A multi-institutional comparison of retrospective deformable dose accumulation for online adaptive magnetic resonance-guided radiotherapy.

Background and Purpose: Application of different deformable dose accumulation (DDA) solutions makes institutional comparisons after online-adaptive magnetic resonance-guided radiotherapy (OA-MRgRT) challenging. The aim of this multi-institutional study was to analyze accuracy and agreement of DDA-implementations in OA-MRgRT. Material and Methods: One gold standard (GS) case deformed with a biomechanical-model and five clinical cases consisting of prostate (2x), cervix, liver, and lymph node cancer, treated with OA-MRgRT, were analyzed. Six centers conducted DDA using institutional implementations. Deformable image registration (DIR) and DDA results were compared using the contour metrics Dice Similarity Coefficient (DSC), surface-DSC, Hausdorff-distance (HD95%), and accumulated dose-volume histograms (DVHs) analyzed via intraclass correlation coefficient (ICC) and clinical dosimetric criteria (CDC). Results: For the GS, median DDA errors ranged from 0.0 to 2.8 Gy across contours and implementations. DIR of clinical cases resulted in DSC > 0.8 for up to 81.3% of contours and a variability of surface-DSC values depending on the implementation. Maximum HD95%=73.3 mm was found for duodenum in the liver case. Although DVH ICC > 0.90 was found after DDA for all but two contours, relevant absolute CDC differences were observed in clinical cases: Prostate I/II showed maximum differences in bladder V28Gy (10.2/7.6%), while for cervix, liver, and lymph node the highest differences were found for rectum D2cm3 (2.8 Gy), duodenum Dmax (7.1 Gy), and rectum D0.5cm3 (4.6 Gy). Conclusion: Overall, high agreement was found between the different DIR and DDA implementations. Case- and algorithm-dependent differences were observed, leading to potentially clinically relevant results. Larger studies are needed to define future DDA-guidelines.

Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy.

PURPOSE: We evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties. MATERIALS AND METHODS: Three evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD-JD), are used to assess the multi-modality (MM), contour-consistency (CC), and image-intensity (II)-based DIR algorithms in the MIM software package, as well as an in-house developed, contour matching-based finite element method (CM-FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One-tailed Wilcoxon-Mann-Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications. RESULTS: For the registrations performed with the MIM-CC, MIM-MM, MIM-II, and CM-FEM algorithms, their average MDAs are 0.62 ± 0.27, 2.39 ± 1.30, 3.07 ± 2.42, 1.04 ± 0.72 mm, and average DSCs are 0.94 ± 0.03, 0.80 ± 0.12, 0.77 ± 0.15, 0.90 ± 0.11, respectively. The p-values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD-JDs and greater than 0.87 for MDA and DSC. CONCLUSIONS: Among the three MIM DIR algorithms, MIM-CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions.

On the correction of respiratory motion-induced image reconstruction errors in positron-emission tomography-guided radiation therapy.

Background and purpose: Free breathing (FB) positron emission tomography (PET) images are routinely used in radiotherapy for lung cancer patients. Respiration-induced artifacts in these images compromise treatment response assessment and obstruct clinical implementation of dose painting and PET-guided radiotherapy. The purpose of this study is to develop a blurry image decomposition (BID) method to correct motion-induced image-reconstruction errors in FB-PETs. Materials and methods: Assuming a blurry PET is represented as an average of multi-phase PETs. A four-dimensional computed-tomography image is deformably registered from the end-inhalation (EI) phase to other phases. With the registration-derived deformation maps, PETs at other phases can be deformed from a PET at the EI phase. To reconstruct the EI-PET, the difference between the blurry PET and the average of the deformed EI-PETs is minimized using a maximum-likelihood expectation-maximization algorithm. The developed method was evaluated with computational and physical phantoms as well as PET/CT images acquired from three patients. Results: The BID method increased the signal-to-noise ratio from 1.88 ± 1.05 to 10.5 ± 3.3 and universal-quality index from 0.72 ± 0.11 to 1.0 for the computational phantoms, and reduced the motion-induced error from 69.9% to 10.9% in the maximum of activity concentration and from 317.5% to 8.7% in the full width at half maximum of the physical PET-phantom. The BID-based corrections increased the maximum standardized-uptake values by 17.7 ± 15.4% and reduced tumor volumes by 12.5 ± 10.4% on average for the three patients. Conclusions: The proposed image-decomposition method reduces respiration-induced errors in PET images and holds potential to improve the quality of radiotherapy for thoracic and abdominal cancer patients.

