TITLE:
Method for Converting Cone-Beam CT Values into Hounsfield Units for Radiation Treatment Planning
AUTHORS:
Tadanori Abe, Kunihiko Tateoka, Yuichi Saito, Takuya Nakazawa, Masaki Yano, Kensei Nakata, Masanori Someya, Masakazu Hori, Koichi Sakata
KEYWORDS:
Cone-Beam Computed Tomography, Hounsfield Unit, Electron Density, Ra-diation Treatment Planning
JOURNAL NAME:
International Journal of Medical Physics, Clinical Engineering and Radiation Oncology,
Vol.6 No.4,
October
12,
2017
ABSTRACT: Cone-beam CT (CBCT) images acquired during radiation
treatment can be used to recalculate the dose distribution as well as to
confirm the treatment location. However, it is difficult to obtain the electron
densities (EDs) necessary for dose calculation from CBCT images because of the
effects of scatter contamination during CBCT image acquisition. This paper
presents a mathematical method for converting the pixel values of CBCT images
(CBCT values) into Hounsfield units (HUs) of
radiation treatment simulation CT (simCT) images for use in radiation
treatment planning. CBCT values are converted into HUs by matching the
histograms of the CBCT values with the histograms of the HUs for each slice via
linear scaling of the CBCT values. For prostate cancer and head-and-neck cancer
patients, the EDs obtained from converted CBCT values (mCBCT values) show good
agreement with the EDs obtained from HUs, within approximately 3.0%, and the
dose calculated on the basis of CBCT images shows good
agreement with the dose calculated on the basis of the simCT images, within
approximately 2.0%. Because the CBCT values are converted for each slice, this
conversion method can account for variation in the CBCT values associated with
differences in body size, body shape, and inner tissue structures, as well as
in longitudinally displaced positions from the isocenter, unlike conventional
methods that use electron density phantoms. This
method improves on conventional CBCT-ED conversion and shows considerable potential for improving the accuracy of radiation treatment planning
using CBCT images.