Phantom-based Quality Assurance of a Clinical Dose Accumulation Technique Used in an Online Adaptive Radiation Therapy Platform.

Purpose: This study aimed to develop a routine quality assurance method for a dose accumulation technique provided by a radiation therapy platform for online treatment adaptation. Methods and Materials: Two commonly used phantoms were selected for the dose accumulation QA: Electron density and anthropomorphic pelvis. On a computed tomography (CT) scan of the electron density phantom, 1 target (gross tumor volume [GTV]; insert at 6 o'clock), a subvolume within this target, and 7 organs at risk (OARs; other inserts) were contoured in the treatment planning system (TPS). Two adaptation sessions were performed in which the GTV was recontoured, first at 7 o'clock and then at 5 o'clock. The accumulated dose was exported from the TPS after delivery. Deformable vector fields were also exported to manually accumulate doses for comparison. For the pelvis phantom, synthetic Gaussian deformations were applied to the planning CT image to simulate organ changes. Two single-fraction adaptive plans were created based on the deformed planning CT and cone beam CT images acquired onboard the radiation therapy platform. A manual dose accumulation was performed after delivery using the exported deformable vector fields for comparison with the system-generated result. Results: All plans were successfully delivered, and the accumulated dose was both manually calculated and derived from the TPS. For the electron density phantom, the average mean dose differences in the GTV, boost volume, and OARs 1 to 7 were 0.0%, -0.2%, 92.0%, 78.4%, 1.8%, 1.9%, 0.0%, 0.0%, and 2.3%, respectively, between the manually summed and platform-accumulated doses. The gamma passing rates for the 3-dimensional dose comparison between the manually generated and TPS-provided dose accumulations were >99% for both phantoms. Conclusions: This study demonstrated agreement between manually obtained and TPS-generated accumulated doses in terms of both mean structure doses and local 3-dimensional dose distributions. Large disagreements were observed for OAR1 and OAR2 defined on the electron density phantom due to OARs having lower deformation priority over the target in addition to artificially large changes in position induced for these structures fraction-by-fraction. The tests applied in this study to a commercial platform provide a straightforward approach toward the development of routine quality assurance of dose accumulation in online adaptation.

Magnetoimpedance Biosensors and Real-Time Healthcare Monitors: Progress, Opportunities, and Challenges.

A small DC magnetic field can induce an enormous response in the impedance of a soft magnetic conductor in various forms of wire, ribbon, and thin film. Also known as the giant magnetoimpedance (GMI) effect, this phenomenon forms the basis for the development of high-performance magnetic biosensors with magnetic field sensitivity down to the picoTesla regime at room temperature. Over the past decade, some state-of-the-art prototypes have become available for trial tests due to continuous efforts to improve the sensitivity of GMI biosensors for the ultrasensitive detection of biological entities and biomagnetic field detection of human activities through the use of magnetic nanoparticles as biomarkers. In this review, we highlight recent advances in the development of GMI biosensors and review medical devices for applications in biomedical diagnostics and healthcare monitoring, including real-time monitoring of respiratory motion in COVID-19 patients at various stages. We also discuss exciting research opportunities and existing challenges that will stimulate further study into ultrasensitive magnetic biosensors and healthcare monitors based on the GMI effect.

Robustness and reproducibility of an artificial intelligence-assisted online segmentation and adaptive planning process for online adaptive radiation therapy.

Clinical implementation of online adaptive radiation therapy requires initial and ongoing performance assessment of the underlying auto-segmentation and adaptive planning algorithms, although a straightforward and efficient process for this in phantom is lacking. The purpose of this work was to investigate robustness and repeatability of the artificial intelligence-assisted online segmentation and adaptive planning process on the Varian Ethos adaptive platform, and to develop an end-to-end test strategy for online adaptive radiation therapy. Five synthetic deformations were generated and applied to a computed tomography image of an anthropomorphic pelvis phantom, and reference treatment plans were generated from each of the resulting deformed images. The undeformed phantom was repeatedly imaged, and the online adaptive process was performed including auto-segmentation, review and manual correction of contours, and adaptive plan creation. One adaptive fractions in five different deformation scenarios were performed. The manually corrected contours had a high degree of consistency (> 93% Dice similarity coefficient and < 1.0 mm mean surface distance) across repeated fractions, with no significant variation across the synthetic deformation instance except for bowel (p = 0.026, one-way ANOVA). Adaptive treatment plans also resulted in highly consistent dose-volume values for targets and organs at risk. A straightforward and efficient process was developed and used to quantify a set of organ specific contouring and dosimetric action levels to help establish uncertainty bounds for an end-to-end test on the Varian Ethos system.

Targetability of cervical cancer by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU)-mediated hyperthermia (HT) for patients receiving radiation therapy.