Development of a multi-layer quality assurance program to evaluate the uncertainty of deformable dose accumulation in adaptive radiotherapy.

PURPOSE: Deformable dose accumulation (DDA) has uncertainties which impede the implementation of DDA-based adaptive radiotherapy (ART) in clinic. The purpose of this study is to develop a multi-layer quality assurance (MLQA) program to evaluate uncertainties in DDA. METHODS: A computer program is developed to generate a pseudo-inverse displacement vector field (DVF) for each deformable image registration (DIR) performed in Accuray's PreciseART. The pseudo-inverse DVF is first used to calculate a pseudo-inverse consistency error (PICE) and then implemented in an energy and mass congruent mapping (EMCM) method to reconstruct a deformed dose. The PICE is taken as a metric to estimate DIR uncertainties. A pseudo-inverse dose agreement rate (PIDAR) is used to evaluate the consequence of the DIR uncertainties in DDA and the principle of energy conservation is used to validate the integrity of dose mappings. The developed MLQA program was tested using the data collected from five representative cancer patients treated with tomotherapy. RESULTS: DIRs were performed in PreciseART to generate primary DVFs for the five patients. The fidelity index and PICE of these DVFs on average are equal to 0.028 mm and 0.169 mm, respectively. With the criteria of 3 mm/3% and 5 mm/5%, the PIDARs of the PreciseART-reconstructed doses are 73.9 ± 4.4% and 87.2 ± 3.3%, respectively. The PreciseART and EMCM-based dose reconstructions have their deposited energy changed by 5.6 ± 3.9% and 2.6 ± 1.5% in five GTVs, and by 9.2 ± 7.8% and 4.7 ± 3.6% in 30 OARs, respectively. CONCLUSIONS: A pseudo-inverse map-based EMCM program has been developed to evaluate DIR and dose mapping uncertainties. This program could also be used as a sanity check tool for DDA-based ART.

Incorporating uncertainty bounds in daily deformable dose accumulation for adaptive radiation therapy of head-and-neck cancer.

BACKGROUND: The widespread use of deformable dose accumulation (DDA) in adaptive radiotherapy (ART) has been limited due to the lack of clinically compatible methods to consider its related uncertainties. PURPOSE: We estimate dose reconstruction uncertainties in daily DDA during CT-guided radiotherapy of head-and-neck cancer (HNC). We project confidence intervals of cumulative dose-volume parameters to the parotids and determine threshold values to guide clinical decision-making in ART. METHODS: Doses from daily images (megavoltage CTs [MVCTs]) of 20 HNC patients treated with tomotherapy were reconstructed and accumulated in the planning CT (PCT) utilizing a commercial DDA algorithm (PreciseART, Accuray, Inc.). For each mapped fraction, we warped the planning contours to the MVCT. Dose-volume histograms (DVHs) calculated in the MVCT (with warped contour and native dose) and the PCT (with native contour and mapped dose) were compared; the observed inconsistencies were associated with dose reconstruction errors. We derived uncertainty bounds for the transferred dose to voxels within the structure of interest in the PCT. The confidence intervals of cumulative dose-volume parameters were mid-treatment projected and evaluated as predictors of the end of treatment cumulative metrics. The need for plan adaptation was tested by comparing the projected uncertainty bounds with the treatment constraint points. RESULTS: Among all cases, the uncertainty in mean values of daily dose distributions mapped to the reference parotid's contours averaged between 2.8% and 3.8% of typical single fraction planning values and less than 1% for the planning target volume (PTV) D95%. These daily inconsistencies were higher in the ipsilateral compared to the contralateral parotid and increased toward the end of treatment. The magnitude of the uncertainty bounds for the cumulative treatment mean dose, D50%, and V20 Gy to the parotids, and PTV D95% were on average 3.5%, 6.6%, 4.6%, and 0.4% of the planned or prescribed values, with confidence intervals of 97.1%-107.0%, 98.2%-110.4%, 95.6%-111.1%, and 98.2%-100.2% respectively. The uncertainty intervals projected at mid-treatment intersected with the end of treatment bounds in 82% of the parotid's metrics; half of them presented an overlapping percentage greater than 60%. In five patients, the cumulative mean doses were projected at mid-treatment to exceed the total treatment constraint point by at least 3%; this threshold was exceeded at the end of treatment in the five cases. Underdosing was projected in only one case; the cumulative PTV D95% at the end of treatment was below the clinical threshold. CONCLUSION: Uncertainty bounds were incorporated into the results of a commercial DDA tool. The cohort's statistics showed that the parotids' cumulative DVH metrics frequently exceeded the planning values if confidence intervals were included. Most of the uncertainty bounds of the PTV metrics were kept within the clinical thresholds. We verified that mid-treatment violation projections led to exceeding the constraint point at the end of the treatment. Based on a 3% threshold, approximately one fourth of the patients are expected to be replanned at mid-treatment for parotids sparing during HNC radiotherapy.

