Comparison of weekly and daily online adaptation for head and neck intensity-modulated proton therapy

The high conformality of intensity-modulated proton therapy (IMPT) dose distributions causes treatment plans to be sensitive to geometrical changes during the course of a fractionated treatment. This can be addressed using adaptive proton therapy (APT). One important question in APT is the frequency of adaptations performed during a fractionated treatment, which is related to the question whether plan adaptation has to be done online or offline. The purpose of this work is to investigate the impact of weekly and daily online IMPT plan adaptation on the treatment quality for head and neck patients. A cohort of ten head and neck patients with daily acquired cone-beam CT (CBCT) images was evaluated retrospectively. Dose tracking of the IMPT treatment was performed for three scenarios: base plan with no adaptation (BP), weekly online adaptation (OA_W), and daily online adaptation (OA_D). Both adaptation schemes used an in-house developed online APT workflow, performing Monte Carlo (MC) dose calculations on scatter-corrected CBCTs. IMPT plan adaptation was achieved by only tuning the weights of a subset of beamlets, based on deformable image registration from the planning CT to each CBCT. Although OA_Dmitigated random delivery errors more effectively than OA_Won a fraction per fraction basis, both OA_Wand OA_Dachieved the clinical goals for all ten patients, while BP failed for six cases. In the high-risk CTV, accumulated values of D_98%ranged between 97.15% and 99.73% of the prescription dose for OA_D, with a median of 98.07%. For OA_W, values between 95.02% and 99.26% were obtained, with a median of 97.61% of the prescription dose. Otherwise, the dose to most organs at risk was similar for all three scenarios. Globally, our results suggest that OA_Wcould be used as an alternative approach to OA_Dfor most patients in order to reduce the clinical workload.

Assessment of daily dose accumulation for robustly optimized intensity modulated proton therapy treatment of prostate cancer

PURPOSE

To implement a daily CBCT based dose accumulation technique in order to assess ideal robust optimization (RO) parameters for IMPT treatment of prostate cancer.

METHODS

Ten prostate cancer patients previously treated with VMAT and having daily CBCT were included. First, RO-IMPT plans were created with ± 3 mm and ± 5 mm patient setup and ± 3% proton range uncertainties, respectively. Second, the planning CT (pCT) was deformably registered to the CBCT to create a synthetic CT (sCT). Both daily and weekly sampling strategies were employed to determine optimal dose accumulation frequency. Doses were recalculated on sCTs for both ± 3 mm/±3% and ± 5 mm/±3% uncertainties and were accumulated back to the pCT. Accumulated doses generated from ± 3 mm/±3% and ± 5 mm/±3% RO-IMPT plans were evaluated using the clinical dose volume constraints for CTV, bladder, and rectum.

RESULTS

Daily accumulated dose based on both ± 3mm/±3% and ±5 mm/±3% uncertainties for RO-IMPT plans resulted in satisfactory CTV coverage (RO-IMPT_3mm/3% CTV_V95 = 99.01 ± 0.87% vs. RO-IMPT_5mm/3% CTV_V95 = 99.81 ± 0.2%, P = 0.002). However, the accumulated dose based on ± 3 mm/3% RO-IMPT plans consistently provided greater OAR sparing than ±5 mm/±3% RO-IMPT plans (RO-IMPT_3mm/3% rectum_V65Gy = 2.93 ± 2.39% vs. RO-IMPT_5mm/3% rectum_V65Gy = 4.38 ± 3%, P < 0.01; RO-IMPT_3mm/3% bladder_V65Gy = 5.2 ± 7.12% vs. RO-IMPT_5mm/3% bladder_V65Gy = 7.12 ± 9.59%, P < 0.01). The gamma analysis showed high dosimetric agreement between weekly and daily accumulated dose distributions.

CONCLUSIONS

This study demonstrated that for RO-IMPT optimization, ±3mm/±3% uncertainty is sufficient to create plans that meet desired CTV coverage while achieving superior sparing to OARs when compared with ± 5 mm/±3% uncertainty. Furthermore, weekly dose accumulation can accurately estimate the overall dose delivered to prostate cancer patients.

Synthetic CT in assessment of anatomical and dosimetric variations in radiotherapy - procedure validation

Introduction: One of many procedures to control the quality of radiotherapy is daily imaging of the patient’s anatomy. The CBCT (Cone Beam Computed Tomography) plays an important role in patient positioning, and dose delivery monitoring. Nowadays, CBCT is a baseline for the calculation of fraction and total dose. Thus, it provides the potential for more comprehensive monitoring of the delivered dose and adaptive radiotherapy. However, due to the poor quality and the presence of numerous artifacts, the replacement of the CBCT image with the corrected one is desired for dose calculation. The aim of the study was to validate a method for generating a synthetic CT image based on deformable image registration.

Material and methods: A Head & Torso Freepoint phantom, model 002H9K (Computerized Imaging Reference Systems, Norfolk, USA) with inserts was imaged with CT (Computed Tomography). Then, contouring and treatment plan were created in Eclipse (Varian Medical Systems, Palo Alto, CA, USA) treatment planning system. The phantom was scanned again with the CBCT. The planning CT was registered and deformed to the CBCT, resulting in a synthetic CT in Velocity software (Varian Medical Systems, Palo Alto, CA, USA). The dose distribution was recalculated based on the created CT image.

Results: Differences in structure volumes and dose statistics calculated both on CT and synthetic CT were evaluated. Discrepancies between the original and delivered plan from 0.0 to 2.5% were obtained. Dose comparison was performed on the DVH (Dose-Volume Histogram) for all delineated inserts.

Conclusions: Our findings suggest the potential utility of deformable registration and synthetic CT for providing dose reconstruction. This study reports on the limitation of the procedure related to the limited length of the CBCT volume and deformable fusion inaccuracies.

