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A direct volume rendering visualization approach for serial PET–CT scans that preserves anatomical consistency

  • Younhyun Jung
  • Jinman KimEmail author
  • Lei Bi
  • Ashnil Kumar
  • David Dagan Feng
  • Michael Fulham
Original Article
  • 165 Downloads

Abstract

Purpose

Our aim was to develop an interactive 3D direct volume rendering (DVR) visualization solution to interpret and analyze complex, serial multi-modality imaging datasets from positron emission tomography–computed tomography (PET–CT).

Methods

Our approach uses: (i) a serial transfer function (TF) optimization to automatically depict particular regions of interest (ROIs) over serial datasets with consistent anatomical structures; (ii) integration of a serial segmentation algorithm to interactively identify and track ROIs on PET; and (iii) parallel graphics processing unit (GPU) implementation for interactive visualization.

Results

Our DVR visualization more easily identifies changes in ROIs in serial scans in an automated fashion and parallel GPU computation which enables interactive visualization.

Conclusions

Our approach provides a rapid 3D visualization of relevant ROIs over multiple scans, and we suggest that it can be used as an adjunct to conventional 2D viewing software from scanner vendors.

Keywords

Direct volume rendering PET–CT visualization Transfer function Serial segmentation 

Notes

Acknowledgements

This study was funded in part by the Australia Research Council (DP160103675).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

For this type of study, formal consent is not required. The testing data were collected at our institution with approval from the institutional review board.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11548_2019_1916_MOESM1_ESM.docx (88 kb)
Supplementary material 1 (DOCX 88 kb)
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Supplementary material 2 (DOCX 2673 kb)
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Supplementary material 3 (DOCX 247 kb)
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Supplementary material 4 (DOCX 1697 kb)

References

  1. 1.
    Pfister H, Lorensen B, Bajaj C, Kindlmann G, Schroeder W, Avila L, Martin K, Machiraju R (2007) The transfer function bake-off. IEEE Comput Graph Appl 21(3):16–22Google Scholar
  2. 2.
    Correa C, Ma K (2011) Visibility histograms and visibility-driven transfer functions. IEEE Trans Vis Comput Graph 17(2):192–204CrossRefPubMedGoogle Scholar
  3. 3.
    Qin H, Ye B, He R (2015) The voxel visibility model: an efficient framework for transfer function design. Comput Med Imaging Graph 40:138–146CrossRefPubMedGoogle Scholar
  4. 4.
    Ma B, Entezari A (2018) Volumetric feature-based classification and visibility analysis for transfer function design. IEEE Trans Vis Comput Graph 25(1):3253–3267CrossRefGoogle Scholar
  5. 5.
    Jung Y, Kim J, Eberl S, Fulham M, Feng D (2013) Visibility-driven PET–CT visualisation with region of interest (ROI) segmentation. Visual Comput 29(6–8):805–815CrossRefGoogle Scholar
  6. 6.
    Tzeng F, Ma K (2005) Intelligent feature extraction and tracking for visualizing large-scale 4D flow simulations. In: Proceedings of ACM/IEEE supercomputing 05Google Scholar
  7. 7.
    Akiba H, Fout N, Ma K (2006) Simultaneous classification of time-vying volume data based on the time histogram. In: Proceedings of EuroVis 06Google Scholar
  8. 8.
    Maciejewski R, Woo I, Chen W, Ebert D (2009) Structuring feature space: a non-parametric method for volumetric transfer function generation. IEEE Trans Vis Comput Graph 15(6):1473–1480CrossRefPubMedGoogle Scholar
  9. 9.
    Kim J, Hu Y, Eberl S, Feng D, Fulham M (2008) A fully automatic bed/linen segmentation for fused PET/CT MIP rendering. In: Proceedings of EMBC 08Google Scholar
  10. 10.
    Klein S, Staring M, Murphy K, Viergever MA, Pluim J (2010) Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29(1):196–205CrossRefPubMedGoogle Scholar
  11. 11.
    Bi L, Kim J, Wen L, Kumar A, Fulham M, Feng D (2013) Cellular automata and anisotropic diffusion filter based interactive tumor segmentation for positron emission tomography. In: Proceedings of EMBC 13Google Scholar
  12. 12.
    Desbordes P, Petitjean C, Ruan S (2016) Segmentation of lymphoma tumor in PET images using cellular automata: a preliminary study. IRBM 37(1):3–10CrossRefGoogle Scholar
  13. 13.
    Wahl R, Jacene H, Kasamon Y, Lodge MA (2009) From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med 50:122–150CrossRefGoogle Scholar
  14. 14.
    Hamamci A, Kucuk N, Karaman K, Engin K, Unal G (2012) Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans Med Imaging 31(3):790–804CrossRefPubMedGoogle Scholar
  15. 15.
    Liu Y, Cheng H, Huang J, Zhang Y, Tang X (2012) An effective approach of lesion segmentation within the breast ultrasound image based on the cellular automata principle. J Digit Imaging 25(5):580–590CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Lagarias J, Reeds J, Wright M, Wright P (1998) Convergence properties of the Nelder–Mead simplex method in low dimensions. SIAM J Optim 9(1):112–147CrossRefGoogle Scholar
  17. 17.
    Kniss J, Kindlmann G, Hansen C (2001) Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets. In: Proceedings of IEEE Vis 01Google Scholar
  18. 18.
    Meyer-Spradow J, Ropinski T, Mensmann J, Hinrichs K (2009) Voreen: a rapid-prototyping environment for ray-casting-based volume visualizations. IEEE Comput Graph Appl 29(6):6–13CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  • Younhyun Jung
    • 1
  • Jinman Kim
    • 1
    Email author
  • Lei Bi
    • 1
  • Ashnil Kumar
    • 1
  • David Dagan Feng
    • 1
    • 2
  • Michael Fulham
    • 3
    • 4
  1. 1.Biomedical & Multimedia Information Technology Research Group, School of Computer ScienceThe University of SydneySydneyAustralia
  2. 2.Med-X Research InstituteShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Sydney Medical SchoolThe University of SydneySydneyAustralia
  4. 4.Department of Molecular ImagingRoyal Prince Alfred HospitalSydneyAustralia

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