Use of a DVH overlay technique for quality assurance of deformable image registration-based dose accumulation.

PURPOSE: We present a DVH overlay technique as a quality assurance (QA) metric for deformable image registration-based dose accumulation (DIR-DA). We use the technique to estimate the uncertainty in a DIR-DA for a revised treatment plan, and to compare two different DIR algorithms. MATERIALS AND METHODS: The required inputs to the DVH overlay workflow are deformably registered primary and secondary images, primary regions-of-interest (ROIs), and secondary dose distribution. The primary ROIs were forward warped to the secondary image, the secondary dose was inversely warped to the primary image, and the DVHs for each image were compiled. Congruent DVHs imply minimal inverse consistency error (ICE) within an ROI. For a pancreas case re-planned after 21 fractions of a 29-fraction course, the workflow was used to quantify dose accumulation error attributable to ICE, based on a hybrid contour-and-intensity-based DIR. The usefulness of the workflow was further demonstrated by assessing the performance of two DIR algorithms (one free-form intensity-based, FFIB, the other using normalized correlation coefficients, NCC, over small neighborhood patches) as applied toward kilovoltage computed tomography (kVCT)-to-megavoltage computed tomography (MVCT) registration and five-fraction dose accumulation of ten male pelvis cases. RESULTS: For the re-planned pancreas case, when applying the DVH-overlay-based uncertainties the resulting accumulated dose remained compliant with all but two of the original plan objectives. Among the male pelvis cases, FFIB and NCC DIR showed good invertibility within the planning target volume (PTV), according to the DVH overlay QA results. NCC DIR exhibited better invertibility for the bladder and rectum compared with FFIB. However, compared with FFIB, NCC DIR exhibited less regional deformation for the bladder and a tendency for increased local contraction of the rectum ROI. For the five-fraction summations, ICE for the PTV V100%Rx is comparable for both algorithms (FFIB 0.8 ± 0.7%, NCC 0.7 ± 0.3%). For the bladder and rectum V70%Rx , ICE is greater for FFIB (1.8 ± 0.7% for bladder, 1.7 ± 0.6% for rectum) than for NCC (1.0 ± 0.3% for bladder, 1.0 ± 0.4% for rectum). CONCLUSIONS: The DVH overlay technique identified instances in which a DIR exhibits favorable invertibility, implying low ICE in a DIR-based dose accumulation. Differences in the overlaid DVHs can also estimate dose accumulation errors attributable to ICE for given ROIs.

Patient-specific synthetic magnetic resonance imaging generation from cone beam computed tomography for image guidance in liver stereotactic body radiation therapy.

Objective: Despite its prevalence, cone beam computed tomography (CBCT) has poor soft-tissue contrast, making it challenging to localize liver tumors. We propose a patient-specific deep learning model to generate synthetic magnetic resonance imaging (MRI) from CBCT to improve tumor localization. Methods: A key innovation is using patient-specific CBCT-MRI image pairs to train a deep learning model to generate synthetic MRI from CBCT. Specifically, patient planning CT was deformably registered to prior MRI, and then used to simulate CBCT with simulated projections and Feldkamp, Davis, and Kress reconstruction. These CBCT-MRI images were augmented using translations and rotations to generate enough patient-specific training data. A U-Net-based deep learning model was developed and trained to generate synthetic MRI from CBCT in the liver, and then tested on a different CBCT dataset. Synthetic MRIs were quantitatively evaluated against ground-truth MRI. Results: The synthetic MRI demonstrated superb soft-tissue contrast with clear tumor visualization. On average, the synthetic MRI achieved 28.01, 0.025, and 0.929 for peak signal-to-noise ratio, mean square error, and structural similarity index, respectively, outperforming CBCT images. The model performance was consistent across all three patients tested. Conclusion: Our study demonstrated the feasibility of a patient-specific model to generate synthetic MRI from CBCT for liver tumor localization, opening up a potential to democratize MRI guidance in clinics with conventional LINACs.

