CISI Affiliated Labs

The Center for Imaging and Surgical Innovation (CISI) is an interdisciplinary entity designed to facilitate collaborative interactions and academic exchanges. Its mission is the creation, development, implementation, and clinical evaluation of imaging methods, medical devices, computer algorithms, and systems designed to facilitate image-guided surgery and their outcome. CISI includes technical laboratories spanning the engineering departments (Bioengineering, Mechanical Engineering, and Electrical and Computer Science, Computer Science, Systems Engineering, and Material Science and Engineering) at the Erik Jonsson School of Engineering and Computer Science. There are numerous collaborations with the investigators from the School of Behavioral and Brain Sciences, School of Natural Sciences and Mathematics, School of Arts, Technology, and Emerging Communication at UT Dallas and in the clinical departments (Radiology, Radiation Oncology, Pediatrics, Surgery, Neurological Surgery, AIRC, Otolaryngology, and Urology) at the UT Southwestern. Its goal is to become an internationally recognized designation center for faculty, researchers, trainees, and students in the world. CISI trains and educate the next generation of imaging scientists, biomedical engineers, surgeons, and computer scientists capable of working synergistically on new solutions to complex interventional problems, ultimately resulting in improved patient care.

Biomedical and Geometric Computing Laboratory (BigC)

BigC focus is on developing fast algorithms with guaranteed performance for biomedical applications with inherent geometric structure. Using machine learning and deep learning approaches on multimodal images, such as MRI and digital pathology (WSI) cancer images, and exploiting geometric structures that arise in nuclei and cell distribution, we are developing algorithms that can be used to predict treatment outcome and allow to personalize treatment. Similarly, by modeling 3-dimensional images of organs and tissue in the human body as “importance weighted” geometric objects, we are able to develop software that can be used to guide medical procedures, like biopsy and surgery, for hard to reach places in the human body. All our algorithms rely on proven theoretical properties associated with the problem and exploit those properties to guarantee performance.

Ding Lab

The long-term goal of our research is to advance experimental paradigms for understanding the remodeling process in response to cardiac injury. Our approach integrates high spatiotemporal resolution imaging systems with computational, biomechanical, and biochemical methodologies, allowing for a holistic strategy to assess aberrant cardiac structure and function. We aim to develop state-of-the-art optical imaging methods and the AI-based computational platform, enabling to characterize the contribution of a specific lineage in cardiac development and regeneration. We are also translating our custom-built methods to clinical studies for new biomechanical insights into heart failure. We seek to establish our hybrid platform dovetailed to both genetic models and human subjects, allowing researchers and physician-scientists to uncover the underlying mechanisms of cardiac injury and repair.

Image-Guided Drug Delivery Lab (IGDD)

IGDD concentrates on the development of ultrasound sensitive particles for targeted delivery of experimental therapeutics and toxic compounds in vivo using focused ultrasound energy. Our research includes colloid particle synthesis for simultaneous imaging and drug delivery to tumor tissue. Specifically, we are interested in developing clinically viable methods of both delivering therapeutics to tumor tissue and utilizing quantitative contrast-enhanced ultrasound imaging to monitor biological effects. We are accomplishing this with state-of-the-art contrast agents made from novel lipids and proteins and specifically engineered for delivering high drug payload and excellent contrast enhancement.

Molecular Imaging and Optical Nanotherapeutics (mION) Laboratory

The mION lab focuses on developing optically-activated nanotherapeutics using molecular imaging as an informant for their intelligent engineering, a diagnostic, and a means for image guided surgery and photo-deposition of tumor therapy. Theranostic nanoparticles for cancer imaging and therapy require synchronization between nanotechnology and nanoengineering with tumor biology. Non-invasive techniques in optical, magnetic and radiographic imaging deliver critical insights into the molecular interactions of such theranostics nanoparticles with tumor compartments and biomolecules, thereby aiding in the function-driven optimization of nanoparticles. Furthermore, innovative approaches in tumor activatable nanoparticles are pursued with the goal of enhancing the specificity of tumor detection, tumor margin delineation and efficiency of post-tumor resection phototherapy of residual disease.

Nano-Thermal Bioengineering Laboratory (NTBEL)

NTBEL focuses on fundamental understanding of biotansport issues for the brain and diagnostic systems, and develop nanotechnology-based approaches to better understand the brain and revolutionize point-of-care infectious disease diagnosis. Recent efforts focus on new optical nanomaterials and its application in the brain and diagnostic devices. Specifically, our new experimental techniques and methods have led to new enabling tools for optical protein manipulation, molecular uncaging, and drug delivery for the brain, and innovative diagnostic methods. Our research utilizes state-of-the-art imaging tools such as confocal and multi-photon in vivo imaging, MRI, micro-CT, and electron microscopy.

Quantitative Bioimaging Laboratory (QBIL)

QBIL concentrates on the development and applications of quantitative imaging technologies and image-guided interventions. Specifically, we are interested in synthesizing the information obtained from multiple imaging modalities and sources in order to study disease mechanisms and/or to aid in making clinical decisions. Our research goals are to (1) provide efficient methods and procedures for mapping the properties of tissue in space and time, (2) integrate multiple information streams acquired from different imaging technologies into a single coherent picture, (3) validate and interpret in vivo imaging data for biologic, physiologic, and pathologic interpretation, and (4) develop image-guided technologies for biopsy, surgery, and therapy. The research will combine multimodality imaging and multidimensional data to exploit our current knowledge of the genetic and molecular bases of various diseases and therefore to have substantial positive implications for disease prevention, detection, diagnosis, and therapy.

