Multi-institutional consensus on machine QA for isochronous cyclotron-based systems delivering ultra-high dose rate (FLASH) pencil beam scanning proton therapy in transmission mode.

BACKGROUND: The first clinical trials to assess the feasibility of FLASH radiotherapy in humans have started (FAST-01, FAST-02) and more trials are foreseen. To increase comparability between trials it is important to assure treatment quality and therefore establish a standard for machine quality assurance (QA). Currently, the AAPM TG-224 report is considered as the standard on machine QA for proton therapy, however, it was not intended to be used for ultra-high dose rate (UHDR) proton beams, which have gained interest due to the observation of the FLASH effect. PURPOSE: The aim of this study is to find consensus on practical guidelines on machine QA for UHDR proton beams in transmission mode in terms of which QA is required, how they should be done, which detectors are suitable for UHDR machine QA, and what tolerance limits should be applied. METHODS: A risk assessment to determine the gaps in the current standard for machine QA was performed by an international group of medical physicists. Based on that, practical guidelines on how to perform machine QA for UHDR proton beams were proposed. RESULTS: The risk assessment clearly identified the need for additional guidance on temporal dosimetry, addressing dose rate (constancy), dose spillage, and scanning speed. In addition, several minor changes from AAPM TG-224 were identified; define required dose rate levels, the use of clinically relevant dose levels, and the use of adapted beam settings to minimize activation of detector and phantom materials or to avoid saturation effects of specific detectors. The final report was created based on discussions and consensus. CONCLUSIONS: Consensus was reached on what QA is required for UHDR scanning proton beams in transmission mode for isochronous cyclotron-based systems and how they should be performed. However, the group discussions also showed that there is a lack of high temporal resolution detectors and sufficient QA data to set appropriate limits for some of the proposed QA procedures.

Measurement of the time structure of FLASH beams using prompt gamma rays and secondary neutrons as surrogates.

Objective. The aim of this study was to investigate the feasibility of online monitoring of irradiation time (IRT) and scan time for FLASH proton radiotherapy using a pixelated semiconductor detector.Approach. Measurements of the time structure of FLASH irradiations were performed using fast, pixelated spectral detectors based on the Timepix3 (TPX3) chips with two architectures: AdvaPIX-TPX3 and Minipix-TPX3. The latter has a fraction of its sensor coated with a material to increase sensitivity to neutrons. With little or no dead time and an ability to resolve events that are closely spaced in time (tens of nanoseconds), both detectors can accurately determine IRTs as long as pulse pile-up is avoided. To avoid pulse pile-up, the detectors were placed well beyond the Bragg peak or at a large scattering angle. Prompt gamma rays and secondary neutrons were registered in the detectors' sensors and IRTs were calculated based on timestamps of the first charge carriers (beam-on) and the last charge carriers (beam-off). In addition, scan times inx,y, and diagonal directions were measured. The experiment was carried out for various setups: (i) a single spot, (ii) a small animal field, (iii) a patient field, and (iv) an experiment using an anthropomorphic phantom to demonstratein vivoonline monitoring of IRT. All measurements were compared to vendor log files.Main results. Differences between measurements and log files for a single spot, a small animal field, and a patient field were within 1%, 0.3% and 1%, respectively.In vivomonitoring of IRTs (95-270 ms) was accurate within 0.1% for AdvaPIX-TPX3 and within 6.1% for Minipix-TPX3. The scan times inx,y, and diagonal directions were 4.0, 3.4, and 4.0 ms, respectively.Significance. Overall, the AdvaPIX-TPX3 can measure FLASH IRTs within 1% accuracy, indicating that prompt gamma rays are a good surrogate for primary protons. The Minipix-TPX3 showed a somewhat higher discrepancy, likely due to the late arrival of thermal neutrons to the detector sensor and lower readout speed. The scan times (3.4 ± 0.05 ms) in the 60 mm distance ofy-direction were slightly less than (4.0 ± 0.06 ms) in the 24 mm distance ofx-direction, confirming the much faster scanning speed of the Y magnets than that of X. Diagonal scan speed was limited by the slower X magnets.

Characterization of 250 MeV Protons from the Varian ProBeam PBS System for FLASH Radiation Therapy.

