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PurposeRadiation 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 materialsThe 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.ResultsPeriodic 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

Background: Biology‐guided radiotherapy (BgRT) is a novel radiotherapy delivery technique that utilizes the tumor itself to guide dynamic delivery of treatment dose to the tumor. The RefleXion X1 system is the first radiotherapy system developed to deliver SCINTIX® BgRT. The X1 is characterized by its split arc design, employing two 90‐degree positron emission tomography (PET) arcs to guide therapeutic radiation beams in real time, currently cleared by FDA to treat bone and lung tumors. Purpose: This study aims to comprehensively evaluate the capabilities of the SCINTIX radiotherapy delivery system by evaluating its sensitivity to changes in PET contrast, its adaptability in the context of patient motion, and its performance across a spectrum of prescription doses. Methods: A series of experimental scenarios, both static and dynamic, were designed to assess the SCINTIX BgRT system's performance, including an end‐to‐end test. These experiments involved a range of factors, including changes in PET contrast, motion, and prescription doses. Measurements were performed using a custom‐made ArcCHECK insert which included a 2.2 cm spherical target and a c‐shape structure that can be filled with a PET tracer with varying concentrations. Sinusoidal and cosine4 motion patterns, simulating patient breathing, was used to test the SCINTIX system's ability to deliver BgRT during motion‐induced challenges. Each experiment was evaluated against specific metrics, including Activity Concentration (AC), Normalized Target Signal (NTS), and Biology Tracking Zone (BTZ) bounded dose‐volume histogram (bDVH) pass rates. The accuracy of the delivered BgRT doses on ArcCHECK and EBT‐XD film were evaluated using gamma 3%/2 mm and 3%/3 mm analysis. Results: In static scenarios, the X1 system consistently demonstrated precision and robustness in SCINTIX dose delivery. The end‐to‐end delivery to the spherical target yielded good results, with AC and NTS values surpassing the critical thresholds of 5 kBq/mL and 2, respectively. Furthermore, bDVH analysis consistently confirmed 100% pass rates. These results were reaffirmed in scenarios involving changes in PET contrast, emphasizing the system's ability to adapt to varying PET avidities. Gamma analysis with 3%/2 mm (10% dose threshold) criteria consistently achieved pass rates > 91.5% for the static tests. In dynamic SCINTIX delivery scenarios, the X1 system exhibited adaptability under conditions of motion. Sinusoidal and cosine4 motion patterns resulted in 3%/3 mm gamma pass rates > 87%. Moreover, the comparison with gated stereotactic body radiotherapy (SBRT) delivery on a conventional c‐arm Linac resulted in 93.9% gamma pass rates and used as comparison to evaluate the interplay effect. The 1 cm step shift tests showed low overall gamma pass rates of 60.3% in ArcCHECK measurements, while the doses in the PTV agreed with the plan with 99.9% for 3%/3 mm measured with film. Conclusions: The comprehensive evaluation of the X1 radiotherapy delivery system for SCINTIX BgRT demonstrated good agreement for the static tests. The system consistently achieved critical metrics and delivered the BgRT doses per plan. The motion tests demonstrated its ability to co‐localize the dose where the PET signal is and deliver acceptable BgRT dose distributions. [ABSTRACT FROM AUTHOR]

PurposeRange verification in proton therapy using the proton-acoustic signal induced in the Bragg peak was investigated for typical clinical scenarios. The signal generation and detection processes were simulated in order to determine the signal-to-noise limits.MethodsAn analytical model was used to calculate the dose distribution and local pressure rise (per proton) for beams of different energy (100 and 160 MeV) and spot widths (1, 5, and 10 mm) in a water phantom. In this method, the acoustic waves propagating from the Bragg peak were generated by the general 3D pressure wave equation implemented using a finite element method. Various beam pulse widths (0.1-10 μs) were simulated by convolving the acoustic waves with Gaussian kernels. A realistic PZT ultrasound transducer (5 cm diameter) was simulated with a Butterworth bandpass filter with consideration of random noise based on a model of thermal noise in the transducer. The signal-to-noise ratio on a per-proton basis was calculated, determining the minimum number of protons required to generate a detectable pulse. The maximum spatial resolution of the proton-acoustic imaging modality was also estimated from the signal spectrum.ResultsThe calculated noise in the transducer was 12-28 mPa, depending on the transducer central frequency (70-380 kHz). The minimum number of protons detectable by the technique was on the order of 3-30 × 10(6) per pulse, with 30-800 mGy dose per pulse at the Bragg peak. Wider pulses produced signal with lower acoustic frequencies, with 10 μs pulses producing signals with frequency less than 100 kHz.ConclusionsThe proton-acoustic process was simulated using a realistic model and the minimal detection limit was established for proton-acoustic range validation. These limits correspond to a best case scenario with a single large detector with no losses and detector thermal noise as the sensitivity limiting factor. Our study indicated practical proton-acoustic range verification may be feasible with approximately 5 × 10(6) protons/pulse and beam current.

