AAPM Medical Physics Practice Guideline 8.b: Linear accelerator performance tests.

The purpose of this guideline is to provide a list of critical performance tests to assist the Qualified Medical Physicist (QMP) in establishing and maintaining a safe and effective quality assurance (QA) program. The performance tests on a linear accelerator (linac) should be selected to fit the clinical patterns of use of the accelerator and care should be given to perform tests which are relevant to detecting errors related to the specific use of the accelerator. Current recommendations for linac QA were reviewed to determine any changes required to those tests highlighted by the original report as well as considering new components of the treatment process that have become common since its publication. Recommendations are made on the acquisition of reference data, routine establishment of machine isocenter, basing performance tests on clinical use of the linac, working with vendors to establish QA tests and performing tests after maintenance and upgrades. The recommended tests proposed in this guideline were chosen based on consensus of the guideline's committee after assessing necessary changes from the previous report. The tests are grouped together by class of test (e.g., dosimetry, mechanical, etc.) and clinical parameter tested. Implementation notes are included for each test so that the QMP can understand the overall goal of each test. This guideline will assist the QMP in developing a comprehensive QA program for linacs in the external beam radiation therapy setting. The committee sought to prioritize tests by their implication on quality and patient safety. The QMP is ultimately responsible for implementing appropriate tests. In the spirit of the report from American Association of Physicists in Medicine Task Group 100, individual institutions are encouraged to analyze the risks involved in their own clinical practice and determine which performance tests are relevant in their own radiotherapy clinics.

Automatic segmentation of high-risk clinical target volume for tandem-and-ovoids brachytherapy patients using an asymmetric dual-path convolutional neural network.

PURPOSES: Preimplant diagnostic magnetic resonance imaging is the gold standard for image-guided tandem-and-ovoids (T&O) brachytherapy for cervical cancer. However, high dose rate brachytherapy planning is typically done on postimplant CT-based high-risk clinical target volume (HR-CTVCT ) because the transfer of preimplant Magnetic resonance (MR)-based HR-CTV (HR-CTVMR ) to the postimplant planning CT is difficult due to anatomical changes caused by applicator insertion, vaginal packing, and the filling status of the bladder and rectum. This study aims to train a dual-path convolutional neural network (CNN) for automatic segmentation of HR-CTVCT on postimplant planning CT with guidance from preimplant diagnostic MR. METHODS: Preimplant T2-weighted MR and postimplant CT images for 65 (48 for training, eight for validation, and nine for testing) patients were retrospectively solicited from our institutional database. MR was aligned to the corresponding CT using rigid registration. HR-CTVCT and HR-CTVMR were manually contoured on CT and MR by an experienced radiation oncologist. All images were then resampled to a spatial resolution of 0.5 × 0.5 × 1.25 mm. A dual-path 3D asymmetric CNN architecture with two encoding paths was built to extract CT and MR image features. The MR was masked by HR-CTVMR contour while the entire CT volume was included. The network put an asymmetric weighting of 18:6 for CT: MR. Voxel-based dice similarity coefficient (DSCV ), sensitivity, precision, and 95% Hausdorff distance (95-HD) were used to evaluate model performance. Cross-validation was performed to assess model stability. The study cohort was divided into a small tumor group (<20 cc), medium tumor group (20-40 cc), and large tumor group (>40 cc) based on the HR-CTVCT for model evaluation. Single-path CNN models were trained with the same parameters as those in dual-path models. RESULTS: For this patient cohort, the dual-path CNN model improved each of our objective findings, including DSCV , sensitivity, and precision, with an average improvement of 8%, 7%, and 12%, respectively. The 95-HD was improved by an average of 1.65 mm compared to the single-path model with only CT images as input. In addition, the area under the curve for different networks was 0.86 (dual-path with CT and MR) and 0.80 (single-path with CT), respectively. The dual-path CNN model with asymmetric weighting achieved the best performance with DSCV of 0.65 ± 0.03 (0.61-0.70), 0.79 ± 0.02 (0.74-0.85), and 0.75 ± 0.04 (0.68-0.79) for small, medium, and large group. 95-HD were 7.34 (5.35-10.45) mm, 5.48 (3.21-8.43) mm, and 6.21 (5.34-9.32) mm for the three size groups, respectively. CONCLUSIONS: An asymmetric CNN model with two encoding paths from preimplant MR (masked by HR-CTVMR ) and postimplant CT images was successfully developed for automatic segmentation of HR-CTVCT for T&O brachytherapy patients.

