VISE Affiliated Labs
The Vanderbilt Institute for Surgery and Engineering (VISE) is an interdisciplinary, trans-institutional entity designed to facilitate these interactions and exchanges. Its mission is the creation, development, implementation, clinical evaluation and commercialization of methods, devices, algorithms, and systems designed to facilitate interventional processes and their outcome. Its goal is to become the premier center for the training of the next generation of surgeons, engineers, and computer scientists capable of working synergistically on new solutions to complex interventional problems, ultimately resulting in improved patient care. VISE includes ten technical laboratories spanning three engineering departments (Biomedical Engineering, Mechanical Engineering, and Electrical Engineering and Computer Science) and the Otolaryngology department as well as clinical departments that include Surgery, Neurological Surgery, Radiology, Otolaryngology, Hearing and Speech, Oncology, Gastroenterology, Surgical and Radiological Oncology, Ophthalmology, Urology, and Thoracic Surgery.
Advanced Robotics and Mechanism Applications (ARMA) Laboratory
PI: Nabil Simaan, Professor of Mechanical Engineering & Otolaryngology
ARMA is focused on advanced robotics research including robotics, mechanism design, control, and telemanipulation for medical applications. We focus on enabling technologies that necessitate novel design solutions that require contributions in design modeling and control. ARMA has lead the way in advancing several robotics technologies for medical applications including high dexterity snake-like robots for surgery, steerable electrode arrays for cochlear implant surgery, robotics for single port access surgery and natural orifice surgery. Current and past funded research includes transurethral bladder cancer resection (NIH), trans-oral minimally invasive surgery of the upper airways (NIH), single port access surgery (NIH), technologies for robot surgical situational awareness (National Robotics Initiative), Micro-vascular surgery and micro surgery of the retina (VU Discovery Grant), Robotics for cochlear implant surgery (Cochlear Corporation). We collaborate closely with industry on translation our research. Examples include technologies for snake robots licensed to Intuitive Surgical, technologies for micro-surgery of the retina which lead to the formation of AURIS surgical robotics Inc., the IREP single port surgery robot which has been licensed to Titan Medical Inc. and serves as the research prototype behind the Titan Medial Inc. SPORT (Single Port Orifice Robotic Technology). Contact Nabil Simaan
View ARMA lab video here:
Biomedical Elasticity and Acoustic Measurement (BEAM) Laboratory
PI: Brett Byram, Assistant Professor of Biomedical Engineering
The biomedical elasticity and acoustic measurement (BEAM) lab is interested in pursuing ultrasonic solutions to clinical problems. Brett Byram and the BEAM lab’s members have experience with most aspects of systems level ultrasound research, but our current efforts focus on advanced pulse sequencing and algorithm development for motion estimation and beamforming. The goal of our beamforming work is to make normal ultrasound images as clear as intraoperative ultrasound, the gold-standard for many applications. We have recently demonstrated non-contrast tissue perfusion imaging with ultrasound at clinical frequencies, and we are developing novel ultrasound transducers to enhance guidance for percutaneous procedures.
Contact Brett Byram
Biomedical Image Analysis for Image Guided Interventions (BAGL) Laboratory
PI: Jack H. Noble, Assistant Professor of Electrical Engineering & Computer Science,
Biomedical image analysis techniques are transforming the way many clinical interventions are performed and enabling the creation of new computer-assisted interventions and surgical procedures. The Biomedical Image Analysis for Image-Guided Interventions Lab (BAGL) investigates novel medical image processing and analysis techniques with emphasis on creating image analysis-based solutions to clinical problems. The lab explores state-of-the-art image analysis techniques, such as machine learning, statistical shape models, graph search methods, level set techniques, image registration techniques, and image-based bio-models. The lab is currently developing novel systems for cochlear implant procedures including systems that use image analysis techniques for (1) comprehensive pre-operative surgery planning and intra-operative guidance and (2) post-operative informatics to optimize hearing outcomes. Contact Jack Noble
Biomedical Modeling Laboratory (BML)
PI: Michael I. Miga, Harvie Branscomb Professor, Professor of Biomedical Engineering, Radiology & Radiological Sciences, and Neurological Surgery
