Plan Verification Research Articles

Technical note: Patient-specific quality assurance for multi-target single-isocenter SRS-A target-specific approach.

As radiotherapy techniques advance, so do planning methods for multi-target intracranial SRS cases. Multi-target-single-isocenter (MTSI) planning offers high-precision beam delivery with shortened duration. However, accommodating all targets in a single Patient-Specific-Quality-Assurance (PSQA) with QA devices like SRS MapCHECK (SRS MC) is generally impractical. Consequently, we conducted PSQA, using a custom script, by relocating each Target or Neighboring-Target-Group (T-NTG) relative to the beam isocenter on the PSQA device, ensuring each target's dose coverage at high precision. SRS treatment plans use 6MV-FFF beams, consisting of four Volumetric Modulated ARC Therapy (VMAT) arcs, including one full-arc and three half arcs with couch-kicks. A custom script calculated T-NTG coordinates relative to the beam isocenter. QA verification plans were created for each T-NTG, redefining the beam isocenter for precise alignment with the center of the SRS MC. CBCT images were acquired during PSQA for SRS MC alignment, and gamma-index analysis (GIA) was performed. A single-tail paired t-test assessed the passing rate (PR) for 75 QA verification plans. GIA with l.0mm/2.0% criteria for each QA plan yielded a PR>95.5%, with an average of 98.9%. Plans achieving PR>99.0% and>97.0% constituted 63% and 92% of studied plans, respectively. Statistical significance was observed in a t-test with an ideal PR value of 100%, while insignificance was found with a PR value of 99%, suggesting that PSQA for individual targets consistently approaches 99% PR. In MTSI cases using 6MV-FFF beams, targets within the lateral dose-fall-off region require careful verification for acceptability. Our clinical study on individual T-NTG relocation demonstrates that the presented PSQA methods are generally acceptable, supported by a statistically insignificant PR against a 99% PR value. Presented statistical analysis results indicate that the proposed PSQA approach can serve as a reliable tool in clinical settings.

Research on the correction method for radiotherapy verification plans based on displaced electronic portal imaging device.

It has been observed that under the single isocenter conditions, the potential shifts of the electronic portal imaging devices (EPID) may be introduced when executing portal dosimetry (PD) plans for bilateral breast cancer, pleural mesothelioma, and lymphoma. These shifts are relative to the calibration positions of EPID and result in significant discrepancies in the plan verification results. To explore methods including correction model and specific correction matrices to revise the data obtained from displaced EPID. Two methods, the correction model and the specific correction matrices, were applied to correct the data. Five experiments were designed and conducted to build correction model and to validate the effectiveness of these two methods. Gamma passing rates were calculated and data profiles along X-axis and Y-axis were captured. The gamma passing rates for the EPID-displaced IMRT validation plans after applying correction model, along with the application of specific correction matrices to VMAT and IMRT validation plans, exhibit results that are comparable to the cases with non-displaced EPID. Except for the VMAT plans applied correction model which showed larger discrepancies (0.041±0.028, 0.049±0.030), the other three exhibit minimal differences in discrepancy values. In all profiles, the corrected data from displaced EPID exhibit a high level of agreement with data obtained from non-displaced EPID. Good consistency is observed in actual application of the correction model and the specific correction matrices between gamma passing rates of data corrected and those of non-displaced data. The proposed methods involving correction model and specific correction matrices can correct the data collected from the displaced EPID, and the gamma passing rates of the corrected data show results that are comparable to some extent with those of non-displaced data. Particularly, the results corrected by specific correction matrices closely resemble the data from non-displaced EPID.

Using Earley Parser for Verification of Totally Ordered Hierarchical Plans

Hierarchical planning extends classical planning by capturing the hierarchical structure of tasks via decomposition of tasks to subtasks. Hierarchical plan verification is the problem of determining whether a given plan is valid according to that structure. As decomposition trees in totally ordered hierarchical planning domains resemble parsing trees of context-free grammars, one may exploit the techniques for context-free grammars also for hierarchical plans. In this paper, we study how the Earley parser, proposed for checking whether a given word belongs to a language defined by context-free grammar, can be extended to verification of totally ordered hierarchical plans.

