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The clinical use of surface imaging has increased dramatically, with demonstrated utility for initial patient positioning, real-time motion monitoring, and beam gating in a variety of anatomical sites. The Therapy Physics Subcommittee and the Imaging for Treatment Verification Working Group of the American Association of Physicists in Medicine commissioned Task Group 302 to review the current clinical uses of surface imaging and emerging clinical applications. The specific charge of this task group was to provide technical guidelines for clinical indications of use for general positioning, breast deep-inspiration breath hold treatment, and frameless stereotactic radiosurgery. Additionally, the task group was charged with providing commissioning and on-going quality assurance (QA) requirements for surface-guided radiation therapy (SGRT) as part of a comprehensive QA program including risk assessment. Workflow considerations for other anatomic sites and for computed tomography simulation, including motion management, are also discussed. Finally, developing clinical applications, such as stereotactic body radiotherapy (SBRT) or proton radiotherapy, are presented. The recommendations made in this report, which are summarized at the end of the report, are applicable to all video-based SGRT systems available at the time of writing.

Purpose: Deformable image registration (DIR) has been increasingly used in radiation therapy (RT). The accuracy of DIR algorithms and how it impacts on the RT plan dosimetrically were examined in our study for abdominal sites using biomechanically modeled deformations. Methods: Five pancreatic cancer patients were enrolled in this study. Following the guidelines of AAPM TG‐132, a patient‐specific quality assurance (QA) workflow was developed to evaluate DIR for the abdomen using the TG‐132 recommended virtual simulation software ImSimQA (Shrewsbury, UK). First, the planning CT was deformed to simulate respiratory motion using the embedded biomechanical model in ImSimQA. Additionally, 5 mm translational motion was added to the stomach, duodenum, and small bowel. The original planning CT and the deformed CT were then imported into Eclipse and MIM to perform DIR. The output displacement vector fields (DVFs) were compared with the ground truth from ImSimQA. Furthermore, the original treatment plan was recalculated on the ground‐truth deformed CT and the deformed CT (with Eclipse and MIM DVF). The dose errors were calculated on a voxel‐to‐voxel basis. Results: Data analysis comparing DVF from Eclipse versus MIM show the average mean DVF magnitude errors of 2.8 ± 1.0 versus 1.1 ± 0.7 mm for stomach and duodenum, 5.2 ± 4.0 versus 2.5 ± 1.0 mm for small bowel, and 4.8 ± 4.1 versus 2.7 ± 1.1 mm for the gross tumor volume (GTV), respectively, across all patients. The mean dose error on stomach+duodenum and small bowel were 2.3 ± 0.6% for Eclipse, and 1.0 ± 0.3% for MIM. As the DIR magnitude error increases, the dose error range increase, for both Eclipse and MIM. Conclusion: In our study, an initial assessment was conducted to evaluate the accuracy of DIR and its dosimetric impact on radiotherapy. A patient‐specific DIR QA workflow was developed for pancreatic cancer patients. This workflow exhibits promising potential for future implementation as a clinical workflow. [ABSTRACT FROM AUTHOR]

Objective: To investigate the feasibility of standardizing RT simulation CT scanner protocols between vendors using target‐based image quality (IQ) metrics. Method and materials: A systematic assessment process in phantom was developed to standardize clinical scan protocols for scanners from different vendors following these steps: (a) images were acquired by varying CTDIvol and using an iterative reconstruction (IR) method (IR: iDose and model‐based iterative reconstruction [IMR] of CTp‐Philips Big Bore scanner, SAFIRE of CTs‐Siemens biograph PETCT scanner), (b) CT exams were classified into body and brain protocols, (c) the rescaled noise power spectrum (NPS) was calculated, (d) quantified the IQ change due to varied CTDIvol and IR, and (e) matched the IR strength level. IQ metrics included noise and texture from NPS, contrast, and contrast‐to‐noise ratio (CNR), low contrast detectability (d′). Area under curve (AUC) of the receiver operation characteristic curve of d′ was calculated and compared. Results: The level of change in the IQ ratio was significant (>0.6) when using IMR. The IQ ratio change was relatively low to moderate when using either iDose in CTp (0.1–0.5) or SAFIRE in CTs (0.1–0.6). SAFIRE‐2 in CTs showed a closer match to the reference body protocol when compared to iDose‐3 in CTp. In the brain protocol, iDose‐3 in CTp could be matched to the low to moderate level of SAFIRE in CTs. The AUC of d′ was highest when using IMR in CTp with lower CTDIvol, and SAFIRE in CTs performed better than iDose in CTp Conclusion: It is possible to use target‐based IQ metrics to evaluate the performance of the system and operations across various scanners in a phantom. This can serve as an initial reference to convert clinical scanned protocols from one CT simulation scanner to another. [ABSTRACT FROM AUTHOR]