Accumulation of the delivered dose based on cone-beam CT and deformable image registration for non-small cell lung cancer treated with hypofractionated radiotherapy

Background

This study aimed to quantify the dosimetric differences between the planned and delivered dose to tumor and normal organs in locally advanced non-small cell lung cancer (LANSCLC) treated with hypofractionated radiotherapy (HRT), and to explore the necessity and identify optimal candidates for adaptive radiotherapy (ART).

Methods

Twenty-seven patients with stage III NSCLC were enrolled. Planned radiation dose was 51Gy in 17 fractions with cone-beam CT (CBCT) acquired at each fraction. Virtual CT was generated by deformable image registration (DIR) of the planning CT to CBCT for dose calculation and accumulation. Dosimetric parameters were compared between original and accumulated plans using Wilcoxon signed rank test. Correlations between dosimetric differences and clinical variables were analyzed using Mann-Whitney U test or Chi-square test.

Results

Patients had varied gross tumor volume (GTV) reduction by HRT (median reduction rate 11.1%, range − 2.9-44.0%). The V₅₁ of planning target volume for GTV (PTV-GTV) was similar between original and accumulated plans (mean, 88.2% vs. 87.6%, p = 0.452). Only 11.1% of patients had above 5% relative decrease in V₅₁ of PTV-GTV in accumulated plans. Compared to the original plan, limited increase (median relative increase < 5%) was observed in doses of total lung (mean dose, V₂₀ and V₃₀), esophagus (mean dose, maximum dose) and heart (mean dose, V₃₀ and V₄₀) in accumulated plans. Less than 30% of patients had above 5% relative increase of lung or heart doses. Patients with quick tumor regression or baseline obstructive pneumonitis showed more notable increase in doses to normal structures. Patients with baseline obstructive atelectasis showed notable decrease (10.3%) in dose coverage of PTV-GTV.

Conclusions

LANSCLC patients treated with HRT had sufficient tumor dose coverage and acceptable normal tissue dose deviation. ART should be applied in patients with quick tumor regression and baseline obstructive pneumonitis/atelectasis to spare more normal structures.

Supplementary Information

Supplementary information accompanies this paper at 10.1186/s12885-020-07617-3.

Evaluation of an a priori scatter correction algorithm for cone-beam computed tomography based range and dose calculations in proton therapy

BACKGROUND AND PURPOSE

Scatter correction of cone-beam computed tomography (CBCT) projections may enable accurate online dose-delivery estimations in photon and proton-based radiotherapy. This study aimed to evaluate the impact of scatter correction in CBCT-based proton range/dose calculations, in scans acquired in both proton and photon gantries.

MATERIAL AND METHODS

CBCT projections of a Catphan and an Alderson phantom were acquired on both a proton and a photon gantry. The scatter corrected CBCTs (corrCBCTs) and the clinical reconstructions (stdCBCTs) were compared against CTs rigidly registered to the CBCTs (rigidCTs). The CBCTs of the Catphan phantom were segmented by materials for CT number analysis. Water equivalent path length (WEPL) maps were calculated through the Alderson phantom while proton plans optimized on the rigidCT and recalculated on all CBCTs were compared in a gamma analysis.

RESULTS

In medium and high-density materials, the corrCBCT CT numbers were much closer to those of the rigidCT than the stdCBCTs. E.g. in the 50% bone segmentations the differences were reduced from above 300 HU (with stdCBCT) to around 60-70 HU (with corrCBCT). Differences in WEPL from the rigidCT were typically well below 5 mm for the corrCBCTs, compared to well above 10 mm for the stdCBCTs with the largest deviations in the head and thorax regions. Gamma pass rates (2%/2mm) when comparing CBCT-based dose re-calculations to rigidCT calculations were improved from around 80% (with stdCBCT) to mostly above 90% (with corrCBCT).

CONCLUSION

Scatter correction leads to substantial artefact reductions, improving accuracy of CBCT-based proton range/dose calculations.

Comparison of CBCT-based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning

PURPOSE

Anatomical variations occur during head and neck (H&N) radiotherapy treatment. kV cone-beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) from CBCT to perform dose calculation. This study aims to evaluate the accuracy of a DLM and to compare this method with three existing methods of dose calculation from CBCT in H&N cancer radiotherapy.

METHODS

Forty-four patients received VMAT for H&N cancer (70-63-56 Gy). For each patient, reference CT (Bigbore, Philips) and CBCT images (XVI, Elekta) were acquired. The DLM was based on a generative adversarial network. The three compared methods were: (a) a method using a density to Hounsfield Unit (HU) relation from phantom CBCT image (HU-D curve method), (b) a water-air-bone density assignment method (DAM), and iii) a method using deformable image registration (DIR). The imaging endpoints were the mean absolute error (MAE) and mean error (ME) of HU from pCT and reference CT (CT_ref ). The dosimetric endpoints were dose discrepancies and 3D gamma analyses (local, 2%/2 mm, 30% dose threshold). Dose discrepancies were defined as the mean absolute differences between DVHs calculated from the CT_ref and pCT of each method.

RESULTS

In the entire body, the MAEs and MEs of the DLM, HU-D curve method, DAM, and DIR method were 82.4 and 17.1 HU, 266.6 and 208.9 HU, 113.2 and 14.2 HU, and 95.5 and -36.6 HU, respectively. The MAE obtained using the DLM differed significantly from those of other methods (Wilcoxon, P ≤ 0.05). The DLM dose discrepancies were 7 ± 8 cGy (maximum = 44 cGy) for the ipsilateral parotid gland D_mean and 5 ± 6 cGy (max = 26 cGy) for the contralateral parotid gland mean dose (D_mean ). For the parotid gland D_mean , no significant dose difference was observed between the DLM and other methods. The mean 3D gamma pass rate ± standard deviation was 98.1 ± 1.2%, 91.0 ± 5.3%, 97.9 ± 1.6%, and 98.8 ± 0.7% for the DLM, HU-D method, DAM, and DIR method, respectively. The gamma pass rates and mean gamma results of the HU-D curve method, DAM, and DIR method differed significantly from those of the DLM.

