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Description
Coronary computed tomography angiography (CTA) has a high negative predictive value for ruling out coronary artery disease with non-invasive evaluation of the coronary arteries. My work has attempted to provide metrics that could increase the positive predictive value of coronary CTA through the use of dual energy CTA imaging. After developing an algorithm for obtaining calcium scores from a CTA exam, a dual energy CTA exam was performed on patients at dose levels equivalent to levels for single energy CTA with a calcium scoring exam. Calcium Agatston scores obtained from the dual energy CTA exam were within ±11% of scores obtained with conventional calcium scoring exams. In the presence of highly attenuating coronary calcium plaques, the virtual non-calcium images obtained with dual energy CTA were able to successfully measure percent coronary stenosis within 5% of known stenosis values, which is not possible with single energy CTA images due to the presence of the calcium blooming artifact. After fabricating an anthropomorphic beating heart phantom with coronary plaques, characterization of soft plaque vulnerability to rupture or erosion was demonstrated with measurements of the distance from soft plaque to aortic ostium, percent stenosis, and percent lipid volume in soft plaque. A classification model was developed, with training data from the beating heart phantom and plaques, which utilized support vector machines to classify coronary soft plaque pixels as lipid or fibrous. Lipid versus fibrous classification with single energy CTA images exhibited a 17% error while dual energy CTA images in the classification model developed here only exhibited a 4% error. Combining the calcium blooming correction and the percent lipid volume methods developed in this work will provide physicians with metrics for increasing the positive predictive value of coronary CTA as well as expanding the use of coronary CTA to patients with highly attenuating calcium plaques.
ContributorsBoltz, Thomas (Author) / Frakes, David (Thesis advisor) / Towe, Bruce (Committee member) / Kodibagkar, Vikram (Committee member) / Pavlicek, William (Committee member) / Bouman, Charles (Committee member) / Arizona State University (Publisher)
Created2013
Description
Three dimensional (3-D) ultrasound is safe, inexpensive, and has been shown to drastically improve system ease-of-use, diagnostic efficiency, and patient throughput. However, its high computational complexity and resulting high power consumption has precluded its use in hand-held applications.
In this dissertation, algorithm-architecture co-design techniques that aim to make hand-held 3-D ultrasound a reality are presented. First, image enhancement methods to improve signal-to-noise ratio (SNR) are proposed. These include virtual source firing techniques and a low overhead digital front-end architecture using orthogonal chirps and orthogonal Golay codes.
Second, algorithm-architecture co-design techniques to reduce the power consumption of 3-D SAU imaging systems is presented. These include (i) a subaperture multiplexing strategy and the corresponding apodization method to alleviate the signal bandwidth bottleneck, and (ii) a highly efficient iterative delay calculation method to eliminate complex operations such as multiplications, divisions and square-root in delay calculation during beamforming. These techniques were used to define Sonic Millip3De, a 3-D die stacked architecture for digital beamforming in SAU systems. Sonic Millip3De produces 3-D high resolution images at 2 frames per second with system power consumption of 15W in 45nm technology.
Third, a new beamforming method based on separable delay decomposition is proposed to reduce the computational complexity of the beamforming unit in an SAU system. The method is based on minimizing the root-mean-square error (RMSE) due to delay decomposition. It reduces the beamforming complexity of a SAU system by 19x while providing high image fidelity that is comparable to non-separable beamforming. The resulting modified Sonic Millip3De architecture supports a frame rate of 32 volumes per second while maintaining power consumption of 15W in 45nm technology.
Next a 3-D plane-wave imaging system that utilizes both separable beamforming and coherent compounding is presented. The resulting system has computational complexity comparable to that of a non-separable non-compounding baseline system while significantly improving contrast-to-noise ratio and SNR. The modified Sonic Millip3De architecture is now capable of generating high resolution images at 1000 volumes per second with 9-fire-angle compounding.