Four-dimensional computed tomography-based biomechanical measurements of pulmonary function and their correlation with clinical outcome for lung stereotactic body radiation therapy patients.

BACKGROUND: Functional image guided radiotherapy allows for the delivery of an equivalent dose to tumor targets while sparing high ventilation lung tissues. In this study, we investigate whether radiation dose to functional lung is associated with clinical outcome for stereotactic body radiation therapy (SBRT) patients. METHODS: Four-dimensional computed tomography (4DCT) images were used to assess lung function. Deformable image registration (DIR) was performed from the end-inhale phase to the end-exhale phase with resultant displacement vectors used to calculate ventilation maps. In addition to the Jacobian-based ventilation we introduce a volumetric variation method (Rv) based on a biomechanical finite element method (FEM), to assess lung ventilation. Thirty NSCLC patients, treated with SBRT, were evaluated in this study. 4DCT images were used to calculate both Jacobian and Rv-based ventilation images. Areas under the receiver operating characteristic curve (AUC) were used to assess the predictive power of functional metrics. Metrics were calculated over the whole lung as well as high and low ventilated regions. RESULTS: Ventilation in dose regions between 1 and 5 Gy had higher AUC values compared to other dose regions. Rv based ventilation imaging method also showed to be less spatially variant and less heterogeneous, and the resultant Rv metrics had higher AUC values for predicting grade 2+ dyspnea. CONCLUSIONS: Low dose delivered to high ventilation areas may also increase the risk of compromised pulmonary function. Rv based ventilation images could be useful for the prediction of clinical toxicity for lung SBRT patients.

4-Dimensional computed tomography-based ventilation and compliance images for quantification of radiation-induced changes in pulmonary function.

INTRODUCTION: 4-Dimensional computed tomography (4DCT)-based ventilation imaging is a promising technique for evaluating pulmonary function, but lung elasticity and mechanics are usually not part of the ventilation image analysis. In this study we demonstrate a 4DCT-based imaging technique that can be used to calculate regional lung compliance changes after radiotherapy (RT). METHODS: Six lung cancer patients were included in this study. Four of the patients had 4DCT images acquired pre-RT, 3 and 9 months post-RT. Ventilation and compliance were calculated from the deformable image registration (DIR) of 4DCTs, performed from the end-inhale to the end-exhale breathing phase. Regional compliance was defined as the ratio of volumetric variation and associated stress in each voxel, representing lung elasticity and computed using a FEM-based framework. Ventilation, compliance and CT density were calculated for all pre-RT and post-RT 4DCTs and evaluation metrics were computed. RESULTS: Average CT density changes were 13.6 ± 11.4HU after 3 months and 26.9 ± 15.8HU after 9 months. Ventilation was reduced at 3 months, but improved at 9 months in regions with dose ≥ 35 Gy, encompassing about 10% of the lung volume; compliance was reduced at both time-points. Radiation dose ≥ 35 Gy caused major change in lung density and ventilation, which was higher than that previously reported in the literature (i.e. 24 Gy). CONCLUSION: Lung tissue response is diverse with respect to CT density, ventilation and compliance. Combination of ventilation and compliance with CT density could be beneficial for understanding radiation-induced lung damage and consequently could help develop improved treatment protocols for lung cancer patients.

Kinetic modeling of tumor regression incorporating the concept of cancer stem-like cells for patients with locally advanced lung cancer.

BACKGROUND: Personalized medicine for patients receiving radiation therapy remains an elusive goal due, in part, to the limits in our understanding of the underlying mechanisms governing tumor response to radiation. The purpose of this study was to develop a kinetic model, in the context of locally advanced lung cancer, connecting cancer cell subpopulations with tumor volumes measured during the course of radiation treatment for understanding treatment outcome for individual patients. METHODS: The kinetic model consists of three cell compartments: cancer stem-like cells (CSCs), non-stem tumor cells (TCs) and dead cells (DCs). A set of ordinary differential equations were developed to describe the time evolution of each compartment, and the analytic solution of these equations was iterated to be aligned with the day-to-day tumor volume changes during the course of radiation treatment. A least squares fitting method was used to estimate the parameters of the model that include the proportion of CSCs and their radio-sensitivities. This model was applied to five patients with stage III lung cancer, and tumor volumes were measured from 33 cone-beam computed tomography (CBCT) images for each of these patients. The analytical solution of these differential equations was compared with numerically simulated results. RESULTS: For the five patients with late stage lung cancer, the derived proportions of CSCs are 0.3 on average, the average probability of the symmetry division is 0.057 and the average surviving fractions of CSCs is 0.967, respectively. The derived parameters are comparable to the results from literature and our experiments. The preliminary results suggest that the CSC self-renewal rate is relatively small, compared to the proportion of CSCs for locally advanced lung cancers. CONCLUSIONS: A novel mathematical model has been developed to connect the population of cancer stem-like cells with tumor volumes measured from a sequence of CBCT images. This model may help improve our understanding of tumor response to radiation therapy, and is valuable for development of new treatment regimens for patients with locally advanced lung cancer.

