Omni-tomography: Next-generation Biomedical Imaging (original) (raw)

Abstract

Systems biomedicine represents one of the major efforts towards personalized and preventive medicine. Recent progresses are largely based on multi-platform and high-throughput-omics data from tissue samples and bioinformatics tools. There is a huge gap between these in vitro data and phenotypic in vivo features. We envision that futuristic research and its translation could greatly benefit from in vivo multiplatform, high-throughput and tomographic information about disease and pre-disease conditions. Inparallel to the development of novel multi-functional probes, multi-physics modeling, high-tech engineering and advanced image reconstruction present new opportunities to peek into living biological systems noninvasively without limitations in space and time. Building blocks are now either available or emerging for the birth of next-generation biomedical imaging-"Omni-tomography" (Wang, Zhang et al. 2012). CT, MRI, PET, SPECT, ultrasound are all medical imaging modalities, each of which has a well-defined role. Over the past decade, we have seen an increasing popularity of multi-modality systems, such as PET-CT and PET-MR, gaining advantages by sequential or contemporaneous data acquisition (Cherry 2009; Patton, Townsend et al. 2009; van der Hoeven, Schalij et al. 2012). However, these paired modalities impose limitations that compromise our understanding of physiological processes relative to fine details and rapid changes driven by a beating heart. With omni-tomography, more or previously non-compatible imaging modalities can be fused together for a comprehensive study of local transient phenomena.

Figures (1)

Omni-tomography offers biological, technical, physical, mathematical, and economic opportunitie: Biologically, the “all-in-one” and “all-at-once”’ imaging power allows observation of well-registere spatiotemporal features in vivo. Physically, multi-physics modeling suggests new imaging modes fc synergistic information (such as photoacoustic imaging which combines ultrasound resolution and opticé contrast). Technically, a paradigm shift of system engineering is required to marry different types of imagin components. Economically, a "one-stop-shop" for diagnosis and intervention may be realized that could b often more cost-effective than a full-fledged imaging center with independent modalities. Omni-tomograph does have limitations due to an ROl-oriented restriction, increased complexity and possible tradeoffs a more imaging contrast mechanisms are involved. Nevertheless, these are tractable with innovativ technology and methods. Figure 1: From interior tomography to omni-tomography. Left: While conventional tomography achieves exact global reconstruction of an object from a non-truncated scan, interior tomography targets exact region-of-interest (ROI) reconstruction from a truncated scan; Right: Omni-tomography is for grand fusion of multiple modalities, with an exemplary CT-MRI scanner design (rendered by Dr. Fenglin Liu with Biomedical Imaging Division at Virginia Tech) in which a pair of magnetic rings leaves space in the middle for CT, and potentially other modalities.

Omni-tomography offers biological, technical, physical, mathematical, and economic opportunitie: Biologically, the “all-in-one” and “all-at-once”’ imaging power allows observation of well-registere spatiotemporal features in vivo. Physically, multi-physics modeling suggests new imaging modes fc synergistic information (such as photoacoustic imaging which combines ultrasound resolution and opticé contrast). Technically, a paradigm shift of system engineering is required to marry different types of imagin components. Economically, a "one-stop-shop" for diagnosis and intervention may be realized that could b often more cost-effective than a full-fledged imaging center with independent modalities. Omni-tomograph does have limitations due to an ROl-oriented restriction, increased complexity and possible tradeoffs a more imaging contrast mechanisms are involved. Nevertheless, these are tractable with innovativ technology and methods. Figure 1: From interior tomography to omni-tomography. Left: While conventional tomography achieves exact global reconstruction of an object from a non-truncated scan, interior tomography targets exact region-of-interest (ROI) reconstruction from a truncated scan; Right: Omni-tomography is for grand fusion of multiple modalities, with an exemplary CT-MRI scanner design (rendered by Dr. Fenglin Liu with Biomedical Imaging Division at Virginia Tech) in which a pair of magnetic rings leaves space in the middle for CT, and potentially other modalities.

Loading...

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

References (33)

