Rigid Registration Techniques Research Articles

Changes in Organs at Risk Dose Estimates Due to Anatomic Changes and Registration Method During Head and Neck Radiotherapy and its Relationship to Percent Weight Loss

<h3>Purpose/Objective(s)</h3> Weight loss and anatomic changes are common during radiation therapy for head and neck cancers. The best method for assessing the impact of anatomic changes on dose distribution is unknown. We compared planned doses to organs at risk (OARs) to delivered dose estimates using a cone beam computed tomography (CBCT) from the final week of treatment. <h3>Materials/Methods</h3> Ten patients who underwent bilateral neck radiation and had weekly extended CBCT's available to capture critical OARs were identified. CBCT from the final week was reconstructed to 2mm slices to match the resolution of the CT simulation (CTsim). The CBCT's were registered to the sim using both rigid and deformable registration techniques. The parotid glands, spinal cord, and skin (defined as 3mm from body contour) were contoured on the CBCT's. Mean dose to the parotids, and maximum dose (D0.035cc) to the spinal cord and skin were extracted from the treatment plan based on CTsim, rigid CBCT and deformed CBCT respectively. Independent t-tests were used to determine and compare mean percent (%) dose changes in OAR's from initially planned to delivered dose estimates using the rigid and deformed CBCT's. Linear regression was then performed to identify which set of OAR doses were the best predictors of the variation in % mean weight change during the treatment course. Statistical software was used for the statistical analyses. <h3>Results</h3> Among the 10 patients analyzed, mean % change in mean right parotid dose was -6.99% between planned and rigid CBCT estimated dose and +12.02% between planned and deformed CBCT estimated dose, for a mean total difference of 19.01% (SE 5.61%, p=0.004). These differences were statistically significant. A similar trend was observed for the left parotid gland, with mean % changes of -1.67%, +6% and 7.67% respectively (SE 5.62%, p=0.19). No differences were observed for cord (mean difference 0.19%, SE 1.27%, p = 0.89) or skin (mean difference 0.26%, SE 2.83%, p = 0.92). On regression analysis the delivered dose estimates from the rigid CBCT model were a better predictor of % weight change (R²=0.543) compared to the deformed CBCT (R²=0.463) or CT sim planned doses (R²=0.460) <h3>Conclusion</h3> The data shows a significant difference in estimating dose delivered to the right parotid gland using final week CBCT between using rigid and deformable registration techniques. Change in dose to left parotid gland also trended towards significance and may prove to be meaningful with a larger sample size. There were no significant differences in cord or skin % dose changes. We found that doses collected from the Rigid CBCT were the best predictors of % weight change during the whole course of treatment on regression analysis. Future work will focus on mid treatment dose estimates using a larger sample size, and correlation with clinical outcomes.

Use of deformable image registration techniques to estimate dose to organs at risk following prostate external beam radiation therapy and high-dose-rate brachytherapy.

PurposeThe purpose of this investigation was to examine differences in estimates of accumulated rectal dose when using deformable image registration (DIR) compared with rigid image registration (RIR) methods, and parameter addition methods for combined transrectal ultrasound (TRUS)-based high-dose-rate brachytherapy (HDR-BT) and external beam radiation therapy (EBRT) treatments of prostate cancer.Material and methodsIn this retrospective study, data from 10 patients who had previously received HDR-BT in one 15 Gy fraction, followed by 46 Gy EBRT in twenty-three fractions were used. To estimate total combined dose to the rectum, dose accumulation using both DIR and RIR methods were compared with parameter addition methods, which assume the same region of rectal anatomy receives the maximum dose from both treatment modalities. For both rigid and deformable image registration techniques, the quality of image registration was evaluated through metrics, including mean distance to agreement and dice similarity coefficient of prostate contours. Total D1cc and D2cc for the rectum was calculated and compared using each method.ResultsThe parameter addition methods predicted the highest accumulated dose to the rectum. On average, the predicted D2cc dose was higher than that calculated by the DIR method by 6.59 Gy EQD2 (range, –3.03 to 13.68 Gy EQD2) for partial parameter addition (PPA), and 4.88 Gy EQD2 (range, –3.41 to 11.97 Gy EQD2) for the full parameter addition (FPA) methods. Similarly, RIR predicted higher average doses compared with DIR, with a difference of 3.46 Gy EQD2 (range, –5.50 to 7.90 Gy EQD2). The results showed a significant difference between DIR and parameter addition methods for dose estimation.ConclusionsThis retrospective study demonstrates significant differences in accumulated rectal dose prediction using different image registration methods. Each method has limitations in its application, and when used with real-time HDR-BT dose planning, awareness of these limitations is essential.

