Linac- and CyberKnife-based MRI-only treatment planning of prostate SBRT using an optimized synthetic CT calibration curve.

PURPOSE: CT Hounsfield Units (HUs) are converted to electron density using a calibration curve obtained from physical measurements of an electron density phantom. HU values assigned to an MRI-derived synthetic computed tomography (sCT) may present a different relationship with electron density compared to CT HU. Correct assignment of sCT HU values is critical for accurate dose calculation and delivery. The goals of this work were to develop a sCT calibration curve using patient data acquired on a clinically commissioned CT scanner and assess for CyberKnife- and volumetric modulated arc therapy (VMAT)-based MR-only treatment planning of prostate SBRT. METHODS: Same-day CT and MRI simulation in the treatment position were performed on 10 patients treated with SBRT to the prostate. Dixon in-phase and out-of-phase MRIs were acquired on a 3T scanner using a 3D T1-weighted gradient-echo sequence to generate sCTs using a commercial sCT algorithm. CT and sCT datasets were co-registered and HU values compared using mean absolute error (MAE). An optimized HU-to-density calibration curve was created based on average HU values across an institutional patient database for each of the four sCT tissue types. Clinical CyberKnife and VMAT treatment plans were generated on each patient CT and recomputed onto corresponding sCTs. Dose distributions computed using CT and sCT were compared using gamma criteria and dose-volume-histograms. RESULTS: For the optimized calibration curve, HU values were -96, 37, 204, and 1170 and relative electron densities were 0.95, 1.04, 1.1, and 1.7 for adipose, soft tissue, inner bone, and outer bone, respectively. The proposed sCT protocol produced total MAE of 94 ± 20HU. Gamma values mean ± std (min-max) were 98.9% ± 0.9% (97.1%-100%) and 97.7% ± 1.3% (95.3%-99.3%) for VMAT and CyberKnife plans, respectively. CONCLUSION: MRI-derived sCT using the proposed approach shows excellent dosimetric agreement with conventional CT simulation, demonstrating the feasibility of MRI-derived sCT for prostate SBRT treatment planning.

End-to-end validation of fiducial tracking accuracy in robotic radiosurgery using MRI-only simulation imaging.

BACKGROUND: Image-guided radiation-therapy (IGRT)-based robotic radiosurgery using magnetic resonance imaging (MRI)-only simulation could allow for improved target definition with highly conformal radiotherapy treatments. Fiducial marker (FM)-based alignment is used with robotic radiosurgery treatments of sites such as the prostate because it aids in accurate target localization. Synthetic CT (sCT) images are generated in the MRI-only workflow but FMs used for IGRT appear as signal voids in MRIs and do not appear in MR-generated sCTs, hindering the ability to use sCTs for fiducial-based IGRT. PURPOSE: In this study we evaluate the fiducial tracking accuracy for a novel artificial fiducial insertion method in sCT images that allows for fiducial marker tracking in robotic radiosurgery, using MRI-only simulation imaging (MRI-only workflow). METHODS: Artificial fiducial markers were inserted into sCT images at the site of the real marker implantation as visible in MRI. Two phantoms were used in this study. A custom anthropomorphic pelvis phantom was designed to validate the tracking accuracy for a variety of artificial fiducials in an MRI-only workflow. A head phantom containing a hidden target and orthogonal film pair inserts was used to perform end-to-end tests of artificial fiducial configurations inserted in sCT images. The setup and end-to-end targeting accuracy of the MRI-only workflow were compared to the computed tomography (CT)-based standard. Each phantom had six FMs implanted with a minimum spacing of 2 cm. For each phantom a bulk-density sCT was generated, and artificial FMs were inserted at the implantation location. Several methods of FM insertion were tested including: (1) replacing HU with a fixed value (10000HU) (voxel-burned); (2) using a representative fiducial image derived from a linear combination of fiducial templates (composite-fiducial); (3) computationally simulating FM signal voids using a digital phantom containing FMs and inserting the corresponding signal void into sCT images (simulated-fiducial). All tests were performed on a CyberKnife system (Accuray, Sunnyvale, CA). Treatment plans and digital-reconstructed-radiographs were generated from the original CT and sCTs with embedded fiducials and used to align the phantom on the treatment couch. Differences in the initial phantom alignment (3D translations/rotations) and tracking parameters between CT-based plans and sCT-based plans were analyzed. End-to-end plans for both scenarios were generated and analyzed following our clinical protocol. RESULTS: For all plans, the fiducial tracking algorithm was able to identify the fiducial locations. The mean FM-extraction uncertainty for the composite and simulated FMs was below 48% for fiducials in both the anthropomorphic pelvis and end-to-end phantoms, which is below the 70% treatment uncertainty threshold. The total targeting error was within tolerance (<0.95 mm) for end-to-end tests of sCT images with the composite and head-on simulated FMs (0.26, 0.44, and 0.35 mm for the composite fiducial in sCT, head-on simulated fiducial in sCT, and fiducials in original CT, respectively. CONCLUSIONS: MRI-only simulation for robotic radiosurgery could potentially improve treatment accuracy and reduce planning margins. Our study has shown that using a composite-derived or simulated FM in conjunction with sCT images, MRI-only workflow can provide clinically acceptable setup accuracy in line with CT-based standards for FM-based robotic radiosurgery.