In this dissertation, algorithm-architecture co-design techniques that aim to make hand-held 3-D ultrasound a reality are presented. First, image enhancement methods to improve signal-to-noise ratio (SNR) are proposed. These include virtual source firing techniques and a low overhead digital front-end architecture using orthogonal chirps and orthogonal Golay codes.
Second, algorithm-architecture co-design techniques to reduce the power consumption of 3-D SAU imaging systems is presented. These include (i) a subaperture multiplexing strategy and the corresponding apodization method to alleviate the signal bandwidth bottleneck, and (ii) a highly efficient iterative delay calculation method to eliminate complex operations such as multiplications, divisions and square-root in delay calculation during beamforming. These techniques were used to define Sonic Millip3De, a 3-D die stacked architecture for digital beamforming in SAU systems. Sonic Millip3De produces 3-D high resolution images at 2 frames per second with system power consumption of 15W in 45nm technology.
Third, a new beamforming method based on separable delay decomposition is proposed to reduce the computational complexity of the beamforming unit in an SAU system. The method is based on minimizing the root-mean-square error (RMSE) due to delay decomposition. It reduces the beamforming complexity of a SAU system by 19x while providing high image fidelity that is comparable to non-separable beamforming. The resulting modified Sonic Millip3De architecture supports a frame rate of 32 volumes per second while maintaining power consumption of 15W in 45nm technology.
Next a 3-D plane-wave imaging system that utilizes both separable beamforming and coherent compounding is presented. The resulting system has computational complexity comparable to that of a non-separable non-compounding baseline system while significantly improving contrast-to-noise ratio and SNR. The modified Sonic Millip3De architecture is now capable of generating high resolution images at 1000 volumes per second with 9-fire-angle compounding.
ContributorsYang, Ming (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Karam, Lina (Committee member) / Frakes, David (Committee member) / Ogras, Umit Y. (Committee member) / Arizona State University (Publisher)
Created2015
Description
The rate of progress in improving survival of patients with solid tumors is slow due to late stage diagnosis and poor tumor characterization processes that fail to effectively reflect the nature of tumor before treatment or the subsequent change in its dynamics because of treatment. Further advancement of targeted therapies relies on advancements in biomarker research. In the context of solid tumors, bio-specimen samples such as biopsies serve as the main source of biomarkers used in the treatment and monitoring of cancer, even though biopsy samples are susceptible to sampling error and more importantly, are local and offer a narrow temporal scope.
Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers.
This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes.
Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers.
This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes.
ContributorsRanjbar, Sara (Author) / Kaufman, David (Thesis advisor) / Mitchell, Joseph R. (Thesis advisor) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2017
Description
A direct Magnetic Resonance (MR)-based neural activity mapping technique with high spatial and temporal resolution may accelerate studies of brain functional organization.
The most widely used technique for brain functional imaging is functional Magnetic Resonance Image (fMRI). The spatial resolution of fMRI is high. However, fMRI signals are highly influenced by the vasculature in each voxel and can be affected by capillary orientation and vessel size. Functional MRI analysis may, therefore, produce misleading results when voxels are nearby large vessels. Another problem in fMRI is that hemodynamic responses are slower than the neuronal activity. Therefore, temporal resolution is limited in fMRI. Furthermore, the correlation between neural activity and the hemodynamic response is not fully understood. fMRI can only be considered an indirect method of functional brain imaging.
Another MR-based method of functional brain mapping is neuronal current magnetic resonance imaging (ncMRI), which has been studied over several years. However, the amplitude of these neuronal current signals is an order of magnitude smaller than the physiological noise. Works on ncMRI include simulation, phantom experiments, and studies in tissue including isolated ganglia, optic nerves, and human brains. However, ncMRI development has been hampered due to the extremely small signal amplitude, as well as the presence of confounding signals from hemodynamic changes and other physiological noise.
Magnetic Resonance Electrical Impedance Tomography (MREIT) methods could have the potential for the detection of neuronal activity. In this technique, small external currents are applied to a body during MR scans. This current flow produces a magnetic field as well as an electric field. The altered magnetic flux density along the main magnetic field direction caused by this current flow can be obtained from phase images. When there is neural activity, the conductivity of the neural cell membrane changes and the current paths around the neurons change consequently. Neural spiking activity during external current injection, therefore, causes differential phase accumulation in MR data. Statistical analysis methods can be used to identify neuronal-current-induced magnetic field changes.
