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BackgroundThis study utilizes interviews of clinical medical physicists to investigate self-reported shortcomings of the current weekly chart check workflow and opportunities for improvement.MethodsNineteen medical physicists were recruited for a 30-minute semi-structured interview, with a particular focus placed on image review and the use of automated tools for image review in weekly checks. Survey-type questions were used to gather quantitative information about chart check practices and importance placed on reducing chart check workloads versus increasing chart check effectiveness. Open-ended questions were used to probe respondents about their current weekly chart check workflow, opinions of the value of weekly chart checks and perceived shortcomings, and barriers and facilitators to the implementation of automated chart check tools. Thematic analysis was used to develop common themes across the interviews.ResultsPhysicists ranked highly the value of reducing the time spent on weekly chart checks (average 6.3 on a scale from 1 to 10), but placed more value on increasing the effectiveness of checks with an average of 9.2 on a 1-10 scale. Four major themes were identified: (1) weekly chart checks need to adapt to an electronic record-and-verify chart environment, (2) physicists could add value to patient care by analyzing images without duplicating the work done by physicians, (3) greater support for trending analysis is needed in weekly checks, and (4) automation has the potential to increase the value of physics checks.ConclusionThis study identified several key shortcomings of the current weekly chart check process from the perspective of the clinical medical physicist. Our results show strong support for automating components of the weekly check workflow in order to allow for more effective checks that emphasize follow-up, trending, failure modes and effects analysis, and allow time to be spent on other higher value tasks that improve patient safety.

Objective. In image-guided radiotherapy (IGRT), off-by-one vertebral body misalignments are rare but potentially catastrophic. In this study, a novel detection method for such misalignments in IGRT was investigated using densely-connected convolutional networks (DenseNets) for applications towards real-time error prevention and retrospective error auditing.Approach. A total of 4213 images acquired from 527 radiotherapy patients aligned with planar kV or MV radiographs were used to develop and test error-detection software modules. Digitally reconstructed radiographs (DRRs) and setup images were retrieved and co-registered according to the clinically applied alignment contained in the DICOM REG files. A semi-automated algorithm was developed to simulate patient positioning errors on the anterior-posterior (AP) and lateral (LAT) images shifted by one vertebral body. A DenseNet architecture was designed to classify either AP images individually or AP and LAT image pairs. Receiver-operator characteristic curves (ROC) and areas under the curves (AUC) were computed to evaluate the classifiers on test subsets. Subsequently, the algorithm was applied to the entire dataset in order to retrospectively determine the absolute off-by-one vertebral body error rate for planar radiograph guided RT at our institution from 2011-2021.Main results. The AUCs for the kV models were 0.98 for unpaired AP and 0.99 for paired AP-LAT. The AUC for the MV AP model was 0.92. For a specificity of 95%, the paired kV model achieved a sensitivity of 99%. Application of the model to the entire dataset yielded a per-fraction off-by-one vertebral body error rate of 0.044% [0.0022%, 0.21%] for paired kV IGRT including one previously unreported error.Significance. Our error detection algorithm was successful in classifying vertebral body positioning errors with sufficient accuracy for retrospective quality control and real-time error prevention. The reported positioning error rate for planar radiograph IGRT is unique in being determined independently of an error reporting system.

PurposePresent knowledge of patient setup and alignment errors in image guided radiation therapy (IGRT) relies on voluntary reporting, which is thought to underestimate error frequencies. A manual retrospective patient-setup misalignment error search is infeasible owing to the bulk of cases to be reviewed. We applied a deep learning-based misalignment error detection algorithm (EDA) to perform a fully automated retrospective error search of clinical IGRT databases and determine an absolute gross patient misalignment error rate.Methods and materialsThe EDA was developed to analyze the registration between planning scans and pretreatment cone beam computed tomography scans, outputting a misalignment score ranging from 0 (most unlikely) to 1 (most likely). The algorithm was trained using simulated translational errors on a data set obtained from 680 patients treated at 2 radiation therapy clinics between 2017 and 2022. A receiver operating characteristic analysis was performed to obtain target thresholds. DICOM Query and Retrieval software was integrated with the EDA to interact with the clinical database and fully automate data retrieval and analysis during a retrospective error search from 2016 to 2017 and from 2021 to 2022 for the 2 institutions, respectively. Registrations were flagged for human review using both a hard-thresholding method and a prediction trending analysis over each individual patient's treatment course. Flagged registrations were manually reviewed and categorized as errors (>1 cm misalignment at the target) or nonerrors.ResultsA total of 17,612 registrations were analyzed by the EDA, resulting in 7.7% flagged events. Three previously reported errors were successfully flagged by the EDA, and 4 previously unreported vertebral body misalignment errors were discovered during case reviews. False positive cases often displayed substantial image artifacts, patient rotation, and soft tissue anatomy changes.ConclusionsOur results validated the clinical utility of the EDA for bulk image reviews and highlighted the reliability and safety of IGRT, with an absolute gross patient misalignment error rate of 0.04% ± 0.02% per delivered fraction.

Technological advances in MRI-guided radiation therapy (MRIgRT) have improved real-time visualization of the prostate and its surrounding structures over CT-guided radiation therapy. Seminal studies have demonstrated safe dose escalation achieved through ultrahypofractionation with MRIgRT due to planning target volume (PTV) margin reduction and treatment gating. On-table adaptation with MRI-based technologies can also incorporate real-time changes in target shape and volume and can reduce high doses of radiation to sensitive surrounding structures that may move into the treatment field. Ongoing clinical trials seek to refine ultrahypofractionated radiotherapy treatments for prostate cancer using MRIgRT. Though these studies have the potential to demonstrate improved biochemical control and reduced side effects, limitations concerning patient treatment times and operational workflows may preclude wide adoption of this technology outside of centers of excellence. In this review, we discuss the advantages and limitations of MRIgRT for prostate cancer, as well as clinical trials testing the efficacy and toxicity of ultrafractionation in patients with localized or post-prostatectomy recurrent prostate cancer.

PURPOSE: Automation and computer assistance can support quality assurance tasks in radiotherapy. Retrospective image review requires significant human resources, and automation of image review remains a noteworthy missing element in previous work. Here, we present initial findings from a proof-of-concept clinical implementation of an AI-assisted review of CBCT registrations used for patient setup. METHODS: An automated pipeline was developed and executed nightly, utilizing python scripts to interact with the clinical database through DICOM networking protocol and automate data retrieval and analysis. A previously developed artificial intelligence (AI) algorithm scored CBCT setup registrations based on misalignment likelihood, using a scale from 0 (most unlikely) through 1 (most likely). Over a 45-day period, 1357 pre-treatment CBCT registrations from 197 patients were retrieved and analyzed by the pipeline. Daily summary reports of the previous days registrations were produced. Initial action levels targeted 10% of cases to highlight for in-depth physics review. A validation subset of 100 cases was scored by three independent observers to characterize AI-model performance. RESULTS: Following an ROC analysis, a global threshold for model predictions of 0.87 was determined, with a sensitivity of 100% and specificity of 82%. Inspecting the observer scores for the stratified validation dataset showed a statistically significant correlation between observer scores and model predictions. CONCLUSION: In this work, we describe the implementation of an automated AI-analysis pipeline for daily quantitative analysis of CBCT-guided patient setup registrations. The AI-model was validated against independent expert observers, and appropriate action levels were determined to minimize false positives without sacrificing sensitivity. Case studies demonstrate the potential benefits of such a pipeline to bolster quality and safety programs in radiotherapy. To the authors knowledge, there are no previous works performing AI-assisted assessment of pre-treatment CBCT-based patient alignment.

