Biomechanical Deformable Image Registration Research Articles

Identification of A0 minimum ablative margins for colorectal liver metastases: multicentre, retrospective study using deformable CT registration and artificial intelligence-based autosegmentation.

Several ablation confirmation software methods for minimum ablative margin assessment have recently been developed to improve local outcomes for patients undergoing thermal ablation of colorectal liver metastases. Previous assessments were limited to single institutions mostly at the place of development. The aim of this study was to validate the previously identified 5 mm minimum ablative margin (A0) using autosegmentation and biomechanical deformable image registration in a multi-institutional setting. This was a multicentre, retrospective study including patients with colorectal liver metastases undergoing CT- or ultrasound-guided microwave or radiofrequency ablation during 2009-2022, reporting 3-year local disease progression (residual unablated tumour or local tumour progression) rates by minimum ablative margin across all institutions and identifying an intraprocedural contrast-enhanced CT-based minimum ablative margin associated with a 3-year local disease progression rate of less than 1%. A total of 400 ablated colorectal liver metastases (median diameter of 1.5 cm) in 243 patients (145 men; median age of 62 [interquartile range 54-70] years) were evaluated, with a median follow-up of 26 (interquartile range 17-40) months. A total of 119 (48.9%) patients with 186 (46.5%) colorectal liver metastases were from international institutions B, C, and D that were not involved in the software development. Three-year local disease progression rates for 0 mm, >0 and <5 mm, and 5 mm or larger minimum ablative margins were 79%, 15%, and 0% respectively for institution A (where the software was developed) and 34%, 19%, and 2% respectively for institutions B, C, and D combined. Local disease progression risk decreased to less than 1% with an intraprocedurally confirmed minimum ablative margin greater than 4.6 mm. A minimum ablative margin of 5 mm or larger demonstrates optimal local oncological outcomes. It is proposed that an intraprocedural minimum ablative margin of 5 mm or larger, confirmed using biomechanical deformable image registration, serves as the A0 for colorectal liver metastasis thermal ablation.

Incidental Ring-hyperenhancing Liver Micronodules at CT Hepatic Arteriography-guided Percutaneous Thermal Ablation of Colorectal Liver Metastases.

CT during hepatic arteriography (CTHA) is a highly sensitive imaging method for detecting colorectal liver metastases (CLMs), which supports its use during percutaneous thermal liver ablation. In contrast to its high sensitivity, its specificity for incidental small CLMs not detected at preablation cross-sectional imaging is believed to be low given the absence of specific imaging signatures and the common presence of pseudolesions. In this retrospective study of 22 patients (mean age, 55 years ± 10.6 [SD]; 63.6% male, 36.4% female) with CLMs undergoing CTHA-guided microwave percutaneous thermal ablation between November 2017 and October 2022, the authors provided a definition of incidental ring-hyperenhancing liver micronodules (RHLMs) and investigated whether there is a correlation of RHLMs with histologic analysis or intrahepatic tumor progression at imaging follow-up after applying a biomechanical deformable image registration method. The analysis revealed 25 incidental RHLMs in 41.7% (10 of 24) of the CTHA images from the respective guided ablation sessions. Of those, four RHLMs were ablated. Among the remaining 21 RHLMs, 71.4% (15 of 21) were confirmed to be CLM with either histology (n = 3) or imaging follow-up (n = 12). The remaining 28.6% (six of 21) of RHLMs were not observed at follow-up imaging. This suggests that RHLMs at CTHA may be an early indicator of incidental small CLMs. Keywords: Colorectal Neoplasms, Liver, Angiography, CT, Incidental Findings, Ablation Supplemental material is available for this article. © RSNA, 2024.

Ablative margin quantification using deformable versus rigid image registration in colorectal liver metastasis thermal ablation: a retrospective single-center study.

