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PurposeOur purpose was to assess the use of machine learning methods and Mobius 3D (M3D) dose calculation software to reduce the number of physical ion chamber (IC) dose measurements required for patient-specific quality assurance during corona virus disease 2019.Methods and materialsIn this study, 1464 inversely planned treatments using Pinnacle or Raystation treatment planning software (TPS) were delivered using Elekta Versa HD and Varian Truebeam and Truebeam STx linear accelerators between June 2018 and November 2019. For each plan, an independent dose calculation was performed using M3D, and an absolute dose measurement was taken using a Pinpoint IC inside the Mobius phantom. The point dose differences between the TPS and M3D calculation and between TPS and IC measurements were calculated. Agreement between the TPS and IC was used to define the ground truth plan failure. To reduce the on-site personnel during the pandemic, 2 methods of receiver operating characteristic analysis (n = 1464) and machine learning (n = 603) were used to identify patient plans that would require physical dose measurements.ResultsIn the receiver operating characteristic analysis, a predelivery M3D difference threshold of 3% identified plans that failed an IC measurement at a 4% threshold with 100% sensitivity and 76.3% specificity. This indicates that fewer than 25% of plans required a physical dose measurement. A threshold of 1% on a machine learning model was able to identify plans that failed an IC measurement at a 3% threshold with 100% sensitivity and 54.3% specificity, leading to fewer than 50% of plans that required a physical dose measurement.ConclusionsIt is possible to identify plans that are more likely to fail IC patient-specific quality assurance measurements before delivery. This possibly allows for a reduction of physical measurements taken, freeing up significant clinical resources and reducing the required amount of on-site personnel while maintaining patient safety.

Single neutral pion production via muon antineutrino charged-current interactions in plastic scintillator (CH) is studied using the MINERvA detector exposed to the NuMI low-energy, wideband antineutrino beam at Fermilab. Measurement of this process constrains models of neutral pion production in nuclei, which is important because the neutral-current analog is a background for ν-e appearance oscillation experiments. The differential cross sections for π0 momentum and production angle, for events with a single observed π0 and no charged pions, are presented and compared to model predictions. These results comprise the first measurement of the π0 kinematics for this process.

Single neutral pion production via muon antineutrino charged-current interactions in plastic scintillator (CH) is studied using the MINERvA detector exposed to the NuMI low-energy, wideband antineutrino beam at Fermilab. Measurement of this process constrains models of neutral pion production in nuclei, which is important because the neutral-current analog is a background for ν-e appearance oscillation experiments. The differential cross sections for π0 momentum and production angle, for events with a single observed π0 and no charged pions, are presented and compared to model predictions. These results comprise the first measurement of the π0 kinematics for this process.

Accurate energy calibration is critical for the application of energy-resolved photon-counting detectors in spectral imaging. The aim of this study is to investigate the feasibility of energy response calibration and characterization of a photon-counting detector using x-ray fluorescence. A comprehensive Monte Carlo simulation study was performed using Geant4 Application for Tomographic Emission (GATE) to investigate the optimal technique for x-ray fluorescence calibration. Simulations were conducted using a 100 kVp tungsten-anode spectra with 2.7 mm Al filter for a single pixel cadmium telluride (CdTe) detector with 3 × 3 mm(2) in detection area. The angular dependence of x-ray fluorescence and scatter background was investigated by varying the detection angle from 20° to 170° with respect to the beam direction. The effects of the detector material, shape, and size on the recorded x-ray fluorescence were investigated. The fluorescent material size effect was considered with and without the container for the fluorescent material. In order to provide validation for the simulation result, the angular dependence of x-ray fluorescence from five fluorescent materials was experimentally measured using a spectrometer. Finally, eleven of the fluorescent materials were used for energy calibration of a CZT-based photon-counting detector. The optimal detection angle was determined to be approximately at 120° with respect to the beam direction, which showed the highest fluorescence to scatter ratio (FSR) with a weak dependence on the fluorescent material size. The feasibility of x-ray fluorescence for energy calibration of photon-counting detectors in the diagnostic x-ray energy range was verified by successfully calibrating the energy response of a CZT-based photon-counting detector. The results of this study can be used as a guideline to implement the x-ray fluorescence calibration method for photon-counting detectors in a typical imaging laboratory.