Background and purposeCurrently, there is no robust indicator within the Cone-Beam Computed Tomography (CBCT) DICOM headers as to which anatomical region is present on the scan. This can be a predicament to CBCT-based algorithms trained on specific body regions, such as auto-segmentation and radiomics tools used in the radiotherapy workflow. We propose an anatomical region labeling (ARL) algorithm to classify CBCT scans into four distinct regions: head & neck, thoracic-abdominal, pelvis, and extremity.Materials and methodsAlgorithm training and testing was performed on 3,802 CBCT scans from 596 patients treated at our radiotherapy center. The ARL model, which consists of a convolutional neural network, makes use of a single CBCT coronal slice to output a probability of occurrence for each of the four classes. ARL was evaluated on the test dataset composed of 1,090 scans and compared to a support vector machine (SVM) model. ARL was also used to label CBCT treatment scans for 22 consecutive days as part of a proof-of-concept implementation. A validation study was performed on the first 100 unique patient scans to evaluate the functionality of the tool in the clinical setting.ResultsARL achieved an overall accuracy of 99.2% on the test dataset, outperforming the SVM (91.5% accuracy). Our validation study has shown strong agreement between the human annotations and ARL predictions, with accuracies of 99.0% for all four regions.ConclusionThe high classification accuracy demonstrated by ARL suggests that it may be employed as a pre-processing step for site-specific, CBCT-based radiotherapy tools.

BackgroundIn cone-beam computed tomography (CBCT)-guided radiotherapy, off-by-one vertebral-body misalignments are rare but serious errors that lead to wrong-site treatments.PurposeAn automatic error detection algorithm was developed that uses a three-branch convolutional neural network error detection model (EDM) to detect off-by-one vertebral-body misalignments using planning computed tomography (CT) images and setup CBCT images.MethodsAlgorithm training and test data consisted of planning CTs and CBCTs from 480 patients undergoing radiotherapy treatment in the thoracic and abdominal regions at two radiotherapy clinics. The clinically applied registration was used to derive true-negative (no error) data. The setup and planning images were then misaligned by one vertebral-body in both the superior and inferior directions, simulating the most likely misalignment scenarios. For each of the aligned and misaligned 3D image pairs, 2D slice pairs were automatically extracted in each anatomical plane about a point within the vertebral column. The three slice pairs obtained were then inputted to the EDM that returned a probability of vertebral misalignment. One model (EDM1 ) was trained solely on data from institution 1. EDM1 was further trained using a lower learning rate on a dataset from institution 2 to produce a fine-tuned model, EDM2 . Another model, EDM3 , was trained from scratch using a training dataset composed of data from both institutions. These three models were validated on a randomly selected and unseen dataset composed of images from both institutions, for a total of 303 image pairs. The model performances were quantified using a receiver operating characteristic analysis. Due to the rarity of vertebral-body misalignments in the clinic, a minimum threshold value yielding a specificity of at least 99% was selected. Using this threshold, the sensitivity was calculated for each model, on each institution's test set separately.ResultsWhen applied to the combined test set, EDM1 , EDM2 , and EDM3 resulted in an area under curve of 99.5%, 99.4%, and 99.5%, respectively. EDM1 achieved a sensitivity of 96% and 88% on Institution 1 and Institution 2 test set, respectively. EDM2 obtained a sensitivity of 95% on each institution's test set. EDM3 achieved a sensitivity of 95% and 88% on Institution 1 and Institution 2 test set, respectively.ConclusionThe proposed algorithm demonstrated accuracy in identifying off-by-one vertebral-body misalignments in CBCT-guided radiotherapy that was sufficiently high to allow for practical implementation. It was found that fine-tuning the model on a multi-facility dataset can further enhance the generalizability of the algorithm.

PurposeAccurate and robust auto-segmentation of highly deformable organs (HDOs), for example, stomach or bowel, remains an outstanding problem due to these organs' frequent and large anatomical variations. Yet, time-consuming manual segmentation of these organs presents a particular challenge to time-limited modern radiotherapy techniques such as on-line adaptive radiotherapy and high-dose-rate brachytherapy. We propose a machine-assisted interpolation (MAI) that uses prior information in the form of sparse manual delineations to facilitate rapid, accurate segmentation of the stomach from low field magnetic resonance images (MRI) and the bowel from computed tomography (CT) images.MethodsStomach MR images from 116 patients undergoing 0.35T MRI-guided abdominal radiotherapy and bowel CT images from 120 patients undergoing high dose rate pelvic brachytherapy treatment were collected. For each patient volume, the manual delineation of the HDO was extracted from every 8th slice. These manually drawn contours were first interpolated to obtain an initial estimate of the HDO contour. A two-channel 64 × 64 pixel patch-based convolutional neural network (CNN) was trained to localize the position of the organ's boundary on each slice within a five-pixel wide road using the image and interpolated contour estimate. This boundary prediction was then input, in conjunction with the image, to an organ closing CNN which output the final organ segmentation. A Dense-UNet architecture was used for both networks. The MAI algorithm was separately trained for the stomach segmentation and the bowel segmentation. Algorithm performance was compared against linear interpolation (LI) alone and against fully automated segmentation (FAS) using a Dense-UNet trained on the same datasets. The Dice Similarity Coefficient (DSC) and mean surface distance (MSD) metrics were used to compare the predictions from the three methods. Statistically significance was tested using Student's t test.ResultsFor the stomach segmentation, the mean DSC from MAI (0.91 ± 0.02) was 5.0% and 10.0% higher as compared to LI and FAS, respectively. The average MSD from MAI (0.77 ± 0.25 mm) was 0.54 and 3.19 mm lower compared to the two other methods. Only 7% of MAI stomach predictions resulted in a DSC

