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The master's degree in BME is a 32 credit program with or without thesis that provides breadth and specialized experience in BME. The thesis option culminates in research experience resulting in publishable work. The coursework for thesis and non-thesis options consists of a biomedical engineering core comprising 15 credits, 9 credits of approved electives, and two 1-credit seminars. For the thesis option, there are 6 credits of research. For the non-thesis option, 6 additional credits are taken in approved electives. 

Core Graduate Courses

Biomedical Engineering I and II (3 credits each)
This course sequence covers aspects of biomedical engineering, tying in physical and life sciences to engineering concepts. Subject areas include instrumentation, applied molecular and cell biology, thermodynamics, fluid dynamics, and other areas of physics and engineering related to living systems. 

Engineering Computation I and II (3 credits each)
This course covers applied mathematics needed for biomedical engineering. Emphasis is placed on software tools and computational methods. A rigorous overview of statistics is covered, including frequentist and Bayesian methods. 

Biomedical Ethics (3 credits)
This course covers ethical matters related to healthcare and research with human subjects. Institutional Review Board, clinical trials, and regulatory pathways will be considered with respect to ethical questions. 

Seminar in Biomedical Engineering (1 credit each)
This course will cover timely topics in biomedical research and practice. This course may be repeated for a total of 2 credits.

Elective Courses

Biomaterials & Characterization Methods (3 credits)
This course will cover the standard characterization methods used on various biomaterials such as engineered heart valves typically encountered by biomedical engineers in the field. The course will cover theory, use, and limitations of various characterization methods such as electron microscopy, spectroscopy, optical imaging, and other typical characterization methods. Students will gain hands on use for various instruments and will learn the practical applications and limitations of real characterization devices for biomaterials. 

Introduction to Biomedical Imaging (3 credits)
This course introduces the fundamental principles of imaging and image processing from major modalities - X-ray, CT, MRI, Ultrasound and optical imaging systems including microscopy - used in clinical medicine and biomedical research. The course is a combination of lectures as well as demonstrations that introduce the fundamentals of acquiring and processing images from a signals and systems standpoint, grounded on mathematical modeling of imaging systems. A strong foundational understanding of imaging techniques will be established through assignments involving simulation of image acquisition processes as well as basic algorithmic image processing for image filtering and de-noising. To this end, course will involve programming focused assignments in Matlab. 

Introduction to Bio-image Analytics (3 credits)
Introduction to Bio-Image Analytics provides an overview of a wide range of applications of imaging data, including fundamental methods for quantitative analysis of biological image data, deriving quantitative biomarkers of disease or disease progression, image rendering / visualization for surgical planning and real-time interventional guidance. These applications will be studied with a focus on the fundamental medical image processing techniques underpinning them viz. image filtering (i.e. convolution, smoothing, de-noising, etc.), image segmentation and image as well as point-cloud registration techniques. Fundamentals of statistical data analysis (i.e. T-tests, P-values, concepts of statistical significance, etc.) and machine learning based classification will be introduced from the standpoint of establishing the clinical relevance of several imaging based biomarkers of disease. Programmatic implementation of simple to complex image processing pipelines will be learned from the standpoint of contextual examples and case studies, through in-class tutorials and assignments.

Advanced PIC Microcontroller Project with C-code (3 credits)
This course is designed to challenge the student to use C-code and the SPI (Serial Peripheral Interface), of the PIC Microcontroller, to gather data regarding a patient's heart rate, along with data collected from other SPI devices, such as; Oxygen, Hydrogen, Altitude, Temperature and Humidity sensors. This collected data will be saved to a Micro SD card. Additionally, this collected data will be displayed on a LCD (Liquid Crystal Display) and on a GLCD (Graphical Liquid Crystal Display).

Biomed Smart- GLCD Projects (3 credits)
This course is designed to challenge the student to use C-code, along with a PIC microcontroller and a Smart-GLCD (Smart Graphical Liquid Crystal Display), to create a project related to Bio-Medical Engineering. The student should use any Hardware and Software learned in their previous course work, along with any other material, to produce a Smart-GLCD project. A Linear and a switching power supply tutorial and a printed circuit card layout software tutorial will be presented at the beginning of this course. These tutorials will empower the student to power their project and to make a printed circuit card for their project.

Introduction to Tissue Engineering (3 credits)
The principles and practice of tissue engineering will be the focus of this course. Topics include strategies for employing selected cells, biomaterial scaffolds, soluble regulators of gene expression, role of stem cells, and mechanical loading and culture conditions, Tissue fabrication techniques as well as the role of bioreactors in tissue development will be explored. Students will investigate using current literature the application of tissue engineering to specific organs.

Biomedical Optics (3 credits)
This course covers theoretical foundations of biomedical optics, including light-tissue interactions and optical imaging and sensing methods. Emphasis will be placed on skin optics and photoacoustic phenomena. Students will perform computational modeling, including Monte Carlo simulations of photon transport in turbid media.

Environmental Adaptations & Rehabilitation Technology (3 credits)
Assessment and modification of the physical environment to enhance occupational performance including computer resources, assistive technology, home health, environmental controls, and environmental accessibility.