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., Competing Interests: Disclosures None., (Copyright © 2023 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

Background: Misalignment to the incorrect vertebral body remains a rare but serious patient safety risk in image-guided radiotherapy (IGRT)., Purpose: Our group has proposed that an automated image-review algorithm be inserted into the IGRT process as an interlock to detect off-by-one vertebral body errors. This study presents the development and multi-institutional validation of a convolutional neural network (CNN)-based approach for such an algorithm using patient image data from a planar stereoscopic x-ray IGRT system., Methods: X-rays and digitally reconstructed radiographs (DRRs) were collected from 429 spine radiotherapy patients (1592 treatment fractions) treated at six institutions using a stereoscopic x-ray image guidance system. Clinically-applied, physician approved, alignments were used for true-negative, "no-error" cases. "Off-by-one vertebral body" errors were simulated by translating DRRs along the spinal column using a semi-automated method. A leave-one-institution-out approach was used to estimate model accuracy on data from unseen institutions as follows: All of the images from five of the institutions were used to train a CNN model from scratch using a fixed network architecture and hyper-parameters. The size of this training set ranged from 5700 to 9372 images, depending on exactly which five institutions were contributing data. The training set was randomized and split using a 75/25 split into the final training/ validation sets. X-ray/ DRR image pairs and the associated binary labels of "no-error" or "shift" were used as the model input. Model accuracy was evaluated using images from the sixth institution, which were left out of the training phase entirely. This test set ranged from 180 to 3852 images, again depending on which institution had been left out of the training phase. The trained model was used to classify the images from the test set as either "no-error" or "shifted", and the model predictions were compared to the ground truth labels to assess the model accuracy. This process was repeated until each institution's images had been used as the testing dataset., Results: When the six models were used to classify unseen image pairs from the institution left out during training, the resulting receiver operating characteristic area under the curve values ranged from 0.976 to 0.998. With the specificity fixed at 99%, the corresponding sensitivities ranged from 61.9% to 99.2% (mean: 77.6%). With the specificity fixed at 95%, sensitivities ranged from 85.5% to 99.8% (mean: 92.9%)., Conclusion: This study demonstrated the CNN-based vertebral body misalignment model is robust when applied to previously unseen test data from an outside institution, indicating that this proposed additional safeguard against misalignment is feasible., (© 2023 American Association of Physicists in Medicine.)

Recent advancements in artificial intelligence (AI) in the domain of radiation therapy (RT) and their integration into modern software-based systems raise new challenges to the profession of medical physics experts. These AI algorithms are typically data-driven, may be continuously evolving, and their behavior has a degree of (acceptable) uncertainty due to inherent noise in training data and the substantial number of parameters that are used in the algorithms. These characteristics request adaptive, and new comprehensive quality assurance (QA) approaches to guarantee the individual patient treatment quality during AI algorithm development and subsequent deployment in a clinical RT environment. However, the QA for AI-based systems is an emerging area, which has not been intensively explored and requires interactive collaborations between medical doctors, medical physics experts, and commercial/research AI institutions. This article summarizes the current QA methodologies for AI modules of every subdomain in RT with further focus on persistent shortcomings and upcoming key challenges and perspectives., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: The authors previously evaluated risk and time course of adverse radiation effects (AREs) following stereotactic radiosurgery (SRS) for brain metastases, excluding lesions treated after prior SRS. In the present analysis they focus specifically on single-fraction salvage SRS to brain metastases previously treated with SRS or hypofractionated SRS (HFSRS), evaluating freedom from progression (FFP) and the risk and time course of AREs., Methods: Brain metastases treated from September 1998 to May 2019 with single-fraction SRS after prior SRS or HFSRS were analyzed. Serial follow-up magnetic resonance imaging (MRI) and surgical pathology reports were reviewed to score local treatment failure and AREs. The Kaplan-Meier method was used to estimate FFP and risk of ARE measured from the date of repeat SRS with censoring at the last brain MRI., Results: A total of 229 retreated brain metastases in 124 patients were evaluable. The most common primary cancers were breast, lung, and melanoma. The median interval from prior SRS/HFSRS to repeat SRS was 15.4 months, the median prescription dose was 18 Gy, and the median duration of follow-up imaging was 14.5 months. At 1 year after repeat SRS, FFP was 80% and the risk of symptomatic ARE was 11%. The 1-year risk of imaging changes, including asymptomatic RE and symptomatic ARE, was 30%. Among lesions that demonstrated RE, the median time to onset was 6.7 months (IQR 4.7-9.9 months) and the median time to peak imaging changes was 10.1 months (IQR 5.6-13.6 months). Lesion size by quadratic mean diameter (QMD) showed similar results for QMDs ranging from 0.75 to 2.0 cm (1-year FFP 82%, 1-year risk of symptomatic ARE 11%). For QMD < 0.75 cm, the 1-year FFP was 86% and the 1-year risk of symptomatic ARE was only 2%. Outcomes were worse for QMDs 2.01-3.0 cm (1-year FFP 65%, 1-year risk of symptomatic ARE 24%). The risk of symptomatic ARE was not increased with tyrosine kinase inhibitors or immunotherapy before or after repeat SRS., Conclusions: RE on imaging was common after repeat SRS (30% at 1 year), but the risk of a symptomatic ARE was much less (11% at 1 year). The results of repeat single-fraction SRS were good for brain metastases ≤ 2 cm. The authors recommend an interval ≥ 6 months from prior SRS and a prescription dose ≥ 18 Gy. Alternatives such as HFSRS, laser interstitial thermal therapy, or resection with adjuvant radiation should be considered for recurrent brain metastases > 2 cm.

