Analysis of measurement deviations for the patient-specific quality assurance using intensity-modulated spot-scanning particle beams

Evaluation of proton and carbon ion beam models in TReatment Planning for Particles 4D (TRiP4D) referring to a commercial treatment planning system

PURPOSE

To investigate the accuracy of the treatment planning system (TPS) TRiP4D in reproducing doses computed by the clinically used TPS SyngoRT.

METHODS

Proton and carbon ion beam models in TRiP4D were converted from SyngoRT. Cubic plans with different depths in a water-tank phantom (WP) and previously treated and experimentally verified patient plans from SyngoRT were recalculated in TRiP4D. The target mean dose deviation (ΔD_mean,T) and global gamma index (2%-2 mm for the absorbed dose and 3%-3mm for the RBE-weighted dose with 10% threshold) were evaluated.

RESULTS

The carbon and proton absorbed dose gamma passing rates (γ-PRs) were ≥99.93% and ΔD_mean,T smaller than -0.22%. On average, the RBE-weighted dose D_mean,T was -1.26% lower for TRiP4D than SyngoRT for cubic plans. In TRiP4D, the faster analytical 'low dose approximation' (Krämer, 2006) was used, while SyngoRT used a stochastic implementation (Krämer, 2000). The average ΔD_{mean, T} could be reduced to -0.59% when applying the same biological effect calculation algorithm. However, the dose recalculation time increased by a factor of 79-477. ΔD_mean,T variation up to -2.27% and -2.79% was observed for carbon absorbed and RBE-weighted doses in patient plans. The γ-PRs were ≥93.92% and ≥91.83% for patient plans, except for one proton beam with a range shifter (γ-PR of 64.19%).

CONCLUSION

The absorbed dose between TRiP4D and SyngoRT were identical for both proton and carbon ion plans in the WP. Compared to SyngoRT, TRiP4D underestimated the target RBE-weighted dose; however more efficient in RBE-weighted dose calculation. Large variation for proton beam with range shifter was observed. TRiP4D will be used to evaluate doses delivered to moving targets. Uncertainties inherent to the 4D-dose reconstruction calculation are expected to be significantly larger than the dose errors reported here. For this reason, the residual differences between TRiP4D and SyngoRT observed in this study are considered acceptable. The study was approved by the Institutional Research Board of Shanghai Proton and Heavy Ion Center (approval number SPHIC-MP-2020-04, RS).

Deep learning for dose assessment in radiotherapy by the super-localization of vaporized nanodroplets in high frame rate ultrasound imaging

Objective. External beam radiotherapy is aimed to precisely deliver a high radiation dose to malignancies, while optimally sparing surrounding healthy tissues. With the advent of increasingly complex treatment plans, the delivery should preferably be verified by quality assurance methods. Recently, online ultrasound imaging of vaporized radiosensitive nanodroplets was proposed as a promising tool for in vivo dosimetry in radiotherapy. Previously, the detection of sparse vaporization events was achieved by applying differential ultrasound (US) imaging followed by intensity thresholding using subjective parameter tuning, which is sensitive to image artifacts. Approach. A generalized deep learning solution (i.e. BubbleNet) is proposed to localize vaporized nanodroplets on differential US frames, while overcoming the aforementioned limitation. A 5-fold cross-validation was performed on a diversely composed 5747-frame training/validation dataset by manual segmentation. BubbleNet was then applied on a test dataset of 1536 differential US frames to evaluate dosimetric features. The intra-observer variability was determined by scoring the Dice similarity coefficient (DSC) on 150 frames segmented twice. Additionally, the BubbleNet generalization capability was tested on an external test dataset of 432 frames acquired by a phased array transducer at a much lower ultrasound frequency and reconstructed with unconventional pixel dimensions with respect to the training dataset. Main results. The median DSC in the 5-fold cross validation was equal to ∼0.88, which was in line with the intra-observer variability (=0.86). Next, BubbleNet was employed to detect vaporizations in differential US frames obtained during the irradiation of phantoms with a 154 MeV proton beam or a 6 MV photon beam. BubbleNet improved the bubble-count statistics by ∼30% compared to the earlier established intensity-weighted thresholding. The proton range was verified with a −0.8 mm accuracy. Significance. BubbleNet is a flexible tool to localize individual vaporized nanodroplets on experimentally acquired US images, which improves the sensitivity compared to former thresholding-weighted methods.

