Deformable Registration Errors Research Articles

Low dose cone-beam computed tomography reconstruction via hybrid prior contour based total variation regularization (hybrid-PCTV).

Previously, we developed a prior contour based total variation (PCTV) method to use edge information derived from prior images for edge enhancement in low-dose cone-beam computed tomography (CBCT) reconstruction. However, the accuracy of edge enhancement in PCTV is affected by the deformable registration errors and anatomical changes from prior to on-board images. In this study, we develop a hybrid-PCTV method to address this limitation to enhance the robustness and accuracy of the PCTV method. Planning-CT is used as prior images and deformably registered with on-board CBCT reconstructed by the edge preserving TV (EPTV) method. Edges derived from planning CT are deformed based on the registered deformation vector fields to generate on-board edges for edge enhancement in PCTV reconstruction. Reference CBCT is reconstructed from the simulated projections of the deformed planning-CT. Image similarity map is then calculated between reference and on-board CBCT using structural similarity index (SSIM) method to estimate local registration accuracy. The hybrid-PCTV method enhances the edge information based on a weighted edge map that combines edges from both PCTV and EPTV methods. Higher weighting is given to PCTV edges at regions with high registration accuracy and to EPTV edges at regions with low registration accuracy. The hybrid-PCTV method was evaluated using both digital extended-cardiac-torso (XCAT) phantom and lung patient data. In XCAT study, breathing amplitude change, tumor shrinkage and new tumor were simulated from CT to CBCT. In the patient study, both simulated and real projections of lung patients were used for reconstruction. Results were compared with both EPTV and PCTV methods. EPTV led to blurring bony structures due to missing edge information, and PCTV led to blurring tumor edges due to inaccurate edge information caused by errors in the deformable registration. In contrast, hybrid-PCTV enhanced edges of both bone and tumor. In XCAT study using 30 half-fan CBCT projections, compared with ground truth, relative errors (REs) were 1.3%, 1.1% and 0.9% and edge cross-correlation were 0.66, 0.68 and 0.71 for EPTV, PCTV and hybrid-PCTV, respectively. Moreover, in the lung patient data, hybrid-PCTV avoided the wrong edge enhancement in the PCTV method while maintaining enhancements of the correct edges. Hybrid-PCTV further improved the robustness and accuracy of PCTV by accounting for uncertainties in deformable registration and anatomical changes between prior and onboard images. The accurate edge enhancement in hybrid-PCTV will be valuable for target localization in radiation therapy.

MRI-guided mid-position liver radiotherapy: Validation of image processing and registration steps

Background & purposeTo propose a novel mid-position (midP) workflow for MRI-guided liver SBRT and provide a validation of the required midP-MRI generation and registration steps. Materials & methodsThe first step of the midP workflow is the generation of a simulation midP-MRI from a 4D-MRI scan using deformable image registration. Next, a planning midP-CT is warped to the midP-MRI to enable planning in the midP-MRI anatomy. For daily MRI-guidance, three different registration methods to the simulation midP-MRI are proposed; (1) 4D rigid registration of all phases of the daily 4D-MRI, (2) 3D rigid registration of the daily midP-MRI, and (3) 3D deformable registration of the daily midP-MRI. The midP-MRI image quality was assessed with respect to 4D-MRI acquisition time, which is related to over-sampling of the data acquisition (i.e. number of dynamics). The deformable registration precision for the midP-MRI generation was validated using the distance discordance metric (DDM). The deformable CT-MRI and daily MRI-MRI registration accuracies were quantified using the ‘full circle method’. ResultsThe DDM was 1.5 mm (median) within the liver, independent of the number of dynamics. The root-mean-squared difference between midP-MRIs based on 10 and 60 dynamics was only 5.2%. The full circle CT-MRI deformable registration error had a median 3D vector length of 1.8 mm in the liver. The daily MRI-MRI registration error was submillimeter for all three evaluated methods. ConclusionThe feasibility of an MRI-guided mid-position workflow for liver SBRT is supported by the demonstrated high precision of all image processing and registration steps.