Shoot-through proton FLASH radiation therapy has been proposed where the highest energy is extracted from a cyclotron to maximize the dose rate (DR). Although our proton pencil beam scanning system can deliver 250 MeV (the highest energy), this energy is not used clinically, and as such, 250 MeV has yet to be characterized during clinical commissioning. We aim to characterize the 250-MeV proton beam from the Varian ProBeam system for FLASH and assess the usability of the clinical monitoring ionization chamber (MIC) for FLASH use. We measured the following data for beam commissioning: integral depth dose curve, spot sigma, and absolute dose. To evaluate the MIC, we measured output as a function of beam current. To characterize a 250 MeV FLASH beam, we measured (1) the central axis DR as a function of current and spot spacing and arrangement, (2) for a fixed spot spacing, the maximum field size that achieves FLASH DR (ie, > 40 Gy/s), and (3) DR reproducibility. All FLASH DR measurements were performed using an ion chamber for the absolute dose, and irradiation times were obtained from log files. We verified dose measurements using EBT-XD films and irradiation times using a fast, pixelated spectral detector. R90 and R80 from integral depth dose were 37.58 and 37.69 cm, and spot sigma at the isocenter were σx = 3.336 and σy = 3.332 mm, respectively. The absolute dose output was measured as 0.343 Gy*mm2/MU for the commissioning conditions. Output was stable for beam currents up to 15 nA and gradually increased to 12-fold for 115 nA. Dose and DR depended on beam current, spot spacing, and arrangement and could be reproduced with 6.4% and 4.2% variations, respectively. Although FLASH was achieved and the largest field size that delivers FLASH DR was determined as 35 × 35 mm2, the current MIC has DR dependence, and users should measure dose and DR independently each time for their FLASH applications.

Synthesizing high-resolution magnetic resonance imaging using parallel cycle-consistent generative adversarial networks for fast magnetic resonance imaging.

PURPOSE: The common practice in acquiring the magnetic resonance (MR) images is to obtain two-dimensional (2D) slices at coarse locations while keeping the high in-plane resolution in order to ensure enough body coverage while shortening the MR scan time. The aim of this study is to propose a novel method to generate HR MR images from low-resolution MR images along the longitudinal direction. In order to address the difficulty of collecting paired low- and high-resolution MR images in clinical settings and to gain the advantage of parallel cycle consistent generative adversarial networks (CycleGANs) in synthesizing realistic medical images, we developed a parallel CycleGANs based method using a self-supervised strategy. METHODS AND MATERIALS: The proposed workflow consists of two parallely trained CycleGANs to independently predict the HR MR images in the two planes along the directions that are orthogonal to the longitudinal MR scan direction. Then, the final synthetic HR MR images are generated by fusing the two predicted images. MR images, including T1-weighted (T1), contrast enhanced T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR), of the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were processed to evaluate the proposed workflow along the cranial-caudal (CC), lateral, and anterior-posterior directions. Institutional collected MR images were also processed for evaluation of the proposed method. The performance of the proposed method was investigated via both qualitative and quantitative evaluations. Metrics of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), edge keeping index (EKI), structural similarity index measurement (SSIM), information fidelity criterion (IFC), and visual information fidelity in pixel domain (VIFP) were calculated. RESULTS: It is shown that the proposed method can generate HR MR images visually indistinguishable from the ground truth in the investigations on the BraTS2020 dataset. In addition, the intensity profiles, difference images and SSIM maps can also confirm the feasibility of the proposed method for synthesizing HR MR images. Quantitative evaluations on the BraTS2020 dataset shows that the calculated metrics of synthetic HR MR images can all be enhanced for the T1, T1CE, T2, and FLAIR images. The enhancements in the numerical metrics over the low-resolution and bi-cubic interpolated MR images, as well as those genearted with a comparative deep learning method, are statistically significant. Qualitative evaluation of the synthetic HR MR images of the clinical collected dataset could also confirm the feasibility of the proposed method. CONCLUSIONS: The proposed method is feasible to synthesize HR MR images using self-supervised parallel CycleGANs, which can be expected to shorten MR acquisition time in clinical practices.

Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy.

Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT is expected to enable accurate multi-organ segmentation in HN cancer patients. In our proposed method, MR images are firstly synthesized using a pre-trained cycle-consistent generative adversarial network given CBCT. The features of CBCT and synthetic MRI (sMRI) are then extracted using dual pyramid networks for final delineation of organs. CBCT images and their corresponding manual contours were used as pairs to train and test the proposed model. Quantitative metrics including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance, and residual mean square distance (RMS) were used to evaluate the proposed method. The proposed method was evaluated on a cohort of 65 HN cancer patients. CBCT images were collected from those patients who received proton therapy. Overall, DSC values of 0.87 ± 0.03, 0.79 ± 0.10/0.79 ± 0.11, 0.89 ± 0.08/0.89 ± 0.07, 0.90 ± 0.08, 0.75 ± 0.06/0.77 ± 0.06, 0.86 ± 0.13, 0.66 ± 0.14, 0.78 ± 0.05/0.77 ± 0.04, 0.96 ± 0.04, 0.89 ± 0.04/0.89 ± 0.04, 0.83 ± 0.02, and 0.84 ± 0.07 for commonly used OARs for treatment planning including brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord, respectively, were achieved. This study provides a rapid and accurate OAR auto-delineation approach, which can be used for adaptive radiation therapy.

A standardized commissioning framework of Monte Carlo dose calculation algorithms for proton pencil beam scanning treatment planning systems.

PURPOSE: Treatment planning systems (TPSs) from different vendors can involve different implementations of Monte Carlo dose calculation (MCDC) algorithms for pencil beam scanning (PBS) proton therapy. There are currently no guidelines for validating non-water materials in TPSs. Furthermore, PBS-specific parameters can vary by 1-2 orders of magnitude among different treatment delivery systems (TDSs). This paper proposes a standardized framework on the use of commissioning data and steps to validate TDS-specific parameters and TPS-specific heterogeneity modeling to potentially reduce these uncertainties. METHODS: A standardized commissioning framework was developed to commission the MCDC algorithms of RayStation 8A and Eclipse AcurosPT v13.7.20 using water and non-water materials. Measurements included Bragg peak depth-dose and lateral spot profiles and scanning field outputs for Varian ProBeam. The phase-space parameters were obtained from in-air measurements and the number of protons per MU from output measurements of 10 × 10 cm2 square fields at a 2 cm depth. Spot profiles and various PBS field measurements at additional depths were used to validate TPS. Human tissues in TPS, Gammex phantom materials, and artificial materials were used for the TPS benchmark and validation. RESULTS: The maximum differences of phase parameters, spot sigma, and divergence between MCDC algorithms are below 4.5 µm and 0.26 mrad in air, respectively. Comparing TPS to measurements at depths, both MC algorithms predict the spot sigma within 0.5 mm uncertainty intervals, the resolution of the measurement device. Beam Configuration in AcurosPT is found to underestimate number of protons per MU by ~2.5% and requires user adjustment to match measured data, while RayStation is within 1% of measurements using Auto model. A solid water phantom was used to validate the range accuracy of non-water materials within 1% in AcurosPT. CONCLUSIONS: The proposed standardized commissioning framework can detect potential issues during PBS TPS MCDC commissioning processes, and potentially can shorten commissioning time and improve dosimetric accuracies. Secondary MCDC can be used to identify the root sources of disagreement between primary MCDC and measurement.

Prospective International Pilot Study Evaluating the Efficacy of a Self-Guided Contouring Teaching Module With Integrated Feedback for Transitioning From 2D to 3D Treatment Planning.

