Measurement‐based range evaluation for quality assurance of CBCT‐based dose calculations in adaptive proton therapy

Neppl, S.; Kurz, Christopher; Köpl, Daniel; Yohannes, Indra; Schneider, Moritz; Bondesson, David; Rabe, Moritz; Belka, Claus; Dietrich, Olaf; Landry, Guillaume; Parodi, Katia; Kamp, Florian

doi:10.1002/mp.14995

Cited by 8 publications

(7 citation statements)

References 33 publications

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“…vCTs may fail for entities with significant interfractional anatomical changes. 19 An alternative is the use of scatter correction algorithms (SCA) [19][20][21][22][23][24] that utilize the vCT as a prior. Most of these studies focused on 3D images, and the few that considered 4D images typically used a vCT-based approach and focused on phantom validation.…”

Section: Introductionmentioning

confidence: 99%

Scatter correction of 4D cone beam computed tomography to detect dosimetric effects due to anatomical changes in proton therapy for lung cancer

et al. 2023

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BackgroundThe treatment of moving tumor entities is expected to have superior clinical outcomes, using image‐guided adaptive intensity‐modulated proton therapy (IMPT).PurposeFor 21 lung cancer patients, IMPT dose calculations were performed on scatter‐corrected 4D cone beam CTs (4DCBCTcor) to evaluate their potential for triggering treatment adaptation. Additional dose calculations were performed on corresponding planning 4DCTs and day‐of‐treatment 4D virtual CTs (4DvCTs).MethodsA 4DCBCT correction workflow, previously validated on a phantom, generates 4DvCT (CT‐to‐CBCT deformable registration) and 4DCBCTcor images (projection‐based correction using 4DvCT as a prior) with 10 phase bins, using day‐of‐treatment free‐breathing CBCT projections and planning 4DCT images as input. Using a research planning system, robust IMPT plans administering eight fractions of 7.5 Gy were created on a free‐breathing planning CT (pCT) contoured by a physician. The internal target volume (ITV) was overridden with muscle tissue. Robustness settings for range and setup uncertainties were 3% and 6 mm, and a Monte Carlo dose engine was used. On every phase of planning 4DCT, day‐of‐treatment 4DvCT, and 4DCBCTcor, the dose was recalculated. For evaluation, image analysis as well as dose analysis were performed using mean error (ME) and mean absolute error (MAE) analysis, dose–volume histogram (DVH) parameters, and 2%/2‐mm gamma pass rate analysis. Action levels (1.6% ITV D98 and 90% gamma pass rate) based on our previous phantom validation study were set to determine which patients had a loss of dosimetric coverage.ResultsQuality enhancements of 4DvCT and 4DCBCTcor over 4DCBCT were observed. ITV D98% and bronchi D2% had its largest agreement for 4DCBCTcor–4DvCT, and the largest gamma pass rates (>94%, median 98%) were found for 4DCBCTcor–4DvCT. Deviations were larger and gamma pass rates were smaller for 4DvCT–4DCT and 4DCBCTcor–4DCT. For five patients, deviations were larger than the action levels, suggesting substantial anatomical changes between pCT and CBCT projections acquisition.ConclusionsThis retrospective study shows the feasibility of daily proton dose calculation on 4DCBCTcor for lung tumor patients. The applied method is of clinical interest as it generates up‐to‐date in‐room images, accounting for breathing motion and anatomical changes. This information could be used to trigger replanning.

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Section: Introductionmentioning

confidence: 99%

Scatter correction of 4D cone beam computed tomography to detect dosimetric effects due to anatomical changes in proton therapy for lung cancer

et al. 2023

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“…Several groups have studied proton range by using cone-beam CT (CBCT) imaging of the patient on the couch. [8][9][10] The calculated proton range error for CBCT is high as a result of image quality and scatter 11,12 but can be mitigated by scatter correction 13,14 or generation of a synthetic CT. 15,16 An alternative is the use of proton range change in the CBCT from day 1, because we are more interested in the relative change of the range instead of the exact range itself. Without scatter correction,one phantom study showed that the error of change of the calculated water-equivalent thickness (WET; the surrogate of proton range from daily CBCTs) was within 2 mm per 1 cm of phantom size change.…”

Section: Introductionmentioning

confidence: 99%

“…Several groups have studied proton range by using cone‐beam CT (CBCT) imaging of the patient on the couch 8–10 . The calculated proton range error for CBCT is high as a result of image quality and scatter 11,12 but can be mitigated by scatter correction 13,14 or generation of a synthetic CT 15,16 . An alternative is the use of proton range change in the CBCT from day 1, because we are more interested in the relative change of the range instead of the exact range itself.…”