On the improvement of CBCT image quality for soft tissue-based SRS localization.

PURPOSE: We explore the optimal cone-beam CT (CBCT) acquisition parameters to improve CBCT image quality to enhance intracranial stereotactic radiosurgery (SRS) localization and also assess the imaging dose levels associated with each imaging protocol. METHODS: Twenty-six CBCT acquisition protocols were generated on an Edge® linear accelerator (Varian Medical Systems, Palo Alto, CA) with different x-ray tube current and potential settings, gantry rotation trajectories, and gantry rotation speeds. To assess image quality, images of the Catphan 504 phantom were analyzed to evaluate the following image quality metrics: uniformity, HU constancy, spatial resolution, low contrast detection, noise level, and contrast-to-noise ratio (CNR). To evaluate the imaging dose for each protocol, the cone-beam dose index (CBDI) was measured. To validate the phantom results, further analysis was performed with an anthropomorphic head phantom as well as image data acquired for a clinical SRS patient. RESULTS: The Catphan data indicates that adjusting acquisition parameters had direct effects on the image noise level, low contrast detection, and CNR, but had minimal effects on uniformity, HU constancy, and spatial resolution. The noise level was reduced from 34.5 ± 0.3 to 18.5 ± 0.2 HU with a four-fold reduction in gantry speed, and to 18.7 ± 0.2 HU with a four-fold increase in tube current. Overall, the noise level was found to be proportional to inverse square root of imaging dose, and imaging dose was proportional to the product of total tube current-time product and the cube of the x-ray potential. Analysis of the anthropomorphic head phantom data and clinical SRS imaging data also indicates that noise is reduced with imaging dose increase. CONCLUSIONS: Our results indicate that optimization of the imaging protocol, and thereby an increase in the imaging dose, is warranted for improved soft-tissue visualization for intracranial SRS.

Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer.

We proposed a framework to detect and quantify local tumor morphological changes due to chemo-radiotherapy (CRT) using a Jacobian map and to extract quantitative radiomic features from the Jacobian map to predict the pathologic tumor response in locally advanced esophageal cancer patients. In 20 patients who underwent CRT, a multi-resolution BSpline deformable registration was performed to register the follow-up (post-CRT) CT to the baseline CT image. The Jacobian map (J) was computed as the determinant of the gradient of the deformation vector field. The Jacobian map measured the ratio of local tumor volume change where J < 1 indicated tumor shrinkage and J > 1 denoted expansion. The tumor was manually delineated and corresponding anatomical landmarks were generated on the baseline and follow-up images. Intensity, texture and geometry features were then extracted from the Jacobian map of the tumor to quantify tumor morphological changes. The importance of each Jacobian feature in predicting pathologic tumor response was evaluated by both univariate and multivariate analysis. We constructed a multivariate prediction model by using a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO) for feature selection. The SVM-LASSO model was evaluated using ten-times repeated 10-fold cross-validation (10 × 10-fold CV). After registration, the average target registration error was 4.30 ± 1.09 mm (LR:1.63 mm AP:1.59 mm SI:3.05 mm) indicating registration error was within two voxels and close to 4 mm slice thickness. Visually, the Jacobian map showed smoothly-varying local shrinkage and expansion regions in a tumor. Quantitatively, the average median Jacobian was 0.80 ± 0.10 and 1.05 ± 0.15 for responder and non-responder tumors, respectively. These indicated that on average responder tumors had 20% median volume shrinkage while non-responder tumors had 5% median volume expansion. In univariate analysis, the minimum Jacobian (p = 0.009, AUC = 0.98) and median Jacobian (p = 0.004, AUC = 0.95) were the most significant predictors. The SVM-LASSO model achieved the highest accuracy when these two features were selected (sensitivity = 94.4%, specificity = 91.8%, AUC = 0.94). Novel features extracted from the Jacobian map quantified local tumor morphological changes using only baseline tumor contour without post-treatment tumor segmentation. The SVM-LASSO model using the median Jacobian and minimum Jacobian achieved high accuracy in predicting pathologic tumor response. The Jacobian map showed great potential for longitudinal evaluation of tumor response.