Evaluation of deformed image-based dose calculations for adaptive radiotherapy of nasopharyngeal carcinoma

The ultimate goal of adaptive radiotherapy (ART) is to deliver truly customized radiation treatments. Currently, the quality of cone-beam computed tomography (CBCT) images is still inferior to that of conventional CT images in contour delineations and dose calculations for replanning purposes. This retrospective study aims to evaluate the dosimetric accuracy of using deformed conventional CT images for dose calculations, in the hope of inferring the feasibility of ART using planning CT (PCT) images that deformed to up-to-date CBCT images for patients with nasopharyngeal carcinoma (NPC). Thirty consecutive patients with NPC who had undergone 1 replan in their radiotherapy treatments were selected. The pretreatment PCT images were deformed to match the mid-treatment PCT images by deformable image registration. The same volumetric modulated arc therapy plan was then calculated on the deformed PCT images. The resulting dose distributions and dose volume histograms of the tumors and organs at risk (OARs) were compared with the original plan. Five dose levels, D98%, D95%, D50%, D5%, and D2%, were recorded for 9 NPC targets. Four dose levels, Dmax, D10%, D50%, and Dmean, were recorded for 15 OARs. The greatest percentage difference in observed dose for D98%, D95%, D50%, D5%, and D2% of the targets were 1.71%, 1.55%, 0.64%, 0.97%, and 1.13%, respectively. The greatest percentage difference in observed dose for Dmax, D10%, D50%, and Dmean of the OARs were -26.51% (left optic nerve), -17.06% (left optic nerve), 56.70% (spinal cord), and 18.97% (spinal cord), respectively. In addition, 29 of 45 (64%) dosimetric end points of the targets showed statistically significant dose differences (p < 0.05) between the original plan and the plan calculated on deformed images. Forty-nine of 60 (82%) dosimetric end points of the OARs also showed statistically significant dose differences (p < 0.05). Dose calculations using deformed PCT images could result in significant dose uncertainties in target volumes and OARs. Larger dose deviations were found in OARs in comparison with target volumes. The spinal cord and optic nerve showed the greatest percentage dose differences and the clinical significance has yet to be determined. Deformable registration error was believed to be the problem causing the dose deviations. Owing to unknown clinical significanceof dose deviation results obtained from this study, a conventional CT scan is still required for replanning in patients with NPC who are experiencing significant anatomical changes during the course of radiation treatment.

SU-D-17A-05: A Method to Determine the Accuracy of a Proposed Breathing Motion Model-Based 4DCT Technique

Purpose: To develop a technique that measures the accuracy of breathing motion model based 4DCT images by comparing the CT images used to generate the model with images reconstructed using the model at the same breathing phases. Methods: A novel 4DCT technique is employed that employs free-breathing fast helical CT images and a simultaneous breathing surrogate measurement. Deformable image registration of the CT images is used to develop a patient-specific motion model. The model can subsequently generate deformation vector fields that generate a CT image at any user-selected breathing phase, defined here as breathing amplitude and rate. These images will be used for treatment planning. However, unlike existing 4DCT techniques, breathing irregularity-caused errors are not evident to the planner: Each image will look excellent, with no sorting artifacts independent of the model accuracy. To measure model accuracy, CT images are reconstructed at the breathing phases encountered during the free-breathing CT acquisition. In principle, the reconstructed and acquired helical CT images should agree exactly, differences indicate errors in deformable registration and the motion model itself. To evaluate this approach, 5 patient studies were selected and 6 of the original 25 helical CT images compared against the generated images using a semi-automatic landmark-based metric. 200 pairs of spatially uniform distributed corresponding landmark points were evaluated and the mean error standard deviation determined for each image pair. Results: The model reconstructed 4DCT images showed excellent similarity to the 6 helical CT images. The mean errors ranged between sub-millimeter to 2 millimeter with uncertainties mostly in the sub-millimeter. When divided between upper and lower lungs, the mean errors in the upper and lower lungs were less than 2 mm and 3 mm, respectively. Conclusion: The proposed registration and model accuracy test can be used to indicate image geometry errors to a treatment planner. NIH R01CA096679

A method to map errors in the deformable registration of 4DCT images.

