Leveraging deep learning-based segmentation and contours-driven deformable registration for dose accumulation in abdominal structures.

Purpose: Discrepancies between planned and delivered dose to GI structures during radiation therapy (RT) of liver cancer may hamper the prediction of treatment outcomes. The purpose of this study is to develop a streamlined workflow for dose accumulation in a treatment planning system (TPS) during liver image-guided RT and to assess its accuracy when using different deformable image registration (DIR) algorithms. Materials and Methods: Fifty-six patients with primary and metastatic liver cancer treated with external beam radiotherapy guided by daily CT-on-rails (CTOR) were retrospectively analyzed. The liver, stomach and duodenum contours were auto-segmented on all planning CTs and daily CTORs using deep-learning methods. Dose accumulation was performed for each patient using scripting functionalities of the TPS and considering three available DIR algorithms based on: (i) image intensities only; (ii) intensities + contours; (iii) a biomechanical model (contours only). Planned and accumulated doses were converted to equivalent dose in 2Gy (EQD2) and normal tissue complication probabilities (NTCP) were calculated for the stomach and duodenum. Dosimetric indexes for the normal liver, GTV, stomach and duodenum and the NTCP values were exported from the TPS for analysis of the discrepancies between planned and the different accumulated doses. Results: Deep learning segmentation of the stomach and duodenum enabled considerable acceleration of the dose accumulation process for the 56 patients. Differences between accumulated and planned doses were analyzed considering the 3 DIR methods. For the normal liver, stomach and duodenum, the distribution of the 56 differences in maximum doses (D2%) presented a significantly higher variance when a contour-driven DIR method was used instead of the intensity only-based method. Comparing the two contour-driven DIR methods, differences in accumulated minimum doses (D98%) in the GTV were >2Gy for 15 (27%) of the patients. Considering accumulated dose instead of planned dose in standard NTCP models of the duodenum demonstrated a high sensitivity of the duodenum toxicity risk to these dose discrepancies, whereas smaller variations were observed for the stomach. Conclusion: This study demonstrated a successful implementation of an automatic workflow for dose accumulation during liver cancer RT in a commercial TPS. The use of contour-driven DIR methods led to larger discrepancies between planned and accumulated doses in comparison to using an intensity only based DIR method, suggesting a better capability of these approaches in estimating complex deformations of the GI organs.

Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy.

PURPOSE: Objective assessment of deformable image registration (DIR) accuracy often relies on the identification of anatomical landmarks in image pairs, a manual process known to be extremely time-expensive. The goal of this study is to propose a method to automatically detect vessel bifurcations in images and assess their use for the computation of target registration errors (TREs). MATERIALS AND METHODS: Three image datasets were retrospectively analyzed. The first dataset included 10 pairs of inhale/exhale phases from lung 4DCTs and full inhale and exhale breath-hold CT scans from 10 patients presenting with chronic obstructive pulmonary disease, with 300 corresponding landmarks available for each case (DIR-Lab). The second dataset included six pairs of inhale/exhale phases from lung 4DCTs (POPI dataset), with 100 pairs of landmarks for each case. The third dataset included 28 pairs of pre/post-radiotherapy liver contrast-enhanced CT scans, each with five manually picked vessel bifurcation correspondences. For all images, the vasculature was autosegmented by computing and thresholding a vesselness image. Images of the vasculature centerline were computed, and bifurcations were detected based on centerline voxel neighbors' count. The vasculature segmentations were independently registered using a Demons algorithm between representations of their surface with distance maps. Detected bifurcations were considered as corresponding when distant by less than 5 mm after vasculature DIR. The selected pairs of bifurcations were used to calculate TRE after registration of the images considering three algorithms: rigid registration, Anaconda, and a Demons algorithm. For comparison with the ground truth, TRE values calculated using the automatically detected correspondences were interpolated in the whole organs to generate TRE maps. The performance of the method in automatically calculating TRE after image registration was quantified by measuring the correlation with the TRE obtained when using the ground truth landmarks. RESULTS: The median Pearson correlation coefficients between ground truth TRE and corresponding values in the generated TRE maps were r = 0.81 and r = 0.67 for the lung and liver cases, respectively. The correlation coefficients between mean TRE for each case were r = 0.99 and r = 0.64 for the lung and liver cases, respectively. CONCLUSION: For lungs or liver CT scans DIR, a strong correlation was obtained between TRE calculated using manually picked or landmarks automatically detected with the proposed method. This tool should be particularly useful in studies requiring assessing the reliability of a high number of DIRs.