CONCLUSIONS

For H&N radiotherapy, DIR method and DLM appears as the most appealing CBCT-based dose calculation methods among the four methods in terms of dose accuracy as well as calculation time. Using the DIR method or DLM with CBCT images enables dose monitoring in the parotid glands during the treatment course and may be used to trigger replanning.

A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer

•

A deep learning network facilitated dose calculation from CBCT.

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A single network achieved CBCT-based dose calculation generating synthetic CT for head-and-neck, lung, and breast cancer patients with similar performance to a network specifically trained for each anatomical site.

•

Generation of synthetic-CT can be achieved within 10 s, facilitating online adaptive radiotherapy scenarios.

Background and purpose Adaptive radiotherapy based on cone-beam computed tomography (CBCT) requires high CT number accuracy to ensure accurate dose calculations. Recently, deep learning has been proposed for fast CBCT artefact corrections on single anatomical sites. This study investigated the feasibility of applying a single convolutional network to facilitate dose calculation based on CBCT for head-and-neck, lung and breast cancer patients.

Materials and Methods Ninety-nine patients diagnosed with head-and-neck, lung or breast cancer undergoing radiotherapy with CBCT-based position verification were included in this study. The CBCTs were registered to planning CT according to clinical procedures. Three cycle-consistent generative adversarial networks (cycle-GANs) were trained in an unpaired manner on 15 patients per anatomical site generating synthetic-CTs (sCTs). Another network was trained with all the anatomical sites together. Performances of all four networks were compared and evaluated for image similarity against rescan CT (rCT). Clinical plans were recalculated on rCT and sCT and analysed through voxel-based dose differences and γ-analysis.

Results A sCT was generated in 10 s. Image similarity was comparable between models trained on different anatomical sites and a single model for all sites. Mean dose differences <0.5% were obtained in high-dose regions. Mean gamma (3%, 3 mm) pass-rates >95% were achieved for all sites.

Conclusion Cycle-GAN reduced CBCT artefacts and increased similarity to CT, enabling sCT-based dose calculations. A single network achieved CBCT-based dose calculation generating synthetic CT for head-and-neck, lung, and breast cancer patients with similar performance to a network specifically trained for each anatomical site.

Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy

In-room imaging is a prerequisite for adaptive proton therapy. The use of onboard cone-beam computed tomography (CBCT) imaging, which is routinely acquired for patient position verification, can enable daily dose reconstructions and plan adaptation decisions. Image quality deficiencies though, hamper dose calculation accuracy and make corrections of CBCTs a necessity. This study compared three methods to correct CBCTs and create synthetic CTs that are suitable for proton dose calculations. CBCTs, planning CTs and repeated CTs (rCT) from 33 H&N cancer patients were used to compare a deep convolutional neural network (DCNN), deformable image registration (DIR) and an analytical image-based correction method (AIC) for synthetic CT (sCT) generation. Image quality of sCTs was evaluated by comparison with a same-day rCT, using mean absolute error (MAE), mean error (ME), Dice similarity coefficient (DSC), structural non-uniformity (SNU) and signal/contrast-to-noise ratios (SNR/CNR) as metrics. Dosimetric accuracy was investigated in an intracranial setting by performing gamma analysis and calculating range shifts. Neural network-based sCTs resulted in the lowest MAE and ME (37/2 HU) and the highest DSC (0.96). While DIR and AIC generated images with a MAE of 44/77 HU, a ME of -8/1 HU and a DSC of 0.94/0.90. Gamma and range shift analysis showed almost no dosimetric difference between DCNN and DIR based sCTs. The lower image quality of AIC based sCTs affected dosimetric accuracy and resulted in lower pass ratios and higher range shifts. Patient-specific differences highlighted the advantages and disadvantages of each method. For the set of patients, the DCNN created synthetic CTs with the highest image quality. Accurate proton dose calculations were achieved by both DCNN and DIR based sCTs. The AIC method resulted in lower image quality and dose calculation accuracy was reduced compared to the other methods.

Online daily adaptive proton therapy

It is recognized that the use of a single plan calculated on an image acquired some time before the treatment is generally insufficient to accurately represent the daily dose to the target and to the organs at risk. This is particularly true for protons, due to the physical finite range. Although this characteristic enables the generation of steep dose gradients, which is essential for highly conformal radiotherapy, it also tightens the dependency of the delivered dose to the range accuracy. In particular, the use of an outdated patient anatomy is one of the most significant sources of range inaccuracy, thus affecting the quality of the planned dose distribution. A plan should be ideally adapted as soon as anatomical variations occur, ideally online. In this review, we describe in detail the different steps of the adaptive workflow and discuss the challenges and corresponding state-of-the art developments in particular for an online adaptive strategy.

Managing treatment-related uncertainties in proton beam radiotherapy for gastrointestinal cancers

In recent years, there has been rapid adaption of proton beam radiotherapy (RT) for treatment of various malignancies in the gastrointestinal (GI) tract, with increasing number of institutions implementing intensity modulated proton therapy (IMPT). We review the progress and existing literature regarding the technical aspects of RT planning for IMPT, and the existing tools that can help with the management of uncertainties which may impact the daily delivery of proton therapy. We provide an in-depth discussion regarding range uncertainties, dose calculations, image guidance requirements, organ and body cavity filling consideration, implanted devices and hardware, use of fiducials, breathing motion evaluations and both active and passive motion management methods, interplay effect, general IMPT treatment planning considerations including robustness plan evaluation and optimization, and finally plan monitoring and adaptation. These advances have improved confidence in delivery of IMPT for patients with GI malignancies under various scenarios.

Visual enhancement of Cone-beam CT by use of CycleGAN

PURPOSE

Cone-beam computed tomography (CBCT) offers advantages over conventional fan-beam CT in that it requires a shorter time and less exposure to obtain images. However, CBCT images suffer from low soft-tissue contrast, noise, and artifacts compared to conventional fan-beam CT images. Therefore, it is essential to improve the image quality of CBCT.