Utilization of a hybrid finite-element based registration method to quantify heterogeneous tumor response for adaptive treatment for lung cancer patients.

Tumor response to radiation treatment (RT) can be evaluated from changes in metabolic activity between two positron emission tomography (PET) images. Activity changes at individual voxels in pre-treatment PET images (PET1), however, cannot be derived until their associated PET-CT (CT1) images are appropriately registered to during-treatment PET-CT (CT2) images. This study aimed to investigate the feasibility of using deformable image registration (DIR) techniques to quantify radiation-induced metabolic changes on PET images. Five patients with non-small-cell lung cancer (NSCLC) treated with adaptive radiotherapy were considered. PET-CTs were acquired two weeks before RT and 18 fractions after the start of RT. DIR was performed from CT1 to CT2 using B-Spline and diffeomorphic Demons algorithms. The resultant displacements in the tumor region were then corrected using a hybrid finite element method (FEM). Bitmap masks generated from gross tumor volumes (GTVs) in PET1 were deformed using the four different displacement vector fields (DVFs). The conservation of total lesion glycolysis (TLG) in GTVs was used as a criterion to evaluate the quality of these registrations. The deformed masks were united to form a large mask which was then partitioned into multiple layers from center to border. The averages of SUV changes over all the layers were 1.0 ± 1.3, 1.0 ± 1.2, 0.8 ± 1.3, 1.1 ± 1.5 for the B-Spline, B-Spline + FEM, Demons and Demons + FEM algorithms, respectively. TLG changes before and after mapping using B-Spline, Demons, hybrid-B-Spline, and hybrid-Demons registrations were 20.2%, 28.3%, 8.7%, and 2.2% on average, respectively. Compared to image intensity-based DIR algorithms, the hybrid FEM modeling technique is better in preserving TLG and could be useful for evaluation of tumor response for patients with regressing tumors.

Adaptive radiotherapy for NSCLC patients: utilizing the principle of energy conservation to evaluate dose mapping operations.

Tumor regression during the course of fractionated radiotherapy confounds the ability to accurately estimate the total dose delivered to tumor targets. Here we present a new criterion to improve the accuracy of image intensity-based dose mapping operations for adaptive radiotherapy for patients with non-small cell lung cancer (NSCLC). Six NSCLC patients were retrospectively investigated in this study. An image intensity-based B-spline registration algorithm was used for deformable image registration (DIR) of weekly CBCT images to a reference image. The resultant displacement vector fields were employed to map the doses calculated on weekly images to the reference image. The concept of energy conservation was introduced as a criterion to evaluate the accuracy of the dose mapping operations. A finite element method (FEM)-based mechanical model was implemented to improve the performance of the B-Spline-based registration algorithm in regions involving tumor regression. For the six patients, deformed tumor volumes changed by 21.2 ± 15.0% and 4.1 ± 3.7% on average for the B-Spline and the FEM-based registrations performed from fraction 1 to fraction 21, respectively. The energy deposited in the gross tumor volume (GTV) was 0.66 Joules (J) per fraction on average. The energy derived from the fractional dose reconstructed by the B-spline and FEM-based DIR algorithms in the deformed GTV's was 0.51 J and 0.64 J, respectively. Based on landmark comparisons for the 6 patients, mean error for the FEM-based DIR algorithm was 2.5 ± 1.9 mm. The cross-correlation coefficient between the landmark-measured displacement error and the loss of radiation energy was -0.16 for the FEM-based algorithm. To avoid uncertainties in measuring distorted landmarks, the B-Spline-based registrations were compared to the FEM registrations, and their displacement differences equal 4.2 ± 4.7 mm on average. The displacement differences were correlated to their relative loss of radiation energy with a cross-correlation coefficient equal to 0.68. Based on the principle of energy conservation, the FEM-based mechanical model has a better performance than the B-Spline-based DIR algorithm. It is recommended that the principle of energy conservation be incorporated into a comprehensive QA protocol for adaptive radiotherapy.

Evaluation of adaptive treatment planning for patients with non-small cell lung cancer.