PURPOSE: Transitioning from two-dimensional to three-dimensional treatment planning requires developing contouring skills. Contouring atlases are excellent resources, but they do not provide users active feedback. Developing countries may not have many radiation oncologists experienced in three-dimensional planning to provide training. We sought to develop a standardized self-guided educational module with integrated feedback to teach contouring skills. METHODS AND MATERIALS: All 18 oncology residents at Black Lion Hospital/Addis Ababa University in Ethiopia were trained to contour the level II lymph node station. Residents took a baseline pretest quiz, survey, and contouring evaluation. Residents then watched an instructional contouring lecture and performed three additional cases with integrated feedback by comparing their contours to gold-standard contours. Residents then took a post-training quiz, survey, and contouring evaluation. Paired t tests and analysis of variance were used for analysis. RESULTS: Before training, the average number of total cases ever contoured was 2.4 and the average number of head and neck cases contoured was 0.5. Comfort with contouring improved from being "not at all comfortable" to "quite comfortable" after the 3-hour training (P < .001). The standard deviation between the resident contours and gold standard improved from 72.6 cm3 (pretest) to 7.4 cm3 (post-test). The average percentage overlap with the gold-standard contours and Dice similarity coefficient improved with each case performed, from 27.7% and 0.26 (pretest) to 80.1% and 0.77 (post-test), respectively (P < .001). After training, 16 of 18 (88.9%) residents produced a Dice similarity coefficient greater than 0.7, the threshold generally accepted for excellent agreement. CONCLUSION: This self-guided teaching module was an effective tool for developing level II lymph node contouring skills by providing active feedback and resulted in improved user confidence and accuracy compared with a gold standard. This module can be expanded to other disease sites and countries to further facilitate transitioning to three-dimensional treatment planning in developing countries.

Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy.

INTRODUCTION: Cone-beam CT (CBCT) image quality is important for its quantitative analysis in adaptive radiation therapy. However, due to severe artifacts, the CBCTs are primarily used for verifying patient setup only so far. We have developed a learning-based image quality improvement method which could provide CBCTs with image quality comparable to planning CTs (pCTs). The accuracy of dose calculations based on these CBCTs is unknown. In this study, we aim to investigate the dosimetric accuracy of our corrected CBCT (CCBCT) in brain stereotactic radiosurgery (SRS) and pelvic radiotherapy. MATERIALS AND METHODS: We retrospectively investigated a total of 32 treatment plans from 22 patients, each of whom with both original treatment pCTs and CBCTs acquired during treatment setup. The CCBCT and original CBCT (OCBCT) were registered to the pCT for generating CCBCT-based and OCBCT-based treatment plans. The original pCT-based plans served as ground truth. Clinically-relevant dose volume histogram (DVH) metrics were extracted from the ground truth, OCBCT-based and CCBCT-based plans for comparison. Gamma analysis was also performed to compare the absorbed dose distributions between the pCT-based and OCBCT/CCBCT-based plans of each patient. RESULTS: CCBCTs demonstrated better image contrast and more accurate HU ranges when compared side-by-side with OCBCTs. For pelvic radiotherapy plans, the mean dose error in DVH metrics for planning target volume (PTV), bladder and rectum was significantly reduced, from 1% to 0.3%, after CBCT correction. The gamma analysis showed the average pass rate increased from 94.5% before correction to 99.0% after correction. For brain SRS treatment plans, both original and corrected CBCT images were accurate enough for dose calculation, though CCBCT featured higher image quality. CONCLUSION: CCBCTs can provide a level of dose accuracy comparable to traditional pCTs for brain and prostate radiotherapy planning and the correction method proposed here can be useful in CBCT-guided adaptive radiotherapy.

MRI-based treatment planning for brain stereotactic radiosurgery: Dosimetric validation of a learning-based pseudo-CT generation method.

Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast without ionizing radiation compared with computed tomography (CT). However, it requires the generation of pseudo CT from MRI images for patient setup and dose calculation. Our machine-learning-based method to generate pseudo CT images has been shown to provide pseudo CT images with excellent image quality, while its dose calculation accuracy remains an open question. In this study, we aim to investigate the accuracy of dose calculation in brain frameless stereotactic radiosurgery (SRS) using pseudo CT images which are generated from MRI images using the machine learning-based method developed by our group. We retrospectively investigated a total of 19 treatment plans from 14 patients, each of whom has CT simulation and MRI images acquired during pretreatment. The dose distributions of the same treatment plans were calculated on original CT simulation images as ground truth, as well as on pseudo CT images generated from MRI images. Clinically-relevant DVH metrics and gamma analysis were extracted from both ground truth and pseudo CT results for comparison and evaluation. The side-by-side comparisons on image quality and dose distributions demonstrated very good agreement of image contrast and calculated dose between pseudo CT and original CT. The average differences in Dose-volume histogram (DVH) metrics for Planning target volume (PTVs) were less than 0.6%, and no differences in those for organs at risk at a significance level of 0.05. The average pass rate of gamma analysis was 99%. These quantitative results strongly indicate that the pseudo CT images created from MRI images using our proposed machine learning method are accurate enough to replace current CT simulation images for dose calculation in brain SRS treatment. This study also demonstrates the great potential for MRI to completely replace CT scans in the process of simulation and treatment planning.