Purpose: To suggest an attention-aware, cycle-consistent generative adversarial network (A-CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to current state-of-the-art MR-to-CT image translation methods., Materials and Methods: An attention-gating mechanism is incorporated into a discriminator network to encourage a more parsimonious use of network parameters, whereas VAE enhancement enables deeper discrimination architectures without inhibiting model convergence. Findings from 60 patients with head, neck, and brain cancer were used to train and validate A-CycleGAN, and findings from 30 patients were used for the holdout test set and were used to report final evaluation metric results using mean absolute error (MAE) and peak signal-to-noise ratio (PSNR)., Results: A-CycleGAN achieved superior results compared with U-Net, a generative adversarial network (GAN), and a cycle-consistent GAN. The A-CycleGAN averages, 95% confidence intervals (CIs), and Wilcoxon signed-rank two-sided test statistics are shown for MAE (19.61 [95% CI: 18.83, 20.39], P = .0104), structure similarity index metric (0.778 [95% CI: 0.758, 0.798], P = .0495), and PSNR (62.35 [95% CI: 61.80, 62.90], P = .0571)., Conclusion: A-CycleGANs were a superior alternative to state-of-the-art MR-to-CT image translation methods.© RSNA, 2020., Competing Interests: Disclosures of Conflicts of Interest: V.K. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: employed by 1983. Other relationships: disclosed no relevant relationships. B.P.Z. disclosed no relevant relationships. A.P. disclosed no relevant relationships. T.W. disclosed no relevant relationships. J.W.C. disclosed no relevant relationships. L.M. disclosed no relevant relationships. O.M. disclosed no relevant relationships. S.S.Y. disclosed no relevant relationships. T.D.S. disclosed no relevant relationships., (2020 by the Radiological Society of North America, Inc.)

Purpose: As knowledge-based planning (KBP) attempts to augment and potentially supplant manual treatment planning, it is imperative to ensure any implementation maintains or improves overall plan quality in any disease site. The purpose of this study was to demonstrate the overall quality of KBP-driven automated stereotactic radiosurgery (SRS) treatment planning using blinded physician comparison and determine systematic factors predictive of physician plan preference to guide future KBP refinement., Methods and Materials: Automated noncoplanar volume modulated arc therapy KBP routines were developed for 199 plans across 3 clinical SRS scenarios: isolated lesions (isolated), lesions closely abutting (<3 cm) organs at risk (involved), and single-isocenter multiple metastases (multimet). Overall plan quality and preference were assessed via blinded review of the plans by two SRS physicians. Quantitative quality metrics were also compared to determine systematic differences in the treatment plans. Multiple parameters were investigated as predictors of KBP plan selection., Results: For the isolated, involved, and multimet scenarios, the KBP plans were considered to be superior or equivalent to clinical plans 86.7% (91/105), 81.1% (43/53), and 78.1% (32/41) of the time, respectively. All investigated quality metrics were equivalent or indicated more sparing for all KBP plans. The only nondosimetric predictor was planning target volume in the isolated (P = .02) and involved (P = .05) groups. The dosimetric predictors for the isolated group were gradient measure and heterogeneity index (both P < .01). In the multimet category, the only significant dosimetric predictor was interlesion dose (P = .01)., Conclusions: The fully automated KBP SRS plans were equivalent or superior to previously treated plans in 83.4% (166/199) of cases. In clinical implementation, geometric features found to be predictive of KBP performance can be used to identify plans where KBP results might benefit from further refinement, whereas dosimetric predictive features could be used to further refine KBP optimization priorities., (Copyright © 2017 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

