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STRATEGIES TO MAXIMIZE DOSE REDUCTION IN SPECT MYOCARDIAL PERFUSION IMAGING
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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