  1. Albanese, C., O. C. Rodriguez, et al. (2012). "Preclinical Magnetic Resonance Imaging and Systems Biology in Cancer Research: Current Applications and Challenges." Am J Pathol.
  2. Cherry, S. R. (2009). "Multimodality Imaging: Beyond PET/CT and SPECT/CT." Seminars in Nuclear Medicine 39(5): 348-353.
  3. Cho, H., E. Ackerstaff, et al. (2009). "Noninvasive multimodality imaging of the tumor microenvironment: registered dynamic magnetic resonance imaging and positron emission tomography studies of a preclinical tumor model of tumor hypoxia." Neoplasia 11(3): 247-259, 242p following 259.
  4. Cong, W. X., J. S. Yang, et al. (2012). "Differential phase-contrast interior tomography." Physics in Medicine and Biology 57(10): 2905-2914.
  5. Costouros, N. G., D. Lorang, et al. (2002). "Microarray gene expression analysis of murine tumor heterogeneity defined by dynamic contrast-enhanced MRI." Molecular Imaging 1(3): 301-308.
  6. Courdurier, M., F. Noo, et al. (2008). "Solving the interior problem of computed tomography using a priori knowledge." Inverse Problems 24(6): Article ID: 065001, 065012 pages.
  7. Creixell, P., E. M. Schoof, et al. (2012). "Navigating cancer network attractors for tumor-specific therapy." Nature Biotechnology 30(9): 842-848.
  8. Gao, H., H. Y. Yu, et al. (2011). "Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM)." Inverse Problems 27(11).
  9. Jain, R. K. (2005). "Normalization of tumor vasculature: An emerging concept in antiangiogenic therapy." Science 307(5706): 58-62.
  10. Katsevich, E., A. Katsevich, et al. (2012). "Stability of the interior problem with polynomial attenuation in the region of interest." Inverse Problems 28(6).
  11. Kudo, H., M. Courdurier, et al. (2008). "Tiny a priori knowledge solves the interior problem in computed tomography." Phys. Med. Biol. 53(9): 2207-2231.
  12. Marusyk, A., V. Almendro, et al. (2012). "Intra-tumour heterogeneity: a looking glass for cancer?" Nat Rev Cancer 12(5): 323-334.
  13. Natterer, F. (1986). The Mathematics of Computerized Tomography, John Wiley & Sons.
  14. O'Connor, J. P. B., C. J. Rose, et al. (2011). "DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6." British Journal of Cancer 105(1): 139-145.
  15. Patton, J. A., D. W. Townsend, et al. (2009). "Hybrid Imaging Technology: From Dreams and Vision to Clinical Devices." Seminars in Nuclear Medicine 39(4): 247-263.
  16. Sanz, J. and Z. A. Fayad (2008). "Imaging of atherosclerotic cardiovascular disease." Nature 451(7181): 953-957.
  17. Segal, E., C. B. Sirlin, et al. (2007). "Decoding global gene expression programs in liver cancer by noninvasive imaging." Nature Biotechnology 25(6): 675-680.
  18. Shibata, D. (2012). "Cancer. Heterogeneity and tumor history." Science 336(6079): 304-305.
  19. van der Hoeven, B. L., M. J. Schalij, et al. (2012). "Multimodality imaging in interventional cardiology." Nature Reviews Cardiology 9(6): 333-346.
  20. Vancraeynest, D., A. Pasquet, et al. (2011). "Imaging the Vulnerable Plaque." Journal of the American College of Cardiology 57(20): 1961-1979.
  21. Walsh, M. F., A. M. T. Opie, et al. (2011). "First CT using Medipix3 and the MARS-CT-3 spectral scanner." Journal of Instrumentation 6.
  22. Wang, G., Y. Bresler, et al. (2011). "Compressive Sensing for Biomedical Imaging." Ieee Transactions on Medical Imaging 30(5): 1013-1016.
  23. Wang, G. and H. Y. Yu (2010). "Can interior tomography outperform lambda tomography?" Proceedings of the National Academy of Sciences of the United States of America 107(22): E92-E93.
  24. Wang, G., H. Y. Yu, et al. (2009). "A scheme for multisource interior tomography." Medical Physics 36(8): 3575-3581.
  25. Wang, G., J. Zhang, et al. (2012). "Towards Omni-Tomography-Grand Fusion of Multiple Modalities for Simultaneous Interior Tomography." Plos One 7(6).
  26. Yang, J. and et al. (2010). "High-order total variation minimization for interior tomography." Inverse Problems 26(3): 035013.
  27. Yang, J., H. Yu, et al. (2012). "High Order Total Variation Minimization for Interior SPECT." Inverse Problems 28(1): Article ID: 015001, 015024 pages. .
  28. Yang, J. S., W. X. Cong, et al. (2012). "Theoretical study on high order interior tomography " Journal of X-ray Science and Technology 20: 423-436.
  29. Ye, Y., H. Yu, et al. (2007). "A general local reconstruction approach based on a truncated Hilbert transform." International Journal of Biomedical Imaging 2007: Article ID: 63634, 63638 pages.
  30. Yu, H., G. Cao, et al. (2009). "Compressive sampling based interior reconstruction for dynamic carbon nanotube micro-CT." Journal Of X-Ray Science And Technology 17(4): 295-303.
  31. Yu, H. and G. Wang (2009). "Compressed sensing based Interior tomography." Phys Med Biol 54(9): 2791-2805.
  32. Yu, H. Y., J. S. Yang, et al. (2009). "Interior SPECT-exact and stable ROI reconstruction from uniformly attenuated local projections." Communications in Numerical Methods in Engineering 25(6): 693-710.
  33. Zainon, R., J. P. Ronaldson, et al. (2012). "Spectral CT of carotid atherosclerotic plaque: comparison with histology." European Radiology 22(12): 2581-2588.