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Students will gain a solid educational foundation in the principles and practice of preclinical molecular imaging such that students can build on their prior education, training, and experiences to drive the field in new directions and advance their professional goals. The course will focus on key areas of knowledge: imaging physics and instrumentation, molecular probes and contrast agents, molecular interactions, quantitative imaging and modeling, and translation to the clinical imaging of disease. 3 undergraduate hours. 4 graduate hours. Credit is not given toward graduation for BIOE 424 and either BIOE 498 WD or BIOE 598 WD. Prerequisite: BIOE 205 is recommended.

Tissue engineering therapies for cell-based, material-based, and therapeutic-based solutions. Stem cells, immunology, and clinical applications. 3 undergraduate hours. 3 graduate hours. Prerequisite: MCB 150 and BIOE 206.

The frontier of biomedical imaging is computed imaging where multi-dimensional images must be reconstructed from measured data that is otherwise not meaningful to human observers. In this course, computational image reconstruction techniques will be developed and employed across a broad range of radiographic, magnetic resonance, and nuclear imaging modalities. General imaging and detection principles common to all computational modalities will be covered in context of specific biomedical imaging scenarios. X-ray computed tomography will be covered in depth and in the context of the imaging science principles presented the co-requisites; this also includes practical concerns about computing resources and modern GPU-based computing. The physics of magnetic resonance imaging will be presented and related to specific mathematical issues of image reconstruction and under-sampled measurement space. Positron emission tomography (PET) will be covered and specific clinical issues discussed in terms of reconstruction algorithm and parameter choices. 3 undergraduate hours. 4 graduate hours. Prerequisite: BIOE 205, BIOE 210, ECE 380/BIOE 380. Concurrent enrollment in BIOE 485 and BIOE 580; or instructor approval.

Covers fundamental mathematical and computational methods needed to implement computational imaging and machine learning solutions. First, relevant aspects of probability theory, matrix decompositions, and vector calculus will be introduced. Subsequently, methods that underline approximate inference, such as stochastic sampling methods, are introduced. Finally, numerical optimization methods that represent core components of computed imaging and machine learning will be introduced. This will include numerical optimization-based formulations of inverse problems. An emphasis will be placed on first order deterministic and stochastic gradient-based methods. Second order optimization techniques including quasi-Newton and Hessian free methods will also be surveyed. The application of these methods to computed imaging and machine learning problems will be addressed in detail. 4 undergraduate hours. 4 graduate hours. Prerequisite: Restricted to senior undergraduate or graduate standing in an engineering degree program or consent of instructor.

Will introduce students to basic principles and practical applications of scientific computing as they relate to problems in machine learning and computed imaging. In this self-contained course, students will be introduced to a variety of important topics that underlie real-world machine learning and biomedical image computing tasks that are not typically comprised in a single course. The material will be presented in a practical way that will be accessible to engineering students who have a moderate level of experience in scientific computing but lack specific training in computer science. The emphasis will be on immediate applicability of scientific computing techniques as opposed to theoretical knowledge. The course will begin with an overview of good scientific coding practices in Python and introductions to canonical computing architectures. Subsequently, parallel computing concepts will be surveyed that address multi-core CPU and GPU-enabled systems. Distributed GPU computing on a cluster will also be covered. The salient aspects of TensorFlow and/or other relevant machine learning programming environments will be introduced and utilized in applications of machine learning. 3 undergraduate hours. 3 graduate hours. Prerequisite: Familiarity with the Python programming language. Restricted to students with senior undergraduate or graduate standing in an engineering major.

Guest topics will vary, but will typically cover topics of current interest relevant to the bioengineering field. Lecture and discussion on topics relevant to the development, regulatory approval, marketing, and application of systems used in the fields of biomedical imaging, life science research, and pharmaceutical discovery. Emphasis upon case studies on topics that will include regulatory approval, intellectual property, strategy, and technology innovation. Approved for S/U grading only. May be repeated up to 2 hours in separate terms. Prerequisite: For students enrolled in the M.Eng. in Bioengineering degree program.

With special focus on medical imaging, this course will introduce fundamental concepts related to the detection and analysis of biological analytes, biomedical images, and physiological parameters. Topics include signal-to-noise analysis, noise characterization, data aliasing, analog-to-digital conversion, common strategies for noise reduction, exogenous contrast agents and fundamentals of molecular imaging. The fundamental phenomena behind biological measurements such as DNA sequencing, fluorescence microscopy. MRI imaging, OCT imaging, and ultrasound imaging will be discussed along with the factors that influence noise and contrast from the standpoint of fundamental physics, instrumentation/hardware, and post-measurement data/signal processing. Prerequisite: For students enrolled in the M.Eng in Bioengineering degree program.

Introduction to fundamental principles of design, management, and improvement of business operations and product innovations. Strategies and techniques for managing processes, projects, process improvement and new product development. Prerequisite: For students enrolled in the M.Eng in Bioengineering degree program.

Students in the Master of Engineering (M.Eng.) in Bioengineering program will demonstrate their proficiency through a capstone project, where students will work on a translational project to develop solutions for real world problems utilizing principles of design, engineering analysis, and functional operation of engineering systems. Depending on the student's flexibility and availability, capstone projects may include collaboration with other online M.Eng. students on a team-based project, analysis of case studies, or even a self-directed project that directly relates to a specific area of interest or on behalf of their employer. Project presentations and demonstrations may be required at the end of the program. May be repeated for 6 hours in separate semesters. Students in the Master of Engineering program will be required to sign up for BIOE 575 in both the Fall and Spring semesters. Prerequisite: Proficiency in MATLAB and completion of or concurrent enrollment in core classes required for the Master of Engineering (M.Eng.) in Bioengineering program. Class only available to students in the M.Eng. in Bioengineering degree program.

In this course students will complete a project that utilizes machine learning methods and advanced computational tools to solve a problem related to bioimaging. The projects will utilize experimental bioimage data that is contributed by a project sponsor and/or available via open source databases. While these projects need not be publishable or perfectly novel, they will involve the systematic application and evaluation of machine learning and image computing methods. It is expected that, by solving actual problems posed by industry or clinical partners, students will gain marketable experience but, ideally, also will make inroads with their future employers. Prerequisite: BIOE 588, BIOE 484, BIOE 486, BIOE 580. Concurrent enrollmment in BIOE 489 is required. Restricted to students enrolled in the Biomedical Image Computing MS degree program only.

Subject offerings of new and developing areas of knowledge in bioengineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. May be repeated in the same or separate terms if topics vary.