Error detection for radiotherapy planning validation based on deep learning networks.

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3mm; 3%, 2mm; 2%, 2mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

153: Pre-treatment verification of online adaptive SBRT plans for prostate cancer

153: Pre-treatment verification of online adaptive SBRT plans for prostate cancer

3035: Automatization of dosimetric analysis and verification of external radiotherapy treatment plans

3035: Automatization of dosimetric analysis and verification of external radiotherapy treatment plans

Assessment of the deep learning-based gamma passing rate prediction system for 1.5 T magnetic resonance-guided linear accelerator.

Measurement-based verification is impossible for the patient-specific quality assurance (QA) of online adaptive magnetic resonance imaging-guided radiotherapy (oMRgRT) because the patient remains on the couch throughout the session. We assessed a deep learning (DL) system for oMRgRT to predict the gamma passing rate (GPR). This study collected 125 verification plans [reference plan (RP), 100; adapted plan (AP), 25] from patients with prostate cancer treated using Elekta Unity. Based on our previous study, we employed a convolutional neural network that predicted the GPRs of nine pairs of gamma criteria from 1%/1mm to 3%/3mm. First, we trained and tested the DL model using RPs (n=75 and n=25 for training and testing, respectively) for its optimization. Second, we tested the GPR prediction accuracy using APs to determine whether the DL model could be applied to APs. The mean absolute error (MAE) and correlation coefficient (r) of the RPs were 1.22 ± 0.27% and 0.29 ± 0.10 in 3%/2mm, 1.35 ± 0.16% and 0.37 ± 0.15 in 2%/2mm, and 3.62 ± 0.55% and 0.32 ± 0.14 in 1%/1mm, respectively. The MAE and r of the APs were 1.13 ± 0.33% and 0.35 ± 0.22 in 3%/2mm, 1.68 ± 0.47% and 0.30 ± 0.11 in 2%/2mm, and 5.08 ± 0.29% and 0.15 ± 0.10 in 1%/1mm, respectively. The time cost was within 3s for the prediction. The results suggest the DL-based model has the potential for rapid GPR prediction in Elekta Unity.

Investigating the Dosimetric Leaf Gap Correction Factor of Mobius3D Dose Calculation for Volumetric-modulated Arc Radiotherapy Plans.

The dosimetric leaf gap (DLG) is a parameter for correcting radiation transmission through the round leaf end of multileaf collimators. The purpose of this study was to determine and investigate the optimal DLG correction factor for 6 MV volumetric-modulated arc radiotherapy (VMAT) plan dose calculations in Mobius3D. Seventeen VMAT plans were selected for the DLG correction factor optimization process. The optimal DLG correction factor was defined as the minimum difference between the measured dose and the Mobius3D-calculated dose on the Mobius Verification Phantom™ with different DLG correction factors. Subsequently, the optimal DLG correction factor was applied for Mobius3D dose calculation, and accuracy was assessed by comparing the measured and calculated doses. For verification and validation, the 17 previous plans and 10 newly selected plans underwent Mobius3D calculations with the optimal DLG correction factor, and gamma analysis was performed to compare them to the treatment planning system (TPS). Gamma analysis was also performed between the electronic portal imaging device (EPID) and the TPS for cross-comparison between systems. The DLG correction factor was optimized to -1.252, which reduced the average percentage differences between measured and Mobius3D-calculated doses from 2.23% ±1.21% to 0.03% ±1.82%. The cross-comparison between Mobius3D/TPS and EPID/TPS revealed a similar trend in gamma passing rate (>95%) in both the verification and validation plans. The DLG correction factor strongly influences the accuracy of Mobius3D-calculated doses. Applying the optimal DLG correction factor can increase dose agreement and gamma passing rate between calculation and delivered doses of VMAT plans, which emphasizes the importance of optimizing this factor during the commissioning process.