The purpose of this study was to develop metrics to evaluate uncertainties in deformable dose accumulation for patients with non-small cell lung cancer (NSCLC). Initial treatment plans (primary) and cone-beam CT (CBCT) images were retrospectively processed for seven NSCLC patients, who showed significant tumor regression during the course of treatment. Each plan was developed with IMRT for 2 Gy × 33 fractions. A B-spline-based DIR algorithm was used to register weekly CBCT images to a reference image acquired at fraction 21 and the resultant displacement vector fields (DVFs) were then modified using a finite element method (FEM). The doses were calculated on each of these CBCT images and mapped to the reference image using a tri-linear dose interpolation method, based on the B-spline and FEM-generated DVFs. Contours propagated from the planning image were adjusted to the residual tumor and OARs on the reference image to develop a secondary plan. For iso-prescription adaptive plans (relative to initial plans), mean lung dose (MLD) was reduced, on average from 17.3 Gy (initial plan) to 15.2, 14.5 and 14.8 Gy for the plans adapted using the rigid, B-Spline and FEM-based registrations. Similarly, for iso-toxic adaptive plans (considering MLD relative to initial plans) using the rigid, B-Spline and FEM-based registrations, the average doses were 69.9 ± 6.8, 65.7 ± 5.1 and 67.2 ± 5.6 Gy in the initial volume (PTV1), and 81.5 ± 25.8, 77.7 ± 21.6, and 78.9 ± 22.5 Gy in the residual volume (PTV21), respectively. Tumor volume reduction was correlated with dose escalation (for isotoxic plans, correlation coefficient = 0.92), and with MLD reduction (for iso-fractional plans, correlation coefficient = 0.85). For the case of the iso-toxic dose escalation, plans adapted with the B-Spline and FEM DVFs differed from the primary plan adapted with rigid registration by 2.8 ± 1.0 Gy and 1.8 ± 0.9 Gy in PTV1, and the mean difference between doses accumulated using the B-spline and FEM DVF's was 1.1 ± 0.6 Gy. As a dose mapping-induced energy change, energy defect in the tumor volume was 20.8 ± 13.4% and 4.5 ± 2.4% for the B-spline and FEM-based dose accumulations, respectively. The energy defect of the B-Spline-based dose accumulation is significant in the tumor volume and highly correlated to the difference between the B-Spline and FEM-accumulated doses with their correlation coefficient equal to 0.79. Adaptive planning helps escalate target dose and spare normal tissue for patients with NSCLC, but deformable dose accumulation may have a significant loss of energy in regressed tumor volumes when using image intensity-based DIR algorithms. The metric of energy defect is a useful tool for evaluation of adaptive planning accuracy for lung cancer patients.

Deriving detector-specific correction factors for rectangular small fields using a scintillator detector.

The goal of this study was to investigate small field output factors (OFs) for flat-tening filter-free (FFF) beams on a dedicated stereotactic linear accelerator-based system. From this data, the collimator exchange effect was quantified, and detector-specific correction factors were generated. Output factors for 16 jaw-collimated small fields (from 0.5 to 2 cm) were measured using five different detectors including an ion chamber (CC01), a stereotactic field diode (SFD), a diode detector (Edge), Gafchromic film (EBT3), and a plastic scintillator detector (PSD, W1). Chamber, diodes, and PSD measurements were performed in a Wellhofer water tank, while films were irradiated in solid water at 100 cm source-to-surface distance and 10 cm depth. The collimator exchange effect was quantified for rectangular fields. Monte Carlo (MC) simulations of the measured configurations were also performed using the EGSnrc/DOSXYZnrc code. Output factors measured by the PSD and verified against film and MC calculations were chosen as the benchmark measurements. Compared with plastic scintillator detector (PSD), the small volume ion chamber (CC01) underestimated output factors by an average of -1.0% ± 4.9% (max. = -11.7% for 0.5 × 0.5 cm2 square field). The stereotactic diode (SFD) overestimated output factors by 2.5% ± 0.4% (max. = 3.3% for 0.5 × 1 cm2 rectangular field). The other diode detector (Edge) also overestimated the OFs by an average of 4.2% ± 0.9% (max. = 6.0% for 1 × 1 cm2 square field). Gafchromic film (EBT3) measure-ments and MC calculations agreed with the scintillator detector measurements within 0.6% ± 1.8% and 1.2% ± 1.5%, respectively. Across all the X and Y jaw combinations, the average collimator exchange effect was computed: 1.4% ± 1.1% (CC01), 5.8% ± 5.4% (SFD), 5.1% ± 4.8% (Edge diode), 3.5% ± 5.0% (Monte Carlo), 3.8% ± 4.7% (film), and 5.5% ± 5.1% (PSD). Small field detectors should be used with caution with a clear understanding of their behaviors, especially for FFF beams and small, elongated fields. The scintillator detector exhibited good agreement against Gafchromic film measurements and MC simulations over the range of field sizes studied. The collimator exchange effect was found to be impor-tant at these small field sizes. Detector-specific correction factors were computed using the scintillator measurements as the benchmark.

