducing dose by a uniform proportion across all patients) via optimization of image reconstruction strategies. Specifically, we maximize perfusion-defect detection (diagnostic accuracy) over a range of simulated dose levels using clinical data into which we have introduced simulated defects. We measure diagnostic performance using clinically validated model observers from the Quantitative Perfusion SPECT (QPS) software package. We investigate the diagnostic accuracy over a range of dose levels ranging from those currently used in the clinic down to one-eighth of this level. We consider the following image-reconstruction: filtered-backprojection (FBP) with no correction for physics effects, and ordered-subsets expectation-maximization (OS-EM) with several combinations of attenuation correction (AC), scatter correction (SC), and resolution correction (RC).Second, we propose a patient-specific ("personalized") dose reduction approach based on machine learning that aims to predict the minimum radiation dose needed to obtain consistent perfusion-defect detection accuracy for each individual patient. This prediction is based on patient attributes, especially body measurements, and various clinical variables. We compare the diagnostic accuracy produced by predicted personalized doses to that produced by standard clinical dose levels to validate the predictive models.Third, we verify that the dose minimization results obtained in the context of perfusion-defect detection also maintain diagnostic accuracy in evaluating cardiac function, as characterized by myocardial motion.Finally, we propose a deep learning (DL) method to denoise SPECT-MPI reconstructed images. The method is a 3D convolutional neural network trained to predict standard-dose images from low-dose images. We quantify the extent to which dose reduction can be achieved using the proposed DL structure when dose is reduced uniformly across patients or by means of our patient-specific approach.
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