Real-time analysis and display of quantitative measures to track and improve clinical workflow.

PURPOSE: Radiotherapy treatment planning is a complex process with multiple, dependent steps involving an interdisciplinary patient care team. Effective communication and real-time tracking of resources and care path activities are key for clinical efficiency and patient safety. MATERIALS AND METHODS: We designed and implemented a secure, interactive web-based dashboard for patient care path, clinical workflow, and resource utilization management. The dashboard enables visualization of resource utilization and tracks progress in a patient's care path from the time of acquisition of the planning CT to the time of treatment in real-time. It integrates with the departmental electronic medical records (EMR) system without the creation and maintenance of a separate database or duplication of work by clinical staff. Performance measures of workflow were calculated. RESULTS: The dashboard implements a standardized clinical workflow and dynamically consolidates real-time information queried from multiple tables in the EMR database over the following views: (1) CT Sims summarizes patient appointment information on the CT simulator and patient load; (2) Linac Sims summarizes patient appointment times, setup history, and notes, and patient load; (3) Task Status lists the clinical tasks associated with a treatment plan, their due date, status and ownership, and patient appointment details; (4) Documents provides the status of all documents in the patients' charts; and (5) Diagnoses and Interventions summarizes prescription information, imaging instructions and whether the plan was approved for treatment. Real-time assessment and quantification of progress and delays in a patient's treatment start were achieved. CONCLUSIONS: This study indicates it is feasible to develop and implement a dashboard, tailored to the needs of an interdisciplinary team, which derives and integrates information from the EMR database for real-time analysis and display of resource utilization and clinical workflow in radiation oncology. The framework developed facilitates informed, data-driven decisions on clinical workflow management as we seek to optimize clinical efficiency and patient safety.

Network evolution in mesial temporal lobe epilepsy revealed by diffusion tensor imaging.

OBJECTIVE: The objective of the present study is to identify novel, time-indexed imaging biomarkers of epileptogenesis in mesial temporal lobe epilepsy (MTLE). METHODS: We used high-resolution brain diffusion tensor imaging (DTI) of the translationally relevant methionine sulfoximine (MSO) brain infusion model of MTLE. MSO inhibits astroglial glutamine synthetase, which is deficient in the epileptogenic hippocampal formation of patients with MTLE. MSO-infused (epileptogenic) rats were compared with phosphate-buffered saline (PBS)-infused (nonepileptogenic) rats at early (3-4 days) and late (6-9 weeks) time points during epileptogenesis. RESULTS: The epileptogenic rats exhibited significant changes in DTI-measured fractional anisotropy (FA) in numerous brain regions versus nonepileptogenic rats. Changes included decreases and increases in FA in regions such as the entorhinal-hippocampal area, amygdala, corpus callosum, thalamus, striatum, accumbens, and neocortex. The FA changes evolved over time as animals transitioned from early to late epileptogenesis. For example, some areas with significant decreases in FA early in epileptogenesis changed to significant increases late in epileptogenesis. Finally, the FA changes significantly correlated with the seizure load. SIGNIFICANCE: Our results suggest (1) that high-resolution DTI can be used for early identification and tracking of the epileptogenic process in MTLE, and (2) that the process identified by DTI is present in multiple brain areas, even though infusion of MSO is restricted to the unilateral entorhinal-hippocampal region.

Enhancing Safety in AI-Driven Cone Beam CT-based Online Adaptive Radiation Therapy: Development and Implementation of an Interdisciplinary Workflow.