Technical Note: Field size analysis of patient-specific quality assurance in scanned carbon ion radiotherapy

PURPOSE

To evaluate the dose difference between measurement and double Gaussian beam model prediction according to the field size and correct the measurements in patient-specific quality assurance (QA).

METHODS

The field size dependence of the dose was evaluated with volumes of 20 × 20 × 80 mm³ , 40 × 40 × 80 mm³ , 60 × 60 × 80 mm³ , and 80 × 80 × 80 mm³ of 1 Gy uniform dose at three depths. Additional two 80 × 80 × 80 mm³ volumes of nonuniform fields were created: one high-dose field was given 1 Gy at the central 40 × 40 mm² and 0.5 Gy in its surrounding, and the other low-dose field was given 0.5 Gy in the middle and 1 Gy at the periphery. The dose in the center of the spread-out Bragg peak (SOBP) was measured in a water phantom and compared with the treatment planning system (TPS) predication. A field factor based on the two-dimensional (2D) dose distribution was proposed to estimate the field size. The field factor was first evaluated against the dose difference in the square fields, and then used to analyze and correct the patient-specific QA results.

RESULTS

TPS overestimated dose for fields smaller than 80 × 80 mm² . A practically positive correlation was observed between the measured dose and the field factor. In the patient-specific QA, measured doses were lower than the TPS predication as they were calculated a relatively small field factor. The corrected dose differences were no longer field factor dependent.

CONCLUSIONS

Using the proposed field factor, we have shown that all the measurements with a large dose deviation were due to the small-sized field. It is clinically relevant to take into consideration the field size in the QA analysis as long as the double Gaussian beam model being used for the dose calculation. Correction to the measurement can be made based on the field factor.

The root cause analysis on failed patient-specific measurements of pencil beam scanning protons using a 2D detection array with finite size ionization chambers

The aim of this report is to present the root cause analysis on failed patient‐specific quality assurance (QA) measurements of pencil beam scanning (PBS) protons; referred to as PBS‐QA measurement. A criterion to fail a PBS‐QA measurement is having a <95% passing rate in a 3.0%‐3.0 mm gamma index analysis. Clinically, we use a two‐dimensional (2D) gamma index analysis to obtain the passing rate. The IBA MatriXX PT 2D detection array with finite size ionization chamber was utilized. A total of 2488 measurements performed in our PBS beamline were cataloged. The percentage of measurements for the sites of head/neck, breast, prostate, and other are 53.3%, 22.7%, 10.5%, and 13.5%, respectively. The measurements with a passing rate of 100 to >94%, 94 to >88%, and <88% were 93.6%, 5.6%, and 0.8%, respectively. The percentage of failed measurements with a <95% passing rate was 10.9%. After removed the user errors of either re‐measurement or re‐analysis, 8.1% became acceptable. We observed a feature of >3% per mm dose gradient with respect to depth on the failed measurements. We utilized a 2D/three‐dimensional (3D) gamma index analysis toolkit to investigate the effect of depth dose gradient. By utilizing this 3D toolkit, 43.1% of the failed measurements were improved. A feature among measurements that remained sub‐optimal after re‐analysis was a sharp >3% per mm lateral dose gradient that may not be well handled using the detector size of 5.0 mm in‐diameter. An analysis of the sampling of finite size detectors using one‐dimensional (1D) error function showed a large dose deviation at locations of low‐dose areas between two high‐dose plateaus. User error, large depth dose gradient, and the effect of detector size are identified as root causes. With the mitigation of the root causes, the goals of patient‐specific QA, specifically detecting actual deviation of beam delivery or identifying limitations of the dose calculation algorithm of the treatment planning system, can be directly related to failure of the PBS‐QA measurements.