The most widely used technique for brain functional imaging is functional Magnetic Resonance Image (fMRI). The spatial resolution of fMRI is high. However, fMRI signals are highly influenced by the vasculature in each voxel and can be affected by capillary orientation and vessel size. Functional MRI analysis may, therefore, produce misleading results when voxels are nearby large vessels. Another problem in fMRI is that hemodynamic responses are slower than the neuronal activity. Therefore, temporal resolution is limited in fMRI. Furthermore, the correlation between neural activity and the hemodynamic response is not fully understood. fMRI can only be considered an indirect method of functional brain imaging.
Another MR-based method of functional brain mapping is neuronal current magnetic resonance imaging (ncMRI), which has been studied over several years. However, the amplitude of these neuronal current signals is an order of magnitude smaller than the physiological noise. Works on ncMRI include simulation, phantom experiments, and studies in tissue including isolated ganglia, optic nerves, and human brains. However, ncMRI development has been hampered due to the extremely small signal amplitude, as well as the presence of confounding signals from hemodynamic changes and other physiological noise.
Magnetic Resonance Electrical Impedance Tomography (MREIT) methods could have the potential for the detection of neuronal activity. In this technique, small external currents are applied to a body during MR scans. This current flow produces a magnetic field as well as an electric field. The altered magnetic flux density along the main magnetic field direction caused by this current flow can be obtained from phase images. When there is neural activity, the conductivity of the neural cell membrane changes and the current paths around the neurons change consequently. Neural spiking activity during external current injection, therefore, causes differential phase accumulation in MR data. Statistical analysis methods can be used to identify neuronal-current-induced magnetic field changes.
ContributorsFu, Fanrui (Author) / Sadleir, Rosalind (Thesis advisor) / Kodibagkar, Vikram (Committee member) / Kleim, Jeffrey (Committee member) / Muthuswamy, Jitendran (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2019
Description
Autism spectrum disorder (ASD) is a developmental neuropsychiatric condition with early childhood onset, thus most research has focused on characterizing brain function in young individuals. Little is understood about brain function differences in middle age and older adults with ASD, despite evidence of persistent and worsening cognitive symptoms. Functional Magnetic Resonance Imaging (MRI) in younger persons with ASD demonstrate that large-scale brain networks containing the prefrontal cortex are affected. A novel, threshold-selection-free graph theory metric is proposed as a more robust and sensitive method for tracking brain aging in ASD and is compared against five well-accepted graph theoretical analysis methods in older men with ASD and matched neurotypical (NT) participants. Participants were 27 men with ASD (52 +/- 8.4 years) and 21 NT men (49.7 +/- 6.5 years). Resting-state functional MRI (rs-fMRI) scans were collected for six minutes (repetition time=3s) with eyes closed. Data was preprocessed in SPM12, and Data Processing Assistant for Resting-State fMRI (DPARSF) was used to extract 116 regions-of-interest defined by the automated anatomical labeling (AAL) atlas. AAL regions were separated into six large-scale brain networks. This proposed metric is the slope of a monotonically decreasing convergence function (Integrated Persistent Feature, IPF; Slope of the IPF, SIP). Results were analyzed in SPSS using ANCOVA, with IQ as a covariate. A reduced SIP was in older men with ASD, compared to NT men, in the Default Mode Network [F(1,47)=6.48; p=0.02; 2=0.13] and Executive Network [F(1,47)=4.40; p=0.04; 2=0.09], a trend in the Fronto-Parietal Network [F(1,47)=3.36; p=0.07; 2=0.07]. There were no differences in the non-prefrontal networks (Sensory motor network, auditory network, and medial visual network). The only other graph theory metric to reach significance was network diameter in the Default Mode Network [F(1,47)=4.31; p=0.04; 2=0.09]; however, the effect size for the SIP was stronger. Modularity, Betti number, characteristic path length, and eigenvalue centrality were all non-significant. These results provide empirical evidence of decreased functional network integration in pre-frontal networks of older adults with ASD and propose a useful biomarker for tracking prognosis of aging adults with ASD to enable more informed treatment, support, and care methods for this growing population.