To investigate the correlation of minimal ablative margin (MAM) quantification using biomechanical deformable (DIR) versus intensity-based rigid image registration (RIR) with local outcomes following colorectal liver metastasis (CLM) thermal ablation. This retrospective single-institution study included consecutive patients undergoing thermal ablation between May 2016 and October 2021. Patients who did not have intraprocedural pre- and post-ablation contrast-enhanced CT images for MAM quantification or follow-up period less than 1year without residual tumor or local tumor progression (LTP) were excluded. DIR and RIR methods were used to quantify the MAM. The registration accuracy was compared using Dice similarity coefficient (DSC). Area under the receiver operating characteristic curve (AUC) was used to test MAM in predicting local tumor outcomes. A total of 72 patients (mean age 57; 44 men) with 139 tumors (mean diameter 1.5cm ± 0.8 (SD)) were included. During a median follow-up of 29.4months, there was one residual unablated tumor and the LTP rate was 17% (24/138). The ranges of DSC were 0.96-0.98 and 0.67-0.98 for DIR and RIR, respectively (p < 0.001). When using DIR, 27 (19%) tumors were partially or totally registered outside the liver, compared to 46 (33%) with RIR. Using DIR versus RIR, the corresponding median MAM was 4.7mm versus 4.0mm, respectively (p = 0.5). The AUC in predicting residual tumor and 1-year LTP for DIR versus RIR was 0.89 versus 0.72, respectively (p < 0.001). Ablative margin quantified on intra-procedural CT imaging using DIR method outperformed RIR for predicting local outcomes of CLM thermal ablation. The study supports the role of biomechanical deformable image registration as the preferred image registration method over rigid image registration for quantifying minimal ablative margins using intraprocedural contrast-enhanced CT images. • Accurate and reproducible image registration is a prerequisite for clinical application of image-based ablation confirmation methods. • When compared to intensity-based rigid image registration, biomechanical deformable image registration for minimal ablative margin quantification was more accurate for liver registration using intraprocedural contrast-enhanced CT images. • Biomechanical deformable image registration outperformed intensity-based rigid image registration for predicting local tumor outcomes following colorectal liver metastasis thermal ablation.

Intraprocedural Versus Initial Follow-up Minimal Ablative Margin Assessment After Colorectal Liver Metastasis Thermal Ablation: Which One Better Predicts Local Outcomes?

The aim of this study was to investigate the prognostic value of 3-dimensional minimal ablative margin (MAM) quantified by intraprocedural versus initial follow-up computed tomography (CT) in predicting local tumor progression (LTP) after colorectal liver metastasis (CLM) thermal ablation. This single-institution, patient-clustered, tumor-based retrospective study included patients undergoing microwave and radiofrequency ablation between 2016 and 2021. Patients without intraprocedural and initial follow-up contrast-enhanced CT, residual tumors, or with follow-up less than 1 year without LTP were excluded. Minimal ablative margin was quantified by a biomechanical deformable image registration method with segmentations of CLMs on intraprocedural preablation CT and ablation zones on intraprocedural postablation and initial follow-up CT. Prognostic value of MAM to predict LTP was tested using area under the curve and competing-risk regression model. A total of 68 patients (mean age ± standard deviation, 57 ± 12 years; 43 men) with 133 CLMs were included. During a median follow-up of 30.3 months, LTP rate was 17% (22/133). The median volume of ablation zone was 27 mL and 16 mL segmented on intraprocedural and initial follow-up CT, respectively ( P < 0.001), with corresponding median MAM of 4.7 mm and 0 mm, respectively ( P < 0.001). The area under the curve was higher for MAM quantified on intraprocedural CT (0.89; 95% confidence interval [CI], 0.83-0.94) compared with initial follow-up CT (0.66; 95% CI, 0.54-0.76) in predicting 1-year LTP ( P < 0.001). An MAM of 0 mm on intraprocedural CT was an independent predictor of LTP with a subdistribution hazards ratio of 11.9 (95% CI, 4.9-28.9; P < 0.001), compared with 2.4 (95% CI, 0.9-6.0; P = 0.07) on initial follow-up CT. Ablative margin quantified on intraprocedural CT significantly outperformed initial follow-up CT in predicting LTP and should be used for ablation endpoint assessment.

