Ge Wang, PhD, Director SBES Division & ICTAS Center for Biomedical Imaging VT-WFU School of Biomedical Engineering & Sciences Virginia Tech, Blacksburg, VA, USA October 1, 2010

Interior Tomography (2007) Object Beam Source Trajectory ROI Object Beam Trajectory ROI Object Beam Trajectory Known Sub-region ROI Object Beam Trajectory Sparsity Model Regular ReconstructionInterior Problem Landmark-based Interior Tomography Sparsity-based Interior Tomography

First Paper (May 2007)

Independent Work (Oct. 2007)

Interior CT Patent

Literature Analysis

Outline Less Is Deeper Less Is Larger Less Is Faster Less Is Less Less Is More

Less Is Deeper Use of less projection data for accurate image reconstruction demands deeper insight, more advanced theory and more powerful tools.

Computed Tomography (Wholesale) t  Sinogram X-rays Projection: Linear integrals t  y x  Measurement Reconstruction Object

Inner Vision with Local Data (Retail) t Sinogram X-rays Projection: Linear integrals t y x Measurement Reconstruction Object XX

Earliest BPF Formula (1991)

Half-PI-Line Reconstruction (2006) Field of View (FOV) Partial-PI-Line

Extrapolation from a Known Point (2006) FOV ?

Curved Filtering Path (2003) ?

Interior Reconstruction (a) (b) (HU) (Pixel) (c) (HU) (Pixel) (d) Global FBP Local FBP Local SART Interior Recon

HOT

Sparsity-based Interior Recon

Outline Less Is Deeper Less Is Larger Less Is Faster Less Is Less Less Is More

Less Is Larger Acquisition of less projection data is achieved with a narrower beam, and an object larger than the beam width is not a concern.

Preclinical Nano-CT

Potential for Study on Earliest Life Hagadorn JW, et al. (2006) Cellular and subcellular structure of Neoproterozoic embryos. Science 314:291–294

Big Patient Problem

Outline Less Is Deeper Less Is Larger Less Is Faster Less Is Less Less Is More

Less Is Faster Less data means smaller detector size, faster frame rate, and more imaging chains, all of which contribute to accelerate the data acquisition process.

Spiral Cone-beam CT

Dual-source Clinical CT (2005)

Multi-source Interior Tomography X-ray Detectors X-ray Tubes ROI Wang G, Yu H, Ye YB. Virginia Tech Patent Disclosure on May 15, 2007, US Patent Application 12/362,979 allowed on October 21, 2009 Ye YB, Yu HY, Wei YC, Wang G. International Journal of Biomedical Imaging, Article ID:63634, 2007 Wang G, Yu H, Ye Y. Medical Physics. 36: , 2009

From Scanning to Roaming

Outline Less Is Deeper Less Is Larger Less Is Faster Less Is Less Less Is More

Less Is Less Less data is equivalent to less radiation dose, because of not only a narrower beam but also a more relaxed angular sampling requirement in the longitudinal studies or multi-scale scenarios.

Reduced Angular Sampling Rate Need 2 ProjectionsNeed 4 Projections

Statistical Interior Tomography Work in progress from Qiong Xu & Xuanqin Mou (China) in collaboration with Wang G & Yu HY 200,000 photons 50,000 photons ITHT ML

Outline Less Is Deeper Less Is Larger Less Is Faster Less Is Less Less Is More

Use of less data is advantageous in more modalities beyond CT, such as other straight-ray tomographic techniques and even in small-angle curvilinear geometry, and more applications of various types. Furthermore, less data means more computational time!

Interior-MRI ……………………………………

Interior-MRI Zhang J, Yu HY, Corum C, Garwood M, Wang G: Exact and stable interior ROI reconstruction for radial MRI. SPIE 7258: 2585G, 8 pages, Feb. 2009, Orlando, FL, USA Traditional MRI Interior MRI

Interior Electron Tomography Ge Wang, Hengyong Yu

Limited Angle Interior Tomography

Interior SPECT

Interior-SPECT Support FOV Known Ideal data Yu HY, Yang JS, Jiang M, Wang G: Interior SPECT- Exact and stable ROI reconstruction from uniformly attenuated local projections; Communications in Numerical Methods in Engineering, 25(6): , 2009 µ a =0cm -1 µ a =0.15cm -1 µ a =0.3cm -1 Noisy data

Practical Implications

Conclusion Less Is Deeper Less Is Larger Less Is Faster Less Is Less Less Is More

Less Is Not Always Better

Link of Localities Pictures from

Multi-scale Interior Tomography Multi-parameter Interior Tomography Multi-energy Interior Tomography Future Work

SBES Advanced Multi-scale CT Facility

Smaller Scales?

Larger Scales?

Multi-parameter CT

Grating-based Imaging

Wang G, Cong W, Shen H, Zou Y: Varying Collimation for Dark-Field Extraction. International Journal of Biomedical Imaging. 2009, Article ID , 2010 Dark-field Tomography

Multi-energy CT

Theoretical Extension Computational Optimization Systematic Evaluation Biomedical Applications Interdisciplinary Collaboration Future Work

Acknowledgment The results in this presentation are of collaborative nature. Major collaborators include Drs. Hengyong Yu, Yangbo Ye, Jiangsheng Yang, Ming Jiang, Steve Wang, Michael Fesser, Erik Ritman, Deepak Bharkhada, Bruno DeMan, Guohua Cao, Otto Zhou, Alexander Katsevich, et al. The work was partially supported by National Institutes of Health/National Institute of Biomedical Imaging and Bioengineering Grants EB002667, EB009275, and EB as well as National Science Foundation NSF/CMMI

Thank You!