Development of a deformable dosimetric phantom to verify dose accumulation algorithms for adaptive radiotherapy.

Adaptive radiotherapy may improve treatment outcomes for lung cancer patients. Because of the lack of an effective tool for quality assurance, this therapeutic modality is not yet accepted in clinic. The purpose of this study is to develop a deformable physical phantom for validation of dose accumulation algorithms in regions with heterogeneous mass. A three-dimensional (3D) deformable phantom was developed containing a tissue-equivalent tumor and heterogeneous sponge inserts. Thermoluminescent dosimeters (TLDs) were placed at multiple locations in the phantom each time before dose measurement. Doses were measured with the phantom in both the static and deformed cases. The deformation of the phantom was actuated by a motor driven piston. 4D computed tomography images were acquired to calculate 3D doses at each phase using Pinnacle and EGSnrc/DOSXYZnrc. These images were registered using two registration software packages: VelocityAI and Elastix. With the resultant displacement vector fields (DVFs), the calculated 3D doses were accumulated using a mass-and energy congruent mapping method and compared to those measured by the TLDs at four typical locations. In the static case, TLD measurements agreed with all the algorithms by 1.8% at the center of the tumor volume and by 4.0% in the penumbra. In the deformable case, the phantom's deformation was reproduced within 1.1 mm. For the 3D dose calculated by Pinnacle, the total dose accumulated with the Elastix DVF agreed well to the TLD measurements with their differences <2.5% at four measured locations. When the VelocityAI DVF was used, their difference increased up to 11.8%. For the 3D dose calculated by EGSnrc/DOSXYZnrc, the total doses accumulated with the two DVFs were within 5.7% of the TLD measurements which are slightly over the rate of 5% for clinical acceptance. The detector-embedded deformable phantom allows radiation dose to be measured in a dynamic environment, similar to deforming lung tissues, supporting the validation of dose mapping and accumulation operations in regions with heterogeneous mass, and dose distributions.

An adaptive MR-CT registration method for MRI-guided prostate cancer radiotherapy.

Magnetic Resonance images (MRI) have superior soft tissue contrast compared with CT images. Therefore, MRI might be a better imaging modality to differentiate the prostate from surrounding normal organs. Methods to accurately register MRI to simulation CT images are essential, as we transition the use of MRI into the routine clinic setting. In this study, we present a finite element method (FEM) to improve the performance of a commercially available, B-spline-based registration algorithm in the prostate region. Specifically, prostate contours were delineated independently on ten MRI and CT images using the Eclipse treatment planning system. Each pair of MRI and CT images was registered with the B-spline-based algorithm implemented in the VelocityAI system. A bounding box that contains the prostate volume in the CT image was selected and partitioned into a tetrahedral mesh. An adaptive finite element method was then developed to adjust the displacement vector fields (DVFs) of the B-spline-based registrations within the box. The B-spline and FEM-based registrations were evaluated based on the variations of prostate volume and tumor centroid, the unbalanced energy of the generated DVFs, and the clarity of the reconstructed anatomical structures. The results showed that the volumes of the prostate contours warped with the B-spline-based DVFs changed 10.2% on average, relative to the volumes of the prostate contours on the original MR images. This discrepancy was reduced to 1.5% for the FEM-based DVFs. The average unbalanced energy was 2.65 and 0.38 mJ cm(-3), and the prostate centroid deviation was 0.37 and 0.28 cm, for the B-spline and FEM-based registrations, respectively. Different from the B-spline-warped MR images, the FEM-warped MR images have clear boundaries between prostates and bladders, and their internal prostatic structures are consistent with those of the original MR images. In summary, the developed adaptive FEM method preserves the prostate volume during the transformation between the MR and CT images and improves the accuracy of the B-spline registrations in the prostate region. The approach will be valuable for the development of high-quality MRI-guided radiation therapy.