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Published on: March 11, 2021
Stoichiometric CT number calibration using three-parameter fit model for ion therapy
Minoru Nakao1, Masahiro Hayata2, Shuichi Ozawa1
1Hiroshima High-Precision Radiotherapy Cancer Center, 3-2-2, Futabanosato, Higashi-ku, Hiroshima 732-0057, Japan; Department of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan.
A new three-parameter fit model improves computed tomography number (CTN) to stopping-power ratio (SPR) calibration for ion therapy. This advanced model reduces calibration errors, especially for low-density tissues, enhancing treatment planning accuracy.
Area of Science:
- Medical Physics
- Radiotherapy
- Image Analysis
Background:
- Accurate conversion of computed tomography number (CTN) to stopping-power ratio (SPR) is crucial for ion therapy treatment planning.
- Conventional stoichiometric models may introduce inaccuracies in SPR calculations, particularly for diverse tissue types.
Purpose of the Study:
- To develop and validate a novel three-parameter fit stoichiometric CTN-to-SPR calibration model for ion therapy.
- To compare the accuracy of this new model against a conventional stoichiometric model.
Main Methods:
- Eight tissue-equivalent materials were scanned using six CT scanners across five radiotherapy institutes.
- Theoretical CTN-to-SPR calibration tables from the three-parameter fit and conventional models were compared to measured data for lung, adipose/muscle, and cartilage/spongy bone tissues.
- SPR differences were analyzed in all cases and a worst-case scenario.
Main Results:
- The three-parameter fit model showed mean SPR differences of -0.1 ± 1.0% (lung), 0.3 ± 0.7% (adipose/muscle), and 2.4 ± 0.6% (cartilage/spongy bone).
- In a worst-case scenario for lung tissue, the three-parameter fit model yielded an SPR difference of -1.4%, compared to 2.9% for the conventional model.
Conclusions:
- The three-parameter fit model's CTN-to-SPR calibration table demonstrates consistency with measured data.
- This model significantly reduces calibration errors, particularly for low-density tissues, even under challenging conditions.