Analysis and prediction of liver volume change maps derived from computational tomography scans acquired pre- and post-radiation therapy

External beam radiation therapy (EBRT) of liver cancers can cause local liver atrophy as a result of tissue damage or hypertrophy as a result of liver regeneration. Predicting those volumetric changes would enable new strategies for liver function preservation during treatment planning. However, understanding of the spatial dose/volume relationship is still limited. This study leverages the use of deep learning-based segmentation and biomechanical deformable image registration (DIR) to analyze and predict this relationship. Pre- and Post-EBRT imaging data were collected for 100 patients treated for hepatocellular carcinomas, cholangiocarcinoma or CRC with intensity-modulated radiotherapy (IMRT) with prescription doses ranging from 50 to 100 Gy delivered in 10–28 fractions. For each patient, DIR between the portal and venous (PV) phase of a diagnostic computed tomography (CT) scan acquired before radiation therapy (RT) planning, and a PV phase of a diagnostic CT scan acquired after the end of RT (on average 147 ± 36 d) was performed to calculate Jacobian maps representing volume changes in the liver. These volume change maps were used: (i): to analyze the dose/volume relationship in the whole liver and individual Couinaud’s segments; and (ii): to investigate the use of deep-learning to predict a Jacobian map solely based on the pre-RT diagnostic CT and planned dose distribution. Moderate correlations between mean equivalent dose in 2 Gy fractions (EQD2) and volume change was observed for all liver sub-regions analyzed individually with Pearson correlation r ranging from −0.36 to −067. The predicted volume change maps showed a significantly stronger voxel-wise correlation with the DIR-based volume change maps than when considering the original EQD2 distribution (0.63 ± 0.24 versus 0.55 ± 23, respectively), demonstrating the ability of the proposed approach to establish complex relationships between planned dose and liver volume response months after treatment, which represents a promising prediction tool for the development of future adaptive and personalized liver radiation therapy strategies.

Ablative Margins of Colorectal Liver Metastases Using Deformable CT Image Registration and Autosegmentation.

Background Confirming ablation completeness with sufficient ablative margin is critical for local tumor control following colorectal liver metastasis (CLM) ablation. An image-based confirmation method considering patient- and ablation-related biomechanical deformation is an unmet need. Purpose To evaluate a biomechanical deformable image registration (DIR) method for three-dimensional (3D) minimal ablative margin (MAM) quantification and the association with local disease progression following CT-guided CLM ablation. Materials and Methods This single-institution retrospective study included patients with CLM treated with CT-guided microwave or radiofrequency ablation from October 2015 to March 2020. A biomechanical DIR method with AI-based autosegmentation of liver, tumors, and ablation zones on CT images was applied for MAM quantification retrospectively. The per-tumor incidence of local disease progression was defined as residual tumor or local tumor progression. Factors associated with local disease progression were evaluated using the multivariable Fine-Gray subdistribution hazard model. Local disease progression sites were spatially localized with the tissue at risk for tumor progression (<5 mm) using a 3D ray-tracing method. Results Overall, 213 ablated CLMs (mean diameter, 1.4 cm) in 124 consecutive patients (mean age, 57 years ± 12 [SD]; 69 women) were evaluated, with a median follow-up interval of 25.8 months. In ablated CLMs, an MAM of 0 mm was depicted in 14.6% (31 of 213), from greater than 0 to less than 5 mm in 40.4% (86 of 213), and greater than or equal to 5 mm in 45.1% (96 of 213). The 2-year cumulative incidence of local disease progression was 72% for 0 mm and 12% for greater than 0 to less than 5 mm. No local disease progression was observed for an MAM greater than or equal to 5 mm. Among 117 tumors with an MAM less than 5 mm, 36 had local disease progression and 30 were spatially localized within the tissue at risk for tumor progression. On multivariable analysis, an MAM of 0 mm (subdistribution hazard ratio, 23.3; 95% CI: 10.8, 50.5; P < .001) was independently associated with local disease progression. Conclusion Biomechanical deformable image registration and autosegmentation on CT images enabled identification and spatial localization of colorectal liver metastases at risk for local disease progression following ablation, with a minimal ablative margin greater than or equal to 5 mm as the optimal end point. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sofocleous in this issue.

Use of Contrast Media During CT-guided Thermal Ablation of Colorectal Liver Metastasis for Procedure Planning is Associated with Improved Immediate Outcomes.