Fast and Robust Iterative Closest Point

The iterative closest point (ICP) algorithm and its variants are a fundamental technique for rigid registration between two point sets, with wide applications in different areas from robotics to 3D reconstruction. The main drawbacks for ICP are its slow convergence, as well as its sensitivity to outliers, missing data, and partial overlaps. Recent work such as Sparse ICP achieves robustness via sparsity optimization at the cost of computational speed. In this paper, we propose a new method for robust registration with fast convergence. First, we show that the classical point-to-point ICP can be treated as a majorization-minimization (MM) algorithm, and propose an Anderson acceleration approach to speed up its convergence. In addition, we introduce a robust error metric based on the Welsch's function, which is minimized efficiently using the MM algorithm with Anderson acceleration. On challenging datasets with noises and partial overlaps, we achieve similar or better accuracy than Sparse ICP while being at least an order of magnitude faster. Finally, we extend the robust formulation to point-to-plane ICP, and solve the resulting problem using a similar Anderson-accelerated MM strategy. Our robust ICP methods improve the registration accuracy on benchmark datasets while being competitive in computational time.

General first-order target registration error model considering a coordinate reference frame in an image-guided surgical system.

Point-based rigid registration (PBRR) techniques are widely used in many aspects of image-guided surgery (IGS). Accurately estimating target registration error (TRE) statistics is of essential value for medical applications such as optically surgical tool-tip tracking and image registration. For example, knowing the TRE distribution statistics of surgical tool tip can help the surgeon make right decisions during surgery. In the meantime, the pose of a surgical tool is usually reported relative to a second rigid body whose local frame is called coordinate reference frame (CRF). In an n-ocular tracking system, fiducial localization error (FLE) should be considered inhomogeneous, that means FLE is different between fiducials, and anisotropic that indicates FLE is different in all directions. In this paper, we extend the TRE estimation algorithm relative to a CRF from homogeneous and anisotropic to heterogeneous FLE cases. Arbitrary weightings can be assumed in solving the registration problems in the proposed TRE estimation algorithm. Monte Carlo simulation results demonstrate the proposed algorithm's effectiveness for both homogeneous and inhomogeneous FLE distributions. The results are further compared with those using the other two algorithms. When FLE distribution is anisotropic and homogeneous, the proposed TRE estimation algorithm's performance is comparable with that of the first one. When FLE distribution is heterogeneous, proposed TRE estimation algorithm outperforms the other two classical algorithms in all test cases when ideal weighting scheme is adopted in solving two registrations. Possible clinical applications include the online estimation of surgical tool-tip tracking error with respect to a CRF in IGS. Graphical Abstract This paper provides the target registration error model considering a coordinate reference frame in surgical navigation.

Automated Contouring of Contrast and Noncontrast Computed Tomography Liver Images With Fully Convolutional Networks

PurposeThe deformable nature of the liver can make focal treatment challenging and is not adequately addressed with simple rigid registration techniques. More advanced registration techniques can take deformations into account (eg, biomechanical modeling) but require segmentations of the whole liver for each scan, which is a time-intensive process. We hypothesize that fully convolutional networks can be used to rapidly and accurately autosegment the liver, removing the temporal bottleneck for biomechanical modeling. Methods and MaterialsManual liver segmentations on computed tomography scans from 183 patients treated at our institution and 30 scans from the Medical Image Computing & Computer Assisted Intervention challenges were collected for this study. Three architectures were investigated for rapid automated segmentation of the liver (VGG-16, DeepLabv3 +, and a 3-dimensional UNet). Fifty-six cases were set aside as a final test set for quantitative model evaluation. Accuracy of the autosegmentations was assessed using Dice similarity coefficient and mean surface distance. Qualitative evaluation was also performed by 3 radiation oncologists on 50 independent cases with previously clinically treated liver contours. ResultsThe mean (minimum-maximum) mean surface distance for the test groups with the final model, DeepLabv3 +, were as follows: μContrast(N = 17): 0.99 mm (0.47-2.2), μNon_Contrast(N = 19)l: 1.12 mm (0.41-2.87), and μMiccai(N = 30)t: 1.48 mm (0.82-3.96). The qualitative evaluation showed that 30 of 50 autosegmentations (60%) were preferred to manual contours (majority voting) in a blinded comparison, and 48 of 50 autosegmentations (96%) were deemed clinically acceptable by at least 1 reviewing physician. ConclusionsThe autosegmentations were preferred compared with manually defined contours in the majority of cases. The ability to rapidly segment the liver with high accuracy achieved in this investigation has the potential to enable the efficient integration of biomechanical model-based registration into a clinical workflow.