BackgroundStereotactic body radiotherapy (SBRT) is becoming increasingly used in treating localized prostate cancer (PCa), with evidence showing similar toxicity and efficacy profiles when compared with longer courses of definitive radiation. Magnetic resonance imaging (MRI)-guided radiotherapy has multiple potential advantages over standard computed tomography (CT)-guided radiotherapy, including enhanced prostate visualization (abrogating the need for fiducials and MRI fusion), enhanced identification of the urethra, the ability to track the prostate in real-time, and the capacity to perform online adaptive planning. However, it is unknown whether these potential advantages translate into improved outcomes. This phase III randomized superiority trial is designed to prospectively evaluate whether toxicity is lower after MRI-guided versus CT-guided SBRT.MethodsThree hundred men with localized PCa will be randomized in a 1:1 ratio to SBRT using CT or MRI guidance. Randomization will be stratified by baseline International Prostate Symptom Score (IPSS) (≤15 or > 15) and prostate gland volume (≤50 cc or > 50 cc). Five fractions of 8 Gy will be delivered to the prostate over the course of fourteen days, with or without hormonal therapy and elective nodal radiotherapy (to a dose of 5 Gy per fraction) as per the investigator's discretion. The primary endpoint is the incidence of physician-reported acute grade ≥ 2 genitourinary (GU) toxicity (during the first 90 days after SBRT), as assessed by the CTCAE version 4.03 scale. Secondary clinical endpoints include incidence of acute grade ≥ 2 gastrointestinal (GI) toxicity, 5-year cumulative incidences of physician-reported late grade ≥ 2 GU and GI toxicity, temporal changes in patient-reported quality of life (QOL) outcomes, 5-year biochemical recurrence-free survival and the proportion of fractions of MRI-guided SBRT in which online adaptive radiotherapy is used.DiscussionThe MIRAGE trial is the first randomized trial comparing MRI-guided with standard CT-guided SBRT for localized PCa. The primary hypothesis is that MRI-guided SBRT will lead to an improvement in the cumulative incidence of acute grade ≥ 2 GU toxicity when compared to CT-guided SBRT. The pragmatic superiority design focused on an acute toxicity endpoint will allow an early comparison of the two technologies.Trial registrationClinicaltrials.gov identifier: NCT04384770. Date of registration: May 12, 2020. https://clinicaltrials.gov/ct2/show/NCT04384770 PROTOCOL VERSION: Version 2.1, Aug 28, 2020.

Background and purposeStereotactic body radiation therapy (SBRT) has become a standard-of-care option for localized prostate cancer. While prostate SBRT has traditionally been delivered using computed-tomography-guided radiation therapy (CTgRT), MR-imaging-guided radiation therapy (MRgRT) is now available. MRgRT offers real-time soft-tissue visualization and ease of adaptive planning, obviating the need for fiducial markers, and potentially allowing for smaller planning target volume (PTV) margins. Although prior studies have focused on evaluating the cost-effectiveness of MRgRT vs CTgRT from a payor perspective, the difference in provider costs to deliver such treatments remains unknown. This study thus used time-driven activity-based costing (TDABC) to determine the difference in provider resources consumed by delivering prostate SBRT via MRgRT vs CTgRT.MethodsData was collected from a single academic institution where prostate SBRT is routinely performed using both CTgRT and MRgRT. Five-fraction SBRT (40 Gy total dose) was assumed to be delivered through volumetric-modulated arc therapy for CTgRT patients, and through step-and-shoot, fixed-gantry intensity-modulated radiation therapy for MRgRT patients. Process maps were constructed for each portion of the radiation delivery process via interviews/surveys with departmental personnel and by measuring CTgRT and MRgRT treatment times. Prior to simulation, only CTgRT patients underwent placement of three gold fiducial markers. Personnel capacity cost rates were calculated by dividing total personnel costs by the annual minutes worked by a given personnel. Equipment costs included both an annualized purchase price and annual maintenance costs. Ultimately, the total costs of care encompassing personnel, space/equipment, and materials were aggregated across the entire chain of care for both CTgRT and MRgRT patients in a base case.ResultsDirect costs associated with delivering a 5-fraction course of prostate SBRT were $1,497 higher with MRgRT than with CTgRT - comprised of personnel costs ($210 higher with MRgRT), space/equipment ($1,542 higher with MRgRT), and materials ($255 higher with CTgRT). Only CTgRT patients underwent fiducial placement, which accounted for $591. MRgRT patients were assumed to undergo both CT simulation (for electron density calculation) and MRI simulation, with the former accounting for $168. Mean time spent by patients in the treatment vault per fraction was 20 minutes (range 15-26 minutes) for CTgRT, and 31 minutes (range 30-34 minutes) for MRgRT. Patient time spent during fiducial placement (CTgRT only) was 60 minutes. Modifying the number of fractions treated would result in the cost difference of $1,497 (5 fractions) changing to $441 (1 fraction) or to $2,025 (7 fractions).ConclusionThis study provides an approximate comparison of the direct resources required for a radiation oncology provider to deliver prostate SBRT with CTgRT vs MRgRT. We await findings from the currently accruing phase III MIRAGE trial, which is comparing these modalities, and will subsequently measure acute and late genitourinary/gastrointestinal (GU/GI) toxicities, temporal change in quality-of-life outcomes, and 5-year biochemical, recurrence-free survival. Results from studies comparing the efficacy and safety of MRgRT vs CTgRT will ultimately allow us to put this cost difference into context.

PurposeStereotactic body radiotherapy (SBRT) delivers ablative doses with excellent local control. However, implementing SBRT for abdominal and pelvic tumors has been limited by the risk for treatment-related gastrointestinal toxicity. MRI-guided radiotherapy may ameliorate these risks and increase the therapeutic ratio. We report the clinical outcomes of stereotactic MRI-guided adaptive radiotherapy (SMART) for primary and metastatic tumors in the abdomen and pelvis.MethodsFrom November 2014 to August 2017, the first 106 consecutive patients with 121 tumors in the abdomen and pelvis were treated with SMART at a single institution. Of the cohort, 41.5%, 15.1%, and 43.4% had primary, locally recurrent, and oligometastatic tumors, respectively. SMART was delivered using a tri-cobalt-60 gantry with on-board 0.35 Tesla MRI with respiratory breath-hold and daily adaptive re-planning when anatomically necessary. A median of 40Gy in five fractions was prescribed. The Common Terminology Criteria for Adverse Events v.4.03 was used to score treatment-related toxicities. Local control (LC), progression-free survival (PFS), and overall survival (OS) were estimated using Kaplan-Meier method.ResultsOf the 510 treatments, seventy-one (13.9%) were adapted. Fatigue, nausea, and pain were the most common acute toxicities. 0.9 and 0% of patients experienced acute grade three and four toxicities, respectively. 5.2 and 2.1% of patients experienced late grade three and four toxicities, respectively. After a median follow-up of 20.4 months, the 2-year LC rate was 74% on a per-lesion basis. Two-year LC was 96% for lesions that were treated with BED10 ≥100 versus 69% for BED10

PurposeTo estimate the overall spatial distortion on clinical patient images for a 0.35 T MR-guided radiotherapy system.MethodsTen patients with head-and-neck cancer underwent CT and MR simulations with identical immobilization. The MR images underwent the standard systematic distortion correction post-processing. The images were rigidly registered and landmark-based analysis was performed by an anatomical expert. Distortion was quantified using Euclidean distance between each landmark pair and tagged by tissue interface: bone-tissue, soft tissue, or air-tissue. For baseline comparisons, an anthropomorphic phantom was imaged and analyzed.ResultsThe average spatial discrepancy between CT and MR landmarks was 1.15 ± 1.14 mm for the phantom and 1.46 ± 1.78 mm for patients. The error histogram peaked at 0-1 mm. 66% of the discrepancies were