Purpose: Single-isocenter, volumetric-modulated arc therapy (VMAT) stereotactic radiosurgery (SRS) for multiple brain metastases (multimets) can deliver highly conformal dose distributions and reduce overall patient treatment time compared to other techniques. However, treatment planning for multimet cases is highly complex due to variability in numbers and sizes of brain metastases, as well as their relative proximity to organs-at-risk (OARs). The purpose of this study was to automate the VMAT planning of multimet cases through a knowledge-based planning (KBP) approach that adapts single-target SRS dose predictions to multiple target predictions., Methods: Using a previously published artificial neural network (ANN) KBP system trained on single-target, linac-based SRS plans, 3D dose distribution predictions for multimet patients were obtained by treating each brain lesion as a solitary target and subsequently combining individual dose predictions into a single distribution. Spatial dose distributions di(r→) for each of the i = 1…N lesions were merged using the combination function d(r→)=∑iNdin(r→)1/n. The optimal value of n was determined by minimizing root-mean squared (RMS) difference between clinical multimet plans and predicted dose per unit length along the line profile joining each lesion in the clinical cohort. The gradient measure GM=[3/4π]1/3V50%1/3-V100%1/3 is the primary quality metric for SRS plan evaluation at our institution and served as the main comparative metric between clinical plans and the KBP results. A total of 41 previously treated multimet plans, with target numbers ranging from N = 2-10, were used to validate the ANN predictions and subsequent KBP auto-planning routine. Fully deliverable KBP plans were developed by converting predicted dose distribution into patient-specific optimization objectives for the clinical treatment planning system (TPS). Plan parity was maintained through identical arc configuration and target normalization. Overall plan quality improvements were quantified by calculating the difference between SRS quality metrics (QMs): ΔQM = QM clinical  - QM KBP . In addition to GM, investigated QMs were: volume of brain receiving ≥ 10 Gy (V 10 Gy ), volume of brain receiving ≥ 5 Gy (ΔV 5 Gy ), heterogeneity index (HI), dose to 0.1 cc of the brainstem (D 0.1 cc ), dose to 1% of the optic chiasm (D 1% ), and interlesion dose (D IL ). In addition to this quantitative analysis, overall plan quality was assessed via blinded plan comparison of the manual and KBP treatment plans by SRS-specializing physicians., Results: A dose combination factor of n = 8 yielded an integrated dose profile RMS difference of 2.9% across the 41-patient cohort. Multimet dose predictions exhibited ΔGM = 0.07 ± 0.10 cm against the clinical sample, implying either further normal tissue sparing was possible or that dose predictions were slightly overestimating achievable dose gradients. The latter is the more likely explanation, as this bias vanished when dose predictions were converted to deliverable KBP plans ΔGM = 0.00 ± 0.08 cm. Remaining QMs were nearly identical or showed modest improvements in the KBP sample. Equivalent QMs included: ΔV 10 Gy  = 0.37 ± 3.78 cc, ΔHI = 0.02 ± 0.08 and ΔD IL  = -2.22 ± 171.4 cGy. The KBP plans showed a greater degree of normal tissue sparing as indicated by brain ΔV 5 Gy  = 4.11± 24.05 cc, brainstem ΔD 0.1 cc  = 42.8 ± 121.4 cGy, and chiasm ΔD 1%  = 50.8 ± 83.0 cGy. In blinded review by SRS-specializing physicians, KBP-generated plans were deemed equivalent or superior in 32/41(78.1%) of the cases., Conclusion: Heuristic KBP-driven automated planning in linac-based, single-isocenter treatments for multiple brain metastases maintained or exceeded overall plan quality., (© 2017 American Association of Physicists in Medicine.)