The aim of this study was to analyze the impact of using intra-procedural pre-ablation contrast-enhanced CT prior to percutaneous thermal ablation (pre-ablation CECT) of colorectal liver metastases (CLM) on local outcomes. This retrospective analysis of a prospectively collected liver ablation registry included 144 consecutive patients (median age 57years IQR [49, 65], 60% men) who underwent 173 CT-guided ablation sessions for 250 CLM between October 2015 and March 2020. In addition to oncologic outcomes, technical success was retrospectively evaluated using a biomechanical deformable image registration software for 3D-minimal ablative margin (3D-MAM) quantification. Bayesian regression was used to estimate effects of pre-ablation CECT on residual unablated tumor, 3D-MAM, and local tumor progression-free survival (LTPFS). Pre-ablation CECT was acquired in 71/173 (41%) sessions. Residual unablated tumor was present in one (0.9%) versus nine tumors (6.6%) ablated with versus without using pre-ablation CECT, respectively (p = 0.024). Pre-ablation CECT use decreased the odds of residual disease on first follow-up by 78% (CI95% [5, 86]) and incomplete ablation (3D-MAM ≤ 0mm) by 58% (CI95% [13, 122]). The odds ratio for residual unablated tumor for larger CLM was lower when pre-ablation CECT was used (odds ratio 1.0 with pre-ablation CECT vs. 2.52 without). Pre-ablation CECT use was not associated with improvements on LTPFS. Pre-ablation CECT is associated with improved immediate outcomes by significantly reducing the incidence of residual unablated tumor and by mitigating the risk of incomplete ablation for larger CLM. We recommend performing baseline intra-procedural pre-ablation CECT as a standard imaging protocol. Level 3 (retrospective cohort study).

Study Protocol COVER-ALL: Clinical Impact of a Volumetric Image Method for Confirming Tumour Coverage with Ablation on Patients with Malignant Liver Lesions.

This study aims to evaluate the intra-procedural use of a novel ablation confirmation (AC) method, consisting of biomechanical deformable image registration incorporating AI-based auto-segmentation, and its impact on tumor coverage by quantitative three-dimensional minimal ablative margin (MAM) CT-generated assessment. This single-center, randomized, phase II, intent-to-treat trial is enrolling 100 subjects with primary and secondary liver tumors (≤ 3 tumors, 1-5cmin diameter) undergoing microwave or radiofrequency ablation with a goal of achieving ≥ 5mm MAM. For the experimental arm, the proposed novel AC method is utilized for ablation applicator(s) placement verification and MAM assessment. For the control arm, the same variables are assessed by visual inspection and anatomical landmarks-based quantitative measurements aided by co-registration of pre- and post-ablation contrast-enhanced CT images. The primary objective is to evaluate the impact of the proposed AC method on the MAM. Secondary objectives are 2-year LTP-free survival, complication rates, quality of life, liver function, other oncological outcomes, and impact of AC method on procedure workflow. The COVER-ALL trial will provide information on the role of a biomechanical deformable image registration-based ablation confirmation method incorporating AI-based auto-segmentation for improving MAM, which might translate in improvements of liver ablation efficacy. The COVER-ALL trial aims to provide information on the role of a novel intra-procedural AC method for improving MAM, which might translate in improvements of liver ablation efficacy. ClinicalTrials.gov identifier: NCT04083378.

A novel use of biomechanical model-based deformable image registration (DIR) for assessing colorectal liver metastases ablation outcomes.

Purpose:Colorectal cancer is the third most common form of cancer in the United States, and up to 60% of these patients develop liver metastasis. While hepatic resection is the curative treatment of choice, only 20% of patients are candidates at the time of diagnosis. While percutaneous thermal ablation (PTA) has demonstrated 24%–51% overall 5-year survival rates, assurance of sufficient ablation margin delivery (5 mm) can be challenging, with current methods of 2D distance measurement not ensuring 3D minimum margin. We hypothesized that biomechanical model-based deformable image registration (DIR) can reduce spatial uncertainties and differentiate local tumor progression (LTP) patients from LTP-free patients.Methods:We retrospectively acquired 30 patients (16 LTP and 14 LTP-free) at our institution who had undergone PTA and had a contrast-enhanced pre-treatment and post-ablation CT scan. Liver, disease, and ablation zone were manually segmented. Biomechanical model-based DIR between the pre-treatment and post-ablation CT mapped the gross tumor volume onto the ablation zone and measured 3D minimum delivered margin (MDM). An in-house cone-tracing algorithm determined if progression qualitatively collocated with insufficient 5 mm margin achieved.Results:Mann–Whitney U test showed a significant difference (p < 0.01) in MDM from the LTP and LTP-free groups. A total of 93% (13/14) of patients with LTP had a correlation between progression and missing 5 mm of margin volume.Conclusions:Biomechanical DIR is able to reduce spatial uncertainty and allow measurement of delivered 3D MDM. This minimum margin can help ensure sufficient ablation delivery, and our workflow can provide valuable information in a clinically useful timeframe.

