Thompson R.,University of Toronto |
Thompson R.,Odette Cancer Center |
Lu Y.,University of Toronto |
Potvin M.,University of Toronto |
And 15 more authors.
Practical Radiation Oncology | Year: 2017
Purpose: Interprofessional, educational live simulations were compared with group discussion-based exercises in terms of their ability to improve radiation medicine trainees' ability to detect hazards and incidents and understand behaviors that may prevent them. Methods and materials: Trainees and recent graduates of radiation therapy, medical physics, and radiation oncology programs were recruited and randomized to either a simulation-based or group discussion-based training intervention. Participants engaged in hazard and incident detection, analysis, and a discussion of potential preventive measures and the concept of the "highly reliable team." A video examination tool modeled on actual incidents, using 5-minute videos created by faculty, students, and volunteers, was created to test hazard and incident recognition ability before and after training. Hazard and incident detection sensitivity and specificity analyses were conducted, and a survey of the participants' and facilitators' perceptions was conducted. Results: Twenty-seven participants were assigned to the simulation (n = 15) or discussion group (n = 12). Hazard and incident-detection sensitivity ranged from 0.04 to 0.56 before and 0.04 to 0.35 after training for the discussion and simulation groups, respectively. The pre- and posttraining difference in sensitivity between groups was 0.03 (P = .75) for the minimum and 0.33 (P = .034) for the maximum reaction time. Participant perceptions of the training's educational value in a variety of domains ranged from a mean score of 6.58 to 8.17 and 7 to 8.07 for the discussion and simulation groups, respectively. Differences were not statistically significant. Twenty-six of the 27 participants indicated that they would recommend this event to a colleague. Conclusions: Participants' ability to detect hazards and incidents as portrayed in 5-minute videos in this study was low both before and after training, and simulation-based training was not superior to discussion-based training. However, levels of satisfaction and perceptions of the training's educational value were high, especially with simulation-based training. © 2017.
Rink A.,University of Toronto |
Rink A.,TECHNA Institute |
Borg J.,University of Toronto |
Simeonov A.,University of Toronto |
And 8 more authors.
Brachytherapy | Year: 2017
Purpose: To assess changes in implant and treatment volumes through the course of a prostate high-dose-rate brachytherapy procedure and their impact on plan quality metrics. Methods and Materials: Sixteen MRI-guided high-dose-rate procedures included a post-treatment MR (ptMR) immediately after treatment delivery (135 min between MR scans). Target and organs at risk (OARs) were contoured, and catheters were reconstructed. The delivered treatment plan was applied to the ptMR image set. Volumes and dosimetric parameters in the ptMR were evaluated and compared with the delivered plan using a paired two-tailed t-test with p < 0.05 considered statistically significant. Results: An average increase of 8.9% in prostate volume was observed for whole-gland treatments, resulting in reduction in coverage for both prostate and planning target volume, reflected in decreased V 100 (mean 3.3% and 4.6%, respectively, p < 0.05), and D 90 (mean 7.1% and 7.6%, respectively, of prescription dose, p < 0.05). There was no significant change in doses to OARs. For partial-gland treatments, there was an increase in planning target volume (9.1%), resulting in reduced coverage and D 90 (mean 3.6% and 12.4%, respectively, p < 0.05). A decrease in D 0.5cc for bladder (3%, p < 0.05) was observed, with no significant changes in dose to other OARs. Conclusions: Volumetric changes were observed during the time between planning MR and ptMR. Nonetheless, treatment plans for both whole- and partial-gland therapies remained clinically acceptable. These results apply to clinical settings in which patients remain in the same position and under anesthesia during the entire treatment process. © 2017 American Brachytherapy Society.
McIntosh C.,University of Toronto |
McIntosh C.,TECHNA Institute |
Welch M.,University of Toronto |
McNiven A.,University of Toronto |
And 4 more authors.
Physics in Medicine and Biology | Year: 2017
Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12-13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities. © 2017 Institute of Physics and Engineering in Medicine.