Purpose: The emerging online adaptive radiation therapy (OART) treatment strategy based on cone beam computed tomography allows for real-time replanning according to a patient's current anatomy. However, implementing this procedure requires a new approach across the patient's care path and monitoring of the "black box" adaptation process. This study identifies high-risk failure modes (FMs) associated with AI-driven OART and proposes an interdisciplinary workflow to mitigate potential medical errors from highly automated processes, enhance treatment efficiency, and reduce the burden on clinicians. Methods and Materials: An interdisciplinary working group was formed to identify safety concerns in each process step using failure mode and effects analysis (FMEA). Based on the FMEA results, the team designed standardized procedures and safety checklists to prevent errors and ensure successful task completion. The Risk Priority Numbers (RPNs) for the top twenty FMs were calculated before and after implementing the proposed workflow to evaluate its effectiveness. Three hundred seventy-four adaptive sessions across 5 treatment sites were performed, and each session was evaluated for treatment safety and FMEA assessment. Results: The OART workflow has 4 components, each with 4, 8, 13, and 4 sequentially executed tasks and safety checklists. Site-specific template preparation, which includes disease-specific physician directives and Intelligent Optimization Engine template testing, is one of the new procedures introduced. The interdisciplinary workflow significantly reduced the RPNs of the high-risk FMs, with an average decrease of 110 (maximum reduction of 305.5 and minimum reduction of 27.4). Conclusions: This study underscores the importance of addressing high-risk FMs associated with AI-driven OART and emphasizes the significance of safety measures in its implementation. By proposing a structured interdisciplinary workflow and integrated checklists, the study provides valuable insights into ensuring the safe and efficient delivery of OART while facilitating its effective integration into clinical practice.

Clinical capability of modern brain tumor segmentation models.

PURPOSE: State-of-the-art automated segmentation methods achieve exceptionally high performance on the Brain Tumor Segmentation (BraTS) challenge, a dataset of uniformly processed and standardized magnetic resonance generated images (MRIs) of gliomas. However, a reasonable concern is that these models may not fare well on clinical MRIs that do not belong to the specially curated BraTS dataset. Research using the previous generation of deep learning models indicates significant performance loss on cross-institutional predictions. Here, we evaluate the cross-institutional applicability and generalzsability of state-of-the-art deep learning models on new clinical data. METHODS: We train a state-of-the-art 3D U-Net model on the conventional BraTS dataset comprising low- and high-grade gliomas. We then evaluate the performance of this model for automatic tumor segmentation of brain tumors on in-house clinical data. This dataset contains MRIs of different tumor types, resolutions, and standardization than those found in the BraTS dataset. Ground truth segmentations to validate the automated segmentation for in-house clinical data were obtained from expert radiation oncologists. RESULTS: We report average Dice scores of 0.764, 0.648, and 0.61 for the whole tumor, tumor core, and enhancing tumor, respectively, in the clinical MRIs. These means are higher than numbers reported previously on same institution and cross-institution datasets of different origin using different methods. There is no statistically significant difference when comparing the dice scores to the inter-annotation variability between two expert clinical radiation oncologists. Although performance on the clinical data is lower than on the BraTS data, these numbers indicate that models trained on the BraTS dataset have impressive segmentation performance on previously unseen images obtained at a separate clinical institution. These images differ in the imaging resolutions, standardization pipelines, and tumor types from the BraTS data. CONCLUSIONS: State-of-the-art deep learning models demonstrate promising performance on cross-institutional predictions. They considerably improve on previous models and can transfer knowledge to new types of brain tumors without additional modeling.

The Current State of Departmental Diversity, Equity, and Inclusion Efforts Within US Academic Radiation Oncology Departments.

PURPOSE: Promoting a diverse workforce of health care professionals that delivers equitable patient care is an important goal in oncology, as in all of medicine. Although most medical schools have a diversity office and associated initiatives, little is known about radiation oncology (RO) department-level efforts to promote diversity, equity, and inclusion (DEI). We describe the current state of DEI leadership and initiatives in RO departments in the US to guide future policies and programs. METHODS AND MATERIALS: A total of 124 US RO departments affiliated with a medical school were contacted to identify departmental DEI leadership. Identified DEI leaders were asked to complete an anonymous survey assessing characteristics of their departmental DEI leadership, committee/organizational structure, activities, and perceived barriers to, and effect of, their work. Descriptive statistics are reported. RESULTS: Among 85 RO departments that responded (68.5% response rate), 48 (56.5%) reported having a departmental DEI leader. Thirty-four DEI leaders completed the survey (70.8%). Of those who answered each survey question, most DEI leaders were assistant or associate professors (n = 24, 82.8%), women (n = 19, 73.1%), and identified with at least one non-White race or Hispanic ethnicity (n = 15, 53.6%). Nineteen (57.6%) had an associated departmental DEI committee; with 10 of these starting in 2020 or later. Few DEI leaders had administrative support (38.2%), funding (29.4%), protected time (23.5%), or increased compensation for added duties Fifteen (50.0%) believed their DEI-focused efforts were considered for promotion. The most reported initiatives included offering programming/education, supporting students from backgrounds underrepresented in medicine, improving recruitment practices/hiring, and implementing pipeline/pathway projects. The perceived impact of DEI initiatives included an increased culture of respect (89.7%), improved health care disparity awareness (75.9%), and improved systemic/structural racism awareness (79.3%). CONCLUSIONS: Departmental DEI efforts are increasingly common within RO, however, the structure, resources, and recognition associated with DEI work are variable. Additional dedicated resources and recognition for these efforts will help ensure a culture of inclusive excellence for the RO workforce and patients.