METHODS

In this paper, we propose a synthetic approach to translate CBCT images with deep neural networks. Our method requires only unpaired and unaligned CBCT images and planning fan-beam CT (PlanCT) images for training. The CBCT images and PlanCT images may be obtained from other patients as long as they are acquired with the same scanner settings. Once trained, three-dimensionally reconstructed CBCT images can be directly translated into high-quality PlanCT-like images.

RESULTS

We demonstrate the effectiveness of our method with images obtained from 20 prostate patients, and provide a statistical and visual comparison. The image quality of the translated images shows substantial improvement in voxel values, spatial uniformity, and artifact suppression compared to those of the original CBCT. The anatomical structures of the original CBCT images were also well preserved in the translated images.

CONCLUSIONS

Our method produces visually PlanCT-like images from CBCT images while preserving anatomical structures.

Beam angle optimization using angular dependency of range variation assessed via water equivalent path length (WEPL) calculation for head and neck proton therapy

PURPOSE

To investigate angular sensitivity of proton range variation due to anatomic change in patients and patient setup error via water equivalent path length (WEPL) calculations.

METHODS

Proton range was estimated by calculating WEPL to the distal edge of target volume using planning CT (pCT) and weekly scatter-corrected cone-beam CT (CBCT) images of 11 head and neck patients. Range variation was estimated as the difference between the distal WEPLs calculated on pCT and scatter-corrected CBCT (cCBCT). This WEPL analysis was performed every five degrees ipsilaterally to the target. Statistics of the distal WEPL difference were calculated over the distal area to compare between different beam angles. Physician-defined contours were used for the WEPL calculation on both pCT and cCBCT, not considering local deformation of target volume. It was also tested if a couch kick (10°) can mitigate the range variation due to anatomic change and patient setup error.

RESULTS

For most of the patients considered, median, 75% quantile, and 95% quantile of the distal WEPL difference were largest for posterior oblique angles, indicating a higher chance of overdosing normal tissues at distal edge with these angles. Using a couch kick resulted in decrease in the WEPL difference for some posterior oblique angles.

CONCLUSIONS

It was demonstrated that the WEPL change has angular dependency for the cohort of head and neck cancer patients. Selecting beam configuration robust to anatomic change in patient and patient setup error may improve the treatment outcome of head and neck proton therapy.

CBCT correction using a cycle-consistent generative adversarial network and unpaired training to enable photon and proton dose calculation

In presence of inter-fractional anatomical changes, clinical benefits are anticipated from image-guided adaptive radiotherapy. Nowadays, cone-beam CT (CBCT) imaging is mostly utilized during pre-treatment imaging for position verification. Due to various artifacts, image quality is typically not sufficient for photon or proton dose calculation, thus demanding accurate CBCT correction, as potentially provided by deep learning techniques. This work aimed at investigating the feasibility of utilizing a cycle-consistent generative adversarial network (cycleGAN) for prostate CBCT correction using unpaired training. Thirty-three patients were included. The network was trained to translate uncorrected, original CBCT images (CBCT_org) into planning CT equivalent images (CBCT_cycleGAN). HU accuracy was determined by comparison to a previously validated CBCT correction technique (CBCT_cor). Dosimetric accuracy was inferred for volumetric-modulated arc photon therapy (VMAT) and opposing single-field uniform dose (OSFUD) proton plans, optimized on CBCT_cor and recalculated on CBCT_cycleGAN. Single-sided SFUD proton plans were utilized to assess proton range accuracy. The mean HU error of CBCT_cycleGAN with respect to CBCT_cor decreased from 24 HU for CBCT_org to  -6 HU. Dose calculation accuracy was high for VMAT, with average pass-rates of 100%/89% for a 2%/1% dose difference criterion. For proton OSFUD plans, the average pass-rate for a 2% dose difference criterion was 80%. Using a (2%, 2 mm) gamma criterion, the pass-rate was 96%. 93% of all analyzed SFUD profiles had a range agreement better than 3 mm. CBCT correction time was reduced from 6-10 min for CBCT_cor to 10 s for CBCT_cycleGAN. Our study demonstrated the feasibility of utilizing a cycleGAN for CBCT correction, achieving high dose calculation accuracy for VMAT. For proton therapy, further improvements may be required. Due to unpaired training, the approach does not rely on anatomically consistent training data or potentially inaccurate deformable image registration. The substantial speed-up for CBCT correction renders the method particularly interesting for adaptive radiotherapy.

Daily adaptive proton therapy - the key to innovative planning approaches for paranasal cancer treatments

Background: For proton therapy of paranasal tumors, field directions avoiding volumes that might change during therapy are typically used. If the plan is optimized on the daily anatomy using daily adapted proton therapy (DAPT) however, field directions crossing the nasal cavities might be feasible. In this study, we investigated the effectiveness of DAPT for enabling narrow-field treatment approaches. Material and methods: For five paranasal tumor patients, representing a wide patient spectrum, anatomically robust 4-field-star and narrow-field plans were calculated and their robustness to anatomical and setup uncertainties was compared with and without DAPT. Based on the nominal planning CTs, per patient up to 125 simulated CTs (simCTs) with different nasal cavity fillings were created and random translations and rotations due to patient setup uncertainties were further simulated. Plans were recalculated or re-optimized on all error scenarios, representing non-adapted and DAPT fractions, respectively. From these, 100 possible treatments (60 GyRBE, 30 fx) were simulated and changes in integral dose, target and organs at risk (OARs) doses evaluated. Results: In comparison to the 4-field-star approach, the use of narrow-fields reduced integral dose between 29% and 56%. If OARs did not overlap with the target, OAR doses were also reduced. Finally, the significantly reduced target coverage in non-adapted treatments (mean V95 reductions of up to 34%) could be almost fully restored with DAPT in all cases (differences <1%). Conclusions: DAPT was found to be not only an effective way to increase plan robustness to anatomical and positional uncertainties, but also opened the possibility to use improved and more conformal field arrangements.

Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy

Throughout the course of delivering a radiation therapy treatment, which may take several weeks, a patient's anatomy may change drastically, and adaptive radiation therapy (ART) may be needed. Cone-beam computed tomography (CBCT), which is often available during the treatment process, can be used for both patient positioning and ART re-planning. However, due to the prominent amount of noise, artifacts, and inaccurate Hounsfield unit (HU) values, the dose calculation based on CBCT images could be inaccurate for treatment planning. One way to solve this problem is to convert CBCT images to more accurate synthesized CT (sCT) images. In this work, we have developed a cycle-consistent generative adversarial network framework (CycleGAN) to synthesize CT images from CBCT images. This model is capable of image-to-image translation using unpaired CT and CBCT images in an unsupervised learning setting. The sCT images generated from CBCT through this CycleGAN model are visually and quantitatively similar to real CT images with decreased mean absolute error (MAE) from 69.29 HU to 29.85 HU for head-and-neck (H&N) cancer patients. The dose distributions calculated on the sCT by CycleGAN demonstrated a higher accuracy than those on CBCT in a 3D gamma index analysis with increased gamma index pass rate from 86.92% to 96.26% under 1 mm/1% criteria, when using the deformed planning CT image (dpCT) as the reference. We also compared the CycleGAN model with other unsupervised learning methods, including deep convolutional generative adversarial networks (DCGAN) and progressive growing of GANs (PGGAN), and demonstrated that CycleGAN outperformed the other two models. A phantom study has been conducted to compare sCT with dpCT, and the increase of structural similarity index from 0.91 to 0.93 shows that CycleGAN performed better than DIR in terms of preserving anatomical accuracy.

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformation-based planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of re-irradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.

RIDOS: A new system for online computation of the delivered dose distributions in scanning ion beam therapy

PURPOSE

To describe a new system for scanned ion beam therapy, named RIDOS (Real-time Ion DOse planning and delivery System), which performs real time delivered dose verification integrating the information from a clinical beam monitoring system with a Graphic Processing Unit (GPU) based dose calculation in patient Computed Tomography.

METHODS

A benchmarked dose computation algorithm for scanned ion beams has been parallelized and adapted to run on a GPU architecture. A workstation equipped with a NVIDIA GPU has been interfaced through a National Instruments PXI-crate with the dose delivery system of the Italian National Center of Oncological Hadrontherapy (CNAO) to receive in real-time the measured beam parameters. Data from a patient monitoring system are also collected to associate the respiratory phases with each spot during the delivery of the dose. Using both measured and planned spot properties, RIDOS evaluates during the few seconds of inter-spill time the cumulative delivered and prescribed dose distributions and compares them through a fast γ-index algorithm.

RESULTS

The accuracy of the GPU-based algorithms was assessed against the CPU-based ones and the differences were found below 1‰. The cumulative planned and delivered doses are computed at the end of each spill in about 300 ms, while the dose comparison takes approximatively 400 ms. The whole operation provides the results before the next spill starts.

CONCLUSIONS

RIDOS system is able to provide a fast computation of the delivered dose in the inter-spill time of the CNAO facility and allows to monitor online the dose deposition accuracy all along the treatment.

Comparing Unet training with three different datasets to correct CBCT images for prostate radiotherapy dose calculations

Image intensity correction is crucial to enable cone beam computed tomography (CBCT) based radiotherapy dose calculations. This study evaluated three different deep learning based correction methods using a U-shaped convolutional neural network architecture (Unet) in terms of their photon and proton dose calculation accuracy. CT and CBCT imaging data of 42 prostate cancer patients were included. For target ground truth data generation, a CBCT correction method based on CT to CBCT deformable image registration (DIR) was used. The method yields a deformed CT called (i) virtual CT (vCT) which is used to generate (ii) corrected CBCT projections allowing the reconstruction of (iii) a final corrected CBCT image. The single Unet architecture was trained using these three different datasets: (Unet1) raw and corrected CBCT projections, (Unet2) raw CBCT and vCT image slices and (Unet3) raw and reference corrected CBCT image slices. Volumetric arc therapy (VMAT) and proton pencil beam scanning (PBS) single field uniform dose (SFUD) plans were optimized on the reference corrected image and recalculated on the obtained Unet-corrected CBCT images. The mean error (ME) and mean absolute error (MAE) for Unet1/2/3 were [Formula: see text] Hounsfield units (HU) and [Formula: see text] HU. The 1% dose difference pass rates were better than 98.4% for VMAT for 8 test patients not seen during training, with little difference between Unets. Gamma evaluation results were even better. For protons a gamma evaluation was employed to account for small range shifts, and [Formula: see text] mm pass rates for Unet1/2/3 were better than [Formula: see text] and 91%. A 3 mm range difference threshold was established. Only for Unet3 the 5th and 95th percentiles of the range difference distributions over all fields, test patients and dose profiles were within this threshold. A single Unet architecture was successfully trained using both CBCT projections and CBCT image slices. Since the results of the other Unets were poorer than Unet3, we conclude that training using corrected CBCT image slices as target data is optimal for PBS SFUD proton dose calculations, while for VMAT all Unets provided sufficient accuracy.

On-line dose-guidance to account for inter-fractional motion during proton therapy

Background and purpose

Proton therapy (PT) of extra-cranial tumour sites is challenged by density changes caused by inter-fractional organ motion. In this study we investigate on-line dose-guided PT (DGPT) to account inter-fractional target motion, exemplified by internal motion in the pelvis.