Comprehensive Measurement, Monitoring, Verification Planning Enables Safe CO2 Storage, Risk Reduction, and Operating Cost Optimisation

Comprehensive Measurement, Monitoring, Verification Planning Enables Safe CO2 Storage, Risk Reduction, and Operating Cost Optimisation

On the Computational Complexity of Plan Verification, (Bounded) Plan-Optimality Verification, and Bounded Plan Existence

In this paper we study the computational complexity of several reasoning tasks centered around the bounded plan existence problem. We do this for standard classical planning and hierarchical task network (HTN) planning and each for a grounded and a lifted representation. Whereas bounded plan existence complexity is known for classical planning, it has not yet been studied for HTN planning. For plan verification, results were available for both formalisms except for the lifted HTN planning. We will present lower and upper bounds of the complexity of plan verification in lifted HTN planning and provide novel insights into its grounded counterpart, in which we show that verification is not just NP-complete in the general case, but already for a severely restricted special case. Finally, we show the complexity concerning verifying the optimality of a given plan and discuss its connection to the bounded plan existence problem.

Symbolic Reasoning Methods for AI Planning

Planning is the act of deliberative thinking before acting. It is based on a symbolic model of the world and the options to act in it, usually defined in function-free first-order logic. The task is to find a sequence of actions (a plan) that leads from a given current state to a desired goal state. The basic, purely physical description may be augmented with a partially ordered grammar-like structure (a Hierarchical Task Network or HTN), which can describe expert knowledge, or practical, legal, or operational requirements. In this talk, I will survey a variety of methods for automatically deriving plans using symbolic methods for planning -- from both my past and future research. These symbolic methods -- in some sense -- translate planning problems into other, simpler symbolic representations and reason over them to find plans. As a basis for these methods, I will firstly introduce relevant theoretical results on planning. First, I will discuss the expressive power of planning formalisms (ECAI'14, ICAPS'16) and second, the computational complexity of HTN planning and related tasks such as HTN plan verification, plan modification, and plan recognition (ICAPS'15, ICAPS'16). Based on these theoretical results, I will develop why SAT-based HTN planning is possible and how it can be implemented. To this end, I will survey several of my publications at top-tier conferences, including papers at ICAPS'17, AAAI'18, AAAI'19, IJCAI'19, AAAI'20, and ICAPS'21 -- in which I developed an highly SAT-based planner for HTN problems including the ability to find optimal plans as well as the grounding as a preprocessing step. Here I will also give an outlook on future developments and new ideas that I propose for SAT-based planning -- including the exploitation of structures in plan (e.g.\ landmarks or operator-counting constraints). Next, I will present the idea of expressing lifted classical planning as SAT (ICAPS'22). The resulting planner LiSAT was the first lifted SAT-based planner -- and proved highly efficient and outperformed all other lifted planners at the time of publication. Notably, LiSAT was the first planner (lifted or grounded) and still is the only one to solve the challenging OrganicSynthesis benchmark -- and could even prove optimality for all plans. I will also outline future ideas to further improve the efficiency of LiSAT. Lastly, I introduce the notion of planning with symbolic symbolic representations (AAAI'21 and ICAPS'23). Here one uses Binary Decision Diagrams to encode large sets of states efficiently. For expressing the additional structure encoded by HTNs, I show how BDDs can be suitably integrated into finite automata. Based on this representation, an efficient and optimal planning algorithm can be derived. Additionally, I show how this algorithm can be extended to also cover oversubscription planning.

