Biological optimization for hybrid proton-photon radiotherapy.

Objective.Hybrid proton-photon radiotherapy (RT) is a cancer treatment option to broaden access to proton RT. Additionally, with a refined treatment planning method, hybrid RT has the potential to offer superior plan quality compared to proton-only or photon-only RT, particularly in terms of target coverage and sparing organs-at-risk (OARs), when considering robustness to setup and range uncertainties. However, there is a concern regarding the underestimation of the biological effect of protons on OARs, especially those in close proximity to targets. This study seeks to develop a hybrid treatment planning method with biological dose optimization, suitable for clinical implementation on existing proton and photon machines, with each photon or proton treatment fraction delivering a uniform target dose.Approach.The proposed hybrid biological dose optimization method optimized proton and photon plan variables, along with the number of fractions for each modality, minimizing biological dose to the OARs and surrounding normal tissues. To mitigate underestimation of hot biological dose spots, proton biological dose was minimized within a ring structure surrounding the target. Hybrid plans were designed to be deliverable separately and robustly on existing proton and photon machines, with enforced uniform target dose constraints for the proton and photon fraction doses. A probabilistic formulation was utilized for robust optimization of setup and range uncertainties for protons and photons. The nonconvex optimization problem, arising from minimum monitor unit constraint and dose-volume histogram constraints, was solved using an iterative convex relaxation method.Main results.Hybrid planning with biological dose optimization effectively eliminated hot spots of biological dose, particularly in normal tissues surrounding the target, outperforming proton-only planning. It also provided superior overall plan quality and OAR sparing compared to proton-only or photon-only planning strategies.Significance.This study presents a novel hybrid biological treatment planning method capable of generating plans with reduced biological hot spots, superior plan quality to proton-only or photon-only plans, and clinical deliverability on existing proton and photon machines, separately and robustly.

An iterative convex relaxation method for proton LET optimization.

Objective:A constant relative biological effectiveness of 1.1 in current clinical practice of proton radiotherapy (RT) is a crude approximation and may severely underestimate the biological dose from proton RT to normal tissues, especially near the treatment target at the end of Bragg peaks that exhibits high linear energy transfer (LET). LET optimization can account for biological effectiveness of protons during treatment planning, for minimizing biological proton dose and hot spots to normal tissues. However, the LET optimization is usually nonlinear and nonconvex to solve, for which this work will develop an effective optimization method based on iterative convex relaxation (ICR).Approach: In contrast to the generic nonlinear optimization method, such as Quasi-Newton (QN) method, that does not account for specific characteristics of LET optimization, ICR is tailored to LET modeling and optimization in order to effectively and efficiently solve the LET problem. Specifically, nonlinear dose-averaged LET term is iteratively linearized and becomes convex during ICR, while nonconvex dose-volume constraint and minimum-monitor-unit constraint are also handled by ICR, so that the solution for LET optimization is obtained by solving a sequence of convex and linearized convex subproblems. Since the high LET mostly occurs near the target, a 1 cm normal-tissue expansion of clinical target volume (CTV) (excluding CTV), i.e. CTV1cm, is defined to as an auxiliary structure during treatment planning, where LET is minimized.Main results: ICR was validated in comparison with QN for abdomen, lung, and head-and-neck cases. ICR was effective for LET optimization, as ICR substantially reduced the LET and biological dose in CTV1cm the ring, with preserved dose conformality to CTV. Compared to QN, ICR had smaller LET, physical and biological dose in CTV1cm, and higher conformity index values; ICR was also computationally more efficient, which was about 3 times faster than QN.Significance: A LET-specific optimization method based on ICR has been developed for solving proton LET optimization, which has been shown to be more computationally efficient than generic nonlinear optimizer via QN, with better plan quality in terms of LET, biological and physical dose conformality.

Simultaneous dose and dose rate optimization (SDDRO) of the FLASH effect for pencil-beam-scanning proton therapy.