PurposeStereotactic magnetic resonance image-guided adaptive radiation therapy (SMART) is an emerging technique that shows promise in the treatment of pancreatic cancer and other abdominopelvic malignancies. However, it is unknown whether the time-limited nature of on-table adaptive planning may result in dosimetrically suboptimal plans. The purpose of this study was to quantitatively address that question through systemic retrospective replanning of treated on-table adaptive pancreatic cancer cases.Methods and materialsOf 74 consecutive adapted fractions, 30 were retrospectively replanned based on deficiencies in planning target volume (PTV) and gross tumor volume (GTV) coverage or doses to organs-at-risk (OARs) that exceeded ideal constraints. Retrospective plans were created by adjusting dose-volume objectives in an iterative fashion until deemed optimized. The goal of replanning was to improve PTV/GTV coverage while keeping the dose to gastrointestinal OARs the same or lower or to reduce OAR doses while keeping PTV coverage the same or higher. The global maximum dose was required to be maintained within 2% of that of the treated adaptive plan to eliminate it as a confounding factor. A threshold of 5% improvement in PTV coverage or 5% decrease in OAR dose was used to define a clinically significant improvement.ResultsOf the 30 replans, 7 obtained at least 5% PTV coverage improvement. The average increase in PTV coverage for these plans was 11%. No plans were clinically significantly improved in terms of OAR sparing. Changes in beam-on time did not show any correlation. Statistical analysis via a linear mixed-effects model with a nested random effect suggested that both GTV and PTV coverage were improved over SMART process plans by 0.91 cc (P = .02) and 2.03 cc (P < .001), respectively.ConclusionsDosimetric plan quality of at least 10% of SMART fractions may be improved through more extensive replanning than is currently performed on-table. Further work is needed to develop an automated replanning workflow to streamline the in-depth replanning process to better fit into an on-table adaptive workflow.

PurposeThe ambulatory patient experience is heavily influenced by wait times for provider care. Delayed patient visit start times may negatively affect overall satisfaction, and increased wait times affect the perception of the information, instructions, and treatment given by health care providers. Improving institutional practices overall requires the determination of the essential quality metrics that will make such an achievement possible. A protracted time leading up to the initiation of radiation therapy may promote poor satisfaction and perceived quality of care for both patients and referring providers alike, which may then create a barrier to patients being treated with radiation therapy. This institution piloted and sucessfully completed a study into improving the timeliness of initiation of patient radiation therapy for our patients.Methods and materialsThis work sought to identify inefficiencies in radiation therapy treatment planning to shorten the time each patient waited for treatment. We examined the time between simulation to the start of the first fraction of treatment. This period includes simulation, contouring, treatment planning, and quality assurance of the plan.ResultsBefore the study, the planning process would typically take 2 weeks. Target and organs-at-risk contouring were found to be the main inefficiency delaying treatment start dates. This delineating process includes drawing contours on radiologic images, typically computed tomography and magnetic resonance imaging. We focused on the time needed for the contouring process to be completed and took steps to increase efficiency. The length of time from simulation to contour approval was decreased by more than 60%, a reduction from an average of more than 4 days to less than 1.5 days. Overall planning time dropped from 2 weeks to less than 5 days.ConclusionsProcess improvements and implementation of task-specific tools improved the timeliness of patient treatments, reducing the overall planning time from simulation to treatments to less than 5 days. Continuous monitoring and modification of these processes revealed that the successes achieved toward better quality of care have been sustained.

Proton radiotherapy has clear dosimetric advantages over photon radiotherapy. In contrast to photons, which are absorbed exponentially, protons have a finite range dependent on the initial proton energy. Protons therefore do not deposit dose beyond the tumor, resulting in great conformality, and offers the promise of dose escalation to increase tumor control while minimizing toxicity. In this review, we discuss the rationale for using proton radiotherapy in the treatment of upper abdominal tumors-hepatocellular carcinomas, cholangiocarcinomas and pancreatic cancers. We also review the clinical outcomes and technical challenges of using proton radiotherapy for the treatment of these malignancies. Finally, we discuss the ongoing clinical trials implementing proton radiotherapy for the treatment of primary liver and pancreatic tumors.

Purpose:Previous studies have shown that stereotactic ablative radiation therapy (SABR) increases local control for cholangiocarcinoma, but gastrointestinal toxicity resulting from this treatment approach remains a concern. SABR using magnetic resonance-guided radiation therapy (MRgRT) may improve the therapeutic ratio of treatment for cholangiocarcinoma patients given the radiosensitivity of neighboring gastrointestinal organs. Methods:Seventeen consecutive patients with unresectable locally advanced cholangiocarcinoma were treated with SABR using MRgRT between May 2015 and August 2017, subsequent to our previously reported series of patients treated using a standard Linac with cone beam computed tomography. Twelve patients presented with extrahepatic cholangiocarcinoma and 5 patients with intrahepatic tumors. MRgRT-based SABR was administered at a median dose of 40 Gy in 5 fractions. Results:The median overall survival (OS) was 18.5 months, with a 1-year OS of 76% and 2-year OS of 46.1%. Three of the 17 patients progressed locally, yielding a 1-year local control of 85.6% and a 2-year local control of 73.3%. Although 12 of 17 patients experienced an acute grade 1 toxicity, none experienced acute grade 2 toxicities. One patient had an acute grade 3 duodenal ulcer with perforation (6%), and one patient had a late radiation-related toxicity grade 2 gastritis/colitis. Conclusions:Our findings demonstrate diminished toxicity and excellent overall survival and local control. The clinical outcomes and safety profile of SABR delivered with MRgRT suggest that MRgRT is a promising treatment approach for treating cholangiocarcinoma.

PurposeOn-board magnetic resonance imaging (MRI) greatly enhances real-time target tracking capability during radiotherapy treatments. However, multislice and volumetric MRI techniques are frame rate limited and introduce unacceptable latency between the target moving out of position and the beam being turned off. We present a technique to estimate continuous volumetric tissue motion using motion models built from a repeated acquisition of a stack of MR slices. Applications including multislice target visualization and out-of-slice motion estimation during MRI-guided radiotherapy are demonstrated.MethodsEight healthy volunteer studies were performed using a 0.35 T MRI-guided radiotherapy system. Images were acquired at three frames per second in an interleaved fashion across ten adjacent sagittal slice positions covering 4.5 cm using a balanced steady-state-free precession sequence. A previously published five-dimensional (5D) linear motion model used for MRI-guided radiotherapy gating was extended to include multiple slices. This model utilizes an external respiratory bellows signal recorded during imaging to simultaneously estimate motion across all imaged slices. For comparison to an image-based approach, the manifold learning technique local linear embedding (LLE) was used to derive a respiratory surrogate for motion modeling. Manifolds for every slice were aligned during LLE in a group-wise fashion, enabling motion estimation outside the current imaged slice using a motion model, a process which we denote as mSGA. Additionally, a method is developed to evaluate out-of-slice motion estimates. The multislice motion model was evaluated in a single slice with each newly acquired image using a leave-one-out approach. Model-generated gating decision accuracy and beam-on positive predictive value (PPV) are reported along with the median and 95th percentile distance between model and ground truth target centroids.ResultsThe average model gating decision accuracy and PPV across all volunteer studies was 93.7% and 92.8% using the 5D model, and 96.8% and 96.1% using the mSGA model, respectively. The median and 95th percentile distance between model and ground truth target centroids was 0.91 and 2.90 mm, respectively, using the 5D model and 0.58 and 1.49 mm using the mSGA model, averaged over all eight subjects. The mSGA motion model provided a statistically significant improvement across all evaluation metrics compared to the external surrogate-based 5D model.ConclusionThe proposed techniques for out-of-slice target motion estimation demonstrated accuracy likely sufficient for clinical use. Results indicate the mSGA model may provide higher accuracy, however, the external surrogate-based model allows for unbiased in vivo accuracy evaluation.