The focus of the Biomedical Modeling Laboratory (BML) is on new paradigms in detection, diagnosis, characterization, and treatment of disease through the integration of computational models into research and clinical practice. With the continued improvements in high performance computing, the ability to translate computational modeling from predictive roles to ones that are more integrated within diagnostic and therapeutic applications is becoming a rapid reality. With respect to therapeutic applications, efforts in deformation correction for image-guided surgery applications in brain, liver, kidney, and breast are being investigated. Other applications in deep brain stimulation, ablative therapies, neoadjuvant chemotherapy, and convective chemotherapy are also being investigated. With respect to diagnostic imaging, applications in elastography, strain imaging, model-based chemotherapeutic tumor response and radio-therapy response parameterizations are also of particular interest. The common thread that ties the work together is that, throughout each research project, the integration of mathematical models, tissue mechanics, instrumentation, and analysis is present with a central focus at translating the information to directing therapy/intervention or characterizing tissue changes for diagnostic value. Contact Michael Miga
View BML lab video here:
Brain Imaging and Electrophysiology Network (BIEN) Laboratory
PI: Dario Englot, Assistant Professor of Neurological Surgery, Radiology and Radiological Sciences, and Biomedical Engineering
The BIEN lab integrates human neuroimaging and electrophysiology techniques to study brain networks in both neurological diseases and normal brain states. The lab is led by Dario Englot, a functional neurosurgeon at Vanderbilt. One major focus of the lab is to understand the complex network perturbations in patients with epilepsy, by relating network changes to neurocognitive problems, disease parameters, and changes in vigilance in this disabling disease. Multimodal data from human intracranial EEG, functional MRI, diffusion tensor imaging, and other tools are utilized to evaluate resting-state, seizure-related, and task-based paradigms. Other interests of the lab include the effects of brain surgery and neurostimulation on brain networks in epilepsy patients, and whether functional and structural connectivity patterns may change in patients after neurosurgical intervention. Through studying disease-based models, the group also hopes to achieve a better understanding of normal human brain network physiology related to consciousness, cognition, and arousal. Finally, surgical outcomes in functional neurosurgery, including deep brain stimulation, procedures for pain disorders, and epilepsy, are also being investigated. Contact Dario Englot
Chang Lab: Neuroimaging and Brain Dynamics
PI: Catie Chang, Assistant Professor of Computer Science, Electrical Engineering, Computer Engineering
The goal of the Chang Lab is to advance understanding of human brain function in health and disease. We develop new approaches for studying human brain activity by integrating functional neuroimaging (fMRI, EEG) and computational analysis techniques. In one major avenue, we are examining the dynamics of large-scale brain networks across cognitive and physiological state changes, and translating this information into novel fMRI biomarkers. To enable clearer inferences about brain function with fMRI, we also work toward resolving the complex neural and physiological underpinnings of fMRI signal fluctuations. A complementary branch of our research strives to improve neuroimaging data quality, such as through algorithms for reducing artifacts in fMRI and EEG signals. Our research is highly interdisciplinary and collaborative, bridging fields such as engineering, computer science, neuroscience, psychology, and medicine. Contact Catie Chang
Computer Assisted Otologic Surgery (CAOS) Laboratory
PI: Robert F Labadie, Professor of Otolaryngology- Head and Neck Surgery, Professor of Biomedical Engineering
The aim of the CAOS lab is to develop novel methods and tools to improve otologic surgery. Our multi-disciplinary team consists of members with both surgical and engineering backgrounds and expertise in Otolaryngology, Audiology, Mechanical Engineering, Electrical Engineering, and Computer Science. We use a variety of medical image analysis, image-guidance and robotic techniques in an effort to decrease the invasiveness of surgery, make surgical procedures safer, and improve patient outcomes. Some of our current projects include: minimally-invasive cochlear implantation surgery, cochlear implant programming based on medical image analysis, assessment of electrode placement and audiological outcomes in cochlear implant patients, robot-assisted bone milling for inner ear access, patient-specific modeling and planning for robotic surgery, natural orifice middle ear endoscopy, and thermal monitoring of surgical procedures.