PURPOSE: Compared to CONV-RT (with conventional dose rate), FLASH-RT (with ultra-high dose rate) can provide biological dose sparing for organs-at-risk (OARs) via the so-called FLASH effect, in addition to physical dose sparing. However, the FLASH effect only occurs, when both dose and dose rate meet certain minimum thresholds. This work will develop a simultaneous dose and dose rate optimization (SDDRO) method accounting for both FLASH dose and dose rate constraints during treatment planning for pencil-beam-scanning proton therapy. METHODS: SDDRO optimizes the FLASH effect (specific to FLASH-RT) as well as the dose distribution (similar to CONV-RT). The nonlinear dose rate constraint is linearized, and the reformulated optimization problem is efficiently solved via iterative convex relaxation powered by alternating direction method of multipliers. To resolve and quantify the generic tradeoff of FLASH-RT between FLASH and dose optimization, we propose the use of FLASH effective dose based on dose modifying factor (DMF) owing to the FLASH effect. RESULTS: FLASH-RT via transmission beams (TB) (IMPT-TB or SDDRO) and CONV-RT via Bragg peaks (BP) (IMPT-BP) were evaluated for clinical prostate, lung, head-and-neck (HN), and brain cases. Despite the use of TB, which is generally suboptimal to BP for normal tissue sparing, FLASH-RT via SDDRO considerably reduced FLASH effective dose for high-dose OAR adjacent to the target. For example, in the lung SBRT case, the max esophageal dose constraint 27 Gy was only met by SDDRO (24.8 Gy), compared to IMPT-BP (35.3 Gy) or IMPT-TB (36.6 Gy); in the brain SRS case, the brain constraint V12Gy≤15cc was also only met by SDDRO (13.7cc), compared to IMPT-BP (43.9cc) or IMPT-TB (18.4cc). In addition, SDDRO substantially improved the FLASH coverage from IMPT-TB, e.g., an increase from 37.2% to 67.1% for lung, from 39.1% to 58.3% for prostate, from 65.4% to 82.1% for HN, from 50.8% to 73.3% for the brain. CONCLUSIONS: Both FLASH dose and dose rate constraints are incorporated into SDDRO for FLASH-RT that jointly optimizes the FLASH effect and physical dose distribution. FLASH effective dose via FLASH DMF is introduced to reconcile the tradeoff between physical dose sparing and FLASH sparing, and quantify the net effective gain from CONV-RT to FLASH-RT.

An adaptive spot placement method on Cartesian grid for pencil beam scanning proton therapy.

Pencil beam scanning proton radiotherapy (RT) offers flexible proton spot placement near treatment targets for delivering tumoricidal radiation dose to tumor targets while sparing organs-at-risk. Currently the spot placement is mostly based on a non-adaptive sampling (NS) strategy on a Cartesian grid. However, the spot density or spacing during NS is a constant for the Cartesian grid that is independent of the geometry of tumor targets, and thus can be suboptimal in terms of plan quality (e.g. target dose conformality) and delivery efficiency (e.g. number of spots). This work develops an adaptive sampling (AS) spot placement method on the Cartesian grid that fully accounts for the geometry of tumor targets. Compared with NS, AS places (1) a relatively fine grid of spots at the boundary of tumor targets to account for the geometry of tumor targets and treatment uncertainties (setup and range uncertainty) for improving dose conformality, and (2) a relatively coarse grid of spots in the interior of tumor targets to reduce the number of spots for improving delivery efficiency and robustness to the minimum-minitor-unit (MMU) constraint. The results demonstrate that (1) AS achieved comparable plan quality with NS for regular MMU and substantially improved plan quality from NS for large MMU, using merely about 10% of spots from NS, where AS was derived from the same Cartesian grid as NS; (2) on the other hand, with similar number of spots, AS had better plan quality than NS consistently for regular and large MMU.

Does the sequence of high-dose rate brachytherapy boost and IMRT for prostate cancer impact early toxicity outcomes? Results from a single institution analysis.

BACKGROUND: We present the first report comparing early toxicity outcomes with high-dose rate brachytherapy (HDR-BT) boost upfront versus intensity modulated RT (IMRT) upfront combined with androgen deprivation therapy (ADT) as definitive management for intermediate risk or higher prostate cancer. METHODS AND MATERIALS: We reviewed all non-metastatic prostate cancer patients who received HDR-BT boost from 2014 to 2019. HDR-BT boost was offered to patients with intermediate-risk disease or higher. ADT use and IMRT target volume was based on NCCN risk group. IMRT dose was typically 45 Gy in 25 fractions to the prostate and seminal vesicles ± pelvic lymph nodes. HDR-BT dose was 15 Gy in 1 fraction, delivered approximately 3 weeks before or after IMRT. The sequence was based on physician preference. Biochemical recurrence was defined per ASTRO definition. Gastrointestinal (GI) and Genitourinary (GU) toxicity was graded per CTCAE v5.0. Pearson Chi-squared test and Wilcoxon tests were used to compare toxicity rates. P-value < 0.05 was significant. RESULTS: Fifty-eight received HDR-BT upfront (majority 2014-2016) and 57 IMRT upfront (majority 2017-2018). Median follow-up was 26.0 months. The two cohorts were well-balanced for baseline patient/disease characteristics and treatment factors. There were differences in treatment sequence based on the year in which patients received treatment. Overall, rates of grade 3 or higher GI or GU toxicity were <1%. There was no significant difference in acute or late GI or GU toxicity between the two groups. CONCLUSION: We found no significant difference in GI/GU toxicity in intermediate-risk or higher prostate cancer patients receiving HDR-BT boost upfront versus IMRT upfront combined with ADT. These findings suggest that either approach may be reasonable. Longer follow-up is needed to evaluate late toxicity and long-term disease control.