PURPOSE: To evaluate the targetability of late-stage cervical cancer by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU)-induced hyperthermia (HT) as an adjuvant to radiation therapy (RT). METHODS: Seventy-nine cervical cancer patients (stage IIIB-IVA) who received RT with lesions visible on positron emission tomography-computed tomography (PET-CT) were retrospectively analyzed for targetability using a commercially-available HT-capable MRgHIFU system. Targetability was assessed for both primary targets and/or any metastatic lymph nodes using both posterior (supine) and anterior (prone) patient setups relative to the transducer. Thirty-four different angles of rotation along subjects' longitudinal axis were analyzed. Targetability was categorized as: (1) Targetable with/without minimal intervention; (2) Not targetable. To determine if any factors could be used for prospective screening of patients, potential associations between demographic/anatomical factors and targetability were analyzed. RESULTS: 72.15% primary tumors and 33.96% metastatic lymph nodes were targetable from at least one angle. 49.37% and 39.24% of primary tumors could be targeted with patient laying in supine and prone positions, respectively. 25°-30° rotation and 0° rotation had the highest rate of the posterior and anterior targetability, respectively. The ventral depth of the tumor and its distance to the coccyx were statistically correlated with the anterior and posterior targetability, respectively. CONCLUSION: Most late-stage cervical cancer primaries were targetable by MRgHIFU HT requiring either no/minimal intervention. A rotation of 0° or 25°-30° relative to the transducer might benefit anterior and posterior targetability, respectively. Certain demographic/anatomic parameters might be useful in screening patients for treatability.

Characterization of magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU)-induced large-volume hyperthermia in deep and superficial targets in a porcine model.

PURPOSE: To characterize temperature fields and tissue damage profiles of large-volume hyperthermia (HT) induced by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) in deep and superficial targets in vivo in a porcine model. METHODS: Nineteen HT sessions were performed in vivo with a commercial MRgHIFU system (Sonalleve® V2, Profound Medical Inc., Mississauga, ON, Canada) in hind leg muscles of eight pigs with temperature fields of cross-sectional diameter of 58-mm. Temperature statistics evaluated in the target region-of-interest (tROI) included accuracy, temporal variation, and uniformity. The impact of the number and location of imaging planes for feedback-based temperature control were investigated. Temperature fields were characterized by time-in-range (TIR, the duration each voxel stays within 40-45 °C) maps. Tissue damage was characterized by contrast-enhanced MRI, and macroscopic and histopathological analysis. The performance of the Sonalleve® system was benchmarked against a commercial phantom. RESULTS: Across all HT sessions, the mean difference between the average temperature (Tavg) and the desired temperature was -0.4 ± 0.5 °C; the standard deviation of temperature 1.2 ± 0.2 °C; the temporal variation of Tavg for 30-min HT was 0.6 ± 0.2 °C, and the temperature uniformity was 1.5 ± 0.2 °C. A difference of 2.2-cm (in pig) and 1.5-cm (in phantom) in TIR dimensions was observed when applying feedback-based plane(s) at different locations. Histopathology showed 62.5% of examined HT sessions presenting myofiber degeneration/necrosis within the target volume. CONCLUSION: Large-volume MRgHIFU-mediated HT was successfully implemented and characterized in a porcine model in deep and superficial targets in vivo with heating distributions modifiable by user-definable parameters.

Long-term Inter-protocol kV CBCT image quality assessment for a ring-gantry linac via automated QA approach.

We develop a fully automated QA process to compare the image quality of all kV CBCT protocols on a Halcyon linac with ring gantry design, and evaluate image quality stability over a 10-month period. A total of 19 imaging scan and reconstruction protocols were characterized with measurement on a newly released QUART phantom. A set of image analysis algorithms were developed and integrated into an automated analysis suite to derive key image quality metrics, including HU value accuracy on density inserts, HU uniformity using the background plate, high contrast resolution with the modulation transfer function (MTF) from the edge profiles, low contrast resolution using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), slice thickness with the air gap modules, and geometric accuracy with the diameter of the phantom. Image quality data over 10 months was tracked and analyzed to evaluate the stability of the Halcyon kV imaging system. The HU accuracy over all 19 protocols is within tolerance (±50HU). The maximum uniformity deviation is 12.2 HU. The SNR and CNR, depending on the protocol selected, range from 18.5-911.9 and 1.9-102.8, respectively. A much-improved SNR and CNR were observed for iterative reconstruction (iCBCT) modes and protocols designed for large subjects over low dose and fast scanning modes. The Head and Image Gently protocols have the greatest high contrast resolution with MTF10% over 1 lp/mm and MTF50% over 0.6 lp/mm. The iCBCT mode slightly improved the MTF10% and MTF50% compared to the Feldkamp-Davis-Kress approach. The slice thickness (maximum error of 0.31 mm) and geometry metrics (maximum error of 0.7 mm) are all within tolerance (±0.5 mm for slice thickness and ±1 mm for geometry metrics). The long-term study over 10-month showed no significant drift for all key image quality metrics, which indicated the kV CBCT image quality is stable over time.