ContributorsCatchings, Michael Thomas (Author) / Braden, Brittany B (Thesis advisor) / Greger, Bradley (Thesis advisor) / Schaefer, Sydney (Committee member) / Arizona State University (Publisher)
Created2019
Description
Rapid intraoperative diagnosis of brain tumors is of great importance for planning treatment and guiding the surgeon about the extent of resection. Currently, the standard for the preliminary intraoperative tissue analysis is frozen section biopsy that has major limitations such as tissue freezing and cutting artifacts, sampling errors, lack of immediate interaction between the pathologist and the surgeon, and time consuming.
Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation.
This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides.
These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.
Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation.
This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides.
These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.
ContributorsIzady Yazdanabadi, Mohammadhassan (Author) / Preul, Mark (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Nakaji, Peter (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2019
Description
The challenge of radiation therapy is to maximize the dose to the tumor while simultaneously minimizing the dose elsewhere. Proton therapy is well suited to this challenge due to the way protons slow down in matter. As the proton slows down, the rate of energy loss per unit path length continuously increases leading to a sharp dose near the end of range. Unlike conventional radiation therapy, protons stop inside the patient, sparing tissue beyond the tumor. Proton therapy should be superior to existing modalities, however, because protons stop inside the patient, there is uncertainty in the range. “Range uncertainty” causes doctors to take a conservative approach in treatment planning, counteracting the advantages offered by proton therapy. Range uncertainty prevents proton therapy from reaching its full potential.
A new method of delivering protons, pencil-beam scanning (PBS), has become the new standard for treatment over the past few years. PBS utilizes magnets to raster scan a thin proton beam across the tumor at discrete locations and using many discrete pulses of typically 10 ms duration each. The depth is controlled by changing the beam energy. The discretization in time of the proton delivery allows for new methods of dose verification, however few devices have been developed which can meet the bandwidth demands of PBS.
In this work, two devices have been developed to perform dose verification and monitoring with an emphasis placed on fast response times. Measurements were performed at the Mayo Clinic. One detector addresses range uncertainty by measuring prompt gamma-rays emitted during treatment. The range detector presented in this work is able to measure the proton range in-vivo to within 1.1 mm at depths up to 11 cm in less than 500 ms and up to 7.5 cm in less than 200 ms. A beam fluence detector presented in this work is able to measure the position and shape of each beam spot. It is hoped that this work may lead to a further maturation of detection techniques in proton therapy, helping the treatment to reach its full potential to improve the outcomes in patients.
A new method of delivering protons, pencil-beam scanning (PBS), has become the new standard for treatment over the past few years. PBS utilizes magnets to raster scan a thin proton beam across the tumor at discrete locations and using many discrete pulses of typically 10 ms duration each. The depth is controlled by changing the beam energy. The discretization in time of the proton delivery allows for new methods of dose verification, however few devices have been developed which can meet the bandwidth demands of PBS.
In this work, two devices have been developed to perform dose verification and monitoring with an emphasis placed on fast response times. Measurements were performed at the Mayo Clinic. One detector addresses range uncertainty by measuring prompt gamma-rays emitted during treatment. The range detector presented in this work is able to measure the proton range in-vivo to within 1.1 mm at depths up to 11 cm in less than 500 ms and up to 7.5 cm in less than 200 ms. A beam fluence detector presented in this work is able to measure the position and shape of each beam spot. It is hoped that this work may lead to a further maturation of detection techniques in proton therapy, helping the treatment to reach its full potential to improve the outcomes in patients.
