Preliminary clinical evaluation of a 4D-CBCT estimation technique using prior information and limited-angle projections

You Zhang, Fang Fang Yin, Tinsu Pan, Irina Vergalasova, Lei Ren

Research output: Contribution to journalArticle

27 Scopus citations

Abstract

Background and purpose A technique has been previously reported to estimate high-quality 4D-CBCT using prior information and limited-angle projections. This study is to investigate its clinical feasibility through both phantom and patient studies. Materials and methods The new technique used to estimate 4D-CBCT is called MMFD-NCC. It is based on the previously reported motion modeling and free-form deformation (MMFD) method, with the introduction of normalized-cross-correlation (NCC) as a new similarity metric. The clinical feasibility of this technique was evaluated by assessing the accuracy of estimated anatomical structures in comparison to those in the 'ground-truth' reference 4D-CBCTs, using data obtained from a physical phantom and three lung cancer patients. Both volume percentage error (VPE) and center-of-mass error (COME) of the estimated tumor volume were used as the evaluation metrics. Results The average VPE/COME of the tumor in the prior image was 257.1%/10.1 mm for the phantom study and 55.6%/3.8 mm for the patient study. Using only orthogonal-view 30° projections, the MMFD-NCC has reduced the corresponding values to 7.7%/1.2 mm and 9.6%/1.1 mm, respectively. Conclusion The MMFD-NCC technique is able to estimate 4D-CBCT images with geometrical accuracy of the tumor within 10% VPE and 2 mm COME, which can be used to improve the localization accuracy of radiotherapy.

Original languageEnglish (US)
Pages (from-to)22-29
Number of pages8
JournalRadiotherapy and Oncology
Volume115
Issue number1
DOIs
Publication statusPublished - Apr 1 2015
Externally publishedYes

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Keywords

  • 4D-CBCT
  • Free-form deformation
  • Image estimation
  • Motion modeling

ASJC Scopus subject areas

  • Hematology
  • Oncology
  • Radiology Nuclear Medicine and imaging

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