The effect of breathing irregularities on quantitative accuracy of respiratory gated PET∕CT.

PURPOSE: 4D positron emission tomography and computed tomography (PET∕CT) can be used to reduce motion artifacts by correlating the raw PET data with the respiratory cycle. The accuracy of each PET phase is dependent on the reproducibility and consistency of the breathing cycle during acquisition. The objective of this study is to evaluate the impact of breathing amplitude and phase irregularities on the quantitative accuracy of 4D PET standardized uptake value (SUV) measurements. In addition, the magnitude of quantitative errors due to respiratory motion and partial volume error are compared. METHODS: Phantom studies were performed using spheres filled with (18)F ranging from 9 to 47 mm in diameter with background activity. Motion was simulated using patient breathing data. The authors compared the accuracy of SUVs derived from gated PET (4 bins and 8 bins, phase-based) for ideal, average, and highly irregular breathing patterns. RESULTS: Under ideal conditions, gated PET produced SUVs that were within (-5.4 ± 5.3)% of the static phantom measurements averaged across all sphere sizes. With breathing irregularities, the quantitative accuracy of gated PET decreased. Gated PET SUVs (best of 4 bins) were (-9.6 ± 13.0)% of the actual value for an average breather and decreased to (-17.1 ± 10.8)% for a highly irregular breather. Without gating, the differences in the SUV from actual value were (-28.5 ± 18.2)%, (-25.9 ± 14.4)%, and (-27.9 ± 18.2)% for the ideal, average, and highly irregular breather, respectively. CONCLUSIONS: Breathing irregularities reduce the quantitative accuracy of gated PET∕CT. Current gated PET techniques may underestimate the actual lesion SUV due to phase assignment errors. Evaluation of respiratory trace is necessary to assess accuracy of data binning and its effect on 4D PET SUVs.

Graph-based risk assessment and error detection in radiation therapy.

PURPOSE: The objective of this study was to formalize and automate quality assurance (QA) in radiation oncology. Quality assurance in radiation oncology entails a multistep verification of complex, personalized radiation plans to treat cancer involving an interdisciplinary team and high technology, multivendor software and hardware systems. We addressed the pretreatment physics chart review (TPCR) using methods from graph theory and constraint programming to study the effect of dependencies between variables and automatically identify logical inconsistencies and how they propagate. MATERIALS AND METHODS: We used a modular approach to decompose the TPCR process into tractable units comprising subprocesses, modules and variables. Modules represented the main software entities comprised in the radiation treatment planning workflow and subprocesses grouped the checks to be performed by functionality. Module-associated variables served as inputs to the subprocesses. Relationships between variables were modeled by means of a directed graph. The detection of errors, in the form of inconsistencies, was formalized as a constraint satisfaction problem whereby checks were encoded as logical formulae. The sequence in which subprocesses were visited was described in an activity diagram. RESULTS: The comprehensive model for the TPCR process comprised 5 modules, 19 subprocesses and 346 variables, 225 of which were distinct. Modules included "Treatment Planning System" and "Record and Verify System." Subprocesses included "Dose Prescription," "Documents," "CT Integrity," "Anatomical Contours," "Beam Configuration," "Dose Calculation," "3D Dose Distribution Quality," and "Treatment Approval." Variable inconsistencies, and their source and propagation were determined by checking for constraint violation and through graph traversal. Impact scores, obtained through graph traversal, combined with severity scores associated with an inconsistency, allowed risk assessment. CONCLUSIONS: Directed graphs combined with constraint programming hold promise for formalizing complex QA processes in radiation oncology, performing risk assessment and automating the TPCR process. Though complex, the process is tractable.

MRI demonstration of gadolinium deposition in bone after monthly triple-dose gadopentetate dimeglumine and correlation with frequency of hypophosphatemia.