Learning-based CBCT correction using alternating random forest based on auto-context model.

PURPOSE: Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radiotherapy because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. However, CBCT is currently limited only to patient setup in the clinic because of the severe issues in its image quality. In this study, we develop a learning-based approach to improve CBCT's image quality for extended clinical applications. MATERIALS AND METHODS: An auto-context model is integrated into a machine learning framework to iteratively generate corrected CBCT (CCBCT) with high-image quality. The first step is data preprocessing for the built training dataset, in which uninformative image regions are removed, noise is reduced, and CT and CBCT images are aligned. After a CBCT image is divided into a set of patches, the most informative and salient anatomical features are extracted to train random forests. Within each patch, alternating RF is applied to create a CCBCT patch as the output. Moreover, an iterative refinement strategy is exercised to enhance the image quality of CCBCT. Then, all the CCBCT patches are integrated to reconstruct final CCBCT images. RESULTS: The learning-based CBCT correction algorithm was evaluated using the leave-one-out cross-validation method applied on a cohort of 12 patients' brain data and 14 patients' pelvis data. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indexes, and spatial nonuniformity (SNU) in the selected regions of interest (ROIs) were used to quantify the proposed algorithm's correction accuracy and generat the following results: mean MAE = 12.81 ± 2.04 and 19.94 ± 5.44 HU, mean PSNR = 40.22 ± 3.70 and 31.31 ± 2.85 dB, mean NCC = 0.98 ± 0.02 and 0.95 ± 0.01, and SNU = 2.07 ± 3.36% and 2.07 ± 3.36% for brain and pelvis data. CONCLUSION: Preliminary results demonstrated that the novel learning-based correction method can significantly improve CBCT image quality. Hence, the proposed algorithm is of great potential in improving CBCT's image quality to support its clinical utility in CBCT-guided adaptive radiotherapy.

Does size matter? Investigating the optimal planning target volume margin for postoperative stereotactic radiosurgery to resected brain metastases.

OBJECTIVE: The optimal margin size in postoperative stereotactic radiosurgery (SRS) for brain metastases is unknown. Herein, the authors investigated the effect of SRS planning target volume (PTV) margin on local recurrence and symptomatic radiation necrosis postoperatively. METHODS: Records of patients who received postoperative LINAC-based SRS for brain metastases between 2006 and 2016 were reviewed and stratified based on PTV margin size (1.0 or > 1.0 mm). Patients were treated using frameless and framed SRS techniques, and both single-fraction and hypofractionated dosing were used based on lesion size. Kaplan-Meier and cumulative incidence models were used to estimate survival and intracranial outcomes, respectively. Multivariate analyses were also performed. RESULTS: A total of 133 patients with 139 cavities were identified; 36 patients (27.1%) and 35 lesions (25.2%) were in the 1.0-mm group, and 97 patients (72.9%) and 104 lesions (74.8%) were in the > 1.0-mm group. Patient characteristics were balanced, except the 1.0-mm cohort had a better Eastern Cooperative Group Performance Status (grade 0: 36.1% vs 19.6%), higher mean number of brain metastases (1.75 vs 1.31), lower prescription isodose line (80% vs 95%), and lower median single fraction-equivalent dose (15.0 vs 17.5 Gy) (all p < 0.05). The median survival and follow-up for all patients were 15.6 months and 17.7 months, respectively. No significant difference in local recurrence was noted between the cohorts. An increased 1-year rate of symptomatic radionecrosis was seen in the larger margin group (20.9% vs 6.0%, p = 0.028). On multivariate analyses, margin size > 1.0 mm was associated with an increased risk for symptomatic radionecrosis (HR 3.07, 95% CI 1.13-8.34; p = 0.028), while multifraction SRS emerged as a protective factor for symptomatic radionecrosis (HR 0.13, 95% CI 0.02-0.76; p = 0.023). CONCLUSIONS: Expanding the PTV margin beyond 1.0 mm is not associated with improved local recurrence but appears to increase the risk of symptomatic radionecrosis after postoperative SRS.