PurposeTo develop and validate a technique for radiation therapy gating using slow (≤1 frame per second) magnetic resonance imaging (MRI) and a motion model. Proposed uses of the technique include radiation therapy gating using T2-weighted images and conducting additional imaging studies during gated treatments.Methods and materialsThe technique uses a physiologically guided breathing motion model to interpolate deformed target position between 2-dimensional (2D) MRI images acquired every 1 to 3 seconds. The model is parameterized by a 1-dimensional respiratory bellows surrogate and is continuously updated with the most recently acquired 2D images. A phantom and 8 volunteers were imaged with a 0.35T MRI-guided radiation therapy system. A balanced steady-state free precession sequence with a 2D frame rate of 3 frames per second was used to evaluate the technique. The accuracy and beam-on positive predictive value (PPV) of the model-based gating decisions were evaluated using the gating decisions derived from imaging as a ground truth. A T2-weighted gating offline proof-of-concept study using a half-Fourier, single-shot, turbo-spin echo sequence is reported.ResultsModel-interpolated gating accuracy, beam-on PPV, and median absolute distances between model and image-tracked target centroids were, on average, 98.3%, 98.4%, and 0.33 mm, respectively, in the balanced steady-state free precession phantom studies and 93.7%, 92.1%, and 0.86 mm, respectively, in the volunteer studies. T2 model-interpolated gating in 6 volunteers yielded an average accuracy and PPV of 94.3% and 92.5%, respectively, and the mean absolute median distance between modeled and imaged target centroids was 0.86 mm.ConclusionsThis work demonstrates the concept of model-interpolated gating for MRI-guided radiation therapy. The technique was found to be potentially sufficiently accurate for clinical use. Further development is needed to accommodate out-of-plane motion and the use of an internal MR-based respiratory surrogate.

Locoregional recurrence in the pelvis after definitive treatment for rectal cancer can lead to significant morbidity. Furthermore, the toxicity associated with reirradiation may also negatively impact the quality of life and even survival. Here we present the case of a 39-year-old male with locoregionally recurrent rectal cancer in a left pelvic sidewall lymph node, treated with stereotactic magnetic resonance (MR)-guided ablative radiotherapy after previously receiving long-course chemoradiation that had already exceeded ideal bowel dose constraints. We discuss the distinct advantages of MR-guidance in the setting of pelvic reirradiation, particularly with regard to inter- and intra-fraction visualization of the target and neighboring bowel anatomy. In this context, MR-guidance may allow radiation oncologists to increase target precision and accuracy, while simultaneously decreasing toxicity to neighboring tissues.

We present a case of durable local control achieved in a patient treated with stereotactic magnetic resonance-guided adaptive radiation therapy (SMART) for an abdominal lymph node in the setting of oligometastatic breast cancer. A 50-year-old woman with a history of triple positive metastatic invasive ductal carcinoma of the left breast, stage IV (T3N2M1), underwent neoadjuvant chemotherapy, mastectomy, adjuvant radiotherapy and maintenance hormonal treatment with HER2 targeted therapies. At 20 months after definitive treatment of her primary, imaging showed an isolated progressive enlargement of lymph nodes between hepatic segment V/IVB and the neck of the pancreas. Radiofrequency ablation was considered, however, this approach was decided not to be optimal due to the proximity to stomach, and pancreatic duct. The patient was treated with SMART for 40 Gray in 5 fractions. Two and a half years later, the patient remains without evidence of disease progression. She experienced Grade 2 acute and late toxicity that was successfully managed with medications. This experience shows that SMART is a feasible and effective treatment to control the abdominal oligometastatic disease for breast cancer.

Locally advanced pancreatic cancer (LAPC) is characterized by poor prognosis and low response durability with standard-of-care chemotherapy or chemoradiotherapy treatment. Stereotactic body radiation therapy (SBRT), which has a shorter treatment course than conventionally fractionated radiotherapy and allows for better integration with systemic therapy, may confer a survival benefit but is limited by gastrointestinal toxicity. Stereotactic MRI-guided adaptive radiation therapy (SMART) has recently gained attention for its potential to increase treatment precision and thus minimize this toxicity through continuous real-time soft-tissue imaging during radiotherapy. The case presented here illustrates the promising outcome of a 69-year-old male patient with LAPC treated with SMART with daily adaptive planning and respiratory-gated technique.

ObjectiveA recently proposed "5DCT" protocol uses deformable registration of free-breathing fast-helical CT scans to generate a breathing motion model. In order to allow accurate registration, free-breathing images are required to be free of doubling-artefacts, which arise when tissue motion is greater than scan speed.MethodsUsing a unique set of digital phantoms based on patient data and verified with a motion phantom, this work identifies the minimum scanner parameters required to successfully generate free-breathing artefact-free fast-helical scans. A motion phantom and 5 patients were imaged 25 times under free-breathing conditions in alternating directions with a 64-slice CT scanner employing a low-dose fast-helical protocol. A series of high temporal resolution (0.1 s) 5DCT scan data sets was generated in each case. A simulated CT scanner was used to "image" each free-breathing data set. Various CT scanner detector widths and rotation times were simulated, and verified using the motion phantom results. Motion-induced artefacts were quantified in patient images using structural similarity maps to determine the similarity between axial slices.ResultsIncreasing amounts of motion-induced artefacts were observed with increasing rotation times >0.2 s for 16 mm detector configuration.ConclusionThe current generation of 16-slice CT scanners, which are present in the majority of Radiation Oncology departments, are not capable of generating free-breathing sorting artefact-free images required for 5DCT. Advances in knowledge: A recently proposed "5DCT" protocol uses deformable registration of free-breathing fast-helical CT scans to generate a breathing motion model. In order to allow accurate registration, free-breathing images are required to be free of doubling-artefacts, which arise when tissue motion is greater than scan speed. The results suggest that the current generation of 16-slice CT scanners, present in the majority of Radiation Oncology departments, are not capable of generating the free-breathing images required for 5DCT.

OBJECTIVE:To evaluate variations in intra- and interfractional tumour motion, and the effect on internal target volume (ITV) contour accuracy, using deformable image registration of real-time two-dimensional-sagittal cine-mode MRI acquired during lung stereotactic body radiation therapy (SBRT) treatments. METHODS:Five lung tumour patients underwent free-breathing SBRT treatments on the ViewRay system, with dose prescribed to a planning target volume (defined as a 3-6 mm expansion of the 4DCT-ITV). Sagittal slice cine-MR images (3.5 × 3.5 mm2 pixels) were acquired through the centre of the tumour at 4 frames per second throughout the treatments (3-4 fractions of 21-32 min). Tumour gross tumour volumes (GTVs) were contoured on the first frame of the MR cine and tracked for the first 20 min of each treatment using offline optical-flow based deformable registration implemented on a GPU cluster. A ground truth ITV (MR-ITV20 min) was formed by taking the union of tracked GTV contours. Pseudo-ITVs were generated from unions of the GTV contours tracked over 10 s segments of image data (MR-ITV10 s). RESULTS:Differences were observed in the magnitude of median tumour displacement between days of treatments. MR-ITV10 s areas were as small as 46% of the MR-ITV20 min. CONCLUSION:An ITV offers a "snapshot" of breathing motion for the brief period of time the tumour is imaged on a specific day. Real-time MRI over prolonged periods of time and over multiple treatment fractions shows that ITV size varies. Further work is required to investigate the dosimetric effect of these results. Advances in knowledge: Five lung tumour patients underwent free-breathing MRI-guided SBRT treatments, and their tumours tracked using deformable registration of cine-mode MRI. The results indicate that variability of both intra- and interfractional breathing amplitude should be taken into account during planning of lung radiotherapy.