OBJECTIVES: We retrospectively analyzed data of the BECOME trial to investigate whether serial administration of triple-dose (3-dose) gadopentetate dimeglumine would result in the development of T1 signal-to-noise (S/N) changes in the cranial diploic space and whether S/N changes correlated with on-study hypophosphatemia. METHODS: Signal intensity analysis was performed on the first year's data of the BECOME trial using 3-dose Gd (14 months, maximum number of doses, 39, mean: 36). Routine blood and urine tests were obtained each month for safety monitoring. Linear mixed regression modeling with random intercept was used to analyze monthly signal-to-noise ratio (S/N = Bone/Air) using an ROI of the diploic space created from T2W images and overlaid on T1FS (T1 fat-saturated) images. Incidence of phosphate abnormalities was analyzed using the general estimation equation; correlation of phosphate and S/N change was achieved with type 3 test of fixed effects. RESULTS: Cranial diploic space T1FS S/N increased over 14 months: S/N = 0.039 mean monthly increase (S.E. 0.008; p < 0.0001). Subjects with consistently normal phosphate levels (n = 32) experienced more of a S/N increase than patients with at least one episode of hypophosphatemia (n = 35) (0.057 vs. 0.023, respectively, p = 0.037). Those with moderate hypophosphatemia demonstrated no significant S/N increase. CONCLUSION: Monthly administration of 3-dose gadopentetate dimeglumine is associated with development of increased S/N on T1FS imaging in the cranial diploic space, suggesting Gd retention in bone. Our data suggests MRI could be used as a noninvasive method of tracking Gd retention in bone, which was more pronounced in patients with normal phosphate levels.

Automated position and size selection of round applicators for AccuBoost breast brachytherapy.

PURPOSE: AccuBoost is a complex non-invasive brachytherapy procedure for breast treatment. This technique requires a radiation oncologist to manually select applicator grid position and size by overlaying transparencies over a mammographic image to encompass surgical clips and resected tumor bed. An algorithm was developed in MATLAB™ to automate the selection of round applicators based on surgical clip position. MATERIAL AND METHODS: A total of 42 mammograms belonging to 10 patients were retrospectively analyzed. Images were pre-processed by masking imprinted localization grid and regions around the grid. A threshold was applied to isolate high-intensity pixels and generate a binary image. A set of morphological operations including region dilation, filling, clearing border structures, and erosion were performed to segment the different regions. A support vector machine classification model was trained to categorize segmented regions as either surgical clips or miscellaneous objects based on different region properties (area, perimeter, eccentricity, circularity, minor axis length, and intensity-derived quantities). Applicator center position was determined by calculating the centroid of detected clips. Size of the applicator was determined with the smallest circle that encompassed all clips with an isotropic 1.0 cm margin. RESULTS: The clip identification model classified 946 regions, with a sensitivity of 96.6% and a specificity of 98.2%. Applicator position was correctly predicted for 20 of 42 fractions and was within 0.5 cm of physician-selected position for 33 of 42 fractions. Applicator size was correctly predicted for 25 out of 42 fractions. CONCLUSIONS: The proposed algorithm provided a method to quantitatively determine applicator position and size for AccuBoost treatments, and may serve as a tool for independent verifications. The discrepancy between physician-selected and algorithm-predicted determinations of applicator position and size suggests that the methodology may be further improved by considering radiomic features of breast tissue in addition to clip position.

Gray Matter Nucleus Hyperintensity After Monthly Triple-Dose Gadopentetate Dimeglumine With Long-term Magnetic Resonance Imaging.