A Volumetric Dosimetry Analysis of Vertebral Body Fracture Risk After Single Fraction Spine Stereotactic Body Radiation Therapy.

PURPOSE: Vertebral compression fractures (VCF) are a common and severe complication of spine stereotactic body radiation therapy (SBRT). We sought to analyze how volumetric dosimetry and clinical factors were associated with the risk of VCF. METHODS AND MATERIALS: We evaluated 173 spinal segments that underwent single fraction SBRT in 85 patients from a retrospective database. Vertebral bodies were contoured and dosimetric values were calculated. Competing risk models were used to evaluate the effect of clinical and dosimetry variables on the risk of VCF. RESULTS: Our primary endpoint was development of a post-SBRT VCF. New or progressive fractures were noted in 21/173 vertebrae (12.1%); the median time to fracture was 322 days. Median follow-up time was 426 days. Upon multivariable analysis, the percentages of vertebral body volume receiving >20 Gy and >24 Gy were significantly associated with increased risk of VCF (hazard ratio, 1.036, 1.104; P = .029, .044, respectively). No other patient or treatment factors were found to be significant on multivariable analysis. Sensitivity analysis revealed that the percentages of vertebral body volume receiving >20 Gy and >24 Gy required to obtain 90% sensitivity for predicting vertebral body fracture were 24% and 0%, respectively. CONCLUSIONS: VCF is a common complication after SBRT, with a crude incidence of 12.1%. Treatment plans that permit higher volumes receiving doses >20 Gy and >24 Gy to the vertebral body are associated with increased risk of VCF. To achieve 90% sensitivity for predicting VCF post-SBRT, the percentage of vertebral volume receiving >20 Gy should be <24% and maximum point dose should be <24 Gy. These results may help guide clinicians when evaluating spine SBRT treatment plans to minimize the risk of developing posttreatment VCF.

Automatic detection and segmentation of multiple brain metastases on magnetic resonance image using asymmetric UNet architecture.

Detection of brain metastases is a paramount task in cancer management due both to the number of high-risk patients and the difficulty of achieving consistent detection. In this study, we aim to improve the accuracy of automated brain metastasis (BM) detection methods using a novel asymmetric UNet (asym-UNet) architecture. An end-to-end asymmetric 3D-UNet architecture, with two down-sampling arms and one up-sampling arm, was constructed to capture the imaging features. The two down-sampling arms were trained using two different kernels (3 × 3 × 3 and 1 × 1 × 3, respectively) with the kernel (1 × 1 × 3) dominating the learning. As a comparison, vanilla single 3D UNets were trained with different kernels and evaluated using the same datasets. Voxel-based Dice similarity coefficient (DSCv), sensitivity (S v), precision (P v), BM-based sensitivity (S BM), and false detection rate (F BM) were used to evaluate model performance. Contrast-enhanced T1 MR images from 195 patients with a total of 1034 BMs were solicited from our institutional stereotactic radiosurgery database. The patient cohort was split into training (160 patients, 809 lesions), validation (20 patients, 136 lesions), and testing (15 patients, 89 lesions) datasets. The lesions in the testing dataset were further divided into two subgroups based on the diameters (small S = 1-10 mm, large L = 11-26 mm). In the testing dataset, there were 72 and 17 BMs in the S and L sub-groups, respectively. Among all trained networks, asym-UNet achieved the highest DSCv of 0.84 and lowest F BM of 0.24. Although vanilla 3D-UNet with a single 1 × 1 × 3 kernel achieved the highest sensitivities for the S group, it resulted in the lowest precision and highest false detection rate. Asym-UNet was shown to balance sensitivity and false detection rate as well as keep the segmentation accuracy high. The novel asym-UNet segmentation network showed overall competitive segmentation performance and more pronounced improvement in hard-to-detect small BMs comparing to the vanilla single 3D UNet.

Addition of magnetic resonance imaging to real time trans-rectal ultrasound-based treatment planning for prostate implants.