Onboard magnetic resonance imaging (MRI) guided radiotherapy is now clinically available in nine centers in the world. This technology has facilitated the clinical implementation of online adaptive radiotherapy (OART), or the ability to alter the daily treatment plan based on tumor and anatomical changes in real-time while the patient is on the treatment table. However, due to the time sensitive nature of OART, implementation in a large and busy clinic has many potential obstacles as well as patient-related safety considerations. In this work, we have described the implementation of this new process of care in the Department of Radiation Oncology at the University of California, Los Angeles (UCLA). We describe the rationale, the initial challenges such as treatment time considerations, technical issues during the process of re-contouring, re-optimization, quality assurance, as well as our current solutions to overcome these challenges. In addition, we describe the implementation of a coverage system with a physician of the day as well as online planners (physicists or dosimetrists) to oversee each OART treatment with patient-specific 'hand-off' directives from the patient's treating physician. The purpose of this effort is to streamline the process without compromising treatment quality and patient safety. As more MRI-guided radiotherapy programs come online, we hope that our experience can facilitate successful adoption of OART in a way that maximally benefits the patient.

Spatial distortion results in image deformation that can degrade accurate targeting and dose calculations in MRI-guided adaptive radiotherapy. The authors present a comprehensive assessment of a 0.35 T MRI-guided radiotherapy system's spatial distortion using two commercially-available phantoms with regularly spaced markers. Images of the spatial integrity phantoms were acquired using five clinical protocols on the MRI-guided radiotherapy machine with the radiotherapy gantry positioned at various angles. Software was developed to identify and localize all phantom markers using a template matching approach. Rotational and translational corrections were implemented to account for imperfect phantom alignment. Measurements were made to assess uncertainties arising from susceptibility artifacts, image noise, and phantom construction accuracy. For a clinical 3D imaging protocol with a 1.5 mm reconstructed slice thickness, 100% of spheres within a 50 mm radius of isocenter had a 3D deviation of 1 mm or less. Of the spheres within 100 mm of isocenter, 99.9% had a 3D deviation less than 1 mm. 94.8% and 100% of the spheres within 175 mm were found to be within 1 mm and 2 mm of the expected positions in 3D respectively. Maximum 3D distortions within 50 mm, 100 mm and 175 mm of isocenter were 0.76 mm, 1.15 mm and 1.88 mm respectively. Distortions present in images acquired using the real-time imaging sequence were less than 1 mm for 98.1% and 95.0% of the cylinders within 50 mm and 100 mm of isocenter. The corresponding maximum distortion in these regions was 1.10 mm and 1.67 mm. These results may be used to inform appropriate planning target volume (PTV) margins for 0.35 T MRI-guided radiotherapy. Observed levels of spatial distortion should be explicitly considered when using PTV margins of 3 mm or less or in the case of targets displaced from isocenter by more than 50 mm.

PurposeMagnetic resonance image (MRI) guided radiotherapy enables gating directly on the target position. We present an evaluation of an MRI-guided radiotherapy system's gating performance using an MRI-compatible respiratory motion phantom and radiochromic film. Our evaluation is geared toward validation of our institution's clinical gating protocol which involves planning to a target volume formed by expanding 5 mm about the gross tumor volume (GTV) and gating based on a 3 mm window about the GTV.MethodsThe motion phantom consisted of a target rod containing high-contrast target inserts which moved in the superior-inferior direction inside a body structure containing background contrast material. The target rod was equipped with a radiochromic film insert. Treatment plans were generated for a 3 cm diameter spherical planning target volume, and delivered to the phantom at rest and in motion with and without gating. Both sinusoidal trajectories and tumor trajectories measured during MRI-guided treatments were used. Similarity of the gated dose distribution to the planned, motion-frozen, distribution was quantified using the gamma technique.ResultsWithout gating, gamma pass rates using 4%/3 mm criteria were 22-59% depending on motion trajectory. Using our clinical standard of repeated breath holds and a gating window of 3 mm with 10% target allowed outside the gating boundary, the gamma pass rate was 97.8% with 3%/3 mm gamma criteria. Using a 3 mm window and 10% allowed excursion, all of the patient tumor motion trajectories at actual speed resulting in at least 95% gamma pass rate at 4%/3 mm.ConclusionsOur results suggest that the device can be used to compensate respiratory motion using a 3 mm gating margin and 10% allowed excursion results in conjunction with repeated breath holds. Full clinical validation requires a comprehensive evaluation of tracking performance in actual patient images, outside the scope of this study.

PurposeTo report a single-institutional experience using magnetic resonance imaging (MRI) guided radiation therapy for the reirradiation of recurrent and second cancers of the head and neck.Methods and materialsBetween October 2014 and August 2016, 13 consecutive patients with recurrent or new primary cancers of the head and neck that occurred in a previously irradiated field were prospectively enrolled in an institutional registry trial to investigate the feasibility and efficacy of MRI guided radiation therapy using a 0.35-T MRI scanner with a cobalt-60 radiation therapy source called the ViewRay system (ViewRay Inc., Cleveland, OH). Eligibility criteria included biopsy-proven evidence of recurrent or new primary squamous cell carcinoma of the head and neck, measurable disease, and previous radiation to >60 Gy. MRI guided reirradiation was delivered either using intensity modulated radiation therapy with conventional fractionation to a median dose of 66 Gy or stereotactic body radiation therapy (SBRT) using 7 to 8 Gy fractions on nonconsecutive days to a median dose of 40 Gy. Two patients (17%) received concurrent chemotherapy.ResultsThe 1- and 2-year estimates of in-field control were 72% and 72%, respectively. A total of 227 daily MRI scans were obtained to guide reirradiation. The 2-year estimates of overall survival and progression-free survival were 53% and 59%, respectively. There were no treatment-related fatalities or hospitalizations. Complications included skin desquamation, odynophagia, otitis externa, keratitis and/or conjunctivitis, and 1 case of aspiration pneumonia.ConclusionsOur preliminary findings show that reirradiation with MRI guided radiation therapy results in effective disease control with relatively low morbidity for patients with recurrent and second primary cancers of the head and neck. The superior soft tissue resolution of the MRI scans that were used for planning and delivery has the potential to improve the therapeutic ratio.