ContributorsHolmes, Jason M (Author) / Alarcon, Ricardo (Thesis advisor) / Bues, Martin (Committee member) / Galyaev, Eugene (Committee member) / Chamberlin, Ralph (Committee member) / Arizona State University (Publisher)
Created2019
Description
In medical imaging, a wide variety of methods are used to interrogate structural and physiological differences between soft tissues. One of the most ubiquitous methods in clinical practice is Magnetic Resonance Imaging (MRI), which has the advantage of limited invasiveness, soft tissue discrimination, and adequate volumetric resolution. A myriad of advanced MRI methods exists to investigate the microstructural, physiologic and metabolic characteristics of tissue. For example, Dynamic Contrast Enhanced (DCE) and Dynamic Susceptibility Contrast (DSC) MRI non-invasively interrogates the dynamic passage of an exogenously administered MRI contrast agent through tissue to quantify local tracer kinetic properties like blood flow, vascular permeability and tissue compartmental volume fractions. Recently, an improved understanding of the biophysical basis of DSC-MRI signals in brain tumors revealed a new approach to derive multiple quantitative biomarkers that identify intrinsic sub-voxel cellular and vascular microstructure that can be used differentiate tumor sub-types. One of these characteristic biomarkers called Transverse Relaxivity at Tracer Equilibrium (TRATE), utilizes a combination of DCE and DSC techniques to compute a steady-state metric which is particularly sensitive to cell size, density, and packing properties. This work seeks to investigate the sensitivity and potential utility of TRATE in a range of disease states including Glioblastomas, Amyotrophic Lateral Sclerosis (ALS), and Duchenne’s Muscular Dystrophy (DMD). The MRC measures of TRATE showed the most promise in mouse models of ALS where TRATE values decreased with disease progression, a finding that correlated with reductions in myofiber size and area, as quantified by immunohistochemistry. In the animal models of cancer and DMD, TRATE results were more inconclusive, due to marked heterogeneity across animals and treatment state. Overall, TRATE seems to be a promising new biomarker but still needs further methodological refinement due to its sensitivity to contrast to noise and further characterization owing to its non-specificity with respect to multiple cellular features (e.g. size, density, heterogeneity) that complicate interpretation.
ContributorsFuentes, Alberto (Author) / Quarles, Chad C (Thesis advisor) / Kodibagkar, Vikram (Thesis advisor) / Greger, Bradley (Committee member) / Arizona State University (Publisher)
Created2018
Description
Open source image analytics and data mining software are widely available but can be overly-complicated and non-intuitive for medical physicians and researchers to use. The ASU-Mayo Clinic Imaging Informatics Lab has developed an in-house pipeline to process medical images, extract imaging features, and develop multi-parametric models to assist disease staging and diagnosis. The tools have been extensively used in a number of medical studies including brain tumor, breast cancer, liver cancer, Alzheimer's disease, and migraine. Recognizing the need from users in the medical field for a simplified interface and streamlined functionalities, this project aims to democratize this pipeline so that it is more readily available to health practitioners and third party developers.
ContributorsBaer, Lisa Zhou (Author) / Wu, Teresa (Thesis director) / Wang, Yalin (Committee member) / Computer Science and Engineering Program (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Arizona State University and Banner Thunderbird Hospital have partnered to provide pre-med students with an internship at a local emergency department. Students entering into this program will have access to each patient's vital signs, medical imaging, lab tests, and medications. This access presents students with an opportunity to learn about a variety of tools used in the assessment and treatment of emergency room patients. In order to enhance the amount of knowledge students take away from the program, I created a handbook summarizing a variety of diagnostic tests and medications. The first section of the handbook (assessment) is spilt up into the three following categories: vital signs, medical imaging, and lab tests. The second section (treatment) consists of one category, medications. Each section was written with emphasis on basic physiology, and is intended to provide pre-med students with a foundation for building further medical knowledge. Although this handbook was tailored to the information students are most likely to encounter working in Banner Thunderbird Hospital's emergency department, it is still appropriate for any student interested in learning about emergency medicine.
ContributorsBecker, Bryson (Author) / Orchinik, Miles (Thesis director) / Washo-Krupps, Delon (Committee member) / School of Life Sciences (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05