Journal of Digital Imaging

, Volume 24, Issue 6, pp 1103–1111 | Cite as

NIRViz: 3D Visualization Software for Multimodality Optical Imaging Using Visualization Toolkit (VTK) and Insight Segmentation Toolkit (ITK)

Article

Abstract

Optical imaging using near-infrared light is used for noninvasive probing of tissues to recover vascular and molecular status of healthy and diseased tissues using hemoglobin contrast arising due to absorption of light. While multimodality optical techniques exist, visualization techniques in this area are limited. Addressing this issue, we present a simple framework for image overlay of optical and magnetic resonance (MRI) or computerized tomographic images which is intuitive and easily usable, called NIRViz. NIRViz is a multimodality software platform for the display and navigation of Digital Imaging and Communications in Medicine (DICOM) MRI datasets and 3D optical image solutions geared toward visualization and coregistration of optical contrast in diseased tissues such as cancer. We present the design decisions undertaken during the design of the software, the libraries used in the implementation, and other implementation details as well as preliminary results from the software package. Our implementation uses the Visualization Toolkit library to do most of the work, with a Qt graphical user interface for the front end. Challenges encountered include reslicing DICOM image data and coregistration of image space and mesh space. The resulting software provides a simple and customized platform to display surface and volume meshes with optical parameters such as hemoglobin concentration, overlay them on magnetic resonance images, allow the user to interactively change transparency of different image sets, rotate geometries, clip through the resulting datasets, obtain mesh and optical solution information, and successfully interact with both functional and structural medical image information.

Keywords

MRI Multimodality imaging NIRViz Visualization Toolkit Insight Segmentation Toolkit 

References

  1. 1.
    Cerussi A, et al: In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy. Journal of Biomed Opt 11(4):044005–04400516, 2006CrossRefGoogle Scholar
  2. 2.
    Chance B, et al: Breast cancer detection based on incremental biochemical and physiological properties of breast cancers: a six-year, two-site study. Acad Radiol 12(8):925–933, 2005PubMedCrossRefGoogle Scholar
  3. 3.
    Poplack SP, et al: Electromagnetic breast imaging: results of a pilot study in women with abnormal mammograms. Radiology 243(2):350–359, 2007PubMedCrossRefGoogle Scholar
  4. 4.
    Jiang S, et al: Evaluation of breast tumor response to neoadjuvant chemotherapy with tomographic diffuse optical spectroscopy: case studies of tumor region-of-interest changes. Radiology 252(2):551–560, 2009PubMedCrossRefGoogle Scholar
  5. 5.
    Cerussi A, et al: Predicting response to breast cancer neoadjuvant chemotherapy using diffuse optical spectroscopy. PNAS 104(10):4014–4019, 2007PubMedCrossRefGoogle Scholar
  6. 6.
    Zhou C, et al: Diffuse optical monitoring of blood flow and oxygenation in human breast cancer during early stages of neoadjuvant chemotherapy. J Biomed Opt 12(5):051903, 2007PubMedCrossRefGoogle Scholar
  7. 7.
    Hebden JC, et al: Three-dimensional optical tomography of the premature infant brain. Phys Med Biol 47(23):4155–4166, 2002PubMedCrossRefGoogle Scholar
  8. 8.
    Austin T, et al: Three dimensional optical imaging of blood volume and oxygenation in the neonatal brain. Neuroimage 31(4):1426–1433, 2006PubMedCrossRefGoogle Scholar
  9. 9.
    Joseph DK, et al: Diffuse optical tomography system to image brain activation with improved spatial resolution and validation with functional magnetic resonance imaging. Applied Optics 45(3):8142–8151, 2006PubMedCrossRefGoogle Scholar
  10. 10.
    Nothdurft RE, et al: In vivo fluorescence lifetime tomography. J Biomed Opt 14:024004, 2009PubMedCrossRefGoogle Scholar
  11. 11.
    Hyde D, et al: Hybrid FMT-CT imaging of amyloid-beta plaques in a murine Alzheimer’s disease model. Neuroimage 44(4):1304–1311, 2009PubMedCrossRefGoogle Scholar
  12. 12.
    Carpenter C, et al: Methodology development for three-dimensional MR-guided near infrared spectroscopy of breast tumors. Optics Express 16(22):17903–17914, 2008PubMedCrossRefGoogle Scholar
  13. 13.
    Culver JP, et al: Three-dimensional diffuse optical tomography in the parallel plane transmission geometry: Evaluation of a hybrid frequency domain/continuous wave clinical system for breast imaging. Med Phys 30(2):235–247, 2003PubMedCrossRefGoogle Scholar
  14. 14.
    Dehghani H, Pogue BW, Shudong J, Brooksby B, Paulsen KD: Three dimensional optical tomography: resolution in small object imaging. Applied Optics 42(16):3117–3128, 2002CrossRefGoogle Scholar
  15. 15.
    Eppstein MJ, et al: Three-dimensional Bayesian optical image reconstruction with domain decomposition. IEEE Trans Med Imaging 20(3):147–162, 2001PubMedCrossRefGoogle Scholar
  16. 16.
    Brooksby B, et al: Imaging breast adipose and fibroglandular tissue molecular signatures using hybrid MRI-guided near-infrared spectral tomography. Proc Nat Acad Sci USA 103(23):8828–8833, 2006PubMedCrossRefGoogle Scholar
  17. 17.
    Carpenter C, et al: Image-guided optical spectroscopy provides molecular-specific information in vivo: MRI-guided spectroscopy of breast cancer hemoglobin, water & scatterer size. Optics Letters 32(8):933–935, 2007PubMedCrossRefGoogle Scholar
  18. 18.
    www.slicer.org. 3D Slicer.
  19. 19.
  20. 20.
  21. 21.
    Azar F, et al: Standardized platform for coregistration of nonconcurrent diffuse optical and magnetic resonance breast images obtained in different geometries. J Biomed Opt 12(5):051902, 2007PubMedCrossRefGoogle Scholar
  22. 22.
    Dehghani H, et al: Numerical modelling and image reconstruction in diffuse optical tomography. Philos Transact A Math Phys Eng Sci 367(1900):3073–3093, 2009PubMedCrossRefGoogle Scholar
  23. 23.
    Srinivasan S, et al: A boundary element approach for image-guided near-infrared absorption and scatter estimation. Med Phys 34(11):4545–4557, 2007PubMedCrossRefGoogle Scholar
  24. 24.
    Schroeder, W., The Visualization Toolkit, 3rd edition. 2003: Kitware, Inc.Google Scholar
  25. 25.
    Ibanez, L., et al., The ITK Software Guide, 2nd edition. 2005: Kitware Inc.Google Scholar
  26. 26.
  27. 27.
    Malaterre, M., GDCM Reference Manual, 1st edition. 2008: http://gdcm.sourceforge.net/gdcm.pdf.
  28. 28.

Copyright information

© Society for Imaging Informatics in Medicine 2011

Authors and Affiliations

  1. 1.Department of Computer ScienceDartmouth CollegeHanoverUSA
  2. 2.Thayer School of EngineeringDartmouth CollegeHanoverUSA

Personalised recommendations