To present a new approach to the problem of estimating errors in deformable image registration (DIR) applied to sequential phases of a 4DCT data set. A set of displacement vector fields (DVFs) are made by registering a sequence of 4DCT phases. The DVFs are assumed to display anatomical movement, with the addition of errors due to the imaging and registration processes. The positions of physical landmarks in each CT phase are measured as ground truth for the physical movement in the DVF. Principal component analysis of the DVFs and the landmarks is used to identify and separate the eigenmodes of physical movement from the error eigenmodes. By subtracting the physical modes from the principal components of the DVFs, the registration errors are exposed and reconstructed as DIR error maps. The method is demonstrated via a simple numerical model of 4DCT DVFs that combines breathing movement with simulated maps of spatially correlated DIR errors. The principal components of the simulated DVFs were observed to share the basic properties of principal components for actual 4DCT data. The simulated error maps were accurately recovered by the estimation method. Deformable image registration errors can have complex spatial distributions. Consequently, point-by-point landmark validation can give unrepresentative results that do not accurately reflect the registration uncertainties away from the landmarks. The authors are developing a method for mapping the complete spatial distribution of DIR errors using only a small number of ground truth validation landmarks.

The utilization of consistency metrics for error analysis in deformable image registration

The aim of this study was to investigate the utility of consistency metrics, such as inverse consistency, in contour-based deformable registration error analysis. Four images were acquired of the same phantom that has experienced varying levels of deformation. The deformations were simulated with deformable image registration. Using calculated deformation maps, the inconsistencies within the algorithm were investigated. This can be done, for example, by calculating deformation maps both in forward and reverse directions and applying them subsequently to an image. If the algorithm is not inverse consistent, then this final image will not be the same as the original, as it should be. Other consistency tests were done, for example by comparing different algorithms or by applying the deformation maps to a circular set of multiple deformations, whereby the original and final images are in fact the same. The resulting composite deformation map in this case contains a combination of the errors within those maps, because if error free, the resulting deformation map should be zero everywhere. We have termed this the generalized inverse consistency error map (). The correlation between the consistency metrics and registration error varied considerably depending on the registration algorithm and type of consistency metric. There was also a trend for the actual registration error to be larger than the consistency metrics. A disadvantage of these techniques is that good performance in these consistency checks is a necessary but not sufficient condition for an accurate deformation method.

TU‐C‐M100F‐07: Detecting Dose Reconstruction Errors

Purpose In radiation therapy, delivered doses can be accumulated over fractions for adaptive treatment planning. However, the dose reconstruction fidelity can be compromised by image registration errors. The resultant dose discrepancy is hard to measure. This paper proposes a new method to assess the difference between the delivered dose and the warped dose. Method and Materials: The dose warping discrepancy is dominated by deformable image registration errors and source‐dose distributions. Registration errors can be specified within a finite element framework as an unbalanced energy δR, which is defined at element j by urn:x-wiley:00942405:media:mp1348:mp1348-math-0001 where E0 and di are Young's modulus and the displacement vector fields of the registration R. E0Φj is an assembly of all the element forces relating to the node i. Consequently the dose warping discrepancy at voxel k can be defined as the convolution of the dose divergence Div(D) and δR: urn:x-wiley:00942405:media:mp1348:mp1348-math-0002 The effectiveness of the dose‐discrepancy‐convolution (DDC) defined above is demonstrated through applying it to dose computed on ten prostate‐CT images. An IMRT treatment plan is developed on the planning image. In nine subsequent treatments, images are acquired to calculate the corresponding delivered doses. Their mean and standard‐deviation in Ci, a target contour automatically expanded from prostate delineated on the time‐of‐treatment image i, are compared with those of their warped doses measured on the planning target volume. The differences in their means and standard‐deviations are denoted by Eε and Eσ, respectively.Results δR, εD,R, Eε and Eσ are computed for the nine treatment fractions. Fraction one and eight have the largest warping discrepancies identified in all the four evaluation criterions. Conclusion Defined in terms of the deformable registration error, the voxel‐based DDC can be employed for the automatic evaluation of dose reconstruction in the whole dosing domain. Its measurement is consistent to the contour‐based dose warping evaluation performed in the planning target volume.