The first direct measurement of electron neutrino quasielastic and quasielasticlike scattering on hydrocarbon in the few-GeV region of incident neutrino energy has been carried out using the MINERvA detector in the NuMI beam at Fermilab. The flux-integrated differential cross sections in the electron production angle, electron energy, and Q^{2} are presented. The ratio of the quasielastic, flux-integrated differential cross section in Q^{2} for ν&#95;{e} with that of similarly selected ν&#95;{μ}-induced events from the same exposure is used to probe assumptions that underpin conventional treatments of charged-current ν&#95;{e} interactions used by long-baseline neutrino oscillation experiments. The data are found to be consistent with lepton universality and are well described by the predictions of the neutrino event generator GENIE.

Widespread clinical implementation of dynamic CT myocardial perfusion has been hampered by its limited accuracy and high radiation dose. The purpose of this study was to evaluate the accuracy and radiation dose reduction of a dynamic CT myocardial perfusion technique based on first pass analysis (FPA). To test the FPA technique, a pulsatile pump was used to generate known perfusion rates in a range of 0.96-2.49 mL/min/g. All the known perfusion rates were determined using an ultrasonic flow probe and the known mass of the perfusion volume. FPA and maximum slope model (MSM) perfusion rates were measured using volume scans acquired from a 320-slice CT scanner, and then compared to the known perfusion rates. The measured perfusion using FPA (P(FPA)), with two volume scans, and the maximum slope model (P(MSM)) were related to known perfusion (P(K)) by P(FPA) = 0.91P(K) + 0.06 (r = 0.98) and P(MSM) = 0.25P(K) - 0.02 (r = 0.96), respectively. The standard error of estimate for the FPA technique, using two volume scans, and the MSM was 0.14 and 0.30 mL/min/g, respectively. The estimated radiation dose required for the FPA technique with two volume scans and the MSM was 2.6 and 11.7-17.5 mSv, respectively. Therefore, the FPA technique can yield accurate perfusion measurements using as few as two volume scans, corresponding to approximately a factor of four reductions in radiation dose as compared with the currently available MSM. In conclusion, the results of the study indicate that the FPA technique can make accurate dynamic CT perfusion measurements over a range of clinically relevant perfusion rates, while substantially reducing radiation dose, as compared to currently available dynamic CT perfusion techniques.

Neutrino-induced coherent charged pion production on nuclei νμA→μ(±)π(∓)A is a rare, inelastic interaction in which a small squared four-momentum |t| is transferred to the recoil nucleus, leaving it intact in the reaction. In the scintillator tracker of MINERvA, we remove events with evidence of particles from nuclear breakup and reconstruct |t| from the final-state pion and muon. We select low |t| events to isolate a sample rich in coherent candidates. By selecting low |t| events, we produce a model-independent measurement of the differential cross section for coherent scattering of neutrinos and antineutrinos on carbon. We find poor agreement with the predicted kinematics in neutrino generators used by current oscillation experiments.

We present measurements of ν(μ) charged-current cross section ratios on carbon, iron, and lead relative to a scintillator (CH) using the fine-grained MINERvA detector exposed to the NuMI neutrino beam at Fermilab. The measurements utilize events of energies 2

We report a study of ν(μ) charged-current quasielastic events in the segmented scintillator inner tracker of the MINERvA experiment running in the NuMI neutrino beam at Fermilab. The events were selected by requiring a μ- and low calorimetric recoil energy separated from the interaction vertex. We measure the flux-averaged differential cross section, dσ/dQ², and study the low energy particle content of the final state. Deviations are found between the measured dσ/dQ² and the expectations of a model of independent nucleons in a relativistic Fermi gas. We also observe an excess of energy near the vertex consistent with multiple protons in the final state.

We have isolated ν(μ) charged-current quasielastic (QE) interactions occurring in the segmented scintillator tracking region of the MINERvA detector running in the NuMI neutrino beam at Fermilab. We measure the flux-averaged differential cross section, dσ/dQ², and compare to several theoretical models of QE scattering. Good agreement is obtained with a model where the nucleon axial mass, M(A), is set to 0.99 GeV/c² but the nucleon vector form factors are modified to account for the observed enhancement, relative to the free nucleon case, of the cross section for the exchange of transversely polarized photons in electron-nucleus scattering. Our data at higher Q² favor this interpretation over an alternative in which the axial mass is increased.