Materials and methods

On-line DGPT involved re-calculating dose distributions with the isocenter shifted up to 15 mm from the position corresponding to conventional soft-tissue based image-guided PT (IGPT). The method was applied to patient models with simulated prostate/seminal vesicle target motion of ±3, ±5 and ±10 mm along the three cardinal axes. Treatment plans were created using either two lateral (gantry angles of 90°/270°) or two lateral oblique fields (gantry angles of 35°/325°). Target coverage and normal tissue doses from DGPT were compared to both soft-tissue and bony anatomy based IGPT.

Results

DGPT improved the dose distributions relative to soft-tissue based IGPT for 39 of 90 simulation scenarios using lateral fields and for 50 of 90 scenarios using lateral oblique fields. The greatest benefits of DGPT were seen for large motion, e.g. a median target coverage improvement of 13% was found for 10 mm anterior motion with lateral fields. DGPT also improved the dose distribution in comparison to bony anatomy IGPT in all cases. The best strategy was often to move the fields back towards the original target position prior to the simulated target motion.

Conclusion

DGPT has the potential to better account for large inter-fractional organ motion in the pelvis than IGPT.

Current state and future applications of radiological image guidance for particle therapy

In this review paper, we first give a short overview of radiological image guidance in photon radiotherapy, placing emphasis on the fact that linac based radiotherapy has outpaced particle therapy in the adoption of volumetric image guidance. While cone beam computed tomography (CBCT) has been an established technique in linac treatment rooms for almost two decades, the widespread adoption of volumetric image guidance in particle therapy, whether by means of CBCT or in-room CT imaging, is recent. This lag may be attributable to the bespoke nature and lower number of particle therapy installations, as well as the differences in geometry between those installations and linac treatment rooms. In addition, for particle therapy the so called shift invariance of the dose distribution rarely applies. An overview of the different volumetric image guidance solutions found at modern particle therapy facilities is provided, covering gantry, nozzle, C-arm, and couch-mounted CBCT as well different in-room CT configurations. A summary of the use of in-room volumetric imaging data beyond anatomy-based positioning is also presented as well as the necessary corrections to CBCT images for accurate water equivalent thickness calculation. Finally, the use of non-ionizing imaging modalities is discussed.

Current State of Image Guidance in Radiation Oncology: Implications for PTV Margin Expansion and Adaptive Therapy

Image guidance technology has evolved and seen widespread application in the past several decades. Advancements in the diagnostic imaging field have found new applications in radiation oncology and promoted the development of therapeutic devices with advanced imaging capabilities. A recent example is the development of linear accelerators that offer magnetic resonance imaging for real-time imaging and online adaptive planning. Volumetric imaging, in particular, offers more precise localization of soft tissue targets and critical organs which reduces setup uncertainty and permit the use of smaller setup margins. We present a review of the status of current imaging modalities available for radiation oncology and its impact on target margins and use for adaptive therapy.

Dose-guided patient positioning in proton radiotherapy using multicriteria-optimization

Proton radiotherapy (PT) requires accurate target alignment before each treatment fraction, ideally utilizing 3D in-room X-ray computed tomography (CT) imaging. Typically, the optimal patient position is determined based on anatomical landmarks or implanted markers. In the presence of non-rigid anatomical changes, however, the planning scenario cannot be exactly reproduced and positioning should rather aim at finding the optimal position in terms of the actually applied dose. In this work, dose-guided patient alignment, implemented as multicriterial optimization (MCO) problem, was investigated in the scope of intensity-modulated and double-scattered PT (IMPT and DSPT) for the first time. A method for automatically determining the optimal patient position with respect to pre-defined clinical goals was implemented. Linear dose interpolation was used to access a continuous space of potential patient shifts. Fourteen head and neck (H&N) and eight prostate cancer patients with up to five repeated CTs were included. Dose interpolation accuracy was evaluated and the potential dosimetric advantages of dose-guided over bony-anatomy-based patient alignment investigated by comparison of clinically relevant target and organ-at-risk (OAR) dose-volume histogram (DVH) parameters. Dose interpolation was found sufficiently accurate with average pass-rates of 90% and 99% for an exemplary H&N and prostate patient, respectively, using a 2% dose-difference criterion. Compared to bony-anatomy-based alignment, the main impact of automated MCO-based dose-guided positioning was a reduced dose to the serial OARs (spinal cord and brain stem) for the H&N cohort. For the prostate cohort, under-dosage of the target structures could be efficiently diminished. Limitations of dose-guided positioning were mainly found in reducing target over-dosage due to weight loss for H&N patients, which might require adaptation of the treatment plan. Since labor-intense online quality-assurance is not required for dose-guided patient positioning, it might, nevertheless, be considered an interesting alternative to full online re-planning for initially mitigating the effects of anatomical changes.

Toward adaptive radiotherapy for lung patients: feasibility study on deforming planning CT to CBCT to assess the impact of anatomical changes on dosimetry

Changes in lung architecture during a course of radiotherapy can alter the planned dose distribution to the extent that it becomes clinically unacceptable. This study aims to validate a quantitative method of determining whether a replan is required during the course of conformal radiotherapy. The proposed method uses deformable image registration (DIR) to flexibly map planning CT (pCT) data to the anatomy of online CBCT images. The resulting deformed CT (dCT) images are used as a basis for assessing the effect of anatomical change on dose distributions. The study used retrospective data from a sample of seven replanned lung patients. The settings of an in-house, open-source DIR algorithm were first optimised for CT-to-CBCT registrations of the anatomy of the thorax. Using these optimised parameters, each patient's pCT was deformed to the CBCT acquired immediately before the replan. Registration accuracy was rigorously validated both geometrically and dosimetrically to confirm that the dCTs could reliably be used to inform replan decisions. A retrospective evaluation of the changes in dose delivered over time was then carried out for a single patient to demonstrate the clinical application of the proposed method. The geometric analysis showed good agreement between deformed structures and those same structures manually outlined on the CBCT images. Results were consistently better than those achieved with rigid-only registration. In the dosimetric analysis, dose distributions derived from the dCTs were found to match closely to the 'gold standard' replan CT (rCT) distributions across dose volume histogram and absolute dose difference measures. The retrospective analysis of serial CBCTs of a single patient produced reliable quantitative assessment of the dose delivery. Had the proposed method been available at the time of treatment, it would have enabled a more objective replan decision. DIR is a valuable clinical tool for dose recalculation in adaptive radiotherapy protocols for lung cancer patients.