Improving Image Quality of Cone-Beam CT Using Alternating Regression Forest.

We propose a CBCT image quality improvement method based on anatomic signature and auto-context alternating regression forest. Patient-specific anatomical features are extracted from the aligned training images and served as signatures for each voxel. The most relevant and informative features are identified to train regression forest. The well-trained regression forest is used to correct the CBCT of a new patient. This proposed algorithm was evaluated using 10 patients' data with CBCT and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the correction accuracy of the proposed algorithm. The mean MAE, PSNR and NCC between corrected CBCT and ground truth CT were 16.66HU, 37.28dB and 0.98, which demonstrated the CBCT correction accuracy of the proposed learning-based method. We have developed a learning-based method and demonstrated that this method could significantly improve CBCT image quality. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore, allowing its quantitative use in CBCT-guided adaptive radiotherapy.

A Denoising Algorithm for CT Image Using Low-rank Sparse Coding.

We propose a denoising method of CT image based on low-rank sparse coding. The proposed method constructs an adaptive dictionary of image patches and estimates the sparse coding regularization parameters using the Bayesian interpretation. A low-rank approximation approach is used to simultaneously construct the dictionary and achieve sparse representation through clustering similar image patches. A variable-splitting scheme and a quadratic optimization are used to reconstruct CT image based on achieved sparse coefficients. We tested this denoising technology using phantom, brain and abdominal CT images. The experimental results showed that the proposed method delivers state-of-art denoising performance, both in terms of objective criteria and visual quality.

Image-based Metal Artifact Reduction in X-ray Computed Tomography utilizing Local Anatomical Similarity.

X-ray computed tomography (CT) is widely used in radiation therapy treatment planning in recent years. However, metal implants such as dental fillings and hip prostheses can cause severe bright and dark streaking artifacts in reconstructed CT images. These artifacts decrease image contrast and degrade HU accuracy, leading to inaccuracies in target delineation and dose calculation. In this work, a metal artifact reduction method is proposed based on the intrinsic anatomical similarity between neighboring CT slices. Neighboring CT slices from the same patient exhibit similar anatomical features. Exploiting this anatomical similarity, a gamma map is calculated as a weighted summation of relative HU error and distance error for each pixel in an artifact-corrupted CT image relative to a neighboring, artifact-free image. The minimum value in the gamma map for each pixel is used to identify an appropriate pixel from the artifact-free CT slice to replace the corresponding artifact-corrupted pixel. With the proposed method, the mean CT HU error was reduced from 360 HU and 460 HU to 24 HU and 34 HU on head and pelvis CT images, respectively. Dose calculation accuracy also improved, as the dose difference was reduced from greater than 20% to less than 4%. Using 3%/3mm criteria, the gamma analysis failure rate was reduced from 23.25% to 0.02%. An image-based metal artifact reduction method is proposed that replaces corrupted image pixels with pixels from neighboring CT slices free of metal artifacts. This method is shown to be capable of suppressing streaking artifacts, thereby improving HU and dose calculation accuracy.

A Patch-based CBCT Scatter Artifact Correction Using Prior CT.

We have developed a novel patch-based cone beam CT (CBCT) artifact correction method based on prior CT images. First, we used the image registration to align the planning CT with the CBCT to reduce the geometry difference between the two images. Then, we brought the planning CT-based prior information into the Bayesian deconvolution framework to perform the CBCT scatter artifact correction based on patch-wise nonlocal mean strategy. We evaluated the proposed correction method using a Catphan phantom with multiple inserts based on contrast-to-noise ratios (CNR) and signal-to-noise ratios (SNR), and the image spatial non-uniformity (ISN). All values of CNR SNR and ISN in the corrected CBCT image were much closer to those in the planning CT images. The results demonstrated that the proposed CT-guided correction method could significantly reduce scatter artifacts and improve the image quality. This method has great potential to correct CBCT images allowing its use in adaptive radiotherapy.

Single-Isocenter Multiple-Target Stereotactic Radiosurgery: Risk of Compromised Coverage.