Lin Q.,Techna Institute |
Lin Q.,University of Toronto |
Lin Q.,Huazhong University of Science and Technology |
Jin C.S.,Techna Institute |
And 7 more authors.
Small | Year: 2014
The abilities to deliver siRNA to its intended action site and assess the delivery efficiency are challenges for current RNAi therapy, where effective siRNA delivery will join force with patient genetic profiling to achieve optimal treatment outcome. Imaging could become a critical enabler to maximize RNAi efficacy in the context of tracking siRNA delivery, rational dosimetry and treatment planning. Several imaging modalities have been used to visualize nanoparticle-based siRNA delivery but rarely did they guide treatment planning. We report a multimodal theranostic lipid-nanoparticle, HPPS(NIR)-chol-siRNA, which has a near-infrared (NIR) fluorescent core, enveloped by phospholipid monolayer, intercalated with siRNA payloads, and constrained by apoA-I mimetic peptides to give ultra-small particle size (<30 nm). Using fluorescence imaging, we demonstrated its cytosolic delivery capability for both NIR-core and dye-labeled siRNAs and its structural integrity in mice through intravenous administration, validating the usefulness of NIR-core as imaging surrogate for non-labeled therapeutic siRNAs. Next, we validated the targeting specificity of HPPS(NIR)-chol-siRNA to orthotopic tumor using sequential four-steps (in vivo, in situ, ex vivo and frozen-tissue) fluorescence imaging. The image co-registration of computed tomography and fluorescence molecular tomography enabled non-invasive assessment and treatment planning of siRNA delivery into the orthotopic tumor, achieving efficacious RNAi therapy. Image-guided treatment planning of target specific RNAi therapeutics by HPPS(NIR)-chol-siRNA. A multimodal theranostic HDL-like nanoparticle is developed for in vivo non-invasive assessment of siRNA accumulation in an orthotopic prostate tumor by tracing the NIR fluorescent surrogate using image co-registration of computed tomography and fluorescence molecular tomography, which provides a useful mean for real-time tracking siRNA delivery, rational dosimetry, and treatment planning for efficacious RNAi therapy. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
McIntosh C.,University of Toronto |
McIntosh C.,Techna Institute |
Purdie T.G.,University of Toronto |
Purdie T.G.,Techna Institute
IEEE Transactions on Medical Imaging | Year: 2016
Radiation therapy is an integral part of cancer treatment, but to date it remains highly manual. Plans are created through optimization of dose volume objectives that specify intent to minimize, maximize, or achieve a prescribed dose level to clinical targets and organs. Optimization is NP-hard, requiring highly iterative and manual initialization procedures. We present a proof-of-concept for a method to automatically infer the radiation dose directly from the patient's treatment planning image based on a database of previous patients with corresponding clinical treatment plans. Our method uses regression forests augmented with density estimation over the most informative features to learn an automatic atlas-selection metric that is tailored to dose prediction. We validate our approach on 276 patients from 3 clinical treatment plan sites (whole breast, breast cavity, and prostate), with an overall dose prediction accuracies of 78.68%, 64.76%, 86.83% under the Gamma metric. © 2015 IEEE.
