Development of a 3D optical scanning-based automatic quality assurance system for proton range compensators

Authors


Abstract

Purpose:

A new automatic quality assurance (AutoRCQA) system using a three-dimensional scanner (3DS) with system automation was developed to improve the accuracy and efficiency of the quality assurance (QA) procedure for proton range compensators (RCs). The system performance was evaluated for clinical implementation.

Methods:

The AutoRCQA system consists of a three-dimensional measurement system (3DMS) based on 3DS and in-house developed verification software (3DVS). To verify the geometrical accuracy, the planned RC data (PRC), calculated with the treatment planning system (TPS), were reconstructed and coregistered with the measured RC data (MRC) based on the beam isocenter. The PRC and MRC inner surfaces were compared with composite analysis (CA) using 3DVS, using the CA pass rate for quantitative analysis. To evaluate the detection accuracy of the system, the authors designed a fake PRC by artificially adding small cubic islands with side lengths of 1.5, 2.5, and 3.5 mm on the inner surface of the PRC and performed CA with the depth difference and distance-to-agreement tolerances of [1 mm, 1 mm], [2 mm, 2 mm], and [3 mm, 3 mm]. In addition, the authors performed clinical tests using seven RCs [computerized milling machine (CMM)-RCs] manufactured by CMM, which were designed for treating various disease sites. The systematic offsets of the seven CMM-RCs were evaluated through the automatic registration function of AutoRCQA. For comparison with conventional technique, the authors measured the thickness at three points in each of the seven CMM-RCs using a manual depth measurement device and calculated thickness difference based on the TPS data (TPS-manual measurement). These results were compared with data obtained from 3DVS. The geometrical accuracy of each CMM-RC inner surface was investigated using the TPS data by performing CA with the same criteria. The authors also measured the net processing time, including the scan and analysis time.

Results:

The AutoRCQA system accurately detected all fake objects in accordance with the given criteria. The median systematic offset of the seven CMM-RCs was 0.08 mm (interquartile range: −0.25 to 0.37 mm) and −0.08 mm (−0.58 to 0.01 mm) in the X- and Y-directions, respectively, while the median distance difference was 0.37 mm (0.23–0.94 mm). The median thickness difference of the TPS-manual measurement at points 1, 2, and 3 was −0.4 mm (−0.4 to −0.2 mm), −0.2 mm (−0.3 to 0.0 mm), and −0.3 mm (−0.6 to −0.1 mm), respectively, while the median difference of 3DMS was 0.0 mm (−0.1 to 0.2 mm), 0.0 mm (−0.1 to 0.3 mm), and 0.1 mm (−0.1 to 0.2 mm), respectively. Thus, 3DMS showed slightly better values compared to the manual measurements for points 1 and 3 in statistical analysis (p < 0.05). The average pass rate of the seven CMM-RCs was 97.97% ± 1.68% for 1-mm CA conditions, increasing to 99.98% ± 0.03% and 100% ± 0.00% for 2- and 3-mm CA conditions, respectively. The average net analysis time was 18.01 ± 1.65 min.

Conclusions:

The authors have developed an automated 3DS-based proton RC QA system and verified its performance. The AutoRCQA system may improve the accuracy and efficiency of QA for RCs.

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