PURPOSE: The greater soft tissue contrast of magnetic resonance imaging (MRI) allows improved accuracy in prostate contouring compared to transrectal ultrasound (TRUS) and helps in identifying specific regions within the prostate. This study attempts to evaluate the potential benefit of MRI-TRUS fusion in treatment planning for more accurate prostate contouring and tumor dose escalation. MATERIAL AND METHODS: 14 patients with previous MRI-guided prostate biopsy and an low-dose-rate (LDR) permanent prostate seed implant have been selected. The prostate and tumor (5 patients) were contoured on the MRI images by a radiologist. The prostate was also contoured on TRUS images during LDR procedure together by a urologist and radiation oncologist. MRI and TRUS images were rigidly fused to compare prostate contours in MRI and TRUS. Prostate was then re-contoured by the radiation oncologist using this fusion. Moreover, V100, V150, and D90 differences were evaluated for localized tumor compared to prostate with negative values indicating cold tumor regions. These cases were re-planned to simulate dose escalation. RESULTS: The prostate volume was contoured 8 ±10% smaller in TRUS images, compared to MRI images. The mean percent difference in tumor (compared to prostate) V100 was 0.3 ±-0.4%, V150 was -0.7 ±-24.8%, and D90 was 0.2 ±-12.1%. For the posteriorly located tumors (2 cases), V100 was 0.0 ±-0.3%, D90 was 9.5 ±-3.0%, and V150 was 26.1 ±-5.4%. For anteriorly located tumors (3 cases), V100 was 0.4 ±-0.4%, D90 was -6.0 ±-11.9%, and V150 was -18.5 ±-14.4% (became 15.6 ±14.6% after re-plan). CONCLUSIONS: The MRI-TRUS image fusion is a feasible tool for the visualization of the prostate gland, particularly at the apex and base of the gland. Tumor identification presents the potential for dose escalation using fusion, especially for anteriorly located tumors.

Gamma Knife and volumetric modulated arc therapy stereotactic radiosurgery plan quality and OAR sparing comparison for pituitary adenomas and vestibular schwannomas.

PURPOSE: To compare the plan quality and organs at risk (OAR) sparing of auto-planned volumetric modulated art therapy (VMAT) and Gamma Knife (GK) for stereotactic radiosurgery of pituitary adenomas (PA) and vestibular schwannomas (VS). METHODS: VMAT radiosurgery plans were made using auto planning tool for eight vestibular schwannoma and eight pituitary adenoma patients previously treated with GK. VMAT plans were made with three non-coplanar arcs using 315, 0 and 45 degrees angles, 6MV FFF energy at 1400 MU/min dose rate and 2.5 mm thick MLC leaves. Both GK and VMAT plans were prescribed to similar isodose lines (50% - 60%). RESULTS: Respectively for GK and VMAT, the mean Paddick conformity index (PCI) was 0.62 ± 0.08 and 0.67 ± 0.10 (p > 0.05) for PA and 0.72 ± 0.09 and 0.660 ± 0.13 (p > 0.05) for VS; the mean gradient index (GI) was 2.76 ± 0.14 and 3.14 ± 0.40 Gy (p < 0.05) for PA and 3.71 ± 1.83 and 3.60 ± 0.84 Gy (p > 0.05) for VS; mean brainstem maximum dose was 9.13 ± 3.50 Gy and 7.31 ± 2.01 Gy (p > 0.05) for PA and 11.67 ± 4.56 Gy and 12.22 ± 4.55 Gy (p > 0.05) for VS; mean optic nerve maximum dose was 9.66 ± 1.0 Gy and 7.67 ± 2.58 Gy (p < 0.05); mean cochlea mean dose was 7.31 ± 2.7 Gy and 7.23 ± 3.13 Gy (p > 0.05); and mean treatment time was 68 min and 5 min for PA and 40 min and 3 min for VS. CONCLUSIONS: Auto planning with standard template simplified the planning stage for VMAT and provided clinically acceptable plans. Comparison of GK and VMAT for plan quality and OAR sparing varied across patients but both were overall comparable.

A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer.

