To recommend imaging protocols and establish tolerance levels for microCT image quality assurance (QA) performed on conformal image-guided small animal irradiators. A fully automated QA software SAPA (small animal phantom analyzer) for image analysis of the commercial Shelley micro-CT MCTP 610 phantom was developed, in which quantitative analyses of CT number linearity, signal-to-noise ratio (SNR), uniformity and noise, geometric accuracy, spatial resolution by means of modulation transfer function (MTF), and CT contrast were performed. Phantom microCT scans from eleven institutions acquired with four image-guided small animal irradiator units (including the commercial PXi X-RAD SmART and Xstrahl SARRP systems) with varying parameters used for routine small animal imaging were analyzed. Multi-institutional data sets were compared using SAPA, based on which tolerance levels for each QA test were established and imaging protocols for QA were recommended. By analyzing microCT data from 11 institutions, we established image QA tolerance levels for all image quality tests. CT number linearity set to R2 > 0.990 was acceptable in microCT data acquired at all but three institutions. Acceptable SNR > 36 and noise levels <55 HU were obtained at five of the eleven institutions, where failing scans were acquired with current-exposure time of less than 120 mAs. Acceptable spatial resolution (>1.5 lp mm−1 for MTF = 0.2) was obtained at all but four institutions due to their large image voxel size used (>0.275 mm). Ten of the eleven institutions passed the set QA tolerance for geometric accuracy (<1.5%) and nine of the eleven institutions passed the QA tolerance for contrast (>2000 HU for 30 mgI ml−1). We recommend performing imaging QA with 70 kVp, 1.5 mA, 120 s imaging time, 0.20 mm voxel size, and a frame rate of 5 fps for the PXi X-RAD SmART. For the Xstrahl SARRP, we recommend using 60 kVp, 1.0 mA, 240 s imaging time, 0.20 mm voxel size, and 6 fps. These imaging protocols should result in high quality images that pass the set tolerance levels on all systems. Average SAPA computation time for complete QA analysis for a 0.20 mm voxel, 400 slice Shelley phantom microCT data set was less than 20 s. We present image quality assurance recommendations for image-guided small animal radiotherapy systems that can aid researchers in maintaining high image quality, allowing for spatially precise conformal dose delivery to small animals.

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Purpose: To compare the extended dose profile delivered by 3DCRT and VMAT techniques for flattened and flattening‐filter‐free(FFF) photon beams (6X, 6XFFF,10XFFF), with and without jaw‐tracking (JT) on Varian TrueBeam linac. The goal is to determine which treatment technique/modality will minimize the peripheral photon dose exposure (and ultimately minimize the risk of second malignant neoplasms (SMN)) in pediatric patients. Methods: 3DCRT, VMAT, and jaw tracking VMAT (JTVMAT) plans with 6X, 6XFFF and 10XFFF x‐ray beams were created on a 30×60×22.5cm solid water phantom with a 551 cc PTV. The 3DCRT plans consisted of a 4FLD arrangement. The optimization objectives for the single‐arc VMAT plans was V95%Rx=98% to PTV and minimize dose to a 5cm diameter organ at risk (OAR). The OAR to PTV distance varied from 0–30cm along the long axis at 7.5cm depth. The dose to the center of the OAR was measured using a 0.6cc ion chamber. Results: Relative to the 6X flattened beam, the 10XFFF photon beam had the lowest dose in the penumbra and peripheral region (>15 cm) region by up to 20% and 40%, respectively for all modalities (3DCRT, VMAT, JTVMAT). The 6XFFF beams only showed a dose reduction in the peripheral region (by up to 20%). JT did not significantly affect the peripheral dose for all modalities and energies. Conclusion: Treating pediatric patients with a 10XFFF beam is the most effective way to reduce photon scatter dose in both the penumbra and peripheral regions. However, the neutron dose contribution resulting from the 10MV beam still needs to be considered. For all modalities, 6XFFF was the next effective method to reduce peripheral photon doses. 3DCRT beams had the lowest peripheral doses for all energies compared to VMAT and JTVMAT, however previous publications have shown that this comes at the expense of PTV conformity and OAR sparing.

Background Using radiation therapy (RT) to treat head and neck (H&N) cancers requires precise targeting of the tumor to avoid damaging the surrounding healthy organs. Immobilisation masks and planning target volume margins are used to attempt to mitigate patient motion during treatment, however patient motion can still occur. Patient motion during RT can lead to decreased treatment effectiveness and a higher chance of treatment related side effects. Tracking tumor motion would enable motion compensation during RT, leading to more accurate dose delivery. Purpose The purpose of this paper is to develop a method to detect and segment the tumor in kV images acquired during RT. Unlike previous tumor segmentation methods for kV images, in this paper, a process for generating realistic and synthetic CT deformations was developed to augment the training data and make the segmentation method robust to patient motion. Detecting the tumor in 2D kV images is a necessary step toward 3D tracking of the tumor position during treatment. Method In this paper, a conditional generative adversarial network (cGAN) is presented that can detect and segment the gross tumor volume (GTV) in kV images acquired during H&N RT. Retrospective data from 15 H&N cancer patients obtained from the Cancer Imaging Archive were used to train and test patient‐specific cGANs. The training data consisted of digitally reconstructed radiographs (DRRs) generated from each patient's planning CT and contoured GTV. Training data was augmented by using synthetically deformed CTs to generate additional DRRs (in total 39 600 DRRs per patient or 25 200 DRRs for nasopharyngeal patients) containing realistic patient motion. The method for deforming the CTs was a novel deformation method based on simulating head rotation and internal tumor motion. The testing dataset consisted of 1080 DRRs for each patient, obtained by deforming the planning CT and GTV at different magnitudes to the training data. The accuracy of the generated segmentations was evaluated by measuring the segmentation centroid error, Dice similarity coefficient (DSC) and mean surface distance (MSD). This paper evaluated the hypothesis that when patient motion occurs, using a cGAN to segment the GTV would create a more accurate segmentation than no‐tracking segmentations from the original contoured GTV, the current standard‐of‐care. This hypothesis was tested using the 1‐tailed Mann‐Whitney U‐test. Results The magnitude of our cGAN segmentation centroid error was (mean ± standard deviation) 1.1 ± 0.8 mm and the DSC and MSD values were 0.90 ± 0.03 and 1.6 ± 0.5 mm, respectively. Our cGAN segmentation method reduced the segmentation centroid error (p < 0.001), and MSD (p = 0.031) when compared to the no‐tracking segmentation, but did not significantly increase the DSC (p = 0.294). Conclusions The accuracy of our cGAN segmentation method demonstrates the feasibility of this method for H&N cancer patients during RT. Accurate tumor segmentation of H&N tumors would allow for intrafraction monitoring...