GroupRegNet: a groupwise one-shot deep learning-based 4D image registration method.

Accurate deformable four-dimensional (4D) (three-dimensional in space and time) medical images registration is essential in a variety of medical applications. Deep learning-based methods have recently gained popularity in this area for the significantly lower inference time. However, they suffer from drawbacks of non-optimal accuracy and the requirement of a large amount of training data. A new method named GroupRegNet is proposed to address both limitations. The deformation fields to warp all images in the group into a common template is obtained through one-shot learning. The use of the implicit template reduces bias and accumulated error associated with the specified reference image. The one-shot learning strategy is similar to the conventional iterative optimization method but the motion model and parameters are replaced with a convolutional neural network and the weights of the network. GroupRegNet also features a simpler network design and a more straightforward registration process, which eliminates the need to break up the input image into patches. The proposed method was quantitatively evaluated on two public respiratory-binned 4D-computed tomography datasets. The results suggest that GroupRegNet outperforms the latest published deep learning-based methods and is comparable to the top conventional method pTVreg. To facilitate future research, the source code is available at https://github.com/vincentme/GroupRegNet.

Technical Note: Automatic segmentation of CT images for ventral body composition analysis.

PURPOSE: Body composition is known to be associated with many diseases including diabetes, cancers, and cardiovascular diseases. In this paper, we developed a fully automatic body tissue decomposition procedure to segment three major compartments that are related to body composition analysis - subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and muscle. Three additional compartments - the ventral cavity, lung, and bones - were also segmented during the segmentation process to assist segmentation of the major compartments. METHODS: A convolutional neural network (CNN) model with densely connected layers was developed to perform ventral cavity segmentation. An image processing workflow was developed to segment the ventral cavity in any patient's computed tomography (CT) using the CNN model, then further segment the body tissue into multiple compartments using hysteresis thresholding followed by morphological operations. It is important to segment ventral cavity firstly to allow accurate separation of compartments with similar Hounsfield unit (HU) inside and outside the ventral cavity. RESULTS: The ventral cavity segmentation CNN model was trained and tested with manually labeled ventral cavities in 60 CTs. Dice scores (mean ± standard deviation) for ventral cavity segmentation were 0.966 ± 0.012. Tested on CT datasets with intravenous (IV) and oral contrast, the Dice scores were 0.96 ± 0.02, 0.94 ± 0.06, 0.96 ± 0.04, 0.95 ± 0.04, and 0.99 ± 0.01 for bone, VAT, SAT, muscle, and lung, respectively. The respective Dice scores were 0.97 ± 0.02, 0.94 ± 0.07, 0.93 ± 0.06, 0.91 ± 0.04, and 0.99 ± 0.01 for non-contrast CT datasets. CONCLUSION: A body tissue decomposition procedure was developed to automatically segment multiple compartments of the ventral body. The proposed method enables fully automated quantification of three-dimensional (3D) ventral body composition metrics from CT images.

Vulnerability of Antarctica's ice shelves to meltwater-driven fracture.

Atmospheric warming threatens to accelerate the retreat of the Antarctic Ice Sheet by increasing surface melting and facilitating 'hydrofracturing'1-7, where meltwater flows into and enlarges fractures, potentially triggering ice-shelf collapse3-5,8-10. The collapse of ice shelves that buttress11-13 the ice sheet accelerates ice flow and sea-level rise14-16. However, we do not know if and how much of the buttressing regions of Antarctica's ice shelves are vulnerable to hydrofracture if inundated with water. Here we provide two lines of evidence suggesting that many buttressing regions are vulnerable. First, we trained a deep convolutional neural network (DCNN) to map the surface expressions of fractures in satellite imagery across all Antarctic ice shelves. Second, we developed a stability diagram of fractures based on linear elastic fracture mechanics to predict where basal and dry surface fractures form under current stress conditions. We find close agreement between the theoretical prediction and the DCNN-mapped fractures, despite limitations associated with detecting fractures in satellite imagery. Finally, we used linear elastic fracture mechanics theory to predict where surface fractures would become unstable if filled with water. Many regions regularly inundated with meltwater today are resilient to hydrofracture-stresses are low enough that all water-filled fractures are stable. Conversely, 60 ± 10 per cent of ice shelves (by area) both buttress upstream ice and are vulnerable to hydrofracture if inundated with water. The DCNN map confirms the presence of fractures in these buttressing regions. Increased surface melting17 could trigger hydrofracturing if it leads to water inundating the widespread vulnerable regions we identify. These regions are where atmospheric warming may have the largest impact on ice-sheet mass balance.