PURPOSE: To describe in detail a dataset consisting of serial four-dimensional computed tomography (4DCT) and 4D cone beam CT (4DCBCT) images acquired during chemoradiotherapy of 20 locally advanced, nonsmall cell lung cancer patients we have collected at our institution and shared publicly with the research community. ACQUISITION AND VALIDATION METHODS: As part of an NCI-sponsored research study 82 4DCT and 507 4DCBCT images were acquired in a population of 20 locally advanced nonsmall cell lung cancer patients undergoing radiation therapy. All subjects underwent concurrent radiochemotherapy to a total dose of 59.4-70.2 Gy using daily 1.8 or 2 Gy fractions. Audio-visual biofeedback was used to minimize breathing irregularity during all fractions, including acquisition of all 4DCT and 4DCBCT acquisitions in all subjects. Target, organs at risk, and implanted fiducial markers were delineated by a physician in the 4DCT images. Image coordinate system origins between 4DCT and 4DCBCT were manipulated in such a way that the images can be used to simulate initial patient setup in the treatment position. 4DCT images were acquired on a 16-slice helical CT simulator with 10 breathing phases and 3 mm slice thickness during simulation. In 13 of the 20 subjects, 4DCTs were also acquired on the same scanner weekly during therapy. Every day, 4DCBCT images were acquired on a commercial onboard CBCT scanner. An optically tracked external surrogate was synchronized with CBCT acquisition so that each CBCT projection was time stamped with the surrogate respiratory signal through in-house software and hardware tools. Approximately 2500 projections were acquired over a period of 8-10 minutes in half-fan mode with the half bow-tie filter. Using the external surrogate, the CBCT projections were sorted into 10 breathing phases and reconstructed with an in-house FDK reconstruction algorithm. Errors in respiration sorting, reconstruction, and acquisition were carefully identified and corrected. DATA FORMAT AND USAGE NOTES: 4DCT and 4DCBCT images are available in DICOM format and structures through DICOM-RT RTSTRUCT format. All data are stored in the Cancer Imaging Archive (TCIA, http://www.cancerimagingarchive.net/) as collection 4D-Lung and are publicly available. DISCUSSION: Due to high temporal frequency sampling, redundant (4DCT and 4DCBCT) data at similar timepoints, oversampled 4DCBCT, and fiducial markers, this dataset can support studies in image-guided and image-guided adaptive radiotherapy, assessment of 4D voxel trajectory variability, and development and validation of new tools for image registration and motion management.

Interfraction displacement of primary tumor and involved lymph nodes relative to anatomic landmarks in image guided radiation therapy of locally advanced lung cancer.

PURPOSE: To analyze primary tumor (PT) and lymph node (LN) position changes relative to each other and relative to anatomic landmarks during conventionally fractionated radiation therapy for patients with locally advanced lung cancer. METHODS AND MATERIALS: In 12 patients with locally advanced non-small cell lung cancer PT, LN, carina, and 1 thoracic vertebra were manually contoured on weekly 4-dimensional fan-beam CT scans. Systematic and random interfraction displacements of all contoured structures were identified in the 3 cardinal directions, and resulting setup margins were calculated. Time trends and the effect of volume changes on displacements were analyzed. RESULTS: Three-dimensional displacement vectors and systematic/random interfraction displacements were smaller for carina than for vertebra both for PT and LN. For PT, mean (SD) 3-dimensional displacement vectors with carina-based alignment were 7 (4) mm versus 9 (5) mm with bony anatomy (P<.0001). For LN, smaller displacements were found with carina- (5 [3] mm, P<.0001) and vertebra-based (6 [3] mm, P=.002) alignment compared with using PT for setup (8 [5] mm). Primary tumor and LN displacements relative to bone and carina were independent (P>.05). Displacements between PT and bone (P=.04) and between PT and LN (P=.01) were significantly correlated with PT volume regression. Displacements between LN and carina were correlated with LN volume change (P=.03). CONCLUSIONS: Carina-based setup results in a more reproducible PT and LN alignment than bony anatomy setup. Considering the independence of PT and LN displacement and the impact of volume regression on displacements over time, repeated CT imaging even with PT-based alignment is recommended in locally advanced disease.

Respiratory triggered 4D cone-beam computed tomography: a novel method to reduce imaging dose.

PURPOSE: A novel method called respiratory triggered 4D cone-beam computed tomography (RT 4D CBCT) is described whereby imaging dose can be reduced without degrading image quality. RT 4D CBCT utilizes a respiratory signal to trigger projections such that only a single projection is assigned to a given respiratory bin for each breathing cycle. In contrast, commercial 4D CBCT does not actively use the respiratory signal to minimize image dose. METHODS: To compare RT 4D CBCT with conventional 4D CBCT, 3600 CBCT projections of a thorax phantom were gathered and reconstructed to generate a ground truth CBCT dataset. Simulation pairs of conventional 4D CBCT acquisitions and RT 4D CBCT acquisitions were developed assuming a sinusoidal respiratory signal which governs the selection of projections from the pool of 3600 original projections. The RT 4D CBCT acquisition triggers a single projection when the respiratory signal enters a desired acquisition bin; the conventional acquisition does not use a respiratory trigger and projections are acquired at a constant frequency. Acquisition parameters studied were breathing period, acquisition time, and imager frequency. The performance of RT 4D CBCT using phase based and displacement based sorting was also studied. Image quality was quantified by calculating difference images of the test dataset from the ground truth dataset. Imaging dose was calculated by counting projections. RESULTS: Using phase based sorting RT 4D CBCT results in 47% less imaging dose on average compared to conventional 4D CBCT. Image quality differences were less than 4% at worst. Using displacement based sorting RT 4D CBCT results in 57% less imaging dose on average, than conventional 4D CBCT methods; however, image quality was 26% worse with RT 4D CBCT. CONCLUSIONS: Simulation studies have shown that RT 4D CBCT reduces imaging dose while maintaining comparable image quality for phase based 4D CBCT; image quality is degraded for displacement based RT 4D CBCT in its current implementation.