ObjectiveTo evaluate the feasibility of on-board diffusion-weighted imaging (DWI) with an integrated low-field MRI radiotherapy system to assess responses to neoadjuvant chemoradiation (NAC) in rectal cancer.MethodsA spin echo-based planar imaging diffusion sequence on a 0.35-T MRI radiotherapy system was acquired over the course of NAC. The apparent diffusion coefficients (ADCs) from the tumour regions of interest (ROIs) were calculated. A functional diffusion map (fDM) was created showing a pixelwise ADC analysis of the ROI over the course of treatment. Surgical pathology was correlated with ADC data.ResultsConsecutive patients treated on a 0.35-T MRI radiotherapy system were evaluated. Patient A had the worst pathological response to NAC with a tumour regression score of 1 and was the only patient with a negative slope in the change of ADC values over the entire course of NAC, and during both the first and second half of NAC. The fDM from the first half of NAC for Patient A showed discrete dark areas in the tumour ROI, reflecting subregions with decreasing ADC values during NAC. Patient C had the most favourable pathological response to NAC with a Grade 3 response and was the only patient who had an increase in the slope in the change of ADC values from the first to the second half of NAC.ConclusionDWI using a low-field MRI radiotherapy system for evaluating the responses to NAC is feasible. Advances in knowledge: ADC values obtained using a 0.35-T MRI radiotherapy system over the course of NAC for rectal cancer correlate with pathological responses.

PurposeTo demonstrate the preliminary feasibility of a longitudinal diffusion magnetic resonance imaging (MRI) strategy for assessing patient response to radiotherapy at 0.35 T using an MRI-guided radiotherapy system (ViewRay).MethodsSix patients (three head and neck cancer, three sarcoma) who underwent fractionated radiotherapy were enrolled in this study. A 2D multislice spin echo single-shot echo planar imaging diffusion pulse sequence was implemented on the ViewRay system and tested in phantom studies. The same pulse sequence was used to acquire longitudinal diffusion data (every 2-5 fractions) on the six patients throughout the entire course of radiotherapy. The reproducibility of the apparent diffusion coefficient (ADC) measurements was assessed using reference regions and the temporal variations of the tumor ADC values were evaluated.ResultsIn diffusion phantom studies, the ADC values measured on the ViewRay system matched well with reference ADC values with

PurposeTo develop a technique that assesses the accuracy of the breathing phase-specific volume image generation process by patient-specific breathing motion model using the original free-breathing computed tomographic (CT) scans as ground truths.MethodsSixteen lung cancer patients underwent a previously published protocol in which 25 free-breathing fast helical CT scans were acquired with a simultaneous breathing surrogate. A patient-specific motion model was constructed based on the tissue displacements determined by a state-of-the-art deformable image registration. The first image was arbitrarily selected as the reference image. The motion model was used, along with the free-breathing phase information of the original 25 image datasets, to generate a set of deformation vector fields that mapped the reference image to the 24 nonreference images. The high-pitch helically acquired original scans served as ground truths because they captured the instantaneous tissue positions during free breathing. Image similarity between the simulated and the original scans was assessed using deformable registration that evaluated the pointwise discordance throughout the lungs.ResultsQualitative comparisons using image overlays showed excellent agreement between the simulated images and the original images. Even large 2-cm diaphragm displacements were very well modeled, as was sliding motion across the lung-chest wall boundary. The mean error across the patient cohort was 1.15 ± 0.37 mm, and the mean 95th percentile error was 2.47 ± 0.78 mm.ConclusionThe proposed ground truth-based technique provided voxel-by-voxel accuracy analysis that could identify organ-specific or tumor-specific motion modeling errors for treatment planning. Despite a large variety of breathing patterns and lung deformations during the free-breathing scanning session, the 5-dimensionl CT technique was able to accurately reproduce the original helical CT scans, suggesting its applicability to a wide range of patients.

PurposeTo determine if and by how much the commercial 4DCT protocols under- and overestimate tumor breathing motion.Methods1D simulations were conducted that modeled a 16-slice CT scanner and tumors moving proportionally to breathing amplitude. External breathing surrogate traces of at least 5-min duration for 50 patients were used. Breathing trace amplitudes were converted to motion by relating the nominal tumor motion to the 90th percentile breathing amplitude, reflecting motion defined by the more recent 5DCT approach. Based on clinical low-pitch helical CT acquisition, the CT detector moved according to its velocity while the tumor moved according to the breathing trace. When the CT scanner overlapped the tumor, the overlapping slices were identified as having imaged the tumor. This process was repeated starting at successive 0.1 s time bin in the breathing trace until there was insufficient breathing trace to complete the simulation. The tumor size was subtracted from the distance between the most superior and inferior tumor positions to determine the measured tumor motion for that specific simulation. The effect of the scanning parameter variation was evaluated using two commercial 4DCT protocols with different pitch values. Because clinical 4DCT scan sessions would yield a single tumor motion displacement measurement for each patient, errors in the tumor motion measurement were considered systematic. The mean of largest 5% and smallest 5% of the measured motions was selected to identify over- and underdetermined motion amplitudes, respectively. The process was repeated for tumor motions of 1-4 cm in 1 cm increments and for tumor sizes of 1-4 cm in 1 cm increments.ResultsIn the examined patient cohort, simulation using pitch of 0.06 showed that 30% of the patients exhibited a 5% chance of mean breathing amplitude overestimations of 47%, while 30% showed a 5% chance of mean breathing amplitude underestimations of 36%; with a separate simulation using pitch of 0.1 showing, respectively, 37% overestimation and 61% underestimation.ConclusionsThe simulation indicates that commercial low-pitch helical 4DCT processes potentially yield large tumor motion measurement errors, both over- and underestimating the tumor motion.

PurposeTo demonstrate that a "5DCT" technique which utilizes fast helical acquisition yields the same respiratory-gated images as a commercial technique for regular, mechanically produced breathing cycles.MethodsRespiratory-gated images of an anesthetized, mechanically ventilated pig were generated using a Siemens low-pitch helical protocol and 5DCT for a range of breathing rates and amplitudes and with standard and low dose imaging protocols. 5DCT reconstructions were independently evaluated by measuring the distances between tissue positions predicted by a 5D motion model and those measured using deformable registration, as well by reconstructing the originally acquired scans. Discrepancies between the 5DCT and commercial reconstructions were measured using landmark correspondences.ResultsThe mean distance between model predicted tissue positions and deformably registered tissue positions over the nine datasets was 0.65 ± 0.28 mm. Reconstructions of the original scans were on average accurate to 0.78 ± 0.57 mm. Mean landmark displacement between the commercial and 5DCT images was 1.76 ± 1.25 mm while the maximum lung tissue motion over the breathing cycle had a mean value of 27.2 ± 4.6 mm. An image composed of the average of 30 deformably registered images acquired with a low dose protocol had 6 HU image noise (single standard deviation) in the heart versus 31 HU for the commercial images.ConclusionsAn end to end evaluation of the 5DCT technique was conducted through landmark based comparison to breathing gated images acquired with a commercial protocol under highly regular ventilation. The techniques were found to agree to within 2 mm for most respiratory phases and most points in the lung.

PurposeTo develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing motion model to remove sorting artifacts.Methods and materialsTen patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific motion model parameters were determined using a breathing motion model. The tissue locations predicted by the motion model in the 25 images were compared against the deformably registered tissue locations, allowing a model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The motion model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling.ResultsImages produced using the model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing motion model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm.ConclusionsThe proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact-free images at a patient dose similar to or less than current 4D-CT techniques.