Renal Clearable medicine Laboratory (RCNL)

RCNL concentrates on the development and applications of renal nanomedicines for early disease detection and treatment. By taking advantage of unique functionalities of engineered nanomaterials and unique physiology at the ultrasmall nano scale, RCNL is developing a new generation of nanoprobes that can be readily integrated with a variety of in vivo imaging techniques such as fluorescence, PET, SPECT, X-ray CT and MRI to early detect a variety of diseases ranging from cancer to kidney and liver injuries. In addition, RCNL is also applying these probes as delivery systems and integrating them with these imaging techniques to precisely deliver drugs to disease sites; so that therapy efficacies can be significantly improved while side effects are minimized.

Embedded Machine Learning (EML) Lab

The current research thrusts of the EML Lab are: (1) machine learning and deep learning solutions for signal and image processing applications, and (2) development and real-time implementation of signal and image processing algorithms on mobile devices, in particular on smartphones. In the past, the medical imaging projects conducted in the EML Lab have included: (i) development of a software tool for non-rigid registration of functional and anatomical magnetic resonance images, (ii) development of a deep learning-based real-time smartphone app for first-pass eye examination, and (iii) development of a real-time system by fusing vision and inertial sensing to monitor medication adherence and to carry out senior fitness testing.

Systems and Synthetic Biology Lab

The focus of our research is developing new genome editing technologies and using advanced systems and synthetic biology methodologies to probe cellular pathways. Our projects have significant demands in cell line and spheroid engineering, and characterization using fluorescence microscopy. We perform combinatorial and high-throughput modifications of pathways in cell cancer lines. We use fluorescent proteins in custom synthetic biology-based sensors of endogenous microRNA and transcription factors. We quantify mRNA levels using population-based qPCR and single-mRNA fluorescent probes. We use a custom fluidic/microscopy setup that we have established that allows us to perform controlled time-lapse experiments for prolonged periods (up to 7 days).

Targeted Neuroplasticity Lab (TNPL)

The targeted neuroplasticity lab employs elements of neuroscience and biomedical engineering to develop treatments for human disease. The primary research focus of the lab is to develop new strategies to enhance neuroplasticity, or the ability of the brain to change, in order to treat neurological disease. The majority of current studies evaluate the ability of vagus nerve stimulation (VNS), a putative targeted plasticity therapy, to improve recovery of motor dysfunction. We are rigorously testing VNS to enhance recovery in clinically-relevant animal models of stroke, peripheral neuropathy, and spinal cord injury. In addition to further development and optimization of targeted plasticity therapy, we are investigating the cellular and molecular mechanisms that underlie this recovery. Finally, building on our success in animal models, we are in the process of translating VNS-based targeted plasticity therapy to clinical investigation in patients with chronic stroke and spinal cord injury.

Tissue Mechanics & Remodeling (TMR) Laboratory

The TMR Lab was established at UT Dallas in August 2020. Research in the TMR Lab is aimed at improving fundamental understanding of diseases associated with – and driven by – abnormal tissue mechanics, such cardiovascular disease and cancer. Dr. Ferruzzi worked extensively in the area of cardiovascular biomechanics between the University of Pisa, Texas A&M University, and Yale University. He studied the progression of arterial stiffening and developed methods for “biomechanical phenotyping” arterial sections in vivo and ex vivo. By using experimental and computational methods, he investigated vascular conditions such as arterial aging, chronic hypertension, abdominal aortic aneurysms, and thoracic aortic dissections. While at Boston University, Dr. Ferruzzi explored the role of biomechanics in breast cancer by employing 3D cell culture techniques and label-free imaging methods. He investigated the mechanisms underlying collagen remodeling upon tumor growth and the mechanics of collective cancer invasion. His research program at UT Dallas employs a multidisciplinary and multiscale approach to dissect the relationship between altered mechanical properties, extracellular matrix organization, and cellular function.

Ultrasound Imaging and Therapy Laboratory (USIT)

USIT is focused broadly on the development and advancement of ultrasound technologies that can impact human health and disease. The research is categorized by the following main pillars: 1) Tissue Characterization and Elasticity Imaging, 2) Quantitative US Imaging using Contrast Agents, 3) Molecular US Imaging, and 4) US-Mediated Drug Delivery and Treatment.

Vascular Mechanobiology Laboratory (VMBL)

The Vascular Mechanobiology Laboratory aims to understand and prevent the progression of cardiovascular diseases through the use of experimental and computational models. Improved computational tools to aid in diagnosis and prognosis of atherosclerosis are needed. The VMBL uses an integrative computational approach to predict the growth, remodeling and instability of atherosclerosis in a coronary artery. Specifically, we combine agent based modeling (ABM) with finite element analysis (FEA) and computational fluid dynamics (CFD) to create a modeling tool capable of handling mechano-geometric and chemo-biological complexities to a degree existing approaches cannot. Together this modeling tool will be insightful for individualized decision making, understanding key underlying factors of plaque progression, and foundational for design changes in interventional procedures.