PO-1001 Combined image-based and biomechanical deformable image registration of extreme anatomical changes.

PO-1001 Combined image-based and biomechanical deformable image registration of extreme anatomical changes.

Implementing Radiation Dose-Volume Liver Response in Biomechanical Deformable Image Registration

Understanding anatomic and functional changes in the liver resulting from radiation therapy is fundamental to the improvement of normal tissue complication probability models needed to advance personalized medicine.The ability to link pretreatment and posttreatment imaging is often compromised by significant dose-dependent volumetric changes within the liver that are currently not accounted for in deformable image registration (DIR) techniques. This study investigated using delivered dose, in combination with other patient factors, to biomechanically model longitudinal changes in liver anatomy for follow-up care and re-treatment planning. Population models describing the relationship between dose and hepatic volume response were produced using retrospective data from 33 patients treated with focal radiation therapy. A DIR technique was improved by implementing additional boundary conditions associated with the dose-volume response in series with a previously developed biomechanical DIR algorithm. Evaluation of this DIR technique was performed on computed tomography imaging from 7 patients by comparing the model-predicted volumetric change within the liver with the observed change, tracking vessel bifurcations within the liver through the deformation process, and then determining target registration error between the predicted and identified posttreatment bifurcation points. Evaluation of the proposed DIR technique showed that all lobes were volumetrically deformed to within the respective contour variability of each lobe.The average target registration error achieved was 7.3mm (2.8mm left-right and anterior-posterior and 5.1mm superior-inferior), with the superior-inferior component within the average limiting slice thickness (6.0mm).This represented a significant improvement (P<.01, Wilcoxon test) over the application of the previously published biomechanical DIR algorithm (10.9mm). This study demonstrates the feasibility of implementing dose-driven volumetric response in deformable registration, enabling improved accuracy of modeling liver anatomy changes, which could allow for improved dose accumulation, particularly for patients who require additional liver radiation therapy.

Validation of biomechanical deformable image registration in the abdomen, thorax, and pelvis in a commercial radiotherapy treatment planning system

The accuracy of deformable image registration tools can vary widely between imaging modalities and specific implementations of the same algorithms. A biomechanical model-based algorithm initially developed in-house at an academic institution was translated into a commercial radiotherapy treatment planning system and validated for multiple imaging modalities and anatomic sites. Biomechanical deformable registration (Morfeus) is a geometry-driven algorithm based on the finite element method. Boundary conditions are derived from the model-based segmentation of controlling structures in each image which establishes a point-to-point surface correspondence. For each controlling structure, material properties and fixed or sliding interfaces are assigned. The displacements of internal volumes for controlling structures and other structures implicitly deformed are solved with finite element analysis. Registration was performed for 74 patients with images (mean vector resolution) of thoracic and abdominal 4DCT (2.8mm) and MR (5.3mm), liver CT-MR (4.5mm), and prostate MR (2.6mm). Accuracy was quantified between deformed and actual target images using distance-to-agreement (DTA) for structure surfaces and the target registration error (TRE) for internal point landmarks. The results of the commercial implementation were as follows. The mean DTA was ≤1.0mm for controlling structures and 1.0-3.5mm for implicitly deformed structures on average. TRE ranged from 2.0mm on prostate MR to 5.1mm on lung MR on average, within 0.1mm or lower than the image voxel sizes. Accuracy was not overly sensitive to changes in the material properties or variability in structure segmentations, as changing these inputs affected DTA and TRE by ≤0.8mm. Maximum DTA >5mm occurred for 88% of the structures evaluated although these were within the inherent segmentation uncertainty for 82% of structures. Differences in accuracy between the commercial and in-house research implementations were ≤0.5mm for mean DTA and ≤0.7mm for mean TRE. Accuracy of biomechanical deformable registration evaluated on a large cohort of images in the thorax, abdomen and prostate was similar to the image voxel resolution on average across multiple modalities. Validation of this treatment planning system implementation supports biomechanical deformable registration as a versatile clinical tool to enable accurate target delineation at planning and treatment adaptation.