Validation of a 3D CBCT-based protocol for the follow-up of mandibular condyle remodeling.

Objectives:Three-dimensional models of mandibular condyles provide a way for condylar remodeling follow-up. The overall aim was to develop and validate a user-friendly workflow for cone beam CT (CBCT)-based semi-automatic condylar registration and segmentation.Methods:A rigid voxel-based registration (VBR) technique for registration of two post-operative CBCT-scans was tested. Two modified mandibular rami, with or without gonial angle, were investigated as the volume of interest for registration. Inter- and intraoperator reproducibility of this technique was tested on 10 mandibular rami of orthognathic patients by means of intraclass correlation coefficients (ICC’s) and descriptive statistics of the transformation values from the VBR. The difference in reproducibility between the two modified rami was evaluated using a paired t-test (p < 0.05). For the segmentation, eight fresh frozen cadaver heads were scanned with CBCT and micro-CT. These data were used to test the inter- and intraoperator reproducibility (ICC’s) and accuracy (Bland–Altman plot) of a newly designed workflow based on semi-automated contour enhancement.Results:Excellent ICC’s (0.94–0.99) were obtained for the voxel-based registration technique using both modified rami. If the gonial angle was not included in the volume of interest, there was a trend of increased operator error suggested by significant higher interoperator differences in translation values (p = 0,0036). The segmentation workflow proved to be highly reproducible with excellent ICC’s (0.99), low absolute mean volume differences between operators (23.19 mm3), within operators (28.93 mm3) and low surface distances between models of different operators (<0.20 mm). Regarding the accuracy, CBCT-models slightly overestimate the condylar volume compared to micro-CT.Conclusions:This study provides a validated user-friendly and reproducible method of creating three-dimensional-surface models of mandibular condyles out of longitudinal CBCT-scans.

Minimal Information Exchange for Secure Image Hash-Based Geometric Transformations Estimation

Signal processing applications dealing with secure transmission are enjoying increasing attention lately. This paper provides some theoretical insights as well as a practical solution for transmitting a hash of an image to a central server to be compared with a reference image. The proposed solution employs a rigid image registration technique viewed in a distributed source coding perspective. In essence, it embodies a phase encoding framework to let the decoder estimate the transformation parameters using a very modest amount of information about the original image. The problem is first cast in an ideal setting and then it is solved in a realistic scenario, giving more prominence to low computational complexity in both the transmitter and receiver, minimal hash size, and hash security. Satisfactory experimental results are reported on a standard images set.

A Review on Registration of Medical Images Using Graph Theoretic Approaches

&lt;p&gt;Image registration is very essential for image analysis especially in the medical field. The registered image helps us to find a lot of things like the presence of a tumor, aneurysm, and many more. Image registration is a process of aligning two images that were captured during different conditions that makes analyzing of images possible. There are various methods in which images can be registered. This paper mainly discusses the methods that use the graph approach to register the medical images. The main focus is on rigid and non-rigid registration techniques and gives a brief description of the methods present.&lt;/p&gt;

Robust 3D registration of CBCT images aggregating multiple estimates through random sampling

In this work, we propose a novel 3D rigid registration technique, by applying the Conventional Mutual Information based 3D Registration (CMIR) repeatedly in sampled data sets. Using the statistical distribution in the parameter space, we have considered mean, median, and mode of the distribution, and found that the median and mode provide reasonably good estimates. We call the method Robust Rigid Registration by Multiples Estimates in Sampled data points (R3MES). It provides higher accuracy than the CMIR technique. When the number of iterations of multiple estimates is kept low, the R3MES technique requires less computation, as it works in the sampled data set. The performance of the R3MES technique is better when the sampling rate is greater than 3%. We present a theoretical validation of this observation, considering uniform sampling with replacement. We also demonstrate its application for registering 3D CBCT image volumes of a colo-rectal cancer patient captured on different days. To demonstrate its general applicability, we present its performance on registering a pair of 3D brain MRI image volumes.