OBJECTIVES: Gadolinium deposition is widely believed to occur, but questions regarding accumulation pattern and permanence remain. We conducted a retrospective study of intracranial signal changes on monthly triple-dose contrast-enhanced magnetic resonance imaging (MRI) examinations from the previously published Betaseron vs. Copaxone in Multiple Sclerosis With Triple-Dose Gadolinium and 3-Tesla MRI Endpoints Trial (N = 67) to characterize the dynamics of gadolinium deposition in several deep brain nuclei and track persistence versus washout of gadolinium deposition on long-term follow-up (LTFU) examinations (N = 28) obtained approximately 10 years after enrollment in the Betaseron vs. Copaxone in Multiple Sclerosis With Triple-Dose Gadolinium and 3-Tesla MRI Endpoints Trial. MATERIALS AND METHODS: Using T2 and proton density images and using image analysis software (ITK-SNAP), manual regions of interest were created ascribing boundaries of the caudate nucleus, dentate nucleus, globus pallidus, pulvinar, putamen, white matter, and air. Intensity analysis was conducted on T1-weighted fat-saturated (fat-sat) images using the FSL package. A linear rigid-body transform was calculated from the fat-sat image at each target time point to the region of interest segmentation reference time point fat-sat image. Serial MRI signal was analyzed using linear mixed regression modeling with random intercept. Annual MRI signal changes including LTFU scans were assessed with t test. RESULTS: During monthly scanning, all gray matter structures demonstrated a significant (P < 0.0001) increase in contrast-to-noise ratio. Yearly changes in deposition showed distinctive patterns for the specific nucleus: globus pallidus showed complete retention, pulvinar showed partial washout, while dentate, caudate, and putamen returned to baseline (ie, complete washout). CONCLUSIONS: Monthly increased contrast-to-noise ratio in gray matter nuclei is consistent with gadolinium deposition over time. The study also suggests that some deep gray matter nuclei permanently retain gadolinium, whereas others demonstrate washout of soluble gadolinium.

2D-3D registration for prostate radiation therapy based on a statistical model of transmission images.

PURPOSE: In external beam radiation therapy of pelvic sites, patient setup errors can be quantified by registering 2D projection radiographs acquired during treatment to a 3D planning computed tomograph (CT). We present a 2D-3D registration framework based on a statistical model of the intensity values in the two imaging modalities. METHODS: The model assumes that intensity values in projection radiographs are independently but not identically distributed due to the nonstationary nature of photon counting noise. Two probability distributions are considered for the intensity values: Poisson and Gaussian. Using maximum likelihood estimation, two similarity measures, maximum likelihood with a Poisson (MLP) and maximum likelihood with Gaussian (MLG), distribution are derived. Further, we investigate the merit of the model-based registration approach for data obtained with current imaging equipment and doses by comparing the performance of the similarity measures derived to that of the Pearson correlation coefficient (ICC) on accurately collected data of an anthropomorphic phantom of the pelvis and on patient data. RESULTS: Registration accuracy was similar for all three similarity measures and surpassed current clinical requirements of 3 mm for pelvic sites. For pose determination experiments with a kilovoltage (kV) cone-beam CT (CBCT) and kV projection radiographs of the phantom in the anterior-posterior (AP) view, registration accuracies were 0.42 mm (MLP), 0.29 mm (MLG), and 0.29 mm (ICC). For kV CBCT and megavoltage (MV) AP portal images of the same phantom, registration accuracies were 1.15 mm (MLP), 0.90 mm (MLG), and 0.69 mm (ICC). Registration of a kV CT and MV AP portal images of a patient was successful in all instances. CONCLUSIONS: The results indicate that high registration accuracy is achievable with multiple methods including methods that are based on a statistical model of a 3D CT and 2D projection images.

Dosimetric and anatomic indicators of late rectal toxicity after high-dose intensity modulated radiation therapy for prostate cancer.

We seek to identify dosimetric and anatomic indicators of late rectal toxicity in prostate cancer patients treated with intensity modulated radiation therapy (IMRT). Data from 49 patients sampled from 698 patients treated for clinically localized prostate cancer at the Memorial Sloan-Kettering Cancer Center with IMRT to a dose of 81 Gy were analyzed. The end point of the study was late Grade 2 or worse rectal toxicity within 30 months of treatment. Dosimetric analysis was performed on the rectum surface in three dimensions and on two-dimensional dose maps obtained by flattening the rectum surface using a conformal mapping procedure. Several parameters including the percentage and absolute surface area of the rectum irradiated, mean dose as a function of location on the rectum, planning target volume (PTV) size and rectum size were analyzed for correlation to toxicity. Significance was set at p < 0.05 for a two-sided t-test. Correlation between absolute areas irradiated and toxicity was observed on both the rectum surface and flattened rectum. Patients with toxicity also received a significantly higher mean dose to the superior 25% of the rectum surface and 15% of the flattened rectum. PTV volume, PTV height, rectum surface area and average cross-sectional area were significantly larger in patients with toxicity. The conformal mapping procedure has potential utility for evaluating dose to the rectum and risk of toxicity. Late rectal toxicity was related to the irradiation of the upper part of the rectum and also to the absolute area irradiated, PTV size, and rectum size on the planning computed tomography (CT) scan.