Single fraction high-dose-rate brachytherapy as monotherapy for low and intermediate risk prostate cancer: toxicities and early outcomes from a single institutional experience.

PURPOSE: High-dose-rate brachytherapy (HDR-BT) delivered in a single fraction as monotherapy is a potential treatment modality for low- and intermediate-risk prostate cancer (LIR-PC); however, outcome data with this technique remain limited. Here we describe our institutional HDR monotherapy experience and report the efficacy and toxicity of this treatment. MATERIAL AND METHODS: LIR-PC patients who received a definitive single fraction HDR-BT during 2013-2017 were retrospectively identified. The intended HDR monotherapy dose was 19 Gy in one fraction. Acute (< 90 days) and late (≥ 90 days) toxicity was assessed using CTCAE version 4.03. Trends in prostate-specific antigen (PSA) and American Urological Association (AUA) symptom scores after treatment were assessed using Bayesian linear mixed models. The Kaplan-Meier method was used to evaluate biochemical failure-free survival (BFFS). RESULTS: 28 patients with median follow-up of 23.6 months were identified. The median age at treatment was 65 years (48-83). The NCCN risk groups were low in 14, favorable intermediate in 10, and unfavorable intermediate in 4 patients. There were 5 (18%) and 0 (0%) acute grade 2 genitourinary (GU) and gastrointestinal (GI) toxicities, respectively, and one (4%) acute grade 3 GU toxicity. There were no late grade 3 toxicities, and 5 (18%) and 0 (0%) late grade 2 GU and GI toxicities respectively. PSA values and AUA symptom scores decreased significantly after treatment. There were 3 biochemical failures with the two- and three-year BFFS of 90.7% and 80.6%, respectively. CONCLUSIONS: Early results from a single institution suggest that single fraction HDR-BT with 19 Gy has limited toxicity, although with suboptimal biochemical control.

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.

Automatic large quantity landmark pairs detection in 4DCT lung images.

PURPOSE: To automatically and precisely detect a large quantity of landmark pairs between two lung computed tomography (CT) images to support evaluation of deformable image registration (DIR). We expect that the generated landmark pairs will significantly augment the current lung CT benchmark datasets in both quantity and positional accuracy. METHODS: A large number of landmark pairs were detected within the lung between the end-exhalation (EE) and end-inhalation (EI) phases of the lung four-dimensional computed tomography (4DCT) datasets. Thousands of landmarks were detected by applying the Harris-Stephens corner detection algorithm on the probability maps of the lung vasculature tree. A parametric image registration method (pTVreg) was used to establish initial landmark correspondence by registering the images at EE and EI phases. A multi-stream pseudo-siamese (MSPS) network was then developed to further improve the landmark pair positional accuracy by directly predicting three-dimensional (3D) shifts to optimally align the landmarks in EE to their counterparts in EI. Positional accuracies of the detected landmark pairs were evaluated using both digital phantoms and publicly available landmark pairs. RESULTS: Dense sets of landmark pairs were detected for 10 4DCT lung datasets, with an average of 1886 landmark pairs per case. The mean and standard deviation of target registration error (TRE) were 0.47 ± 0.45 mm with 98% of landmark pairs having a TRE smaller than 2 mm for 10 digital phantom cases. Tests using 300 manually labeled landmark pairs in 10 lung 4DCT benchmark datasets (DIRLAB) produced TRE results of 0.73 ± 0.53 mm with 97% of landmark pairs having a TRE smaller than 2 mm. CONCLUSION: A new method was developed to automatically and precisely detect a large quantity of landmark pairs between lung CT image pairs. The detected landmark pairs could be used as benchmark datasets for more accurate and informative quantitative evaluation of DIR algorithms.

Improving treatment geometries in total skin electron therapy: Experimental investigation of linac angles and floor scatter dose contributions using Cherenkov imaging.