Biomechanical modeling of radiation dose-induced volumetric changes of the parotid glands for deformable image registration.

PURPOSE: Early animal studies suggest that parotid gland (PG) toxicity prediction could be improved by an accurate estimation of the radiation dose to sub-regions of the PG. Translation to clinical investigation requires voxel-level dose accumulation in this organ that responds volumetrically throughout treatment. To date, deformable image registration (DIR) has been evaluated for the PG using only surface alignment. We sought to develop and evaluate an advanced DIR technique capable of modeling these complex PG volume changes over the course of radiation therapy. MATERIALS AND METHODS: Planning and mid-treatment magnetic resonance images from 19 patients and computed tomography images from nine patients who underwent radiation therapy for head and neck cancer were retrospectively evaluated. A finite element model (FEM)-based DIR algorithm was applied between the corresponding pairs of images, based on boundary conditions on the PG surfaces only (Morfeus-spatial). To investigate an anticipated improvement in accuracy, we added a population model-based thermal expansion coefficient to simulate the dose distribution effect on the volume change inside the glands (Morfeus-spatialDose). The model accuracy was quantified using target registration error for magnetic resonance images, where corresponding anatomical landmarks could be identified. The potential clinical impact was evaluated using differences in mean dose, median dose, D98, and D50 of the PGs. RESULTS: In the magnetic resonance images, the mean (±standard deviation) target registration error significantly reduced by 0.25 ± 0.38 mm (p = 0.01) when using Morfeus-spatialDose instead of Morfeus-spatial. In the computed tomography images, differences in the mean dose, median dose, D98, and D50 of the PGs reached 2.9 ± 0.8, 3.8, 4.1, and 3.8 Gy, respectively, between Morfeus-spatial and Morfeus-spatialDose. CONCLUSION: Differences between Morfeus-spatial and Morfeus-spatialDose may be impactful when considering high-dose gradients of radiation in the PGs. The proposed DIR model can allow more accurate PG alignment than the standard model and improve dose estimation and toxicity prediction modeling.

Accuracy of deformable image registration techniques for alignment of longitudinal cholangiocarcinoma CT images.

PURPOSE: Response assessment of radiotherapy for the treatment of intrahepatic cholangiocarcinoma (IHCC) across longitudinal images is challenging due to anatomical changes. Advanced deformable image registration (DIR) techniques are required to correlate corresponding tissues across time. In this study, the accuracy of five commercially available DIR algorithms in four treatment planning systems (TPS) was investigated for the registration of planning images with posttreatment follow-up images for response assessment or re-treatment purposes. METHODS: Twenty-nine IHCC patients treated with hypofractionated radiotherapy and with pretreatment and posttreatment contrast-enhanced computed tomography (CT) images were analyzed. Liver segmentations were semiautomatically generated on all CTs and the posttreatment CT was then registered to the pretreatment CT using five commercially available algorithms (Demons, B-splines, salient feature-based, anatomically constrained and finite element-based) in four TPSs. This was followed by an in-depth analysis of 10 DIR strategies (plus global and liver-focused rigid registration) in one of the TPSs. Eight of the strategies were variants of the anatomically constrained DIR while the two were based on a finite element-based biomechanical registration. The anatomically constrained techniques were combinations of: (a) initializations with the two rigid registrations; (b) two similarity metrics - correlation coefficient (CC) and mutual information (MI); and (c) with and without a controlling region of interest (ROI) - the liver. The finite element-based techniques were initialized by the two rigid registrations. The accuracy of each registration was evaluated using target registration error (TRE) based on identified vessel bifurcations. The results were statistically analyzed with a one-way analysis of variance (ANOVA) and pairwise comparison tests. Stratified analysis was conducted on the inter-TPS data (plus the liver-focused rigid registration) using treatment volume changes, slice thickness, time between scans, and abnormal lab values as stratifying factors. RESULTS: The complex deformation observed following treatment resulted in average TRE exceeding the image voxel size for all techniques. For the inter-TPS comparison, the Demons algorithm had the lowest TRE, which was significantly superior to all the other algorithms. The respective mean (standard deviation) TRE (in mm) for the Demons, B-splines, salient feature-based, anatomically constrained, and finite element-based algorithms were 4.6 (2.0), 7.4 (2.7), 7.2 (2.6), 6.3 (2.3), and 7.5 (4.0). In the follow-up comparison of the anatomically constrained DIR, the strategy with liver-focused rigid registration initialization, CC as similarity metric and liver as a controlling ROI had the lowest mean TRE - 6.0 (2.0). The maximum TRE for all techniques exceeded 10 mm. Selection of DIR strategy was found to be a statistically significant factor for registration accuracy. Tumor volume change had a significant effect on TRE for finite element-based registration and B-splines DIR. Time between scans had a substantial effect on TRE for all registrations but was only significant for liver-focused rigid, finite element-based and salient feature-based DIRs. CONCLUSIONS: This study demonstrates the limitations of commercially available DIR techniques in TPSs for alignment of longitudinal images of liver cancer presenting complex anatomical changes including local hypertrophy and fibrosis/necrosis. DIR in this setting should be used with caution and careful evaluation.