Image quality improvement in cone-beam CT using the super-resolution technique

This study was conducted to improve cone-beam computed tomography (CBCT) image quality using the super-resolution technique, a method of inferring a high-resolution image from a low-resolution image. This technique is used with two matrices, so-called dictionaries, constructed respectively from high-resolution and low-resolution image bases. For this study, a CBCT image, as a low-resolution image, is represented as a linear combination of atoms, the image bases in the low-resolution dictionary. The corresponding super-resolution image was inferred by multiplying the coefficients and the high-resolution dictionary atoms extracted from planning CT images. To evaluate the proposed method, we computed the root mean square error (RMSE) and structural similarity (SSIM). The resulting RMSE and SSIM between the super-resolution images and the planning CT images were, respectively, as much as 0.81 and 1.29 times better than those obtained without using the super-resolution technique. We used super-resolution technique to improve the CBCT image quality.

Impact of robust treatment planning on single- and multi-field optimized plans for proton beam therapy of unilateral head and neck target volumes

BACKGROUND

Proton beam therapy is promising for the treatment of head and neck cancer (HNC), but it is sensitive to uncertainties in patient positioning and particle range. Studies have shown that the planning target volume (PTV) concept may not be sufficient to ensure robustness of the target coverage. A few planning studies have considered irradiation of unilateral HNC targets with protons, but they have only taken into account the dose on the nominal plan, without considering anatomy changes occurring during the treatment course.

METHODS

Four pencil beam scanning (PBS) proton therapy plans were calculated for 8 HNC patients with unilateral target volumes: single-field (SFO) and multi-field optimized (MFO) plans, either using the PTV concept or clinical target volume (CTV)-based robust optimization. The dose was recalculated on computed tomography (CT) scans acquired during the treatment course. Doses to target volumes and organs at risk (OARs) were compared for the nominal plans, cumulative doses considering anatomical changes, and additional setup and range errors in each fraction. If required, the treatment plan was adapted, and the dose was compared with the non-adapted plan.

RESULTS

All nominal plans fulfilled the clinical specifications for target coverage, but significantly higher doses on the ipsilateral parotid gland were found for both SFO approaches. MFO PTV-based plans had the lowest robustness against range and setup errors. During the treatment course, the influence of the anatomical variation on the dose has shown to be patient specific, mostly independent of the chosen planning approach. Nine plans in four patients required adaptation, which led to a significant improvement of the target coverage and a slight reduction in the OAR dose in comparison to the cumulative dose without adaptation.

CONCLUSIONS

The use of robust MFO optimization is recommended for ensuring plan robustness and reduced doses in the ipsilateral parotid gland. Anatomical changes occurring during the treatment course might degrade the target coverage and increase the dose in the OARs, independent of the chosen planning approach. For some patients, a plan adaptation may be required.

Dosimetric effects of anatomical changes during fractionated photon radiation therapy in pancreatic cancer patients

Pancreatic tumors show large interfractional position variation. In addition, changes in gastrointestinal gas volumes and body contour take place over the course of radiation therapy. We aimed to quantify the effect of these anatomical changes on target dose coverage, for the clinically used fiducial marker‐based patient position verification and, for comparison, also for simulated bony anatomy‐based position verification. Nine consecutive patients were included in this retrospective study. To enable fraction dose calculations on cone‐beam CT (CBCT), the planning CT was deformably registered to each CBCT (13–15 per patient); gas volumes visible on CBCT were copied to the deformed CT. Fraction doses were calculated for the clinically used 10 MV VMAT treatment plan (with for the planning target volume (PTV): D_98% = 95%), according to fiducial marker‐based and bony anatomy‐based image registrations. Dose distributions were rigidly summed to yield the accumulated dose. To evaluate target dose coverage, we defined an iCTV_{+5 mm} volume, i.e., the internal clinical target volume (iCTV) expanded with a 5 mm margin to account for remaining uncertainties including delineation uncertainties. We analyzed D_98%, D_mean, and D_2% for iCTV_{+5 mm} and PTV (i.e., iCTV plus 10 mm margin). We found that for fiducial marker‐based registration, differences between fraction doses and planned dose were minimal. For bony anatomy‐based registration, fraction doses differed considerably, resulting in large differences between planned and accumulated dose for some patients, up to a decrease in D_98% of the iCTV_{+5 mm} from 95.9% to 85.8%. Our study shows that fractionated photon irradiation of pancreatic tumors is robust against variations in body contour and gastrointestinal gas, with dose coverage only mildly affected. However, as a result of interfractional tumor position variations, target dose coverage can severely decline when using bony anatomy for patient position verification. Therefore, the use of intratumoral fiducial marker‐based daily position verification is essential in pancreatic cancer patients.

Effect of DIR uncertainty on prostate passive-scattering proton therapy dose accumulation

Deformable image registration (DIR) is important in dose accumulation. Currently, the impact of DIR-algorithm-associated uncertainties in proton therapy is unclear. Here, we quantify the effect of DIR uncertainties on prostate passive-scattering proton therapy (PSPT) dose accumulation. Ten patients with an intermediate risk for prostate cancer formerly treated by PSPT (PTV D95=78GyE) were studied. Dose distributions from all verification CT images (five images per patient) were warped and accumulated in the planning CT geometries with DIR. The dose-volume histogram parameters (Dmean, V40, and V70) for rectum and bladder were calculated. Two commercially available DIR software packages were employed: Velocity AI (Varian Medical Systems) and RayStation (RaySearch Laboratories). The dice similarity coefficient (DSC) and surface distance, which were calculated between planning CT contours and deformed contours, were used for DIR validation, with the relationship between the dose parameter and DIR uncertainty ultimately investigated. On average, when using RayStation, the DSC increased by 0.14 and surface distance decreased by 6.4mm, as compared to Velocity. For Dmean, V40, and V70 to the rectum, the average differences between the RayStation and Velocity were 3.9GyE, 5.5%, and 3.2%, respectively. For the bladder, the differences were 5.2GyE, 5.8%, and 5.4%, respectively. The maximum differences in V40 between RayStation and Velocity were 14.4% and 22.8% for the rectum and bladder, respectively, when the average DSC and surface distance differences were more than 0.14 and 6.4mm, respectively. Such results suggest that DIR uncertainties might significantly affect prostate PSPT dose accumulations.