Predicting gamma passing rates for portal dosimetry-based IMRT QA using machine learning.

PURPOSE: Intensity-modulated radiation therapy (IMRT) quality assurance (QA) measurements are routinely performed prior to treatment delivery to verify dose calculation and delivery accuracy. In this work, we applied a machine learning-based approach to predict portal dosimetry based IMRT QA gamma passing rates. METHODS: 182 IMRT plans for various treatment sites were planned and delivered with portal dosimetry on two TrueBeam and two Trilogy LINACs. A total of 1497 beams were collected and analyzed using gamma criteria of 2%/2 mm with a 5% threshold. The datasets for building the machine learning models consisted of 1269 beams. Ten-fold cross-validation was utilized to tune the model and prevent "overfitting." A separate test set with the remaining 228 beams was used to evaluate model performance. Each beam was characterized by a set of 31 features including both plan complexity metrics and machine characteristics. Three tree-based machine learning algorithms (AdaBoost, Random Forest, and XGBoost) were used to train the models and predict gamma passing rates. RESULTS: Both AdaBoost and Random Forest had 98% of predictions within 3% of the measured 2%/2 mm gamma passing rates with a maximum error less than 4% and a mean absolute error < 1%. XGBoost showed a slightly worse prediction accuracy with 95% of the predictions within 3% of the measured gamma passing rates and a maximum error of 4.5%. The three models identified the same nine features in the top 10 most important ones that are related to plan complexity and maximum aperture displacement from the central axis or the maximum jaw size in a beam. CONCLUSION: We have demonstrated that portal dosimetry IMRT QA gamma passing rates can be accurately predicted using tree-based ensemble learning models. The machine learning based approach allows physicists to better identify the failures of IMRT QA measurements and to develop proactive QA approaches.

Toward adaptive proton therapy guided with a mobile helical CT scanner.

PURPOSE: To evaluate the feasibility of image-guided adaptive proton therapy (IGAPT) with a mobile helical-CT without rails. METHOD: CT images were acquired with a 32-slice mobile CT (mCT) scanning through a 6 degree-of-freedom robotic couch rotated isocentrically 90 degrees from an initial setup position. The relationship between the treatment isocenter and the mCT imaging isocenter was established by a stereotactic reference frame attached to the treatment couch. Imaging quality, geometric integrity and localization accuracy were evaluated according to AAPM TG-66. Accuracy of relative stopping power ratio (RSPR) was evaluated by comparing water equivalent distance (WED) and dose calculations on anthropomorphic phantoms to that of planning CT (pCT). Feasibility of image-guided adaptive proton therapy was demonstrated on fractional images acquired with the mCT scanner. RESULTS: mCT images showed slightly lower spatial resolution and a higher contrast-to-noise ratio compared to pCT images from the standard helical CT scanner. The geometric accuracy of the mCT was <1 mm. Localization accuracy was <0.4 mm and <0.3° with respect to 2DkV/kV matching. WED differences between mCT and pCT images were negligible, with discrepancies of 0.8 ±â€¯0.6 mm and 1.3 ±â€¯0.9 mm for brain and lung phantoms respectively. 3D gamma analysis (3% and 3 mm) passing rate was >95% on dose computed on mCT, with respect to dose calculation on pCT. CONCLUSION: Our study has demonstrated that the geometric integrity, image quality and RSPR accuracy of the mCT are sufficient for IGAPT.

Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017.

PURPOSE: This report presents the methods and results of the Thoracic Auto-Segmentation Challenge organized at the 2017 Annual Meeting of American Association of Physicists in Medicine. The purpose of the challenge was to provide a benchmark dataset and platform for evaluating performance of autosegmentation methods of organs at risk (OARs) in thoracic CT images. METHODS: Sixty thoracic CT scans provided by three different institutions were separated into 36 training, 12 offline testing, and 12 online testing scans. Eleven participants completed the offline challenge, and seven completed the online challenge. The OARs were left and right lungs, heart, esophagus, and spinal cord. Clinical contours used for treatment planning were quality checked and edited to adhere to the RTOG 1106 contouring guidelines. Algorithms were evaluated using the Dice coefficient, Hausdorff distance, and mean surface distance. A consolidated score was computed by normalizing the metrics against interrater variability and averaging over all patients and structures. RESULTS: The interrater study revealed highest variability in Dice for the esophagus and spinal cord, and in surface distances for lungs and heart. Five out of seven algorithms that participated in the online challenge employed deep-learning methods. Although the top three participants using deep learning produced the best segmentation for all structures, there was no significant difference in the performance among them. The fourth place participant used a multi-atlas-based approach. The highest Dice scores were produced for lungs, with averages ranging from 0.95 to 0.98, while the lowest Dice scores were produced for esophagus, with a range of 0.55-0.72. CONCLUSION: The results of the challenge showed that the lungs and heart can be segmented fairly accurately by various algorithms, while deep-learning methods performed better on the esophagus. Our dataset together with the manual contours for all training cases continues to be available publicly as an ongoing benchmarking resource.