Siddique S.,Princess Margaret Cancer Center |
Siddique S.,University of Toronto |
Fiume E.,Princess Margaret Cancer Center |
Jaffray D.A.,University of Toronto |
Jaffray D.A.,Techna Institute
Medical Physics | Year: 2014
Purpose: X-ray fluoroscopy remains an important imaging modality in a number of image-guided procedures due to its real-time nature and excellent spatial detail. However, the radiation dose delivered raises concerns about its use particularly in lengthy treatment procedures (>0.5 h). The authors have previously presented an algorithm that employs feedback of geometric uncertainty to control dose while maintaining a desired targeting uncertainty during fluoroscopic tracking of fiducials. The method was tested using simulations of motion against controlled noise fields. In this paper, the authors embody the previously reported method in a physical prototype and present changes to the controller required to function in a practical setting.Methods: The metric for feedback used in this study is based on the trace of the covariance of the state of the system, tr(C). The state is defined here as the 2D location of a fiducial on a plane parallel to the detector. A relationship between this metric and the tube current is first developed empirically. This relationship is extended to create a manifold that incorporates a latent variable representing the estimated background attenuati on. The manifold is then used within the controller to dynamically adjust the tube current and maintain a specified targeting uncertainty. To evaluate the performance of the proposed method, an acrylic sphere (1.6 mm in diameter) was tracked at tube currents ranging from 0.5 to 0.9 mA (0.033 s) at a fixed energy of 80 kVp. The images were acquired on a Varian Paxscan 4030A (2048 ×1536 pixels, ∼100 cm source-to-axis distance, ∼160 cm source-to-detector distance). The sphere was tracked using a particle filter under two background conditions: (1) uniform sheets of acrylic and (2) an acrylic wedge. The measured tr(C) was used in conjunction with a learned manifold to modulate the tube current in order to maintain a specified uncertainty as the sphere traversed regions of varying thickness corresponding to the acrylic sheets in the background.Results: With feedback engaged, the tracking error was found to correlate well with the specified targeting uncertainty. Tracking of the fiducial was found to be robust to changes in the attenuation presented by the varying background conditions. For a desired uncertainty of 5.0 mm, comparison of the feedback framework with a comparable system employing fixed exposure demonstrated dose savings of 29%.Conclusions: This work presents a relation between a state descriptor, tr(C), the x-ray tube current used, and an estimate of the background attenuation. This relation is leveraged to modulate the tube current in order to maintain a desired geometric uncertainty during fluoroscopy. The authors work demonstrates the use of the method in a real x-ray fluoroscopy system with physical motion against varying backgrounds. The method offers potential savings in imaging dose to patients and staff while maintaining tracking uncertainty during fluoroscopy-guided treatment procedures. © 2014 American Association of Physicists in Medicine.
Samavati N.,University of Toronto |
Velec M.,Techna Institute |
Brock K.,University of Michigan
Physics in Medicine and Biology | Year: 2015
Deformable image registration (DIR) has been extensively studied over the past two decades due to its essential role in many image-guided interventions (IGI). IGI demands a highly accurate registration that maintains its accuracy across the entire region of interest. This work evaluates the improvement in accuracy and consistency by refining the results of Morfeus, a biomechanical model-based DIR algorithm. A hybrid DIR algorithm is proposed based on, a biomechanical model-based DIR algorithm and a refinement step based on a B-spline intensity-based algorithm. Inhale and exhale reconstructions of four-dimensional computed tomography (4DCT) lung images from 31 patients were initially registered using the biomechanical DIR by modeling contact surface between the lungs and the chest cavity. The resulting deformations were then refined using the intensity-based algorithm to reduce any residual uncertainties. Important parameters in the intensity-based algorithm, including grid spacing, number of pyramids, and regularization coefficient, were optimized on 10 randomly-chosen patients (out of 31). Target registration error (TRE) was calculated by measuring the Euclidean distance of common anatomical points on both images after registration. For each patient a minimum of 30 points/lung were used. Grid spacing of 8 mm, 5 levels of grid pyramids, and regularization coefficient of 3.0 were found to provide optimal results on 10 randomly chosen patients. Overall the entire patient population (n = 31), the hybrid method resulted in mean ± SD (90th%) TRE of 1.5 ± 1.4 (2.9) mm compared to 3.1 ± 1.9 (5.6) using biomechanical DIR and 2.6 ± 2.5 (6.1) using intensity-based DIR alone. The proposed hybrid biomechanical modeling intensity based algorithm is a promising DIR technique which could be used in various IGI procedures. The current investigation shows the efficacy of this approach for the registration of 4DCT images of the lungs with average accuracy of 1.5 mm. © 2015 Institute of Physics and Engineering in Medicine.