Automated 2D-3D registration of portal images and CT data using line-segment enhancement.

In prostate radiotherapy, setup errors with respect to the patient's bony anatomy can be reduced by aligning 2D megavoltage (MV) portal images acquired during treatment to a reference 3D kilovoltage (kV) CT acquired for treatment planning purposes. The purpose of this study was to evaluate a fully automated 2D-3D registration algorithm to quantify setup errors in 3D through the alignment of line-enhanced portal images and digitally reconstructed radiographs computed from the CT. The line-enhanced images were obtained by correlating the images with a filter bank of short line segments, or "sticks" at different orientations. The proposed methods were validated on (1) accurately collected gold-standard data consisting of a 3D kV cone-beam CT scan of an anthropomorphic phantom of the pelvis and 2D MV portal images in the anterior-posterior (AP) view acquired at 15 different poses and (2) a conventional 3D kV CT scan and weekly 2D MV AP portal images of a patient over 8 weeks. The mean (and standard deviation) of the absolute registration error for rotations around the right-lateral (RL), inferior-superior (IS), and posterior-anterior (PA) axes were 0.212 degree (0.214 degree), 0.055 degree (0.033 degree) and 0.041 degree (0.039 degree), respectively. The corresponding registration errors for translations along the RL, IS, and PA axes were 0.161 (0.131) mm, 0.096 (0.033) mm, and 0.612 (0.485) mm. The mean (and standard deviation) of the total registration error was 0.778 (0.543) mm. Registration on the patient images was successful in all eight cases as determined visually. The results indicate that it is feasible to automatically enhance features in MV portal images of the pelvis for use within a completely automated 2D-3D registration framework for the accurate determination of patient setup errors. They also indicate that it is feasible to estimate all six transformation parameters from a 3D CT of the pelvis and a single portal image in the AP view.

2D-3D registration for cranial radiation therapy using a 3D kV CBCT and a single limited field-of-view 2D kV radiograph.

PURPOSE: We present and evaluate a fully automated 2D-3D intensity-based registration framework using a single limited field-of-view (FOV) 2D kV radiograph and a 3D kV CBCT for 3D estimation of patient setup errors during brain radiotherapy. METHODS: We evaluated two similarity measures, the Pearson correlation coefficient on image intensity values (ICC) and maximum likelihood measure with Gaussian noise (MLG), derived from the statistics of transmission images. Pose determination experiments were conducted on 2D kV radiographs in the anterior-posterior (AP) and left lateral (LL) views and 3D kV CBCTs of an anthropomorphic head phantom. In order to minimize radiation exposure and exclude nonrigid structures from the registration, limited FOV 2D kV radiographs were employed. A spatial frequency band useful for the 2D-3D registration was identified from the bone-to-no-bone spectral ratio (BNBSR) of digitally reconstructed radiographs (DRRs) computed from the 3D kV planning CT of the phantom. The images being registered were filtered accordingly prior to computation of the similarity measures. We evaluated the registration accuracy achievable with a single 2D kV radiograph and with the registration results from the AP and LL views combined. We also compared the performance of the 2D-3D registration solutions proposed to that of a commercial 3D-3D registration algorithm, which used the entire skull for the registration. The ground truth was determined from markers affixed to the phantom and visible in the CBCT images. RESULTS: The accuracy of the 2D-3D registration solutions, as quantified by the root mean squared value of the target registration error (TRE) calculated over a radius of 3 cm for all poses tested, was ICCAP : 0.56 mm, MLGAP : 0.74 mm, ICCLL : 0.57 mm, MLGLL : 0.54 mm, ICC (AP and LL combined): 0.19 mm, and MLG (AP and LL combined): 0.21 mm. The accuracy of the 3D-3D registration algorithm was 0.27 mm. There was no significant difference in mean TRE for the 2D-3D registration algorithms using a single 2D kV radiograph with similarity measure and image view point. There was no significant difference in mean TRE between ICCLL , MLGLL , ICC (AP and LL combined), MLG (AP and LL combined), and the 3D-3D registration algorithm despite the smaller FOV used for the 2D-3D registration. While submillimeter registration accuracy was obtained with both ICC and MLG using a single 2D kV radiograph, combining the results from the two projection views resulted in a significantly smaller (P≤0.05) mean TRE. CONCLUSIONS: Our results indicate that it is possible to achieve submillimeter registration accuracy with both ICC and MLG using either single or dual limited FOV 2D kV radiographs of the head in the AP and LL views. The registration accuracy suggests that the 2D-3D registration solutions presented are suitable for the estimation of patient setup errors not only during conventional brain radiation therapy, but also during stereotactic procedures and proton radiation therapy where tighter setup margins are required.