PURPOSE: The purpose of this study was to identify the optimal treatment geometry for total skin electron therapy (TSET) using a new optimization metric from Cherenkov image analysis, and to investigate the sensitivity of the Cherenkov imaging method to floor scatter effects in this unique treatment setup. METHODS: Cherenkov imaging using an intensified charge coupled device (ICCD) was employed to measure the relative surface dose distribution as a 2D image in the total skin electron treatment plane. A 1.2 m × 2.2 m × 1 cm white polyethylene sheet was placed vertically at a source to surface distance (SSD) of 300 cm, and irradiated with 6 MeV high dose rate TSET beams. The linear accelerator coordinate system used stipulates 0° is the bottom of the gantry arc, and progresses counterclockwise so that gantry angle 270° produces a horizontal beam orthogonal to the treatment plane. First, all unique pairs of treatment beams were analyzed to determine the performance of the currently recommended symmetric treatment angles (±20° from the horizontal), compared to treatment geometries unconstrained to upholding gantry angle symmetry. This was performed on two medical linear accelerators (linacs). Second, the extent of the floor scatter contributions to measured surface dose at the extended SSD required for TSET were imaged using three gantry angles of incidence: 270° (horizontal), 253° (-17°), and 240° (-30°). Images of the surface dose profile at each angle were compared to the standard concrete floor when steel plates, polyvinyl chloride (PVC), and solid water were placed on the ground at the base of the treatment plane. Postprocessing of these images allowed for comparison of floor material-based scatter profiles with previously published simulation results. RESULTS: Analysis of the symmetric treatment geometry (270 ± 20°) and the identified optimal treatment geometry (270 + 23° and 270 - 17°) showed a 16% increase in the 90% isodose area for the latter field pair on the first linac. The optimal asymmetric pair for the second linac (270 + 25° and 270 - 17°) provided a 52% increase in the 90% isodose area when compared to the symmetric geometry. Difference images between Cherenkov images captured with test materials (steel, PVC, and solid water) and the control (concrete floor) demonstrated relative changes in the two-dimensional (2D) dose profile over a 1 × 1.9 m region of interest (ROI) that were consistent with published simulation data. Qualitative observation of the residual images demonstrates localized increases and decreases with respect to the change in floor material and gantry angle. The most significant changes occurred when the beam was most directly impinging the floor (gantry angle 240°, horizontal -30°), where the PVC floor material decreased scatter dose by 1-3% in 7.2% of the total ROI area, and the steel plate increased scatter dose by 1-3% in 7.0% of the total ROI area. CONCLUSIONS: An updated Cherenkov imaging method identified asymmetric, machine-dependent TSET field angle pairs that provided much larger 90% isodose areas than the commonly adopted symmetric geometry suggested by Task Group 30 Report 23. A novel demonstration of scatter dose Cherenkov imaging in the TSET field was established.

Tolerance limits and methodologies for IMRT measurement-based verification QA: Recommendations of AAPM Task Group No. 218.

PURPOSE: Patient-specific IMRT QA measurements are important components of processes designed to identify discrepancies between calculated and delivered radiation doses. Discrepancy tolerance limits are neither well defined nor consistently applied across centers. The AAPM TG-218 report provides a comprehensive review aimed at improving the understanding and consistency of these processes as well as recommendations for methodologies and tolerance limits in patient-specific IMRT QA. METHODS: The performance of the dose difference/distance-to-agreement (DTA) and γ dose distribution comparison metrics are investigated. Measurement methods are reviewed and followed by a discussion of the pros and cons of each. Methodologies for absolute dose verification are discussed and new IMRT QA verification tools are presented. Literature on the expected or achievable agreement between measurements and calculations for different types of planning and delivery systems are reviewed and analyzed. Tests of vendor implementations of the γ verification algorithm employing benchmark cases are presented. RESULTS: Operational shortcomings that can reduce the γ tool accuracy and subsequent effectiveness for IMRT QA are described. Practical considerations including spatial resolution, normalization, dose threshold, and data interpretation are discussed. Published data on IMRT QA and the clinical experience of the group members are used to develop guidelines and recommendations on tolerance and action limits for IMRT QA. Steps to check failed IMRT QA plans are outlined. CONCLUSION: Recommendations on delivery methods, data interpretation, dose normalization, the use of γ analysis routines and choice of tolerance limits for IMRT QA are made with focus on detecting differences between calculated and measured doses via the use of robust analysis methods and an in-depth understanding of IMRT verification metrics. The recommendations are intended to improve the IMRT QA process and establish consistent, and comparable IMRT QA criteria among institutions.

Two-and-a-half-year clinical experience with the world's first magnetic resonance image guided radiation therapy system.