A Couch Mounted Smartphone-based Motion Monitoring System for Radiation Therapy.

PURPOSE: Surface-guided radiation-therapy (SGRT) systems are being adopted into clinical practice for patient setup and motion monitoring. However, commercial systems remain cost prohibitive to resource-limited clinics around the world. Our aim is to develop and validate a smartphone-based application using LiDAR cameras (such as on recent Apple iOS devices) for facilitating SGRT in low-resource centers. The proposed SGRT application was tested at multiple institutions and validated using phantoms and volunteers against various commercial systems to demonstrate feasibility. METHODS AND MATERIALS: An iOS application was developed in Xcode and written in Swift using the Augmented-Reality (AR) Kit and implemented on an Apple iPhone 13 Pro with a built-in LiDAR camera. The application contains multiple features: 1) visualization of both the camera and depth video feeds (at a ∼60Hz sample-frequency), 2) region-of-interest (ROI) selection over the patient's anatomy where motion is measured, 3) chart displaying the average motion over time in the ROI, and 4) saving/exporting the motion traces and surface map over the ROI for further analysis. The iOS application was tested to evaluate depth measurement accuracy for: 1) different angled surfaces, 2) different field-of-views over different distances, and 3) similarity to a commercially available SGRT systems (Vision RT AlignRT and Varian IDENTIFY) with motion phantoms and healthy volunteers across 3 institutions. Measurements were analyzed using linear-regressions and Bland-Altman analysis. RESULTS: Compared with the clinical system measurements (reference), the iOS application showed excellent agreement for depth (r = 1.000, P < .0001; bias = -0.07±0.24 cm) and angle (r = 1.000, P < .0001; bias = 0.02±0.69°) measurements. For free-breathing traces, the iOS application was significantly correlated to phantom motion (institute 1: r = 0.99, P < .0001; bias =-0.003±0.03 cm; institute 2: r = 0.98, P < .0001; bias = -0.001±0.10 cm; institute 3: r = 0.97, P < .0001; bias = 0.04±0.06 cm) and healthy volunteer motion (institute 1: r = 0.98, P < .0001; bias = -0.008±0.06 cm; institute 2: r = 0.99, P < .0001; bias = -0.007±0.12 cm; institute 3: r = 0.99, P < .0001; bias = -0.001±0.04 cm). CONCLUSIONS: The proposed approach using a smartphone-based application provides a low-cost platform that could improve access to surface-guided radiation therapy accounting for motion.

Predicting the Effect of Proton Beam Therapy Technology on Pulmonary Toxicities for Patients With Locally Advanced Lung Cancer Enrolled in the Proton Collaborative Group Prospective Clinical Trial.