Dose escalation for locally advanced lung cancer using adaptive radiation therapy with simultaneous integrated volume-adapted boost.

PURPOSE: To test the feasibility of a planned phase 1 study of image-guided adaptive radiation therapy in locally advanced lung cancer. METHODS AND MATERIALS: Weekly 4-dimensional fan beam computed tomographs (4D FBCT) of 10 lung cancer patients undergoing concurrent chemoradiation therapy were used to simulate adaptive radiation therapy: After an initial intensity modulated radiation therapy plan (0-30 Gy/2 Gy), adaptive replanning was performed on week 2 (30-50 Gy/2 Gy) and week 4 scans (50-66 Gy/2 Gy) to adjust for volume and shape changes of primary tumors and lymph nodes. Week 2 and 4 clinical target volumes (CTV) were deformably warped from the initial planning scan to adjust for anatomical changes. On the week 4 scan, a simultaneous integrated volume-adapted boost was created to the shrunken primary tumor with dose increases in 5 0.4-Gy steps from 66 Gy to 82 Gy in 2 scenarios: plan A, lung isotoxicity; plan B, normal tissue tolerance. Cumulative dose was assessed by deformably mapping and accumulating biologically equivalent dose normalized to 2 Gy-fractions (EQD2). RESULTS: The 82-Gy level was achieved in 1 in 10 patients in scenario A, resulting in a 13.4-Gy EQD2 increase and a 22.1% increase in tumor control probability (TCP) compared to the 66-Gy plan. In scenario B, 2 patients reached the 82-Gy level with a 13.9 Gy EQD2 and 23.4% TCP increase. CONCLUSIONS: The tested image-guided adaptive radiation therapy strategy enabled relevant increases in EQD2 and TCP. Normal tissue was often dose limiting, indicating a need to modify the present study design before clinical implementation.

Evaluation of 4-dimensional computed tomography to 4-dimensional cone-beam computed tomography deformable image registration for lung cancer adaptive radiation therapy.

PURPOSE: To evaluate 2 deformable image registration (DIR) algorithms for the purpose of contour mapping to support image-guided adaptive radiation therapy with 4-dimensional cone-beam CT (4DCBCT). METHODS AND MATERIALS: One planning 4D fan-beam CT (4DFBCT) and 7 weekly 4DCBCT scans were acquired for 10 locally advanced non-small cell lung cancer patients. The gross tumor volume was delineated by a physician in all 4D images. End-of-inspiration phase planning 4DFBCT was registered to the corresponding phase in weekly 4DCBCT images for day-to-day registrations. For phase-to-phase registration, the end-of-inspiration phase from each 4D image was registered to the end-of-expiration phase. Two DIR algorithms-small deformation inverse consistent linear elastic (SICLE) and Insight Toolkit diffeomorphic demons (DEMONS)-were evaluated. Physician-delineated contours were compared with the warped contours by using the Dice similarity coefficient (DSC), average symmetric distance, and false-positive and false-negative indices. The DIR results are compared with rigid registration of tumor. RESULTS: For day-to-day registrations, the mean DSC was 0.75 ± 0.09 with SICLE, 0.70 ± 0.12 with DEMONS, 0.66 ± 0.12 with rigid-tumor registration, and 0.60 ± 0.14 with rigid-bone registration. Results were comparable to intraobserver variability calculated from phase-to-phase registrations as well as measured interobserver variation for 1 patient. SICLE and DEMONS, when compared with rigid-bone (4.1 mm) and rigid-tumor (3.6 mm) registration, respectively reduced the average symmetric distance to 2.6 and 3.3 mm. On average, SICLE and DEMONS increased the DSC to 0.80 and 0.79, respectively, compared with rigid-tumor (0.78) registrations for 4DCBCT phase-to-phase registrations. CONCLUSIONS: Deformable image registration achieved comparable accuracy to reported interobserver delineation variability and higher accuracy than rigid-tumor registration. Deformable image registration performance varied with the algorithm and the patient.