Section: Introductionmentioning

confidence: 99%

Use of CBCT plus plan robustness for reducing QACT frequency in intensity‐modulated proton therapy: Head‐and‐neck cases

et al. 2022

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Purpose Anatomic variation has a significant dosimetric impact in intensity‐modulated proton therapy. Weekly or biweekly computed tomography (CT) scans, called quality assurance CTs (QACTs), are used to monitor anatomic and resultant dose changes to determine whether adaptive plans are needed. Frequent CT scans result in unwanted QACT dose and increased clinical workloads. This study proposed utilizing patient setup cone‐beam CTs (CBCTs) and treatment plan robustness to reduce the frequency of QACTs. Methods We retrospectively analyzed data from 27 patients with head‐and‐neck cancer, including 594 CBCTs, 136 QACTs, and 19 adaptive plans. For each CBCT, water‐equivalent thickness (WET) along the pencil‐beam path was calculated. For each treatment plan, the WET of the first‐day CBCT was used as the reference, and the mean WET changes (ΔWET) in each following CBCT was used as the surrogate of proton range change. Using CBCTs acquired prior to a QACT, we predicted the ΔWET on the QACT day by a linear regression model. The impact of range change on target dose was calculated as the predicted ΔWET weighted by the monitor units of each field. In addition, plan robustness was estimated from the robust dose‐volume histograms (DVHs) and combined with ΔWET to reduce QACT frequency. Robustness was estimated from the distance between the DVH curves of the nominal and worst scenarios. Results When the estimated mean ΔWET was <6.5 mm (or <7.5 mm if the robustness was >95%), the QACT could be skipped without missing any adaptive planning; otherwise a QACT was required. Overall, 41% of QACTs could be eliminated when ΔWET was <6.5 mm and 56% when ΔWET was <7.5 mm, and robustness was >95%. At least one QACT could have been omitted in 25 of the 27 cases under skipping thresholds at ΔWETs <7.5 mm and R > 95%. Conclusion This study suggests that the number of QACTs can be greatly reduced by calculating range change in patient setup CBCTs and can be further reduced by combining this information with analyses of plan robustness.

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“…In addition to the conventional CT scan used for initial radiotherapy treatment planning, volumetric on‐board imaging has been integrated into most linear accelerators (linacs) and proton therapy systems to provide image‐guided radiotherapy (IGRT), allowing for pre‐treatment localization, monitoring of anatomical changes, and online plan adaptation. On‐board cone‐beam CT (CBCT), for example, is routinely used for these purposes and has demonstrated utility for daily dose reconstruction 13–15 . While kV imaging systems are widely used in many systems, they can introduce several disadvantages for treatment planning and dose calculation.…”

Section: Introductionmentioning

confidence: 99%

“…Onboard cone-beam CT (CBCT), for example, is routinely used for these purposes and has demonstrated utility for daily dose reconstruction. [13][14][15] While kV imaging systems are widely used in many systems, they can introduce several disadvantages for treatment planning and dose calculation. Onboard kV CBCT imaging requires that additional hardware be integrated into the accelerator, resulting in different imaging and treatment isocenters.…”

Section: Introductionmentioning

confidence: 99%

Generation of synthetic megavoltage CT for MRI‐only radiotherapy treatment planning using a 3D deep convolutional neural network

et al. 2022

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Background: Megavoltage computed tomography (MVCT) has been implemented on many radiotherapy treatment machines for on-board anatomical visualization, localization, and adaptive dose calculation. Implementing an MRonly workflow by synthesizing MVCT from magnetic resonance imaging (MRI) would offer numerous advantages for treatment planning and online adaptation. Purpose: In this work, we sought to synthesize MVCT (sMVCT) datasets from MRI using deep learning to demonstrate the feasibility of MRI-MVCT only treatment planning. Methods: MVCTs and T1-weighted MRIs for 120 patients treated for head-andneck cancer were retrospectively acquired and co-registered. A deep neural network based on a fully-convolutional 3D U-Net architecture was implemented to map MRI intensity to MVCT HU. Input to the model were volumetric patches generated from paired MRI and MVCT datasets. The U-Net was initialized with random parameters and trained on a mean absolute error (MAE) objective function. Model accuracy was evaluated on 18 withheld test exams. sMVCTs were compared to respective MVCTs. Intensity-modulated volumetric radiotherapy (IMRT) plans were generated on MVCTs of four different disease sites and compared to plans calculated onto corresponding sMVCTs using the gamma metric and dose-volume-histograms (DVHs). Results: MAE values between sMVCT and MVCT datasets were 93.3 ± 27.5, 78.2 ± 27.5, and 138.0 ± 43.4 HU for whole body, soft tissue, and bone volumes, respectively. Overall, there was good agreement between sMVCT and MVCT, with bone and air posing the greatest challenges. The retrospective dataset introduced additional deviations due to sinus filling or tumor growth/shrinkage between scans, differences in external contours due to variability in patient positioning,or when immobilization devices were absent from diagnostic MRIs.Dose distributions of IMRT plans evaluated for four test cases showed close agreement between sMVCT and MVCT images when evaluated using DVHs and gamma dose metrics, which averaged to 98.9 ± 1.0% and 96.8 ± 2.6% analyzed at 3%/3 mm and 2%/2 mm, respectively. Conclusions: MVCT datasets can be generated from T1-weighted MRI using a 3D deep convolutional neural network with dose calculation on a sample sMVCT in close agreement with the MVCT. These results demonstrate the feasibility of using MRI-derived sMVCT in an MR-only treatment planning workflow.

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Measurement‐based range evaluation for quality assurance of CBCT‐based dose calculations in adaptive proton therapy

Cited by 8 publications

References 33 publications

Scatter correction of 4D cone beam computed tomography to detect dosimetric effects due to anatomical changes in proton therapy for lung cancer

Scatter correction of 4D cone beam computed tomography to detect dosimetric effects due to anatomical changes in proton therapy for lung cancer

Use of CBCT plus plan robustness for reducing QACT frequency in intensity‐modulated proton therapy: Head‐and‐neck cases

Generation of synthetic megavoltage CT for MRI‐only radiotherapy treatment planning using a 3D deep convolutional neural network

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