PURPOSE: This study aimed to predict the probability of grade ≥2 pneumonitis or dyspnea within 12 months of receiving conventionally fractionated or mildly hypofractionated proton beam therapy for locally advanced lung cancer using machine learning. METHODS AND MATERIALS: Demographic and treatment characteristics were analyzed for 965 consecutive patients treated for lung cancer with conventionally fractionated or mildly hypofractionated (2.2-3 Gy/fraction) proton beam therapy across 12 institutions. Three machine learning models (gradient boosting, additive tree, and logistic regression with lasso regularization) were implemented to predict Common Terminology Criteria for Adverse Events version 4 grade ≥2 pulmonary toxicities using double 10-fold cross-validation for parameter hyper-tuning without leak of information. Balanced accuracy and area under the curve were calculated, and 95% confidence intervals were obtained using bootstrap sampling. RESULTS: The median age of the patients was 70 years (range, 20-97), and they had predominantly stage IIIA or IIIB disease. They received a median dose of 60 Gy in 2 Gy/fraction, and 46.4% received concurrent chemotherapy. In total, 250 (25.9%) had grade ≥2 pulmonary toxicity. The probability of pulmonary toxicity was 0.08 for patients treated with pencil beam scanning and 0.34 for those treated with other techniques (P = 8.97e-13). Use of abdominal compression and breath hold were highly significant predictors of less toxicity (P = 2.88e-08). Higher total radiation delivered dose (P = .0182) and higher average dose to the ipsilateral lung (P = .0035) increased the likelihood of pulmonary toxicities. The gradient boosting model performed the best of the models tested, and when demographic and dosimetric features were combined, the area under the curve and balanced accuracy were 0.75 ± 0.02 and 0.67 ± 0.02, respectively. After analyzing performance versus the number of data points used for training, we observed that accuracy was limited by the number of observations. CONCLUSIONS: In the largest analysis of prospectively enrolled patients with lung cancer assessing pulmonary toxicities from proton therapy to date, advanced machine learning methods revealed that pencil beam scanning, abdominal compression, and lower normal lung doses can lead to significantly lower probability of developing grade ≥2 pneumonitis or dyspnea.

Tungsten Filled 3-Dimensional Printed Lung Blocks for Total Body Irradiation.

PURPOSE: Lung blocks for total-body irradiation are commonly used to reduce lung dose and prevent radiation pneumonitis. Currently, molten Cerrobend containing toxic materials, specifically lead and cadmium, is poured into molds to construct blocks. We propose a streamlined method to create 3-dimensional (3D)-printed lung block shells and fill them with tungsten ball bearings to remove lead and improve overall accuracy in the block manufacturing workflow. METHODS AND MATERIALS: 3D-printed lung block shells were automatically generated using an inhouse software, printed, and filled with 2 to 3 mm diameter tungsten ball bearings. Clinical Cerrobend blocks were compared with the physician drawn blocks as well as our proposed tungsten filled 3D-printed blocks. Physical and dosimetric comparisons were performed on a linac. Dose transmission through the Cerrobend and 3D-printed blocks were measured using point dosimetry (ion-chamber) and the on-board Electronic-Portal-Imaging-Device (EPID). Dose profiles from the EPID images were used to compute the full-width-half-maximum and to compare with the treatment-planning-system. Additionally, the coefficient-of-variation in the central 80% of full-width-half-maximum was computed and compared between Cerrobend and 3D-printed blocks. RESULTS: The geometric difference between treatment-planning-system and 3D-printed blocks was significantly lower than Cerrobend blocks (3D: -0.88 ± 2.21 mm, Cerrobend: -2.28 ± 2.40 mm, P = .0002). Dosimetrically, transmission measurements through the 3D-printed and Cerrobend blocks for both ion-chamber and EPID dosimetry were between 42% to 48%, compared with the open field. Additionally, coefficient-of-variation was significantly higher in 3D-printed blocks versus Cerrobend blocks (3D: 4.2% ± 0.6%, Cerrobend: 2.6% ± 0.7%, P < .0001). CONCLUSIONS: We designed and implemented a tungsten filled 3D-printed workflow for constructing total-body-irradiation lung blocks, which serves as an alternative to the traditional Cerrobend based workflow currently used in clinics. This workflow has the capacity of producing clinically useful lung blocks with minimal effort to facilitate the removal of toxic materials from the clinic.