Contact Robert F. Labadie View CAOS lab video here:
Diagnostic Imaging and Image-Guided Interventions (DIIGI) Laboratory
PI: Yuankai (Kenny) Tao, Assistant Professor of Biomedical Engineering
The Diagnostic Imaging and Image-Guided Interventions (DIIGI) Laboratory develops novel optical imaging systems for clinical diagnostics and therapeutic monitoring in ophthalmology and oncology. Biomedical optics enable non-invasive subcellular visualization of tissue morphology, biological dynamics, and disease pathogenesis. Our ongoing research primarily focuses on clinical translation of therapeutic tools for image-guided intraoperative feedback using modalities including optical coherence tomography (OCT), which provides high-resolution volumetric imaging of weakly scattering tissue; and nonlinear microscopy, which has improved molecular-specificity, imaging depth, and contrast over conventional white-light and fluorescence microscopy. Additionally, we have developed optical imaging techniques that exploit intrinsic functional contrast for in vivo monitoring of blood flow and oxygenation as surrogate biomarkers of cellular metabolism and early indicators of disease. The majority of our research projects are multidisciplinary collaborations between investigators in engineering, basic sciences, and medicine. Contact Yuankai (Kenny) Tao View DIIGI lab video here:
Grissom Laboratory: MRI-Guided Focused Ultrasound
PI: William Grissom, Assistant Professor Biomedical Engineering
A major research focus of the Grissom laboratory is MRI guidance of high intensity focused ultrasound surgery. MRI-guided high intensity focused ultrasound surgery (FUS) is a promising technique for the next generation of non-invasive therapy systems. One important feature of FUS lies in its ability to apply ultrasound from outside the body, without any skin puncture or incision. The ultrasound energy can be focused to a point within the body, with minimal heating of the intervening tissues. MR imaging is used both for treatment planning and to provide temperature measurements during the procedure. The temperature maps are used both to dynamically control the FUS beam during the procedure, and to assess thermal dose afterwards. Our group is focused on the development of MR imaging methods for FUS surgery guidance, including real-time temperature imaging sequences, algorithms to reconstruct temperature maps, and MRI-based methods to autofocus ultrasound beams through bone and inhomogeneous tissue. We also are interested in the development of imagining techniques to exploit novel temperature contrast mechanisms, and algorithms to dynamically and automatically steer and control the power of the FUS beam. Our current clinical applications are ablation of uterine fibroids and diffuse adenomyosis, anti-tumor immune response modulation of breast cancer, modulation of drug uptake in pancreatic cancer, and tumor and tissue ablation in the brain for functional neurosurgery, blood-brain barrier disruption, and sonothrombolysis for treatment acute ischemic stroke. Contact William Grissom
tHe biomedical data Representation and Learning laB
PI: Yuankai Huo, Assistant Professor in Computer Science
The HRLB lab aims to facilitate data-driven healthcare and improve patient outcomes through innovations in medical image analysis as well as multi-modal data representation and learning. Our current focus efforts on quantifying high-resolution and spatial-temporal data from microscopy imaging techniques, including renal pathology, cancer pathology, cytology, computational biology. The quantitative imaging information is associated with molecular, genetic, and clinical features for precise diagnosis and treatment.
Medical-image Analysis and Statistical Interpretation (MASI) Laboratory
PI: Bennett Landman, Electrical Engineering, Biomedical Engineering, Computer Science, Radiology and Image Science, Chancellor Faculty Fellow
Three-dimensional medical images are changing the way we understand our minds, describe our bodies, and care for ourselves. In the MASI lab, we believe that only a small fraction of this potential has been tapped. We are applying medical image processing to capture the richness of human variation at the population level to learn about complex factors impacting individuals. Our focus is on innovations in robust content analysis, modern statistical methods, and imaging informatics. We partner broadly with clinical and basic science researchers to recognize and resolve technical, practical, and theoretical challenges to translating medical image computing techniques for the benefit of patient care. Contact Bennett Landman
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Medical Engineering and Discovery (MED) Laboratory
PI: Robert J. Webster III, Mechanical Engineering, Electrical Engineering, Otolaryngology, Neurological Surgery, and Urologic Surgery
The Vanderbilt School of Engineering’s Medical Engineering and Discovery (MED) Laboratory pursues research at the interface of surgery and engineering. Our mission is to enhance the lives of patients by engineering better devices and tools to assist physicians. Much of our current research involves designing and constructing the next generation of surgical robotic systems that are less invasive, more intelligent, and more accurate. These devices typically work collaboratively with surgeons, assisting them with image guidance and dexterity in small spaces. Creating these devices involves research in design, modeling, control, and human interfaces for novel robots. Specific current projects include needle-sized tentacle-like robots, advanced manual laparoscopic instruments with wrists and elbows, image guidance for high-accuracy inner ear surgery and abdominal soft tissue procedures, and swallowable pill-sized robots for interventions in the gastrointestinal tract. Contact RobertWebster .