Structural Parameters of the Proximal Femur by 3-Dimensional Dual-Energy X-ray Absorptiometry Software: Comparison With Quantitative Computed Tomography

Structural parameters of the proximal femur evaluate the strength of the bone and its susceptibility to fracture. These parameters are computed from dual-energy X-ray absorptiometry (DXA) or from quantitative computed tomography (QCT). The 3-dimensional (3D)-DXA software solution provides 3D models of the proximal femur shape and bone density from anteroposterior DXA scans. In this paper, we present and evaluate a new approach to compute structural parameters using 3D-DXA software. A cohort of 60 study subjects (60.9 ± 14.7 yr) with DXA and QCT examinations was collected. 3D femoral models obtained by QCT and 3D-DXA software were aligned using rigid registration techniques for comparison purposes. Geometric, cross-sectional, and volumetric structural parameters were computed at the narrow neck, intertrochanteric, and lower shaft regions for both QCT and 3D-DXA models. The accuracy of 3D-DXA structural parameters was evaluated in comparison with QCT. Correlation coefficients (r) between geometric parameters computed by QCT and 3D-DXA software were 0.86 for the femoral neck axis length and 0.71 for the femoral neck shaft angle. Correlation coefficients ranged from 0.86 to 0.96 for the cross-sectional parameters and from 0.84 to 0.97 for the volumetric structural parameters. Our study demonstrated that accurate estimates of structural parameters for the femur can be obtained from 3D-DXA models. This provides clinicians with 3D indexes related to the femoral strength from routine anteroposterior DXA scans, which could potentially improve osteoporosis management and fracture prevention.

MRI-based radiotherapy planning method using rigid image registration technique combined with outer body correction scheme: a feasibility study.

An alternative pseudo CT generation method for magnetic resonance image (MRI)-based radiotherapy planning was investigated in the work. A pseudo CT was initially generated using the rigid image registration between the planning MRI and previously acquired diagnostic CT scan. The pseudo CT generated was then refined to have the same morphology with that of the referenced planning image scan by applying the outer body correction scheme. This method was applied to some sample of brain image data and the feasibility of the method was assessed by comparing dosimetry results with those from the current gold standard CT-based calculations. Validation showed that nearly the entire pixel doses calculated from pseudo CT were agreed well with those from actual planning CT within 2% in dosimetric and 1mm in geometric uncertainty ranges. The results demonstrated that the method suggested in the study was sufficiently accurate, and thus could be applicable to MRI-based brain radiotherapy planning.

SP-0310: Rigid registration techniques for different imaging modalities

SP-0310: Rigid registration techniques for different imaging modalities

Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data.

In this paper, a method of vascular structure identification in intraoperative 3D Contrast-Enhanced Ultrasound (CEUS) data is presented. Ultrasound imaging is commonly used in brain tumor surgery to investigate in real time the current status of cerebral structures. The use of an ultrasound contrast agent enables to highlight tumor tissue, but also surrounding blood vessels. However, these structures can be used as landmarks to estimate and correct the brain shift. This work proposes an alternative method for extracting small vascular segments close to the tumor as landmark. The patient image dataset involved in brain tumor operations includes preoperative contrast T1MR (cT1MR) data and 3D intraoperative contrast enhanced ultrasound data acquired before (3D-iCEUS) and after (3D-iCEUS) tumor resection. Based on rigid registration techniques, a preselected vascular segment in cT1MR is searched in 3D-iCEUS and 3D-iCEUS data. The method was validated by using three similarity measures (Normalized Gradient Field, Normalized Mutual Information and Normalized Cross Correlation). Tests were performed on data obtained from ten patients overcoming a brain tumor operation and it succeeded in nine cases. Despite the small size of the vascular structures, the artifacts in the ultrasound images and the brain tissue deformations, blood vessels were successfully identified.

Application of Independent Component Analysis techniques in speckle noise reduction of retinal OCT images

Optical Coherence Tomography (OCT) is an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging, etc. OCT images usually suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. Therefore the need for speckle noise reduction techniques is of high importance. To the best of our knowledge, use of Independent Component Analysis (ICA) techniques has never been explored for speckle reduction of OCT images. Here, a comparative study of several ICA techniques (InfoMax, JADE, FastICA and SOBI) is provided for noise reduction of retinal OCT images. Having multiple B-scans of the same location, the eye movements are compensated using a rigid registration technique. Then, different ICA techniques are applied to the aggregated set of B-scans for extracting the noise-free image. Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR) and Equivalent-Number-of-Looks (ENL), as well as analysis on the computational complexity of the methods, are considered as metrics for comparison. The results show that use of ICA can be beneficial, especially in case of having fewer number of B-scans.