SU‐F‐J‐88: Comparison of Two Deformable Image Registration Algorithms for CT‐To‐CT Contour Propagation

Purpose:To compare the contour propagation accuracy of two deformable image registration (DIR) algorithms in the Raystation treatment planning system – the “Hybrid” algorithm based on image intensities and anatomical information; and the “Biomechanical” algorithm based on linear anatomical elasticity and finite element modeling.Methods:Both DIR algorithms were used for CT‐to‐CT deformation for 20 lung radiation therapy patients that underwent treatment plan revisions. Deformation accuracy was evaluated using landmark tracking to measure the target registration error (TRE) and inverse consistency error (ICE). The deformed contours were also evaluated against physician drawn contours using Dice similarity coefficients (DSC). Contour propagation was qualitatively assessed using a visual quality score assigned by physicians, and a refinement quality score (0&lt;RQS&lt;1) assigned by dosimetrists based on the extent of manual refinement needed for acceptable quality.Results:Both algorithms showed similar ICE (&lt; 1.5 mm), but the hybrid DIR (TRE = 3.2 mm) performed better than the biomechanical DIR (TRE = 4.3 mm) with landmark tracking. Both algorithms had comparable DSC (DSC &gt; 0.9 for lungs, &gt; 0.85 for heart, &gt; 0.8 for liver) and similar qualitative assessments (VQS &lt; 0.35, RQS &gt; 0.75 for lungs). When anatomical structures were used to control the deformation, the DSC improved more significantly for the biomechanical DIR compared to the hybrid DIR, while the VQS and RQS improved only for the controlling structures. However, while the inclusion of controlling structures improved the TRE for the hybrid DIR, it increased the TRE for the biomechanical DIR.Conclusion:The hybrid DIR was found to perform slightly better than the biomechanical DIR based on lower TRE while the DSC, VQS, and RQS studies yielded comparable results for both. The use of controlling structures showed considerable improvement in the hybrid DIR results and is recommended for clinical use in contour propagation.

WE‐AB‐202‐05: Validation of Lung Stress Maps for CT‐Ventilation Imaging

Purpose:To date, lung CT‐ventilation imaging has been based on quantification of local breathing‐induced changes in Hounsfield Units (HU) or volume. This work investigates the use of a stress map resulting from a biomechanical deformable image registration (DIR) algorithm as a metric of the ventilation function.Method:Eight lung cancer patients presenting different kinds of ventilation defects were retrospectively analyzed. Additionally, to the 4DCT acquired for radiotherapy planning, five of them had PET and three had SPECT imaging following inhalation of Ga‐68 and Tc‐99m, respectively. For each patient, the inhale phase of the 4DCT was registered to the exhale phase using Morfeus, a biomechanical DIR algorithm based on the determination of boundary conditions on the lung surfaces and vessel tree. To take into account the heterogeneity of the tissue stiffness in the stress map estimation, each tetrahedral element of the finite‐element model was assigned a Young's modulus ranging from 60kPa to 12MPa, as a function of the HU in the inhale CT. The node displacements and element stresses resulting from the numerical simulation were used to generate three CT‐ventilation maps based on: (i) volume changes (Jacobian determinant), (ii) changes in HU, (iii) the maximum principal stress. The voxel‐wise correlation between each CT‐ventilation map and the PET or SPECT V image was computed in a lung mask.Results:For patients with PET, the mean (min‐max) Spearman correlation coefficients r were: 0.33 (0.19–0.45), 0.36 (0.16–0.51) and 0.42 (0.21–0.59) considering the Jacobian, changes in HU and maximum principal stress, respectively. For patients with SPECT V, the mean r were: 0.12 (−0.12–0.43), 0.29 (0.22–0.45) and 0.33 (0.25–0.39).Conclusion:The maximum principal stress maps showed a stronger correlation with the ventilation images than the previously proposed Jacobian or change in HU maps. This metric thus appears promising for CT‐ventilation imaging.This work was funded in part by NIH P01CA059827.

