Abstract
Introduction Some artifacts caused by prosthesis implants or gold seeds for prostate treatment create uncertainties in CT-Scan images (SOMATOM Scope). This will cause several approximations in radiation therapy (contouring, dose calculation). To improve the treatment, Siemens Healthineers, developed an iterative metal artifact reduction algorithm: iMAR. The aim of this study is to compare HU values reconstructed by the CT-Scan with and without iMAR for prostate treatment. And finally, evaluating dosimetrics effects by calculation of dose on iMAR corrected images. Methods The study is based on three steps. Firstly, on 30 patients with and without prosthesis/gold seeds to evaluate the average of HU into and outside prostate, before and after iMAR correction. Secondly, created customized phantom (QuasarTM) with known material inserts (CIRS Model 062) to simulate human pelvic linked with previous results. We have done CT-Scan acquisitions of the phantom to evaluate accuracy in retrieving correct HU values and Standard Deviation (SD). The reference acquisition is made with QuasarTM acrylic insert, and an acquisition with double metals prosthesis inserts (with/without iMAR reconstruction) is performed. Same ROI (4 cm2) are calculated in this three reconstructions CT images to compare HU values and SD. Thirdly, 23MV VMAT treatment plan in the TPS Eclipse® is performed for both reconstruction (iMAR and no iMAR) to evaluate the dose distribution and to be able to do a dose measurement on the Clinac to determine if iMAR reconstruction is more accurate in dose calculation. Results On patients CT images, iMAR allows to regain information lost by metals artefacts. Moreover, the phantom study demonstrating that lost information (up to −100 HU and ± 100 SD) are improved with iMAR correction (up to + 10 HU and ± 15 SD) and outside the streaking artifact, HU and SD are constant. From a dosimetry point of view, the TPS variation in dose calculation may increase up to + 3.32 Gy in one pixel and the dose covering is improved by iMAR for muscle insert (dose constraint is applied). Therefore, iMAR has an impact in the dose calculation. Finally, the dose verification between treatment in Linac and calculation in TPS (in two measured points) shows that calculation in TPS is more accurate with iMAR ( ≠ 3% out of dose constraint and ≠ 0.2% in dose constraint) compared without iMAR ( ≠ 8.1% out of dose constraint and ≠ 1% in dose constraint). Conclusions iMAR algorithm increases confidence in contouring due to a visualisation of organ’s outlines with a very close reproduction of the HU values and SD. This, enables a stability of the information in TPS for dose calculation. Therefore, dose calculation is improved for the dose covering and dose calculation in TPS is closer to reality.
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