A machine learning approach to the accurate prediction of monitor units for a compact proton machine.

PURPOSE: Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning-based approach was developed to predict output (cGy/MU) and derive MUs, incorporating the dependencies on gantry angle and field size for a single-room proton therapy system. The goal of this study was to develop a secondary check tool for OF measurements and eventually eliminate patient-specific OF measurements. METHOD: The OFs of 1754 fields previously measured in a water phantom with calibrated ionization chambers and electrometers for patient-specific fields with various range and modulation width combinations for 23 options were included in this study. The training data sets for machine learning models in three different methods (Random Forest, XGBoost and Cubist) included 1431 (~81%) OFs. Ten-fold cross-validation was used to prevent "overfitting" and to validate each model. The remaining 323 (~19%) OFs were used to test the trained models. The difference between the measured and predicted values from machine learning models was analyzed. Model prediction accuracy was also compared with that of the semi-empirical model developed by Kooy (Phys. Med. Biol. 50, 2005). Additionally, gantry angle dependence of OFs was measured for three groups of options categorized on the selection of the second scatters. Field size dependence of OFs was investigated for the measurements with and without patient-specific apertures. RESULTS: All three machine learning methods showed higher accuracy than the semi-empirical model which shows considerably large discrepancy of up to 7.7% for the treatment fields with full range and full modulation width. The Cubist-based solution outperformed all other models (P < 0.001) with the mean absolute discrepancy of 0.62% and maximum discrepancy of 3.17% between the measured and predicted OFs. The OFs showed a small dependence on gantry angle for small and deep options while they were constant for large options. The OF decreased by 3%-4% as the field radius was reduced to 2.5 cm. CONCLUSION: Machine learning methods can be used to predict OF for double-scatter proton machines with greater prediction accuracy than the most popular semi-empirical prediction model. By incorporating the gantry angle dependence and field size dependence, the machine learning-based methods can be used for a sanity check of OF measurements and bears the potential to eliminate the time-consuming patient-specific OF measurements.

Mapping of Rice Varieties and Sowing Date Using X-Band SAR Data.

Rice is a major staple food for nearly half of the world's population and has a considerable contribution to the global agricultural economy. While spaceborne Synthetic Aperture Radar (SAR) data have proved to have great potential to provide rice cultivation area, few studies have been performed to provide practical information that meets the user requirements. In rice growing regions where the inter-field crop calendar is not uniform such as in the Mekong Delta in Vietnam, knowledge of the start of season on a field basis, along with the planted rice varieties, is very important for correct field management (timing of irrigation, fertilization, chemical treatment, harvest), and for market assessment of the rice production. The objective of this study is to develop methods using SAR data to retrieve in addition to the rice grown area, the sowing date, and the distinction between long and short cycle varieties. This study makes use of X-band SAR data from COSMO-SkyMed acquired from 19 August to 23 November 2013 covering the Chau Thanh and Thoai Son districts in An Giang province, Viet Nam, characterized by a complex cropping pattern. The SAR data have been analyzed as a function of rice parameters, and the temporal and polarization behaviors of the radar backscatter of different rice varieties have been interpreted physically. New backscatter indicators for the detection of rice paddy area, the estimation of the sowing date, and the mapping of the short cycle and long cycle rice varieties have been developed and assessed. Good accuracy has been found with 92% in rice grown area, 96% on rice long or short cycle, and a root mean square error of 4.3 days in sowing date. The results have been discussed regarding the generality of the methods with respect to the rice cultural practices and the SAR data characteristics.

Unsupervised Feature Learning Classification With Radial Basis Function Extreme Learning Machine Using Graphic Processors.

Ever-increasing size and complexity of data sets create challenges and potential tradeoffs of accuracy and speed in learning algorithms. This paper offers progress on both fronts. It presents a mechanism to train the unsupervised learning features learned from only one layer to improve performance in both speed and accuracy. The features are learned by an unsupervised feature learning (UFL) algorithm. Then, those features are trained by a fast radial basis function (RBF) extreme learning machine (ELM). By exploiting the massive parallel computing attribute of modern graphics processing unit, a customized compute unified device architecture (CUDA) kernel is developed to further speed up the computing of the RBF kernel in the ELM. Results tested on Canadian Institute for Advanced Research and Mixed National Institute of Standards and Technology data sets confirm the UFL RBF ELM achieves high accuracy, and the CUDA implementation is up to 20 times faster than CPU and the naive parallel approach.