PURPOSE: To determine the dosimetric effects of rotational errors on target coverage using volumetric modulated arc therapy (VMAT) for multitarget stereotactic radiosurgery (SRS). METHODS AND MATERIALS: This retrospective study included 50 SRS cases, each with 2 intracranial planning target volumes (PTVs). Both PTVs were planned for simultaneous treatment to 21 Gy using a single-isocenter, noncoplanar VMAT SRS technique. Rotational errors of 0.5°, 1.0°, and 2.0° were simulated about all axes. The dose to 95% of the PTV (D95) and the volume covered by 95% of the prescribed dose (V95) were evaluated using multivariate analysis to determine how PTV coverage was related to PTV volume, PTV separation, and rotational error. RESULTS: At 0.5° rotational error, D95 values and V95 coverage rates were ≥95% in all cases. For rotational errors of 1.0°, 7% of targets had D95 and V95 values <95%. Coverage worsened substantially when the rotational error increased to 2.0°: D95 and V95 values were >95% for only 63% of the targets. Multivariate analysis showed that PTV volume and distance to isocenter were strong predictors of target coverage. CONCLUSIONS: The effects of rotational errors on target coverage were studied across a broad range of SRS cases. In general, the risk of compromised coverage increased with decreasing target volume, increasing rotational error and increasing distance between targets. Multivariate regression models from this study may be used to quantify the dosimetric effects of rotational errors on target coverage given patient-specific input parameters of PTV volume and distance to isocenter.

MRI-based computed tomography metal artifact correction method for improving proton range calculation accuracy.

PURPOSE: Computed tomography (CT) artifacts can severely degrade dose calculation accuracy in proton therapy. Prompted by the recently increased popularity of magnetic resonance imaging (MRI) in the radiation therapy clinic, we developed an MRI-based CT artifact correction method for improving the accuracy of proton range calculations. METHODS AND MATERIALS: The proposed method replaces corrupted CT data by mapping CT Hounsfield units (HU number) from a nearby artifact-free slice, using a coregistered MRI. MRI and CT volumetric images were registered with use of 3-dimensional (3D) deformable image registration (DIR). The registration was fine-tuned on a slice-by-slice basis by using 2D DIR. Based on the intensity of paired MRI pixel values and HU from an artifact-free slice, we performed a comprehensive analysis to predict the correct HU for the corrupted region. For a proof-of-concept validation, metal artifacts were simulated on a reference data set. Proton range was calculated using reference, artifactual, and corrected images to quantify the reduction in proton range error. The correction method was applied to 4 unique clinical cases. RESULTS: The correction method resulted in substantial artifact reduction, both quantitatively and qualitatively. On respective simulated brain and head and neck CT images, the mean error was reduced from 495 and 370 HU to 108 and 92 HU after correction. Correspondingly, the absolute mean proton range errors of 2.4 cm and 1.7 cm were reduced to less than 2 mm in both cases. CONCLUSIONS: Our MRI-based CT artifact correction method can improve CT image quality and proton range calculation accuracy for patients with severe CT artifacts.

Clinical outcomes for a novel 6 degrees of freedom image guided localization method for frameless radiosurgery for intracranial brain metastases.

Stereotactic radiosurgery (SRS) is an accepted method of treatment for intracranial brain metastases with sub-millimeter accuracy. Frameless radiosurgery (FRS) is becoming an alternative to framed SRS due to its less invasive requirements. The purpose of this study is to describe the clinical outcomes and local patterns of failure for a novel 6 degrees of freedom CT guided method of localization for FRS of intracranial brain metastases. 42 patients underwent linear accelerator-based FRS to 94 intracranial brain metastases between 01/2009 and 07/2011. 78 and 22 % of treated sites were intact metastases and resection cavities, respectively. 55 % of patients had undergone prior brain radiotherapy (45 % SRS, 26 % whole brain radiation therapy). The 1 year actuarial local recurrence rate was 18 %, with a median imaging follow-up period of 13.2 months. Single fraction equivalent dose was the most important predictor of local recurrence. The 1 year actuarial first distant brain recurrence and total intracranial recurrence rate was 58 and 69 %, respectively. The crude radiographic radiation necrosis rate was 3 %. Of the 10 local recurrence events, 8 (80 %) were in-field only, 1 (10 %) was marginal only, and 1 (10 %) was both. The preponderance of in-field only patterns of failure suggests that geographic miss is not a major contributor to local recurrence using this novel localization method for FRS. The 1 year local control rate is comparable to other similar published series of framed and frameless radiosurgery.

