Biomedical Imaging and Instrumentation

At Cornell, collaborations among engineers, physical scientists, life scientists and clinicians provide superb opportunities to create and improve these tools. BME faculty focus on time-resolved and spectrally-resolved measurement and visualization of biological structures across scales, with spatial scales ranging from macromolecular complexes to cells to whole organisms, temporal scales ranging from milliseconds to years, and spectral scales ranging from megahertz radiofrequency waves to exahertz x-rays. 

A wide range of imaging modalities and methods for achieving contrast are developed and used, including optical imaging, MRI, and CT. Cornell is known for pioneering development and application of nonlinear optical imaging techniques for in vivo imaging and our researchers are also inventing new image analysis methods and novel contrast agents for clinical and research use. BME faculty apply these imaging tools to a diverse set of human health problems including neurodegenerative disease, cancer, and heart disease. Biomedical imaging is also interconnected with other areas of BME, providing in vitro and in vivo tools to evaluate biomaterials, validate systems biology models, monitor drug delivery, and map biomechanical properties. 

Faculty research interests

Prof. Steven Adie’s lab develops optical coherence tomography (OCT)-based methods for 4D imaging of biophysical cell-matrix mechanical properties, including methods for high-resolution imaging of soft tissue biomechanics. These new capabilities support collaborative studies on the role of biophysical factors in physiological processes (e.g. stem cell function) and disease (e.g. cancer). His group also develops new image formation paradigms for OCT by synergistically combining physics-based computed imaging techniques and hardware adaptive optics. These approaches are used to extend the spatiotemporal coverage and imaging depth of OCT, and have applications to ultra-deep multimodal imaging of neural network activity in animal models. 

Prof. James Antaki’s research involves development of diagnostic devices for the home and point-of-care to improve patient engagement, currently focusing on diabetic foot ulcers and breast lesions. He is also developing clinical decision-support tools for severe heart failure, based on deep-learning statistical models to predict the risk of adverse events for various clinical interventions, such as heart-assist devices.

Prof. Jonathan Butcher’s lab applies different imaging modalities to study embryonic morphogenesis, the dynamics of cardiac function and small animal models of congenital and acquired cardiac disease. His lab uses multiphoton microscopy, high frequency ultrasound and micro-CT to investigate cardiac structure-function dynamics in living embryonic and adult model animals.

Prof. Peter Doerschuk’s group develops quantitative image analysis algorithms, using ideas from statistics, machine learning, and high performance computing applied to a diverse set of image and signal problems, including determining the 3-D reconstruction of biological and synthetic nano particles from 2-D electron microscopy images and inferring the state of the brain’s neurovascular system from optical images.

Prof. Nozomi Nishimura’s lab is interested in understanding how inflammation, blood flow and cell death are linked in several different diseases. The strategy is to develop novel tools such as multiphoton microscopy to image the contribution of multiple physiological systems to diseases with in vivo animal models. The lab uses these new vivo optical imaging developments in mouse models to study the diversity of cellular phenotypes and structures in a whole living organism. Targeted applications include heart disease, neurodegeneration and stem cells in the intestine.

Prof. Mert Sabuncu’s research focuses on developing computational tools to analyze and exploit biomedical data, in particular imaging and genetic data, primarily for neurology and neuroscience. Previously at MIT and Harvard Medical School, his Cornell lab develops cutting-edge machine-learning algorithms for a range of biomedical applications that often involve large-scale and multi-modal datasets.

Prof. Chris Schaffer’s lab employs light not only to visualize biological systems, but also for targeted ablation and manipulation. For example, using extremely short laser pulses, Schaffer’s lab causes localized injuries to individual blood vessels in the brains of rodents, triggering a small stroke. These targeted microstrokes allow the lab to study the role of microvascular lesions in neurodegenerative diseases such as Alzheimer ’s disease.

Prof. Yi Wang holds a joint appointment with Radiology at Weill Cornell Medical College, where he is the Director of the MRI Research Institute, which is equipped with a state-of-the-art MRI systems including human 7T, MR/PET and MRgFUS. His lab develops biomedical imaging methods using tools from computer science, electronic engineering, mathematics, physics, and using knowledge in biology, chemistry, life science and medicine. His lab pioneers quantitative susceptibility mapping (QSM), superresolution 4D imaging in MRI, mass transport inverse solution, multi-scale functional imaging - concurrent multi photon microscopy and MRI, and simultaneous neuromodulation and imaging, and works closely with clinicians on various diseases including heart diseases, Parkinson’s diseases, multiple sclerosis, and liver and prostate cancer.

Prof. Warren Zipfel’s lab develops and applies novel methods of fluorescence microscopy and bioanalytical techniques. He was involved in the early development and commercialization of multiphoton microscopy at Cornell and continues to apply multiphoton, as well as confocal and super-resolution microscopies, in a variety of research areas ranging from transcriptional regulation and 3D nuclear structure to cancer biology. Prof. Zipfel serves as the Faculty Advisor to Cornell’s BRC Imaging Facility and as Director of the Biophysics and Metabolic Imaging Core of Cornell’s Center on the Physics of Cancer Metabolism.