Intensity-modulated radiation therapy (IMRT) currently plays an important role in radiotherapy, but its treatment plan quality can vary significantly among institutions and planners. Treatment plan quality control (QC) is a necessary component for individual clinics to ensure that patients receive treatments with high therapeutic gain ratios. The voxel-weighting factor-based plan re-optimization mechanism has been proved able to explore a larger Pareto surface (solution domain) and therefore increase the possibility of finding an optimal treatment plan. In this study, we incorporated additional modules into an in-house developed voxel weighting factor-based re-optimization algorithm, which was enhanced as a highly automated and accurate IMRT plan QC tool (TPS-QC tool). After importing an under-assessment plan, the TPS-QC tool was able to generate a QC report within 2 minutes. This QC report contains the plan quality determination as well as information supporting the determination. Finally, the IMRT plan quality can be controlled by approving quality-passed plans and replacing quality-failed plans using the TPS-QC tool. The feasibility and accuracy of the proposed TPS-QC tool were evaluated using 25 clinically approved cervical cancer patient IMRT plans and 5 manually created poor-quality IMRT plans. The results showed high consistency between the QC report quality determinations and the actual plan quality. In the 25 clinically approved cases that the TPS-QC tool identified as passed, a greater difference could be observed for dosimetric endpoints for organs at risk (OAR) than for planning target volume (PTV), implying that better dose sparing could be achieved in OAR than in PTV. In addition, the dose-volume histogram (DVH) curves of the TPS-QC tool re-optimized plans satisfied the dosimetric criteria more frequently than did the under-assessment plans. In addition, the criteria for unsatisfied dosimetric endpoints in the 5 poor-quality plans could typically be satisfied when the TPS-QC tool generated re-optimized plans without sacrificing other dosimetric endpoints. In addition to its feasibility and accuracy, the proposed TPS-QC tool is also user-friendly and easy to operate, both of which are necessary characteristics for clinical use.

Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson's tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson's tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study's results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control. [ABSTRACT FROM AUTHOR]

Background: Quality assurance of deformable image registration (DIR) is challenging because the ground truth is often unavailable. In addition, current approaches that rely on artificial transformations do not adequately resemble clinical scenarios encountered in adaptive radiotherapy. Purpose: We developed an atlas‐based method to create a variety of patient‐specific serial digital phantoms with CBCT‐like image quality to assess the DIR performance for longitudinal CBCT imaging data in adaptive lung radiotherapy. Methods: A library of deformations was created by extracting the longitudinal changes observed between a planning CT and weekly CBCT from an atlas of lung radiotherapy patients. The planning CT of an inquiry patient was first deformed by mapping the deformation pattern from a matched atlas patient, and subsequently appended with CBCT artifacts to imitate a weekly CBCT. Finally, a group of digital phantoms around an inquiry patient was produced to simulate a series of possible evolutions of tumor and adjacent normal structures. We validated the generated deformation vector fields (DVFs) to ensure numerically and physiologically realistic transformations. The proposed framework was applied to evaluate the performance of the DIR algorithm implemented in the commercial Eclipse treatment planning system in a retrospective study of eight inquiry patients. Results: The generated DVFs were inverse consistent within less than 3 mm and did not exhibit unrealistic folding. The deformation patterns adequately mimicked the observed longitudinal anatomical changes of the matched atlas patients. Worse Eclipse DVF accuracy was observed in regions of low image contrast or artifacts. The structure volumes exhibiting a DVF error magnitude of equal or more than 2 mm ranged from 24.5% (spinal cord) to 69.2% (heart) and the maximum DVF error exceeded 5 mm for all structures except the spinal cord. Contour‐based evaluations showed a high degree of alignment with dice similarity coefficients above 0.8 in all cases, which underestimated the overall DVF accuracy within the structures. Conclusions: It is feasible to create and augment digital phantoms based on a particular patient of interest using multiple series of deformation patterns from matched patients in an atlas. This can provide a semi‐automated procedure to complement the quality assurance of CT‐CBCT DIR and facilitate the clinical implementation of image‐guided and adaptive radiotherapy that involve longitudinal CBCT imaging studies. [ABSTRACT FROM AUTHOR]