Six degrees of freedom CBCT-based positioning for intracranial targets treated with frameless stereotactic radiosurgery.

Frameless radiosurgery is an attractive alternative to the framed procedure if it can be performed with comparable precision in a reasonable time frame. Here, we present a positioning approach for frameless radiosurgery based on in-room volumetric imaging coupled with an advanced six-degrees-of-freedom (6 DOF) image registration technique which avoids use of a bite block. Patient motion is restricted with a custom thermoplastic mask. Accurate positioning is achieved by registering a cone-beam CT to the planning CT scan and applying all translational and rotational shifts using a custom couch mount. System accuracy was initially verified on an anthropomorphic phantom. Isocenters of delineated targets in the phantom were computed and aligned by our system with an average accuracy of 0.2 mm, 0.3 mm, and 0.4 mm in the lateral, vertical, and longitudinal directions, respectively. The accuracy in the rotational directions was 0.1°, 0.2°, and 0.1° in the pitch, roll, and yaw, respectively. An additional test was performed using the phantom in which known shifts were introduced. Misalignments up to 10 mm and 3° in all directions/rotations were introduced in our phantom and recovered to an ideal alignment within 0.2 mm, 0.3 mm, and 0.4 mm in the lateral, vertical, and longitudinal directions, respectively, and within 0.3° in any rotational axis. These values are less than couch motion precision. Our first 28 patients with 38 targets treated over 63 fractions are analyzed in the patient positioning phase of the study. Mean error in the shifts predicted by the system were less than 0.5 mm in any translational direction and less than 0.3° in any rotation, as assessed by a confirmation CBCT scan. We conclude that accurate and efficient frameless radiosurgery positioning is achievable without the need for a bite block by using our 6DOF registration method. This system is inexpensive compared to a couch-based 6 DOF system, improves patient comfort compared to systems that utilize a bite block, and is ideal for the treatment of pediatric patients with or without general anesthesia, as well as of patients with dental issues. From this study, it is clear that only adjusting for 4 DOF may, in some cases, lead to significant compromise in PTV coverage. Since performing the additional match with 6 DOF in our registration system only adds a relatively short amount of time to the overall process, we advocate making the precise match in all cases.

Current dosing paradigm for stereotactic radiosurgery alone after surgical resection of brain metastases needs to be optimized for improved local control.

PURPOSE: To describe the use of radiosurgery (RS) alone to the resection cavity after resection of brain metastases as an alternative to adjuvant whole-brain radiotherapy (WBRT). METHODS AND MATERIALS: Sixty-two patients with 64 cavities were treated with linear accelerator-based RS alone to the resection cavity after surgical removal of brain metastases between March 2007 and August 2010. Fifty-two patients (81%) had a gross total resection. Median cavity volume was 8.5 cm(3). Forty-four patients (71%) had a single metastasis. Median marginal and maximum doses were 18 Gy and 20.4 Gy, respectively. Sixty-one cavities (95%) had gross tumor volume to planning target volume expansion of ≥1 mm. RESULTS: Six-month and 1-year actuarial local recurrence rates were 14% and 22%, respectively, with a median follow-up period of 9.7 months. Six-month and 1-year actuarial distant brain recurrence, total intracranial recurrence, and freedom from WBRT rates were 31% and 51%, 41% and 63%, and 91% and 74%, respectively. The symptomatic cavity radiation necrosis rate was 8%, with 2 patients (3%) undergoing surgery. Of the 11 local failures, 8 were in-field, 1 was marginal, and 2 were both (defined as in-field if ≥90% of recurrence within the prescription isodose and marginal if ≥90% outside of the prescription isodose). CONCLUSIONS: The high rate of in-field cavity failure suggests that geographic misses with highly conformal RS are not a major contributor to local recurrence. The current dosing regimen derived from Radiation Therapy Oncology Group protocol 90-05 should be optimized in this patient population before any direct comparison with WBRT.