Integrative Imaging

In vivo rat mammary tumor boundary
In vivo stain-free imaging of tumor invasion in rat mammary cancer. On the upper right as well as the lower left are the tumor clusters. The other parts of the image reveals the tumor-promoting microenvironment surrounding the tumor, including angiogenesis and increase in the number of vesicles.

The Integrative Imaging research theme is dedicated to bringing together ideas, modalities, and people in imaging to foster the interdisciplinary discovery of fundamental principles in imaging science.

Stephen Boppart
Zhi-Pei Liang

The Integrative Imaging research them has three main areas of focus: Cancer, Neuroscience, and Computational Imaging.

Integrative Neuroimaging

Brain imaging is one of the most exciting frontiers of contemporary science. It provides this generation of scientists the opportunity to make major advances on a historic question: how the brain works and what goes wrong when it is injured or diseased. The field of cognitive neuroscience in general and the application of neuroscientific methods and theories to the study of cognitive aging in particular has blossomed in the past decade, largely due to the development of the neuroimaging methods. The Beckman Institute has a unique research program on neurocognitive aging with active research to: (a) understand changes in cognition and brain structure/function across the adult lifespan, and (b) examine the influence of interventions (such as fitness and cognition training) on the brain structure and function of older adults (whether such interventions can be effective in slowing or reversing the negative effects of aging). Engineering imaging faculty and neuroscientists can develop a new multimodal (structural, diffusion, functional, mechanical, metabolic, etc.) integrative neuroimaging paradigm that will substantially enhance our ability to understand changes in brain and cognition across the adult lifespan.

Multi-Scale Multi-Modality Cancer Imaging

The multidimensionality of cancer as a disease represents a complex challenge that can now be better addressed with advances in multi-scale, multi-modality biomedical imaging technologies and computational algorithms for integration of image data. Despite decades of research, and many notable advances in cancer detection, treatment, and monitoring, there remains a critical need to fundamentally understand and image carcinogenesis in vivo, how cancers respond to established and experimental treatment regimens, and how the local and systemic changes in cancer impact human health. The Cancer Moonshot Initiative has rejuvenated our realization that new technology and new approaches are still needed, and our Cancer Community at Illinois along with our newly established Cancer Center, led by full-time Beckman and IntIm faculty member, Rohit Bhargava, are well positioned to integrate with faculty to leverage their imaging expertise and technologies across many scales. This effort will continue the historical trend where advances in imaging and visualization technologies have led scientists and clinicians toward new discoveries in biology and medical science, with the goals of early molecular-level detection, and rapid image-based indicators and predictors of treatment response.

Computational Imaging

Computational techniques play a central role in modern imaging systems, especially biomedical imaging systems. They are fundamental to the noninvasive acquisition of images that reveal the structures, functions, and metabolism of internal tissues or organs in human or other biological systems. Although early imaging technologies were developed under the constraints of limited computing power available in practical imaging systems, today many of these computational constraints are lifted owing to the rapid progress of high-performance computing technology. This group of researchers is actively working on developing and applying advanced computational methods, algorithms and tools to improve and transform image acquisition and processing. On-going research covers the entire imaging process from end-to-end synergistically, from building physics-based forward models, to biology-based interpretation models, to solving large-scale inverse problems, to developing machine learning-based intelligent data acquisition and processing methods. We expect that the resulting computational methods and tools will enable new imaging paradigms that will fundamentally change the way in which imaging data are acquired, processed, and interpreted, thereby overcoming the technical barriers in imaging speed, spatial resolution, and signal-to-noise ratio of existing imaging systems.