A frequency-based approach to locate common structure for 2D-3D intensity-based registration of setup images in prostate radiotherapy.

In many radiotherapy clinics, geometric uncertainties in the delivery of 3D conformal radiation therapy and intensity modulated radiation therapy of the prostate are reduced by aligning the patient's bony anatomy in the planning 3D CT to corresponding bony anatomy in 2D portal images acquired before every treatment fraction. In this paper, we seek to determine if there is a frequency band within the portal images and the digitally reconstructed radiographs (DRRs) of the planning CT in which bony anatomy predominates over non-bony anatomy such that portal images and DRRs can be suitably filtered to achieve high registration accuracy in an automated 2D-3D single portal intensity-based registration framework. Two similarity measures, mutual information and the Pearson correlation coefficient were tested on carefully collected gold-standard data consisting of a kilovoltage cone-beam CT (CBCT) and megavoltage portal images in the anterior-posterior (AP) view of an anthropomorphic phantom acquired under clinical conditions at known poses, and on patient data. It was found that filtering the portal images and DRRs during the registration considerably improved registration performance. Without filtering, the registration did not always converge while with filtering it always converged to an accurate solution. For the pose-determination experiments conducted on the anthropomorphic phantom with the correlation coefficient, the mean (and standard deviation) of the absolute errors in recovering each of the six transformation parameters were Theta(x):0.18(0.19) degrees, Theta(y):0.04(0.04) degrees, Theta(z):0.04(0.02) degrees, t(x):0.14(0.15) mm, t(y):0.09(0.05) mm, and t(z):0.49(0.40) mm. The mutual information-based registration with filtered images also resulted in similarly small errors. For the patient data, visual inspection of the superimposed registered images showed that they were correctly aligned in all instances. The results presented in this paper suggest that robust and accurate registration can be achieved with intensity-based methods by focusing on rigid bony structures in the images while diminishing the influence of artifacts with similar frequencies as soft tissue.

Automated 2D-3D registration of a radiograph and a cone beam CT using line-segment enhancement.

The objective of this study was to develop a fully automated two-dimensional (2D)-three-dimensional (3D) registration framework to quantify setup deviations in prostate radiation therapy from cone beam CT (CBCT) data and a single AP radiograph. A kilovoltage CBCT image and kilovoltage AP radiograph of an anthropomorphic phantom of the pelvis were acquired at 14 accurately known positions. The shifts in the phantom position were subsequently estimated by registering digitally reconstructed radiographs (DRRs) from the 3D CBCT scan to the AP radiographs through the correlation of enhanced linear image features mainly representing bony ridges. Linear features were enhanced by filtering the images with "sticks," short line segments which are varied in orientation to achieve the maximum projection value at every pixel in the image. The mean (and standard deviations) of the absolute errors in estimating translations along the three orthogonal axes in millimeters were 0.134 (0.096) AP(out-of-plane), 0.021 (0.023) ML and 0.020 (0.020) SI. The corresponding errors for rotations in degrees were 0.011 (0.009) AP, 0.029 (0.016) ML (out-of-plane), and 0.030 (0.028) SI (out-of-plane). Preliminary results with megavoltage patient data have also been reported. The results suggest that it may be possible to enhance anatomic features that are common to DRRs from a CBCT image and a single AP radiography of the pelvis for use in a completely automated and accurate 2D-3D registration framework for setup verification in prostate radiotherapy. This technique is theoretically applicable to other rigid bony structures such as the cranial vault or skull base and piecewise rigid structures such as the spine.

Quantifying cell migration distance as a contributing factor to the development of rectal toxicity after prostate radiotherapy.