A systematic review and meta-analysis of liver tumor position variability during SBRT using various motion management and IGRT strategies.

PURPOSE: To suggest PTV margins for liver SBRT with different motion management strategies based on a systematic review and meta-analysis. METHODS: In accordance with Preferred-Reporting-Items-for-Systematic-Reviews-and-Meta-Analyses (PRISMA), a systematic review in PubMed, Embase and Medline databases was performed for liver tumor position variability. From an initial 533 studies published before October 2020, 36 studies were categorized as 18 free-breathing (FB; npatients = 401), 9 abdominal compression (AC; npatients = 145) and 9 breath-hold (BH; npatients = 126). A meta-analysis was performed on inter- and intra-fraction position variability to report weighted-mean with 95% confidence interval (CI95) in superior-inferior (SI), left-right (LR) and anterior-posterior (AP) directions. Furthermore, weighted-mean ITV margins were computed for FB (nstudies = 15, npatients = 373) and AC (nstudies = 6, npatients = 97) and PTV margins were computed for FB (nstudies = 6, npatients = 95), AC (nstudies = 7, npatients = 106) and BH (nstudies = 8, npatients = 133). RESULTS: The FB weighted-mean intra-fraction variability, ITV margins and weighted-standard-deviation in mm were SI-9.7, CI95 = 9.3-10.1, 13.5 ± 4.9; LR-5.4, CI95 = 5.3-5.6, 7.3 ± 7.9; and AP-4.2, CI95 = 4.0-4.4, 6.3 ± 7.6. The inter-fraction-based results were SI-4.7, CI95 = 4.3-5.1, 5.7 ± 1.7; LR-1.4, CI95 = 1.1-1.6, 3.6 ± 2.7; and AP-2.8, CI95 = 2.5-3.1, 4.8 ± 2.1. For AC intra-fraction results in mm were SI-1.8, CI95 = 1.6-2.0, 2.6 ± 1.2; LR-0.7, CI95 = 0.6-0.8, 1.7 ± 1.5; and AP-0.9, CI95 = 0.8-1.0, 1.9 ± 1.7. The inter-fraction results were SI-2.6, CI95 = 2.3-3.0, 5.2 ± 2.9; LR-1.9, CI95 = 1.7-2.1, 4.0 ± 2.2; and AP-2.9, CI95 = 2.5-3.2, 5.8 ± 2.7. For BH the inter-fraction variability, and the weighted-mean PTV margins and weighted-standard-deviation in mm were SI-2.4, CI95 = 2.1-2.7, 5.6 ± 2.9; LR-1.8, CI95 = 1.3-2.2, 5.5 ± 1.7; and AP-1.4; CI95 = 1.2-1.7, 6.1 ± 2.1. CONCLUSION: Our meta-analysis suggests a symmetric weighted-mean PTV margin of 6 mm might be appropriate for BH. For AC and FB, asymmetric PTV margins (weighted-mean margin of 4 mm (AP), 6 mm (SI/LR)) might be appropriate. For FB, if larger (>ITV margin) intra-fraction variability observed, the additional intra- and inter-fraction variability should be accounted in the PTV margin.

Stereotactic Body Radiation Therapy and High-Dose-Rate Brachytherapy Boost in Combination With Intensity Modulated Radiation Therapy for Localized Prostate Cancer: A Single-Institution Propensity Score Matched Analysis.