Study of Variation in Dose Calculation Accuracy Between kV Cone-Beam Computed Tomography and kV fan-Beam Computed Tomography

Cone-beam computed tomography (CBCT) images are presently used for geometric verification for daily patient positioning. In this work, we have compared the images of CBCT with the images of conventional fan beam CT (FBCT) in terms of image quality and Hounsfield units (HUs). We also compared the dose calculated using CBCT with that of FBCT. Homogenous RW3 plates and Catphan phantom were scanned by FBCT and CBCT. In RW3 and Catphan phantom, percentage depth dose (PDD), profiles, isodose distributions (for intensity modulated radiotherapy plans), and calculated dose volume histograms were compared. The HU difference was within ± 20 HU (central region) and ± 30 HU (peripheral region) for homogeneous RW3 plates. In the Catphan phantom, the difference in HU was ± 20 HU in the central area and peripheral areas. The HU differences were within ± 30 HU for all HU ranges starting from -1000 to 990 in phantom and patient images. In treatment plans done with simple symmetric and asymmetric fields, dose difference (DD) between CBCT plan and FBCT plan was within 1.2% for both phantoms. In intensity modulated radiotherapy (IMRT) treatment plans, for different target volumes, the difference was <2%. This feasibility study investigated HU variation and dose calculation accuracy between FBCT and CBCT based planning and has validated inverse planning algorithms with CBCT. In our study, we observed a larger deviation of HU values in the peripheral region compared to the central region. This is due to the ring artifact and scatter contribution which may prevent the use of CBCT as the primary imaging modality for radiotherapy treatment planning. The reconstruction algorithm needs to be modified further for improving the image quality and accuracy in HU values. However, our study with TG-119 and intensity modulated radiotherapy test targets shows that CBCT can be used for adaptive replanning as the recalculation of dose with the anisotropic analytical algorithm is in full accord with conventional planning CT except in the build-up regions. Patient images with CBCT have to be carefully analyzed for any artifacts before using them for such dose calculations.

Comparing the dosimetric impact of interfractional anatomical changes in photon, proton and carbon ion radiotherapy for pancreatic cancer patients

Radiotherapy using charged particles is characterized by a low dose to the surrounding healthy organs, while delivering a high dose to the tumor. However, interfractional anatomical changes can greatly affect the robustness of particle therapy. Therefore, we compared the dosimetric impact of interfractional anatomical changes (i.e. body contour differences and gastrointestinal gas volume changes) in photon, proton and carbon ion therapy for pancreatic cancer patients. In this retrospective planning study, photon, proton and carbon ion treatment plans were created for 9 patients. Fraction dose calculations were performed using daily cone-beam CT (CBCT) images. To this end, the planning CT was deformably registered to each CBCT; gastrointestinal gas volumes were delineated on the CBCTs and copied to the deformed CT. Fraction doses were accumulated rigidly. To compare planned and accumulated dose, dose-volume histogram (DVH) parameters of the planned and accumulated dose of the different radiotherapy modalities were determined for the internal gross tumor volume, internal clinical target volume (iCTV) and organs-at-risk (OARs; duodenum, stomach, kidneys, liver and spinal cord). Photon plans were highly robust against interfractional anatomical changes. The difference between the planned and accumulated DVH parameters for the photon plans was less than 0.5% for the target and OARs. In both proton and carbon ion therapy, however, coverage of the iCTV was considerably reduced for the accumulated dose compared with the planned dose. The near-minimum dose ([Formula: see text]) of the iCTV reduced with 8% for proton therapy and with 10% for carbon ion therapy. The DVH parameters of the OARs differed less than 3% for both particle modalities. Fractionated radiotherapy using photons is highly robust against interfractional anatomical changes. In proton and carbon ion therapy, such changes can severely reduce the dose coverage of the target.

Water equivalent path length calculations using scatter-corrected head and neck CBCT images to evaluate patients for adaptive proton therapy

Proton therapy has dosimetric advantages due to the well-defined range of the proton beam over photon radiotherapy. When the proton beams, however, are delivered to the patient in fractionated radiation treatment, the treatment outcome is affected by delivery uncertainties such as anatomic change in the patient and daily patient setup error. This study aims at establishing a method to evaluate the dosimetric impact of the anatomic change and patient setup error during head and neck proton therapy. Range variations due to the delivery uncertainties were assessed by calculating water equivalent path length (WEPL) to the distal edge of tumor volume using planning CT and weekly treatment cone-beam CT (CBCT) images. Specifically, mean difference and root mean squared deviation (RMSD) of the distal WEPLs were calculated as the weekly range variations. To accurately calculate the distal WEPLs, an existing CBCT scatter correction algorithm was used. An automatic rigid registration was used to align the planning CT and treatment CBCT images, simulating a six degree-of-freedom couch correction at treatments. The authors conclude that the dosimetric impact of the anatomic change and patient setup error was reasonably captured in the differences of the distal WEPL variation with a range calculation uncertainty of 2%. The proposed method to calculate the distal WEPL using the scatter-corrected CBCT images can be an essential tool to decide the necessity of re-planning in adaptive proton therapy.