View MED lab video here:
Medical Image Computing Laboratory (MedICL)
PI: Ipek Oguz, Assistant Professor of Computer Science
The goal of the Medical Image Computing Lab is to develop novel algorithms for better leveraging the wealth of data available in medical imagery. We are interested in a wide variety of methods including image segmentation, image registration, image prediction/synthesis, and machine learning. One of our current clinical applications is Huntington’s disease, where we are interested in improving the prediction of clinical disease onset through longitudinal segmentation of subcortical and cortical anatomy from brain MRI’s. We are also interested in multiple sclerosis, where we work on improving our understanding of both the inflammatory disease process through lesion quantification and a potential complementary neurodegenerative component through cortical thickness studies. Additional application areas include retinal OCTs and diffusion MRI in Aicardi-Goutières syndrome.
Contact: Ipek Oguz email@example.com
Medical Image Processing (MIP) Laboratory
PI: Benoit Dawant, Professor Electrical Engineering and Computer Science, Biomedical Engineering, Radiology and Radiological Sciences
The medical image processing (MIP) laboratory of the Electrical Engineering and Computer Science (EECS) Department conducts research in the area of medical image processing and analysis. The core algorithmic expertise of the laboratory is image segmentation and registration. The laboratory is involved in a number of collaborative projects both with others in the engineering school and with investigators in the medical school. Ongoing research projects include developing and testing image processing algorithms to (1) automatically localize radiosensitive structures to facilitate radiotherapy planning, (2) assist in the placement and programming of Deep Brain Stimulators used to treat Parkinson’s disease, (3) localize automatically structures that need to be avoided while placing cochlear implants, (4) develop methods for cochlear implant programming or (5) track brain shift during surgery. The laboratory expertise spans the entire spectrum between algorithmic development and clinical deployment. Several projects that have been initiated in the laboratory have been translated to clinical use or have reached the stage of clinical prototype at Vanderbilt and at other collaborative institutions. Components of these systems have been commercialized. Contact Benoit Dawant
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Morgan Lab: Engineering and Imaging in Epilepsy
PI: Vicky Morgan, Professor of Radiology and Radiological Sciences, Professor of Biomedical Engineering, Professor of Neurology, Professor of Neurological Surgery
The Morgan Engineering and Imaging in Epilepsy Lab works closely with the departments of Neurology and Neurosurgery to develop Magnetic Resonance Imaging (MRI) methods to improve neurosurgical outcomes, particularly for patients with epilepsy. We directly support clinical care by developing and providing functional MRI to localize eloquent cortex in the brain to aid in surgical planning to minimize functional and cognitive deficits post surgery. Our research focuses on mapping functional and structural brain networks in epilepsy before and after surgical treatment. Ultimately, we aim to use MRI to fully characterize the spatial and temporal impacts of seizures across the brain to optimize management of epilepsy patients. The Morgan lab has on-going research collaborations with the BIEN (Englot) Lab, the Medical Imaging Processing Laboratory (Dawant), the MASI Lab (Landman) and researchers throughout the Vanderbilt Institute of Imaging Science (VUIIS).
Science and Technology for Robotics in Medicine (STORM) Laboratory
Director STORM Lab USA and PI: Keith L. Obstein, Division of Gastroenterology, Hepatology, and Nutrition; Department of Mechanical Engineering—Vanderbilt University
Director STORM Lab UK and PI: Pietro Valdastri, School of Electronic and Electrical Engineering—University of Leeds; Department of Mechanical and Electrical Engineering, Divisions of Gastroenterology, Hepatology and Nutrition—Vanderbilt University
At the STORM Lab we strive to improve the quality of life for people undergoing endoscopy and abdominal surgery by creating miniature and non-invasive capsule robots.
The continuous quest for miniaturization has made the science fiction vision of miniature capsule robots working inside the human body a reality. At the STORM Lab, we are designing and creating mechatronic and self-contained devices to be used inside specific districts of the human body to detect and cure diseases in a non-invasive and minimally invasive manner.
Capsule robots represent a challenging paradigm for both research and learning. They embed sensors, actuators, digital intelligence, miniature mechanisms, communication systems, and power supply, all in a very small volume. Capsule robots may be autonomous or teleoperated, they can work alone or as a team, and they can be customized to fulfill specific functions. We are currently applying capsule robot technologies to early detection and treatment of gastrointestinal cancers (i.e. colorectal cancer, gastric cancer) and are developing a new generation of surgical robots that can enter the patient’s abdomen by a single tiny incision. Building upon these competences, we are always ready to face new challenges by modifying our capsule robots to emerging medical needs. Contact Keith Obstein
View STORM Lab video