To investigate automation of the preparation of the region of interest (ROI) for surface‐guided radiotherapy (SGRT) of the whole breast with two algorithms based on contour anatomies: using the body contour, and using the breast contour. The patient dataset used for modeling consisted of 39 breast cancer patients previously treated with SGRT. The patient's anatomical structures (body and ipsilateral breast) were retrieved from the planning system, and the clinical ROI (cROI) drawn by the planners was retrieved from the SGRT system for comparison. For the body‐contour‐based algorithm, a convolutional neural network (MobileNet‐v2) was utilized to train a synthetic human model dataset to predict body joint locations. With the body joint location knowledge, an automated ROI (aROIbody) can be created based on: (1) the superior‐inferior (S‐I) borders defined by the joint locations, (2) the left‐right (L‐R) borders defined with 3/4 of chest width, and (3) a curation of the ROI to avoid the ipsilateral armpit. For the breast‐contour‐based algorithm, an aROIbreast was created by first defining the ROI in the S‐I direction with the ipsilateral breast boundaries. Other steps are the same as with the body‐contour‐based algorithm. Among the 39 patients, 24 patients were used to fine‐tune the algorithm parameters, and the remaining 15 patients were used to evaluate the quality of the aROIs against the cROIs. A blinded evaluation was performed by three SGRT expert physicists to rate the acceptability and the quality (1–10 scale) of the aROIs and cROIs, and the dice similarity coefficient (DSC) was also calculated to compare the similarity between the aROIs and cROIs. The results showed that the average acceptability was 14/15 (range: 13/15–15/15) for cROIs, 13.3/15 (range: 13/15–14/15) for aROIbody, and 14.6/15 (range: 14/15–15/15) for aROIbreast. The average quality was 7.4 ± 0.8 for cROIs, 8.1 ± 1.2 for aROIbody, and 8.2 ± 0.9 for aROIbreast. The DSC with cROIs was 0.81 ± 0.06 for aROIbody, and 0.83 ± 0.04 for aROIbreast. The ROI creation time was ∼120 s for clinical, 1.3 s for aROIbody, and 1.2 s for aROIbreast. The proposed automated algorithms can improve the ROI compliance with the SGRT protocol, with a shortened preparation time. It is ready to be integrated into the clinical workflow for automated ROI preparation. [ABSTRACT FROM AUTHOR]

Background. With the rapidly increasing application of adaptive radiotherapy, large datasets of organ geometries based on the patient’s anatomy are desired to support clinical application or research work, such as image segmentation, re-planning, and organ deformation analysis. Sometimes only limited datasets are available in clinical practice. In this study, we propose a new method to generate large datasets of organ geometries to be utilized in adaptive radiotherapy.

This study compared the reproducibility of chestwall and heart position using surface‐guided versus RPM (real‐time position management)‐guided deep inspiration breath hold (DIBH) radiotherapy for left sided breast cancer. Forty DIBH patients under either surface‐guided radiotherapy (SGRT) or RPM guidance were studied. For patients treated with tangential fields, reproducibility was measured as the displacements in central lung distance (CLD) and heart shadow to field edge distance (HFD) between pretreatment MV (megavoltage) images and planning DRRs (digitally reconstructed radiographs). For patients treated with volumetric modulated arc therapy (VMAT), sternum to isocenter (ISO) distance (StID), spine to rib edge distance (SpRD), and heart shadow to central axis (CAX) distance (HCD) between pretreatment kV images and planning DRRs were measured. These displacements were compared between SGRT and RPM‐guided DIBH. In tangential patients, the mean absolute displacements of SGRT versus RPM guidance were 0.19 versus 0.23 cm in CLD, and 0.33 versus 0.62 cm in HFD. With respect to planning DRR, heart appeared closer to the field edge by 0.04 cm with surface imaging versus 0.62 cm with RPM. In VMAT patients, the displacements of surface imaging versus RPM guidance were 0.21 versus 0.15 cm in StID, 0.24 versus 0.19 cm in SpRD, and 0.72 versus 0.41 cm in HCD. Heart appeared 0.41 cm further away from CAX with surface imaging, whereas 0.10 cm closer to field CAX with RPM. None of the differences between surface imaging and RPM guidance was statistically significant. In conclusion, the displacements of chestwall were small and were comparable with SGRT‐ or RPM‐guided DIBH. The position deviations of heart were larger than those of chestwall with SGRT or RPM. Although none of the differences between SGRT and RPM guidance were statistically significant, there was a trend that the position deviations of heart were smaller and more favorable with SGRT than with RPM guidance in tangential patients. [ABSTRACT FROM AUTHOR]