A hybrid biomechanical intensity based deformable image registration of lung 4DCT

Deformable image registration (DIR) has been extensively studied over the past two decades due to its essential role in many image-guided interventions (IGI). IGI demands a highly accurate registration that maintains its accuracy across the entire region of interest. This work evaluates the improvement in accuracy and consistency by refining the results of Morfeus, a biomechanical model-based DIR algorithm.A hybrid DIR algorithm is proposed based on, a biomechanical model–based DIR algorithm and a refinement step based on a B-spline intensity-based algorithm. Inhale and exhale reconstructions of four-dimensional computed tomography (4DCT) lung images from 31 patients were initially registered using the biomechanical DIR by modeling contact surface between the lungs and the chest cavity. The resulting deformations were then refined using the intensity-based algorithm to reduce any residual uncertainties. Important parameters in the intensity-based algorithm, including grid spacing, number of pyramids, and regularization coefficient, were optimized on 10 randomly-chosen patients (out of 31). Target registration error (TRE) was calculated by measuring the Euclidean distance of common anatomical points on both images after registration. For each patient a minimum of 30 points/lung were used.Grid spacing of 8 mm, 5 levels of grid pyramids, and regularization coefficient of 3.0 were found to provide optimal results on 10 randomly chosen patients. Overall the entire patient population (n = 31), the hybrid method resulted in mean ± SD (90th%) TRE of 1.5 ± 1.4 (2.9) mm compared to 3.1 ± 1.9 (5.6) using biomechanical DIR and 2.6 ± 2.5 (6.1) using intensity-based DIR alone.The proposed hybrid biomechanical modeling intensity based algorithm is a promising DIR technique which could be used in various IGI procedures. The current investigation shows the efficacy of this approach for the registration of 4DCT images of the lungs with average accuracy of 1.5 mm.

Utility and validation of biomechanical deformable image registration in low-contrast images

PurposeThe application of a biomechanical deformable image registration algorithm has been demonstrated to overcome the potential limitations in the use of intensity-based algorithms on low-contrast images that lack prominent features. Because validation of deformable registration is particularly challenging on such images, the dose distribution predicted via a biomechanical algorithm was evaluated using the measured dose from a deformable dosimeter. Methods and materialsA biomechanical model–based image registration algorithm registered computed tomographic (CT) images of an elastic radiochromic dosimeter between its undeformed and deformed positions. The algorithm aligns the external boundaries of the dosimeter, created from CT contours, and the internal displacements are solved by modeling the physical material properties of the dosimeter. The dosimeter was planned and irradiated in its deformed position, and subsequently, the delivered dose was measured with optical CT in the undeformed position. The predicted dose distribution, created by applying the deformable registration displacement map to the planned distribution, was then compared with the measured optical CT distribution. ResultsCompared with the optical CT distribution, biomechanical image registration predicted the position and size of the deformed dose fields with mean errors of ≤1 mm (maximum, 3 mm). The accuracy did not differ between cross sections with a greater or lesser deformation magnitude despite the homogenous CT intensities throughout the dosimeter. The overall 3-dimensional voxel passing rate of the predicted distribution was γ3%/3mm = 91% compared with optical CT. ConclusionsBiomechanical registration accurately predicted the deformed dose distribution measured in a deformable dosimeter, whereas previously, evaluations of a commercial intensity-based algorithm demonstrated substantial errors. The addition of biomechanical algorithms to the collection of adaptive radiation therapy tools would be valuable for dose accumulation, particularly in feature-poor images such as cone beam CT and organs such as the liver.