Background: Volumetric reconstruction of magnetic resonance imaging (MRI) from sparse samples is desirable for 3D motion tracking and promises to improve magnetic resonance (MR)‐guided radiation treatment precision. Data‐driven sparse MRI reconstruction, however, requires large‐scale training datasets for prior learning, which is time‐consuming and challenging to acquire in clinical settings. Purpose: To investigate volumetric reconstruction of MRI from sparse samples of two orthogonal slices aided by sparse priors of two static 3D MRI through implicit neural representation (NeRP) learning, in support of 3D motion tracking during MR‐guided radiotherapy. Methods: A multi‐layer perceptron network was trained to parameterize the NeRP model of a patient‐specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first training the network to learn the NeRP of two static 3D MRI with different breathing motion states, prior information of patient breathing motion was embedded into network weights through optimization. The prior information was then augmented from two motion states to 31 motion states by querying the optimized network at interpolated and extrapolated motion state coordinates. Starting from the prior‐augmented NeRP model as an initialization point, we further trained the network to fit sparse samples of two orthogonal MRI slices and the final volumetric reconstruction was obtained by querying the trained network at 3D spatial locations. We evaluated the proposed method using 5‐min volumetric MRI time series with 340 ms temporal resolution for seven abdominal patients with hepatocellular carcinoma, acquired using golden‐angle radial MRI sequence and reconstructed through retrospective sorting. Two volumetric MRI with inhale and exhale states respectively were selected from the first 30 s of the time series for prior embedding and augmentation. The remaining 4.5‐min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to two orthogonal slices and compared model‐reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking. Results: Across the seven patients evaluated, the peak signal‐to‐noise‐ratio between model‐reconstructed and ground truth MR images was 38.02 ± 2.60 dB and the structure similarity index measure was 0.98 ± 0.01. Throughout the 4.5‐min time period, gross tumor volume (GTV) motion estimated by deforming a reference state MRI to model‐reconstructed and ground truth MRI showed good consistency. The 95‐percentile Hausdorff distance between GTV contours was 2.41 ± 0.77 mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1 mm in all three orthogonal directions. Conclusion: A prior‐augmented NeRP model has been developed to reconstruct volumetric MRI from sparse samples of orthogonal cine slices. Only one exhale and one inhale 3D MRI were needed to train the model to learn prior information of patient breathing motion for sparse image reconstruction. The proposed model has the potential of supporting 3D motion tracking during MR‐guided radiotherapy for improved treatment precision and promises a major simplification of the workflow by eliminating the need for large‐scale training datasets. [ABSTRACT FROM AUTHOR]

Background: Eye plaque brachytherapy is currently an optimal therapy for intraocular cancers. Due to the lack of an effective and practical technique to measure the seed radioactivity distribution, current quality assurance (QA) practice according to the American Association of Physicists in Medicine TG129 only stipulates that the plaque assembly be visually inspected. Consequently, uniform seed activity is routinely adopted to avoid possible loading mistakes of differential seed loading. However, modulated dose delivery, which represents a general trend in radiotherapy to provide more personalized treatment for a given tumor and patient, requires differential activities in the loaded seeds. Purpose: In this study, a fast and low‐cost radio‐luminescent imaging and dose calculating system to verify the seed activity distribution for differential loading was developed. Methods: A proof‐of‐concept system consisting of a thin scintillator sheet coupled to a camera/lens system was constructed. A seed‐loaded plaque can be placed directly on the scintillator surface with the radioactive seeds facing the scintillator. The camera system collects the radioluminescent signal generated by the scintillator on its opposite side. The predicted dose distribution in the scintillator's sensitive layer was calculated using a Monte Carlo simulation with the planned plaque loading pattern of I‐125 seeds. Quantitative comparisons of the distribution of relative measured signal intensity and that of the relative predicted dose in the sensitive layer were performed by gamma analysis, similar to intensity‐modulated radiation therapy QA. Results: Data analyses showed high gamma (3%/0.3 mm, global, 20% threshold) passing rates for correct seed loadings and low passing rates with distinguished high gamma value area for incorrect loadings, indicating that possible errors may be detected. The measurement and analysis only required a few extra minutes, significantly shorter than the time to assay the extra verification seeds the physicist already must perform as recommended by TG129. Conclusions: Radio‐luminescent QA can be used to facilitate and assure the implementation of intensity‐modulated, customized plaque loading. [ABSTRACT FROM AUTHOR]