PURPOSE: To perform a propensity-score matched analysis comparing stereotactic body radiation therapy (SBRT) boost and high-dose-rate (HDR) boost for localized prostate cancer. METHODS AND MATERIALS: A single-institution retrospective chart review was conducted of men treated with pelvic external beam radiation therapy (EBRT) and SBRT boost (21 Gy and 19 Gy in 2 fractions) to the prostate for prostate cancer. A cohort treated at the same institution with HDR brachytherapy boost (19 Gy in 2 fractions) was compared. Propensity-score (PS) matching and multivariable Cox regression were used for analysis. Outcomes were biochemical recurrence freedom (BCRF) and metastasis freedom (MF). RESULTS: One hundred thirty-one men were treated with SBRT boost and 101 with HDR boost with median follow-up of 73.4 and 186.0 months, respectively. In addition, 68.8% of men had high-risk and 26.0% had unfavorable-intermediate disease, and 94.3% received androgen deprivation therapy. Five- and 10-year unadjusted BCRF was 88.8% and 85.3% for SBRT and 91.8% and 74.6% for HDR boost (log-rank P = .3), and 5- and 10-year unadjusted MF was 91.7% and 84.3% for SBRT and 95.8% and 82.0% for HDR (log-rank P = .8). After adjusting for covariates, there was no statistically significant difference in BCRF (hazard ratio [HR] 0.81; 95% confidence interval [CI], 0.37-1.79; P = .6) or MF (HR 1.07; 95% CI, 0.44-2.57; P = .9) between SBRT and HDR boost. Similarly, after PS matching, there was no statistically significant difference between SBRT and HDR (BCRF: HR 0.66, 0.27-1.62, P = .4; MF: HR 0.84, 0.31-2.26, P = .7). Grade 3+ genitourinary and gastrointestinal toxicity in the SBRT cohort were 4.6% and 1.5%, and 3.0% and 0.0% in the HDR cohorts (P = .4, Fisher exact test). CONCLUSIONS: SBRT boost plus pelvic EBRT for prostate cancer resulted in similar BCRF and MF to HDR boost in this single institution, PS matched retrospective analysis. Toxicity was modest. Prospective evaluation of SBRT boost for the treatment of unfavorable-intermediate and high-risk prostate cancer is warranted.

Technical Note: Evaluation of audiovisual biofeedback smartphone application for respiratory monitoring in radiation oncology.

PURPOSE: Radiation dose delivered to targets located near the upper abdomen or thorax are significantly affected by respiratory motion, necessitating large margins, limiting dose escalation. Surrogate motion management devices, such as the Real-time Position Management (RPM™) system (Varian Medical Systems, Palo Alto, CA), are commonly used to improve normal tissue sparing. Alternative to current solutions, we have developed and evaluated the feasibility of a real-time position management system that leverages the motion data from the onboard hardware of Apple iOS devices to provide patients with visual coaching with the potential to improve the reproducibility of breathing as well as improve patient compliance and reduce treatment delivery time. METHODS AND MATERIALS: The iOS application, coined the Instant Respiratory Feedback (IRF) system, was developed in Swift (Apple Inc., Cupertino, CA) using the Core-Motion library and implemented on an Apple iPhone® devices. Operation requires an iPhone®, a three-dimensional printed arm, and a radiolucent projector screen system for feedback. Direct comparison between IRF, which leverages sensor fusion data from the iPhone®, and RPM™, an optical-based system, was performed on multiple respiratory motion phantoms and volunteers. The IRF system and RPM™ camera tracking marker were placed on the same location allowing for simultaneous data acquisition. The IRF surrogate measurement of displacement was compared to the signal trace acquired using RPM™ with univariate linear regressions and Bland-Altman analysis. RESULTS: Periodic motion shows excellent agreement between both systems, and subject motion shows good agreement during regular and irregular breathing motion. Comparison of IRF and RPM™ show very similar signal traces that were significantly related across all phantoms, including those motion with different amplitude and frequency, and subjects' waveforms (all r > 0.9, P < 0.0001). We demonstrate the feasibility of performing four-dimensional cone beam computed tomography using IRF which provided similar image quality as RPM™ when reconstructing dynamic motion phantom images. CONCLUSIONS: Feasibility of an iOS application to provide real-time respiratory motion is demonstrated. This system generated comparable signal traces to a commercially available system and offers an alternative method to monitor respiratory motion.