Imaging and characterization of optical emission from ex vivo tissue during conventional and UHDR PBS proton therapy

Objective. Imaging of optical photons emitted from tissue during radiotherapy is a promising technique for real-time visualization of treatment delivery, offering applications in dose verification, treatment monitoring, and retrospective treatment plan comparison. This research aims to explore the feasibility of intensified imaging of tissue luminescence during proton therapy (PT), under both conventional and ultra-high dose rate (UHDR) conditions. Approach. Conventional and UHDR pencil beam scanning (PBS) PT irradiation of fresh ex vivo porcine tissue and tissue-mimicking plastic phantom was imaged using intensified complementary metal-oxide-semiconductor(CMOS) cameras. The optical emission from tissue was characterized during conventional irradiation using both blue and red-sensitive intensifiers to ensure adequate spectral coverage. Spectral characterization was performed using bandpass filters between the lens and sensor. Imaging of conventional proton fields (240 MeV, 10 nA) was performed at 100 Hz frame rate, while UHDR PBS proton delivery (250 MeV, 99 nA) was recorded at 1 kHz frame rate. Dependence of optical emission yield on proton energy was studied using an optical tissue-mimicking plastic phantom and a range shifter. Finally, we demonstrated fast beam tracking capability of fast camera towards in vivo monitoring of FLASH PT. Main results. Under conventional treatment dose rates optical emission was imaged with single spot resolution. Spot profiles were found to agree with the treatment planning system calculation within >90% for all spectral bands and spot intensity was found to vary with spectral filtration. The resultant polychromatic emission presented a maximum intensity at 650 nm and decreasing signal at lower wavelengths, which is consistent with expected attenuation patterns of high fat and muscle tissue. For UHDR beam imaging, optical yield increased with higher proton energy. Imaging at 1 kHz allowed continuous monitoring of delivery during porcine tissue irradiation, with clear identification of individual dwell positions. The number of dwell positions matched the treatment plan in total and per row showing adequate temporal capability of iCMOS imaging. Significance. For the first time, this study characterizes optical emission from tissue during PT and demonstrates our capability of fast optical tracking of pencil proton beam on the tissue anatomy in both conventional and UHDR setting. Similar to the Cherenkov imaging in radiotherapy, this imaging modality could enable a seamless, independent validation of PT treatments.

Nanodosimetric quantity-weighted dose optimization for carbon-ion treatment planning.

Dose verification of treatment plans is an essential step in radiotherapy workflows. In this work, we propose a novel method of treatment planning based on nanodosimetric quantity-weighted dose (NQWD), which could realize biological representation using pure physical quantities for biological-oriented carbon ion-beam treatment plans and their direct verification. The relationship between nanodosimetric quantities and relative biological effectiveness (RBE) was studied with the linear least-squares method for carbon-ion radiation fields. Next, under the framework of the matRad treatment planning platform, NQWD was optimized using the existing RBE-weighted dose (RWD) optimization algorithm. The schemes of NQWD-based treatment planning were compared with the RWD treatment plans in term of the microdosimetric kinetic model (MKM). The results showed that the nanodosimetric quantity F3 - 10 had a good correlation with the radiobiological effect reflected by the relationship between RBE and F3 - 10. Moreover, the NQWD-based treatment plans reproduced the RWD plans generally. Therefore, F3 - 10 could be adopted as a radiation quality descriptor for carbon-ion treatment planning. The novel method proposed herein not only might be helpful for rapid physical verification of biological-oriented ion-beam treatment plans with the development of experimental nanodosimetry, but also makes the direct comparison of ion-beam treatment plans in different institutions possible. Thus, our proposed method might be potentially developed to be a new strategy for carbon-ion treatment planning and improve patient safety for carbon-ion radiotherapy.

Study on the Performance of Sandwich Pressurization System

This study aims to outline a verification plan for domestic scenarios using the sandwich pressurization method. We employed four methods: T.A.B, hot smoke test, FDS, and pathfinder. T.A.B and fire and evacuation simulation did not exhibit any issues. However, the smoke flow was observed in both the upper and lower layers during the H.S.T. Evacuation safety was confirmed through simulation, including the adequacy of the procedure. Nonetheless, the introduction of smoke in the hot smoke test highlighted the need for enhancements to the sandwich pressurization method. Consequently, the hot smoke test, alongside T.A.B and fire and evacuation simulations, will enhance verification standards for the sandwich pressurization method.

