Spatiotemporal Imaging Exploiting Structured Sparsity

Overcoming the conventional limits of spatiotemporal imaging by applying compressed sensing and sparse representations to reduce the amount of data acquired while maintaining high image resolution.

Spatiotemporal imaging contains a large class of imaging problems, which involve collecting a sequence of data sets to resolve both the spatial and temporal (or spectral) distributions of some physics quantity. This capability is exploited in numerous different fields such as remote sensing, security surveillance systems, astronomical imaging, and biomedical imaging. One typical example is hyperspectral imaging, which is a powerful technology for remotely inferring the material properties of the objects in a scene of interest. Ultrasonic and thermal imaging are other important examples of spatiotemporal imaging where high spatial resolution is needed for urban planning, military planning, intelligence and disaster monitoring and evaluation.

Task Activation fMRI Experiment: Spatial support of task activation obtained by correlating fMRI time series with the task paradigm convolved with canonical HRF (top row) and spatial support of active voxels from the corresponding ICA and MCA- KSVD components (middle and last rows).

While spatiotemporal imaging has great potential, acquiring and processing data comes with significant practical challenges. First, spatiotemporal images are extremely high-dimensional which limits fast data acquisition. Second, physical design constraints of the acquisition devices (such as size and weight limitations of the satellite) limit attaining higher spatial resolution. As a result, there is a tradeoff between spatial and temporal (or spectral) resolution when designing a system. For example, expensive multiple detector arrays are usually required for recording multispectral bands. By lowering the spatial resolution of each detector, more bands may be contained on the sensor for the same cost. Similar trade-offs are observed in many other spatiotemporal imaging modalities.

This project studies the feasibility of applying compressed sensing and sparse representations, a recently emerged signal processing technique, to achieve reduction of data acquisition while maintaining high image resolution. The objective of the project is to develop an innovative solution to overcome the conventional limits of spatiotemporal imaging by considering sparsely-sampled data and developing an advanced image reconstruction method utilizing compressed sensing technique.

Effects and capabilities of the method are further investigated and evaluated by applying it to the functional Magnetic Resonance Imaging (fMRI) and Dynamic Contrast-Enhanced (DCE-MRI) breast imaging as case studies. The project performs extensive analysis and evaluation of the proposed imaging method on the existing experimental data acquired by Prof. Dr. Gary H. Glover at Stanford University, Lucas center for MR imaging, Radiological Sciences laboratory, approved by the Stanford ethics review board. In addition, the project demonstrates the applicability of the proposed method in different imaging modality applications such as dynamic contrast-enhanced breast imaging, through the international research collaboration between PI and Dr. Wei Huang, Oregon Health and Science University, Advanced Imaging Research Center.

This work was done by Dr. Hien M. Nguyen of the Vietnamese – German University for the Air Force Research Laboratory.For more information, download the Technical Support Package (free white paper) here under the Photonics category. AFRL-0275

This Brief includes a Technical Support Package (TSP).
Spatiotemporal Imaging Exploiting Structured Sparsity

(reference AFRL-0275) is currently available for download from the TSP library.

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