Biomechanical modeling of neck flexion for deformable alignment of the salivary glands in head and neck cancer images.

During head and neck (HN) cancer radiation therapy, analysis of the dose-response relationship for the parotid glands (PG) relies on the ability to accurately align soft tissue organs between longitudinal images. In order to isolate the response of the salivary glands to delivered dose, from deformation due to patient position, it is important to resolve the patient postural changes, mainly due to neck flexion. In this study we evaluate the use of a biomechanical model-based deformable image registration (DIR) algorithm to estimate the displacements and deformations of the salivary glands due to postural changes. A total of 82 pairs of CT images of HN cancer patients with varying angles of neck flexion were retrospectively obtained. The pairs of CTs of each patient were aligned using bone-based rigid registration. The images were then deformed using biomechanical model-based DIR method that focused on the mandible, C1 vertebrae, C3 vertebrae, and external contour. For comparison, an intensity-based DIR was also performed. The accuracy of the biomechanical model-based DIR was assessed using Dice similarity coefficient (DSC) for all images and for the subset of images where the PGs had a volume change within 20%. The accuracy was compared to the intensity-based DIR. The PG mean ± STD DSC were 0.63 ± 0.18, 0.80 ± 0.08, and 0.82 ± 0.15 for the rigid registration, biomechanical model-based DIR, and intensity based DIR, respectively, for patients with a PG volume change up to 20%. For the entire cohort of patients, where the PG volume change was up to 57%, the PG mean ± STD DSC were 0.60 ± 0.18, 0.78 ± 0.09, and 0.81 ± 0.14 for the rigid registration, biomechanical model-based DIR, and intensity based DIR, respectively. The difference in DSC of the intensity and biomechanical model-based DIR methods was not statistically significant when the volume change was less than 20% (two-sided paired t-test, p  = 0.12). When all volume changes were considered, there was a significant difference between the two registration approaches, although the magnitude was small. These results demonstrate that the proposed biomechanical model with boundary conditions on the bony anatomy can serve to describe the varying angles of neck flexion appearing in images during radiation treatment and to align the salivary glands for proper analysis of dose-response relationships. It also motivates the need for dose response modeling following neck flexion for cases where parotid gland response is noted.

Predictive Models to Determine Clinically Relevant Deviations in Delivered Dose for Head and Neck Cancer.