Equivalent-time-active-cavitation-imaging enables vascular-resolution blood-brain-barrier-opening-therapy planning

Objective. Linking cavitation and anatomy was found to be important for predictable outcomes in focused-ultrasound blood-brain-barrier-opening and requires high resolution cavitation mapping. However, cavitation mapping techniques for planning and monitoring of therapeutic procedures either (1) do not leverage the full resolution capabilities of ultrasound imaging or (2) place constraints on the length of the therapeutic pulse. This study aimed to develop a high-resolution technique that could resolve vascular anatomy in the cavitation map. Approach. Herein, we develop BandPass-sampled-equivalent-time-active-cavitation-imaging (BP-ETACI), derived from bandpass sampling and dual-frequency contrast imaging at 12.5 MHz to produce cavitation maps prior and during blood–brain barrier opening with long therapeutic bursts using a 1.5 MHz focused transducer in the brain of C57BL/6 mice. Main results. The BP-ETACI cavitation maps were found to correlate with the vascular anatomy in ultrasound localization microscopy vascular maps and in histological sections. Cavitation maps produced from non-blood-brain-barrier disrupting doses showed the same cavitation-bearing vasculature as maps produced over entire blood-brain-barrier opening procedures, allowing use for (1) monitoring focused-ultrasound blood-brain-barrier-opening (FUS-BBBO), but also for (2) therapy planning and target verification. Significance. BP-ETACI is versatile, created high resolution cavitation maps in the mouse brain and is easily translatable to existing FUS-BBBO experiments. As such, it provides a means to further study cavitation phenomena in FUS-BBBO.

Customizing Management Strategies for Product Introduction in Low-Volume Manufacturing: Enhancing Information Content Quality

Manufacturing companies characterized by high-mix and low-volume production exhibit distinct features that exert a significant influence on the product introduction process. The quality of information exchange between product design and production interfaces becomes paramount in low-volume manufacturing. Consequently, there is a pressing need to explore various methods for adapting and customizing management strategies to align with the unique characteristics of low-volume manufacturing and its associated product introduction processes. Although this knowledge is critical in low-volume manufacturing, most existing studies focus on the management aspects of high-volume manufacturing. Therefore, this study investigated the customization of management strategies for product introduction in low-volume manufacturing to enhance the quality of information content. Drawing upon a longitudinal analysis of new product introductions within a low-volume manufacturing company, this study identified four management strategies—namely, securing a production test and verification plan, revising the role of product introduction management, developing a formal design and production coordination plan, and developing lessons learned management plan. The paper explains how the examined company adapted and implemented product introduction management strategies to facilitate the enhancement of information content quality across three key stages: pre-implementation, post-implementation, and subsequent modification and refinement of strategies following the initial round of implementation. By shedding light on these strategies, this study offers a comprehensive understanding of management approaches for product introduction in low-volume manufacturing. The study makes a valuable contribution to the discourse on information quality management and design-production interface in the low-volume manufacturing and low-volume product introduction literature.

Evaluating the Impact of Bowel Gas Variations for Wilms’ Tumor in Pediatric Proton Therapy

(1) Background: Proton therapy, a precise form of radiation treatment, can be significantly affected by variations in bowel content. The purpose was to identify the most beneficial gantry angles that minimize deviations from the treatment plan quality, thus enhancing the safety and efficacy of proton therapy for Wilms’ tumor patients. (2) Methods: Thirteen patients with Wilms’ tumor, enrolled in the SJWT21 clinical trial, underwent proton therapy. The variations in bowel gas were systematically monitored using daily Cone Beam Computed Tomography (CBCT) imaging. Air cavities identified in daily CBCT images were analyzed to construct daily verification plans and measure water equivalent path length (WEPL) changes. A worst-case scenario simulation was conducted to identify the safest beam angles. (3) Results: The study revealed a maximum decrease in target dose (ΔD100%) of 8.0%, which corresponded to a WEPL variation (ΔWEPL) of 11.3 mm. The average reduction in target dose, denoted as mean ΔD100%, was found to be 2.8%, with a standard deviation (SD) of 3.2%. The mean ΔWEPL was observed as 3.3 mm, with an SD of 2.7 mm. The worst-case scenario analysis suggested that gantry beam angles oriented toward the patient’s right and posterior aspects from 110° to 310° were associated with minimized WEPL discrepancies. (4) Conclusions: This study comprehensively evaluated the influence of bowel gas variability on treatment plan accuracy and proton range uncertainties in pediatric proton therapy for Wilms’ tumor.