PURPOSE: Magnetic resonance image guided radiation therapy (MR-IGRT) has been used at our institution since 2014. We report on more than 2 years of clinical experience in treating patients with the world's first MR-IGRT system. METHODS AND MATERIALS: A clinical service was opened for MR-IGRT in January 2014 with an MR-IGRT system consisting of a split 0.35T magnetic resonance scanner that straddles a ring gantry with 3 multileaf collimator-equipped 60Co heads. The service was expanded to include online adaptive radiation therapy (ART) MR-IGRT and cine gating after 6 and 9 months, respectively. Patients selected for MR-IGRT were enrolled in a prospective registry between January 2014 and June 2016. Patients were treated with a variety of radiation therapy techniques including intensity modulated radiation therapy and stereotactic body radiation therapy (SBRT). When applicable, online ART was performed and gating on sagittal 2-dimensional cine MR was used. The charts of patients treated with MR-IGRT were reviewed to report on the clinical and treatment characteristics of the initial patients who were treated with this novel technique. RESULTS: A total of 316 patients have been treated with the MR-IGRT system, which has been integrated into a high-volume clinic. The cases were most commonly selected for improved soft tissue visualization, ART, and cine gating. Seventy-six patients were treated with 3-dimensional conformal radiation therapy, 146 patients with intensity modulated radiation therapy, and 94 patients with SBRT. The most commonly treated disease sites were the abdomen (28%), breast (26%), pelvis (22%), thorax (19%), and head and neck (5%). Sixty-seven patients were treated with online ART over a total of 244 adapted fractions. Cine treatment gating was used for a total of 81 patients. CONCLUSIONS: MR-IGRT has been successfully implemented in a high-volume radiation clinic and provides unique advantages in the treatment of a variety of malignancies. Additional clinical trials are in development to formally evaluate MR-IGRT in the treatment of multiple disease sites with techniques such as SBRT and ART.

Development of a 3D remote dosimetry protocol compatible with MRgIMRT.

PURPOSE: To develop a novel remote 3D dosimetry protocol to verify Magnetic Resonance-guided Radiation Therapy (MRgRT) treatments. The protocol was applied to investigate the accuracy of TG-119 IMRT irradiations delivered by the MRIdian® system (ViewRay® , Oakwood Village, OH, USA) allowing for a 48-hour delay between irradiation at a field institution and subsequent readout at a base institution. METHODS: The 3D dosimetry protocol utilizes a novel formulation of PRESAGE® radiochromic dosimeters developed for high postirradiation stability and compatibility with optical-CT readout. Optical-CT readout was performed with an in-house system utilizing telecentric lenses affording high-resolution scanning. The protocol was developed from preparatory experiments to characterize PRESAGE® response in relevant conditions. First, linearity and sensitivity of PRESAGE® dose-response in the presence of a magnetic field was evaluated in a small volume study (4 ml cuvettes) conducted under MRgRT conditions and irradiated with doses 0-15 Gy. Temporal and spatial stability of the dose-response were investigated in large volume studies utilizing large field-of-view (FOV) 2 kg cylindrical PRESAGE® dosimeters. Dosimeters were imaged at t = 1 hr and t = 48 hrs enabling the development of correction terms to model any observed spatial and temporal changes postirradiation. Polynomial correction factors for temporal and spatial changes in PRESAGE® dosimeters (CT and CR respectively) were obtained by numerical fitting to time-point data acquired in six irradiated dosimeters. A remote dosimetry protocol was developed where PRESAGE® change in optical-density (ΔOD) readings at time t = X (the irradiation to return shipment time interval) were corrected back to a convenient standard time t = 1 hr using the CT and CR corrections. This refined protocol was then applied to TG-119 (American Association of Physicists in Medicine, Task Group 119) plan deliveries on the MRIdian® system to evaluate the accuracy of MRgRT in these conditions. RESULTS: In the small volume study, in the presence of a 0.35 T magnetic field, PRESAGE® was observed to respond linearly (R2  = 0.9996) to Co-60 irradiation at t = 48 hrs postirradiation, within the dose ranges of 0 to 15 Gy, with a sensitivity of 0.0305(±0.003) ΔOD cm-1  Gy-1 . In the large volume studies, at t = 1 hr postirradiation, consistent linear response was observed, with average sensitivity of 0.0930 ± 0.002 ΔOD cm-1  Gy-1 . However, dosimeters gradually darkened with time (OD< 5% per day). A small radial dependence to the dosimeter sensitivity was measured (< 3% of maximum dose), which is attributed to a spherically symmetric dosimeter artifact arising from exothermic heating legacy in the PRESAGE® polyurethane substrate during curing. When applied to the TG-119 IMRT irradiations, the remote dosimetry protocol (including correction terms) yielded excellent line-profile and 3D gamma agreement for 3%/3 mm, 10% threshold (mean passing rate = 96.6% ± 4.0%). CONCLUSION: A novel 3D remote dosimetry protocol is introduced for validating off-site dosimetrically complex radiotherapy systems, including MRgRT. The protocol involves correcting for temporal and spatially dependent changes in PRESAGE® radiochromic dosimeters readout by optical-CT. Application of the protocol to TG-119 irradiations enabled verification of MRgRT dose distributions with high resolution.