PURPOSE: This study aimed to improve the understanding of deviations between planned and accumulated doses and to establish metrics to predict clinically significant dosimetric deviations midway through treatment to evaluate the potential need to re-plan during fractionated radiation therapy (RT). METHODS AND MATERIALS: A total of 100 patients with head and neck cancer were retrospectively evaluated. Contours were mapped from the planning computed tomography (CT) scan to each fraction cone beam CT via deformable image registration. The dose was calculated on each cone beam CT and evaluated based on the mapped contours. The mean dose at each fraction was averaged to approximate the accumulated dose for structures with mean dose constraints, and the daily maximum dose was summed to approximate the accumulated dose for structures with maximum dose constraints. A threshold deviation value was calculated to predict for patients needing midtreatment re-planning. This predictive model was applied to 52 patients treated at a separate institution. RESULTS: Dose was accumulated on 10 organs over 100 patients. To generate a threshold deviation that predicted the need to re-plan with 100% sensitivity, the submandibular glands required re-planning if the delivered dose was at least 3.5 Gy higher than planned by fraction 15. This model predicts the need to re-plan the submandibular glands with 98.7% specificity. In the independent evaluation cohort, this model predicts the need to re-plan the submandibular glands with 100% sensitivity and 98.0% specificity. The oral cavity, intermediate clinical target volume, left parotid, and inferior constrictor patient groups each had 1 patient who exceeded the threshold deviation by the end of RT. By fraction 15 of 30 to 35 total fractions, the left parotid gland, inferior constrictor, and intermediate clinical target volume had a dose deviation of 3.1 Gy, 5.9 Gy, and 4.8 Gy, respectively. When a deformable image registration failure was observed, the dose deviation exceeded the threshold for at least 1 organ, demonstrating that an automated deformable image registration-based dose assessment process could be developed with user evaluation for cases that result in dose deviations. CONCLUSIONS: A midtreatment threshold deviation was determined to predict the need to replan for the submandibular glands by fraction 15 of 30 to 35 total fractions of RT.

A simulation study to assess the potential impact of developing normal tissue complication probability models with accumulated dose.

PURPOSE: This study aimed to analyze the potential clinical impact of the differences between planned and accumulated doses on the development and use of normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: Thirty patients who were previously treated with stereotactic body radiation therapy for liver cancer and for whom the accumulated dose was computed were assessed retrospectively. The linear quadratic equivalent dose at 2 Gy per fraction and generalized equivalent uniform dose were calculated for planned and accumulated doses. Stomach and duodenal Lyman-Kutcher-Burman NTCP models (α/ß = 2.5; n = .09) were developed on the basis of planned and accumulated generalized equivalent uniform doses and the differences between the models assessed. In addition, the error in determining the probability of toxicity on the basis of the planned dose was evaluated by comparing planned doses in the NTCP model that were created from accumulated doses. RESULTS: The standard, planned-dose NTCP model overestimates toxicity risk for both the duodenal and stomach models at doses that are below approximately 20 Gy (6 fractions) and underestimates toxicity risk for doses above approximately 20 Gy (6 fractions). Building NTCP models with accumulated rather than planned doses changes the predicted risk by up to 16% (mean: 6%; standard deviation: 7%) for duodenal toxicity and 6% (mean: 2%; standard deviation: 2%) for stomach toxicity. For a protocol that plans a 10% iso-toxicity risk to the duodenum, a 15.7 Gy (6 fractions) maximum dose constraint would be necessary when using standard NTCP models on the basis of a planned dose and a 17.6 Gy (6 fractions) maximum dose would be allowed when using NTCP models on the basis of accumulated doses. CONCLUSIONS: Assuming that accumulated dose is a more accurate representation of the true delivered dose than the planned dose, this simulation study indicates the need for prospective clinical trials to evaluate the impact of building NTCP models on the basis of accumulated doses.

Medical Professional Radiation Dosimeter Usage: Reasons for Noncompliance.

The purpose of this study was to assess the attitudes of occupationally exposed employees at a large teaching hospital about wearing their assigned personal radiation dosimeters. A 16-question multiple-answer survey was used to report the reasons why medical professionals may not wear their dosimetry during procedures involving ionizing radiation. In all, 302 employees responded to the survey. The majority of respondents who reported always or almost always wearing their dosimeters do so because they consider themselves well informed concerning the importance of personal dosimetry measurement and appreciate the importance of federal and state regulations. For respondents who reported not always wearing their dosimeters, the most commonly stated reason was the inconvenience of remembering to bring and wear their dosimeters when working in multiple locations, for which a potential solution would be to provide dosimeters to each affected wearer in each location where they work.