PURPOSE: Spatial information is usually neglected in mathematical models of radiation-induced toxicity. In the presence of inhomogeneous dose distributions produced by intensity modulated radiation therapy (IMRT) and volumetric arc therapy, this may be a limitation. We present a model to quantify the spatial characteristics of the dose distribution on the rectum through the quantification of the distribution of distances between dose points on the surface of the rectum in three-dimensions. The method allows us to evaluate the hypothesis that distances between lower and higher dose regions on the rectum influence radiation damage repair due to the migration of normal cells into damaged areas, and consequently, the development of radiation-induced toxicity in patients treated with radiation for prostate cancer. METHODS: We present a method to compute distances between dose points on the surface of the rectum in three dimensions (3D) and to generate distance maps representing the distances between specific dose regions on the rectum. We introduce the concept of the distance dose surface histogram (DDSH), which is computed from the distance maps. The DDSH is a 2D histogram of rectum area on a grid defined by pairwise combinations of dose and distance. Each bin in the DDSH quantifies the area of the rectum exposed to a given dose and at a given distance from other another dose region on the rectum. By summing across the columns and rows of the DDSH, the authors can generate the dose surface histogram (DSH) and distance surface histogram (DiSH) for a particular dose region. The DiSH is a marginal histogram showing the distribution of distances for the dose points in a specific dose region from another region. We computed the DDSH, DiSH, and DSH for 33 patients treated with IMRT for prostate cancer, nine of whom developed late Grade 2 or higher late rectal toxicity. RESULTS: We show how even though the total area of the rectum exposed to a given dose may be the same for different patients, the distribution of distances for points receiving that dose can be different depending on the shape and contiguity of the region(s) containing those dose points. We also show that area of the rectum in the region receiving more than 75 Gy and at a distance of 16 to 22 mm from the 50 Gy isodose line was significantly correlated to the development of toxicity (p = 0.004, two sided t-test). This suggests that examining the distance distribution of points in specific dose regions could provide valuable additional information in predicting the risk of a patient developing radiation-induced rectal toxicity. CONCLUSIONS: We present a new method to quantify the spatial distribution of points in a given region relative to other regions on the rectum. The method provides a means to evaluate the hypothesis that distances between lower and higher dose regions on the rectum influence radiation damage repair due to the migration of normal cells into damaged areas, and may be a contributing factor to the development of radiation-induced toxicity in patients treated with radiation for prostate cancer.

Dynamic simulation of motion effects in IMAT lung SBRT.

BACKGROUND: Intensity modulated arc therapy (IMAT) has been widely adopted for Stereotactic Body Radiotherapy (SBRT) for lung cancer. While treatment dose is optimized and calculated on a static Computed Tomography (CT) image, the effect of the interplay between the target and linac multi-leaf collimator (MLC) motion is not well described and may result in deviations between delivered and planned dose. In this study, we investigated the dosimetric consequences of the inter-play effect on target and organs at risk (OAR) by simulating dynamic dose delivery using dynamic CT datasets. METHODS: Fifteen stage I non-small cell lung cancer (NSCLC) patients with greater than 10 mm tumor motion treated with SBRT in 4 fractions to a dose of 50 Gy were retrospectively analyzed for this study. Each IMAT plan was initially optimized using two arcs. Simulated dynamic delivery was performed by associating the MLC leaf position, gantry angle and delivered beam monitor units (MUs) for each control point with different respiratory phases of the 4D-CT using machine delivery log files containing time stamps of the control points. Dose maps associated with each phase of the 4D-CT dose were calculated in the treatment planning system and accumulated using deformable image registration onto the exhale phase of the 4D-CT. The original IMAT plans were recalculated on the exhale phase of the CT for comparison with the dynamic simulation. RESULTS: The dose coverage of the PTV showed negligible variation between the static and dynamic simulation. There was less than 1.5% difference in PTV V95% and V90%. The average inter-fraction and cumulative dosimetric effects among all the patients were less than 0.5% for PTV V95% and V90% coverage and 0.8 Gy for the OARs. However, in patients where target is close to the organs, large variations were observed on great vessels and bronchus for as much as 4.9 Gy and 7.8 Gy. CONCLUSIONS: Limited variation in target dose coverage and OAR constraints were seen for each SBRT fraction as well as over all four fractions. Large dose variations were observed on critical organs in patients where these organs were closer to the target.