Performance of a multi leaf collimator system for MR-guided radiation therapy.

PURPOSE: The purpose of this study was to investigate and characterize the performance of a Multi Leaf Collimator (MLC) designed for Cobalt-60 based MR-guided radiation therapy system in a 0.35 T magnetic field. METHODS: The MLC design and unique assembly features in the ViewRay MRIdian system were first reviewed. The RF cage shielding of MLC motor and cables were evaluated using ACR phantoms with real-time imaging and quantified by signal-to-noise ratio. The dosimetric characterizations, including the leaf transmission, leaf penumbra, tongue-and-groove effect, were investigated using radiosensitive films. The output factor of MLC-defined fields was measured with ionization chambers for both symmetric fields from 2.1 × 2.1 cm2 to 27.3 × 27.3 cm2 and asymmetric fields from 10.5 × 10.5 cm2 to 10.5 × 2.0 cm2 . Multi leaf collimator (MLC) positional accuracy was assessed by delivering either a picket fence (PF) style pattern on radiochromic films with wire-jig phantom or double and triple-rectangular patterns on ArcCheck-MR (Sun Nuclear, Melbourne, FL, USA) with gamma analysis as the pass/fail indicator. Leaf speed tests were performed to assess the capability of full range leaf travel within manufacture's specifications. Multi leaf collimator plan delivery reproducibility was tested by repeatedly delivering both open fields and fields with irregular shaped segments over 1-month period. RESULTS: Comparable SNRs within 4% were observed for MLC moving and stationary plans on vendor-reconstructed images, and the direct k-space reconstructed images showed that the three SNRs are within 1%. The maximum leaf transmission for all three MLCs was less than 0.35% and the average leakage was 0.153 ± 0.006%, 0.151 ± 0.008%, and 0.159 ± 0.015% for head 1, 2, and 3, respectively. Both the leaf edge and leaf end penumbra showed comparable values within 0.05 cm, and the measured values are within 0.1 cm with TPS values. The leaf edge TG effect indicated 10% underdose and the leaf end TG showed a shifted dose distribution with 0.3 cm offset. The leaf positioning test showed a 0.2 cm accuracy in the PF style test, and a gamma passing rate above 96% was observed with a 3%/2 mm criteria when comparing the measured double/triple-rectangular pattern fluence with TPS calculated fluence. The average leaf speed when executing the test plan fell in a range from 1.86 to 1.95 cm/s. The measured and TPS calculated output factors were within 2% for squared fields and within 3% for rectangular fields. The reproducibility test showed the deviation of output factors were well within 2% for square fields and the gamma passing rate within 1.5% for fields with irregular segments. The Monte Carlo predicted output factors were within 2% compared to TPS values. 15 out of the 16 IMRT plans have gamma passing rate more than 98% compared to the TPS fluence with an average passing rate of 99.1 ± 0.6%. CONCLUSION: The MRIdian MLC has a good RF noise shielding design, low radiation leakage, good positioning accuracy, comparable TG effect, and can be modeled by an independent Monte Carlo calculation platform.

A method to detect landmark pairs accurately between intra-patient volumetric medical images.

PURPOSES: An image processing procedure was developed in this study to detect large quantity of landmark pairs accurately in pairs of volumetric medical images. The detected landmark pairs can be used to evaluate of deformable image registration (DIR) methods quantitatively. METHODS: Landmark detection and pair matching were implemented in a Gaussian pyramid multi-resolution scheme. A 3D scale-invariant feature transform (SIFT) feature detection method and a 3D Harris-Laplacian corner detection method were employed to detect feature points, i.e., landmarks. A novel feature matching algorithm, Multi-Resolution Inverse-Consistent Guided Matching or MRICGM, was developed to allow accurate feature pairs matching. MRICGM performs feature matching using guidance by the feature pairs detected at the lower resolution stage and the higher confidence feature pairs already detected at the same resolution stage, while enforces inverse consistency. RESULTS: The proposed feature detection and feature pair matching algorithms were optimized to process 3D CT and MRI images. They were successfully applied between the inter-phase abdomen 4DCT images of three patients, between the original and the re-scanned radiation therapy simulation CT images of two head-neck patients, and between inter-fractional treatment MRIs of two patients. The proposed procedure was able to successfully detect and match over 6300 feature pairs on average. The automatically detected landmark pairs were manually verified and the mismatched pairs were rejected. The automatic feature matching accuracy before manual error rejection was 99.4%. Performance of MRICGM was also evaluated using seven digital phantom datasets with known ground truth of tissue deformation. On average, 11855 feature pairs were detected per digital phantom dataset with TRE = 0.77 ± 0.72 mm. CONCLUSION: A procedure was developed in this study to detect large number of landmark pairs accurately between two volumetric medical images. It allows a semi-automatic way to generate the ground truth landmark datasets that allow quantitatively evaluation of DIR algorithms for radiation therapy applications.