Simple DVH parameter addition as compared to deformable registration for bladder dose accumulation in cervix cancer brachytherapy

Background and purposeVariations in organ position, shape, and volume cause uncertainties in dose assessment for brachytherapy (BT) in cervix cancer. The purpose of this study was to evaluate uncertainties associated with bladder dose accumulation based on DVH parameter addition (previously called “the worst case assumption”) in fractionated BT. Materials and methodsForty-seven patients treated for locally advanced cervical cancer were included. All patients received EBRT combined with two individually planned 3D image-guided adaptive BT fractions. D2cm3 and D0.1cm3were estimated by DVH parameter addition and compared to dose accumulations based on an in-house developed biomechanical deformable image registration (DIR) algorithm. ResultsDIR-based DVH analysis was possible in 42/47 patients. DVH parameter addition resulted in mean dose deviations relative to DIR of 0.4±0.3Gyαβ3 (1.5±1.8%) and 1.9±1.6Gyαβ3 (5.2±4.2%) for D2cm3 and D0.1cm3, respectively. Dose deviations greater than 5% occurred in 2% and 38% of the patients for D2cm3 and D0.1cm3, respectively. Visual inspection of the dose distributions showed that hotspots were located in the same region of the bladder during both BT fractions for the majority of patients. ConclusionDVH parameter addition provides a good estimate for D2cm3, whereas D0.1cm3 is less robust to this approximation.

Bladder dose accumulation based on a biomechanical deformable image registration algorithm in volumetric modulated arc therapy for prostate cancer

Variations in bladder position, shape and volume cause uncertainties in the doses delivered to this organ during a course of radiotherapy for pelvic tumors. The purpose of this study was to evaluate the potential of dose accumulation based on repeat imaging and deformable image registration (DIR) to improve the accuracy of bladder dose assessment. For each of nine prostate cancer patients, the initial treatment plan was re-calculated on eight to nine repeat computed tomography (CT) scans. The planned bladder dose–volume histogram (DVH) parameters were compared to corresponding parameters derived from DIR-based accumulations as well as DVH summation based on dose re-calculations. It was found that the deviations between the DIR-based accumulations and the planned treatment were substantial and ranged (−0.5–2.3) Gy and (−9.4–13.5) Gy for D2% and Dmean, respectively, whereas the deviations between DIR-based accumulations and DVH summation were small and well within 1 Gy. For the investigated treatment scenario, DIR-based bladder dose accumulation did not result in substantial improvement of dose estimation as compared to the straightforward DVH summation. Large variations were found in individual patients between the doses from the initial treatment plan and the accumulated bladder doses. Hence, the use of repeat imaging has a potential for improved accuracy in treatment dose reporting.

SU-E-T-640: Development of Liver SBRT Planning and Delivery at the Mean Respiratory Position Using Deformable Image Registration.

Deformable image registration (DIR) allows modeling of liver tumors on respiratory correlated (4D) imaging. The mid-position CT was reconstructed for liver SBRT plans using DIR, and the potential for dose-escalation was investigated. Thirty patients were planned clinically with IMRT for 27-48 Gy in 6 fractions on static exhale 4DCT with PTVs encompassing the breathing amplitude. For research, exhale 4DCT was deformed to the inhale 4DCT using biomechanical DIR. The mid-position CT was created by applying a percentage (the time-averaged normalized position between exhale and inhale calculated from daily 4D cone-beam CT) to this deformation map, assuming a linear trajectory. A probability-based PTV margin, using patient-specific breathing amplitude from DIR of 4DCT, was created around the GTV on the mid-position CT where IMRT was re-optimized. Dose was maximally escalated according to clinical protocol (e.g. liver NTCP <5%). The 4D predicted breathing dose was accumulated by interpolating the elements' positions at exhale, mid-position and inhale onto the respective dose matrices (weighted by time spent nearest each matrix) then summed. Compared the exhale plans, the GTV-to-PTV volume decreased on the mid-position plans by a mean of 31% (p<0.01, range: 24-38%). Static re-planning on the mid-position CT decreased the mean effective liver volume by 7% (p=0.032), enabling escalation of the nominal prescribed dose in 80% of patients of 6-12 Gy. Reconstruction of the 4D predicted breathing dose resulted in a mean increase of 6.7 Gy (p<0.01, maximum increase of 15.0 Gy) in mean GTV dose for the mid-position versus the exhale plan. For the mid-position plan, the minimum 0.5 cm3 GTV dose received 100% of the prescription in the 4D distribution. Liver SBRT Planning at the mean respiratory position enables PTV reduction and a mean dose escalation of 6.7 Gy, potentially improving local control. Dr. Brock has financial interest in deformable registration technology through the licensing of Morfeus to RaySearch Laboratories. Research is funded by NIH 1R01CA124714.