Principle of signal-to-noise ratio direct measurement

The sensitivity of radio receivers and other systems used in all fields of practical applications of electronics is carried out at certain signal-to-noise ratio value. Examples of systems, for which this ratio is one of the important qualitative indicators, are given. The aim of the investigation is to solve the actual problem of signal-to-noise ratio measurement in a wide range of frequencies, simplify instruments and improve their technical and metrological parameters. A principle of signal-to-noise ratio measurement has been proposed, its theoretical foundation has been given. Precise relationship between the signal-to-noise ratio and the parameters of noise amplitude, phase and frequency modulation has been established, thus modulation meters can measure signal-to-noise ratio. There are three options for implementing the developed measurement principle. All of them use one existing industrial modulation meter and provide direct signal-to-noise ratio. For two options there are no restrictions on the origin and type of noise, it may be arbitrary. A pilot test of the measurement principle was conducted, and three implementation options were actually tested. Their discrepancies with the original data of the signal and noise generator lie within error limits of the instrumentation of the former. Mathematical modeling of the principle of signal-to-noise ratio measurement in Mathcad software package was also carried out. Recommendations on the use of the principle are given. The fundamental advantages of the developed principle consist of simple implementation by a single industrial instrument and solving the problem of metrological ensuring of signal-to-noise measuring instruments using the existing state verification plans. The main technical and metrological characteristics obtained by using the principle are determined by parameters of existing modulation meters and are proven to be high, the frequency range is till 26,5 GHz and the measurement error is as low as 8 %.

Comprehensive characterisation of the IBA myQA SRS for SRS and SBRT patient specific quality assurance.

The myQA SRS (IBA) is a new to market 2D complementary metal oxide semiconductor detector array with an active area 140 × 120mm2 and 0.4mm resolution, making it a potential real-time dosimetry alternative to radiochromic film for stereotactic plan verification. Characterisation of the device was completed to assess performance. The dosimetric properties of the device were assessed for 6FF and 6FFF beams from a Varian TrueBeam STx with high definition multileaf collimator. Clinical suitability of the device for Patient Specific Quality Assurance was verified using ten SRS/SBRT plans, compared against other detectors, as well as multi leaf collimator (MLC) tests including picket fence and chair. Gamma analysis was performed using myQA software with criteria of 4%/1mm. The device demonstrated compliance with recommended specifications for basic tests. After the required warm-up period, the maximum deviation in detector signal from initial readings was 0.2%. Short-term and long-term reproducibility was 0.1% (6FF) and 1.0% (6FFF), respectively. Dose linearity was within 0.3% (6FF) and 0.7% (6FFF) and dose-rate dependence within 1.7% (6FF) and 2.9% (6FFF) and were verified with a Farmer type ionization chamber (PTW 30013). Angular dependence was quantified for coplanar and non-coplanar situations. Output factors and beam profiles measured on the device showed agreement within 1% of baseline RAZOR diode (IBA) and CC04 ionisation chamber (IBA) measurements for field sizes 1 × 1 to 10 × 10cm2. The minimum gamma (4%/1mm) pass rates for MLC-pattern tests were 96.5% and 98.1% for the myQA SRS and film, respectively. The average gamma (4%/1mm) pass rates for SBRT and SRS plans were 98.8% and 99.8% respectively. This work represents one of the first studies performed on the commissioning and performance characterisation of this novel device, demonstrating its accuracy and reliability, making it highly useful as a film alternative in stereotactic treatment plan verification.

Comparison of EPID portal dosimetry verification and RadCalc dose verification for VMAT treatment plans

Comparison of EPID portal dosimetry verification and RadCalc dose verification for VMAT treatment plans