A method to reconstruct and apply 3D primary fluence for treatment delivery verification.

MOTIVATION: In this study, a method is reported to perform IMRT and VMAT treatment delivery verification using 3D volumetric primary beam fluences reconstructed directly from planned beam parameters and treatment delivery records. The goals of this paper are to demonstrate that 1) 3D beam fluences can be reconstructed efficiently, 2) quality assurance (QA) based on the reconstructed 3D fluences is capable of detecting additional treatment delivery errors, particularly for VMAT plans, beyond those identifiable by other existing treatment delivery verification methods, and 3) QA results based on 3D fluence calculation (3DFC) are correlated with QA results based on physical phantom measurements and radiation dose recalculations. METHODS: Using beam parameters extracted from DICOM plan files and treatment delivery log files, 3D volumetric primary fluences are reconstructed by forward-projecting the beam apertures, defined by the MLC leaf positions and modulated by beam MU values, at all gantry angles using first-order ray tracing. Treatment delivery verifications are performed by comparing 3D fluences reconstructed using beam parameters in delivery log files against those reconstructed from treatment plans. Passing rates are then determined using both voxel intensity differences and a 3D gamma analysis. QA sensitivity to various sources of errors is defined as the observed differences in passing rates. Correlations between passing rates obtained from QA derived from both 3D fluence calculations and physical measurements are investigated prospectively using 20 clinical treatment plans with artificially introduced machine delivery errors. RESULTS: Studies with artificially introduced errors show that common treatment delivery problems including gantry angle errors, MU errors, jaw position errors, collimator rotation errors, and MLC leaf position errors were detectable at less than normal machine tolerances. The reported 3DFC QA method has greater sensitivity than measurement-based QA methods. Statistical analysis-based Spearman's correlations shows that the 3DFC QA passing rates are significantly correlated with passing rates of physical phantom measurement-based QA methods. CONCLUSION: Among measurement-less treatment delivery verification methods, the reported 3DFC method is less demanding than those based on full dose re-calculations, and more comprehensive than those that solely checks beam parameters in treatment log files. With QA passing rates correlating to measurement-based passing rates, the 3DFC QA results could be useful for complementing the physical phantom measurements, or verifying treatment deliveries when physical measurements are not available. For the past 4+ years, the reported method has been implemented at authors' institution 1) as a complementary metric to physical phantom measurements for pretreatment, patient-specific QA of IMRT and VMAT plans, and 2) as an important part of the log file-based automated verification of daily patient treatment deliveries. It has been demonstrated to be useful in catching both treatment plan data transfer errors and treatment delivery problems.

Automatic and hierarchical segmentation of the human skeleton in CT images.

Accurate segmentation of each bone of the human skeleton is useful in many medical disciplines. The results of bone segmentation could facilitate bone disease diagnosis and post-treatment assessment, and support planning and image guidance for many treatment modalities including surgery and radiation therapy. As a medium level medical image processing task, accurate bone segmentation can facilitate automatic internal organ segmentation by providing stable structural reference for inter- or intra-patient registration and internal organ localization. Even though bones in CT images can be visually observed with minimal difficulty due to the high image contrast between the bony structures and surrounding soft tissues, automatic and precise segmentation of individual bones is still challenging due to the many limitations of the CT images. The common limitations include low signal-to-noise ratio, insufficient spatial resolution, and indistinguishable image intensity between spongy bones and soft tissues. In this study, a novel and automatic method is proposed to segment all the major individual bones of the human skeleton above the upper legs in CT images based on an articulated skeleton atlas. The reported method is capable of automatically segmenting 62 major bones, including 24 vertebrae and 24 ribs, by traversing a hierarchical anatomical tree and by using both rigid and deformable image registration. The degrees of freedom of femora and humeri are modeled to support patients in different body and limb postures. The segmentation results are evaluated using the Dice coefficient and point-to-surface error (PSE) against manual segmentation results as the ground-truth. The results suggest that the reported method can automatically segment and label the human skeleton into detailed individual bones with high accuracy. The overall average Dice coefficient is 0.90. The average PSEs are 0.41 mm for the mandible, 0.62 mm for cervical vertebrae, 0.92 mm for thoracic vertebrae, and 1.45 mm for pelvis bones.