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



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).


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.


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


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.


Direct volume rendering PET–CT visualization Transfer function Serial segmentation 



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

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Supplementary material 1 (DOCX 88 kb)
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Supplementary material 3 (DOCX 247 kb)
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Supplementary material 4 (DOCX 1697 kb)


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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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