Technical Note: Performance of CyberKnife® tracking using low-dose CT and kV imaging.

PURPOSE: To investigate the effects of CT protocol and in-room x-ray technique on CyberKnife® (Accuray Inc.) tracking accuracy by evaluating end-to-end tests. METHODS: End-to-end (E2E) tests were performed for the different tracking methods (6D skull, fiducial, spine, and lung) using an anthropomorphic head phantom (Accuray Inc.) and thorax phantom (CIRS Inc.). Bolus was added to the thorax phantom to simulate a large patient and to evaluate the performance of lung tracking in a more realistic condition. The phantoms were scanned with a Siemens Sensation Open 24 slice CT at low dose (120 kV, 70 mAs, 1.5 mm slice thickness) and high dose (120 kV, 700 mAs, 1.5 mm slice thickness) to generate low-dose and high-dose digitally reconstructed radiographs (DRRs). The difference in initial phantom alignment, Δ(Align), and in total targeting accuracy, E2E, were obtained for all tracking methods with low- and high-dose DRRs. Additionally, Δ(Align) was determined for different in-room x-ray imaging techniques (0.5 to 50 mAs and 100 to 140 kV) using a low-dose lung tracking plan. RESULTS: Low-dose CT scans produced images with high noise; however, for these phantoms the targets could be easily delineated on all scans. End-to-end results were less than 0.95 mm for all tracking methods and all plans. The greatest difference in initial alignment Δ(Align) and E2E results between low- and high-dose CT protocols was 0.32 and 0.24 mm, respectively. Similar results were observed with a large thorax phantom. Tracking using different in-room x-ray imaging techniques (mAs) corresponding to low exposures (resulting in high image noise) or high exposure (resulting in image saturation) had alignment accuracy Δ(Align) greater than 1 mm. CONCLUSIONS: End-to-end targeting accuracy within tolerance (<0.95 mm) was obtained for all tracking methods using low-dose CT protocols, suggesting that CT protocol should be set by target contouring needs. Additionally, high tracking accuracy was achieved for in-room x-ray imaging techniques that produce high-quality images.

MTF and DQE enhancement using an apodized-aperture x-ray detector design.

PURPOSE: Acquisition of high-quality x-ray images using low patient exposures requires detectors with high detective quantum efficiency (DQE). We describe a novel apodized-aperture pixel (AAP) design that increases high-frequency modulation transfer function (MTF) and DQE values. The AAP design makes a separation of physical sensor elements from image pixels by using very small sensor elements (e.g., 0.010-0.025 mm) to synthesize desired larger image pixels (e.g., 0.1-0.2 mm). METHODS: A cascaded systems model of signal and noise propagation is developed to describe the benefits of the AAP approach in terms of the MTF, Wiener noise power spectrum (NPS), and DQE. The theoretical model was validated experimentally using a CMOS/CsI detector with 0.05 mm sensor elements to synthesize 0.20 mm image pixels and a clinical Se detector with 0.07 mm sensor elements to synthesize 0.28 mm pixels. A Monte Carlo study and x-ray images of a star-pattern and rat leg are used to visually compare AAP images. RESULTS: When used with a high-resolution converter layer and sensor elements one quarter the size of image pixels, the MTF is increased by 53% and the DQE by a factor of 2.3× at the image sampling cut-off frequency. Both simulated and demonstration images show improved detectability of high-frequency content and removal of aliasing artifacts. Evidence of Gibbs ringing is sometimes seen near high-contrast edges. CONCLUSIONS: It is shown that the AAP approach preserves the MTF of the small sensor elements and attenuates frequencies above the image sampling cut-off frequency. This has the double benefit of improving the MTF while reducing both signal and noise aliasing, resulting in an increase of the DQE at high spatial frequencies. For optimal implementation, the converter layer must have very high spatial resolution and the detector must have low readout noise.