PurposeThe purpose of this work is to develop a cardiac-induced lung motion model to be integrated into an existing breathing motion model.MethodsThe authors' proposed cardiac-induced lung motion model represents the lung tissue's specific response to the subject's cardiac cycle. The model is mathematically defined as a product of a converging polynomial function h of the cardiac phase (c) and the maximum displacement y(X0) of each voxel (X0) among all the cardiac phases. The function h(c) was estimated from cardiac-gated MR imaging of ten healthy volunteers using an Akaike Information Criteria optimization algorithm. For each volunteer, a total of 24 short-axis and 18 radial planar views were acquired on a 1.5 T MR scanner during a series of 12-15 s breath-hold maneuvers. Each view contained 30 temporal frames of equal time-duration beginning with the end-diastolic cardiac phase. The frames in each of the planar views were resampled to create a set of three-dimensional (3D) anatomical volumes representing thoracic anatomy at different cardiac phases. A 3D multiresolution optical flow deformable image registration algorithm was used to quantify the difference in tissue position between the end-diastolic cardiac phase and the remaining cardiac phases. To account for image noise, voxel displacements whose maximum values were less than 0.3 mm, were excluded. In addition, the blood vessels were segmented and excluded in order to eliminate registration artifacts caused by blood-flow.ResultsThe average cardiac-induced lung motions for displacements greater than 0.3 mm were found to be 0.86 ± 0.74 and 0.97 ± 0.93 mm in the left and right lungs, respectively. The average model residual error for the ten healthy volunteers was found to be 0.29 ± 0.08 mm in the left lung and 0.38 ± 0.14 mm in the right lung for tissue displacements greater than 0.3 mm. The relative error decreased with increasing cardiac-induced lung tissue motion. While the relative error was > 60% for submillimeter cardiac-induced lung tissue motion, the relative error decreased to < 5% for cardiac-induced lung tissue motion that exceeded 10 mm in displacement.ConclusionsThe authors' studies implied that modeling and including cardiac-induced lung motion would improve breathing motion model accuracy for tissues with cardiac-induced motion greater than 0.3 mm.

PurposeTo characterize radiation therapy patient breathing patterns based on measured external surrogate information.MethodsBreathing surrogate data were collected during 4DCT from a cohort of 50 patients including 28 patients with lung cancer and 22 patients without lung cancer. A spirometer and an abdominal pneumatic bellows were used as the surrogates. The relationship between these measurements was assumed to be linear within a small phase difference. The signals were correlated and drift corrected using a previously published method to convert the signal into tidal volume. The airflow was calculated with a first order time derivative of the tidal volume using a window centered on the point of interest and with a window length equal to the CT gantry rotation period. The airflow was compared against the tidal volume to create ellipsoidal patterns that were binned into 25 ml × 25 ml∕s bins to determine the relative amount of time spent in each bin. To calculate the variability of the maximum inhalation tidal volume within a free-breathing scan timeframe, a metric based on percentile volume ratios was defined. The free breathing variability metric (κ) was defined as the ratio between extreme inhalation tidal volumes (defined as >93 tidal volume percentile of the measured tidal volume) and normal inhalation tidal volume (defined as >80 tidal volume percentile of the measured tidal volume).ResultsThere were three observed types of volume-flow curves, labeled Types 1, 2, and 3. Type 1 patients spent a greater duration of time during exhalation with κ = 1.37 ± 0.11. Type 2 patients had equal time duration spent during inhalation and exhalation with κ = 1.28 ± 0.09. The differences between the mean peak exhalation to peak inhalation tidal volume, breathing period, and the 85th tidal volume percentile for Type 1 and Type 2 patients were statistically significant at the 2% significance level. The difference between κ and the 98th tidal volume percentile for Type 1 and Type 2 patients was found to be statistically significant at the 1% significance level. Three patients did not display a breathing stability curve that could be classified as Type 1 or Type 2 due to chaotic breathing patterns. These patients were classified as Type 3 patients.ConclusionsBased on an observed volume-flow curve pattern, the cohort of 50 patients was divided into three categories called Type 1, Type 2, and Type 3. There were statistically significant differences in breathing characteristics between Type 1 and Type 2 patients. The use of volume-flow curves to classify patients has been demonstrated as a physiological characterization metric that has the potential to optimize gating windows in radiation therapy.

PurposeTo quantitatively compare the accuracy of tumor volume segmentation in amplitude-based and phase-based respiratory gating algorithms in respiratory-correlated positron emission tomography (PET).Methods and materialsList-mode fluorodeoxyglucose-PET data was acquired for 10 patients with a total of 12 fluorodeoxyglucose-avid tumors and 9 lymph nodes. Additionally, a phantom experiment was performed in which 4 plastic butyrate spheres with inner diameters ranging from 1 to 4 cm were imaged as they underwent 1-dimensional motion based on 2 measured patient breathing trajectories. PET list-mode data were gated into 8 bins using 2 amplitude-based (equal amplitude bins [A1] and equal counts per bin [A2]) and 2 temporal phase-based gating algorithms. Gated images were segmented using a commercially available gradient-based technique and a fixed 40% threshold of maximum uptake. Internal target volumes (ITVs) were generated by taking the union of all 8 contours per gated image. Segmented phantom ITVs were compared with their respective ground-truth ITVs, defined as the volume subtended by the tumor model positions covering 99% of breathing amplitude. Superior-inferior distances between sphere centroids in the end-inhale and end-exhale phases were also calculated.ResultsTumor ITVs from amplitude-based methods were significantly larger than those from temporal-based techniques (P=.002). For lymph nodes, A2 resulted in ITVs that were significantly larger than either of the temporal-based techniques (P

Background and purposeTo quantify the benefit of motion-specific internal target volumes for FDG-avid mediastinal and hilar lymph nodes generated using 4D-PET, vs. conventional internal target volumes generated using non-respiratory gated PET and 4D-CT scans.Materials and methodsFive patients with FDG-avid tumors metastatic to 11 hilar or mediastinal lymph nodes were imaged with respiratory-correlated FDG-PET (4D-PET) and 4D-CT. FDG-avid nodes were contoured by a radiation oncologist in two ways. Standard-of-care volumes were contoured using conventional un-gated PET, 4D-CT, and breath-hold CT. A second, motion-specific, set of volumes were contoured using 4D-PET.Contours based on 4D-PET corresponded directly to an internal target volume (ITV(4D)), whereas contours based on un-gated PET were expanded by a series of exploratory isotropic margins (from 5 to 13 mm) based on literature recommendations on lymph node motion to form internal target volumes (ITV(3D)).ResultsA 13 mm expansion of the un-gated PET nodal volume was needed to cover the ITV(4D) for 10 of 11 nodes studied. The ITV(3D) based on a 13 mm expansion included on average 45 cm(3) of tissue that was not included in the ITV(4D).ConclusionsMotion-specific lymph-node internal target volumes generated from 4D-PET imaging could be used to improve accuracy and/or reduce normal-tissue irradiation compared to the standard-of-care un-gated PET based internal target volumes.