Robust rigid registration by scanning multiple phase correlation peaks

Rigid image registration is an important image processing problem with applications in medical imaging, biometrics, image mosaicking, video stabilization, and others. A popular technique for rigid image registration consists in estimating the location of the maximum of the phase correlation function. However, multiple motions, non-rigid transformations, non-textured regions and noise introduce artefacts and spurious peaks that may confound the phase correlation method. A solution, which is proposed here, is to explore multiple peaks and estimate an error measure for each plausible transformation, in order to find the correct one. Our tests, performed with both artificially generated and real cases, show that the proposed approach can increase the probability of successful registration by 25–40% and reduce the registration error by up to 35%.

Methodology to study the three-dimensional spatial distribution of prostate cancer and their dependence on clinical parameters.

A methodology to study the relationship between clinical variables [e.g., prostate specific antigen (PSA) or Gleason score] and cancer spatial distribution is described. Three-dimensional (3-D) models of 216 glands are reconstructed from digital images of whole mount histopathological slices. The models are deformed into one prostate model selected as an atlas using a combination of rigid, affine, and B-spline deformable registration techniques. Spatial cancer distribution is assessed by counting the number of tumor occurrences among all glands in a given position of the 3-D registered atlas. Finally, a difference between proportions is used to compare different spatial distributions. As a proof of concept, we compare spatial distributions from patients with PSA greater and less than [Formula: see text] and from patients older and younger than 60 years. Results suggest that prostate cancer has a significant difference in the right zone of the prostate between populations with PSA greater and less than [Formula: see text]. Age does not have any impact in the spatial distribution of the disease. The proposed methodology can help to comprehend prostate cancer by understanding its spatial distribution and how it changes according to clinical parameters. Finally, this methodology can be easily adapted to other organs and pathologies.

Reverse modeling strategy of aero-engine blade based on design intent

Blade geometry plays an extremely important role in the aerodynamic performance of aero-engine. However, in the process of secondary machining for near-net shape or serviced blade, the deviation between the actual and nominal blades can raise serious problems for NC tool path generation. It is even worse that inappropriate reverse modeling may change the design intent and deteriorate the aerodynamic performance. This paper presents an innovative strategy for reconstructing actual profiles from measured data. Comparing with conventional reverse modeling, the proposed strategy emphasizes functional significance of the known feature information concerned with implicit design intent. The preservation of design intent has three levels by cognition of the feature information. Firstly, the established technique for rigid registration is improved by means of incorporating accuracy feature into localization. Secondly, the parametric design methodology of aero-engine blade profile is introduced into the free-form deformation (FFD) of cross sections to control geometry feature, and finally, it guarantees aerodynamic performance feature. Some practical examples are given to demonstrate the effectiveness and superiority of the strategy.

Elastic registration of prostate MR images based on estimation of deformation states.

Magnetic resonance imaging (MRI) is being used increasingly for image-guided targeted biopsy and focal therapy of prostate cancer. In this paper, a combined rigid and deformable registration technique is proposed to register pre-treatment diagnostic 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T scanner before insertion of the needle with patients in the semi-lithotomy position. Both the rigid and deformable registration algorithms employ an intensity-based distance metric defined based on the modality independent neighborhood descriptors (MIND) between images. The optimization routine for estimating the rigid transformation parameters is initialized using four pairs of manually selected approximate corresponding points on the boundaries of the prostate. In this paper, the problem of deformable image registration is approached from the perspective of state estimation for dynamical systems. The registration algorithm employs a rather generic dynamic linear elastic model of the tissue deformation discretized by the finite element method (FEM). We use the model in a classical state estimation framework to estimate the deformation of the prostate based on the distance metric between pre- and intra-treatment images. Our deformable registration results using 17 sets of prostate MR images showed that the proposed method yielded a target registration error (TRE) of 1.87 ± 0.94 mm,2.03 ± 0.94 mm, and 1.70 ± 0.93 mm for the whole gland (WG), central gland (CG), and peripheral zone (PZ), respectively, using 76 manually-identified fiducial points. This was an improvement over the 2.67 ± 1.31 mm, 2.95 ± 1.43 mm, and 2.34 ± 1.11 mm, respectively for the WG, CG, and PZ after rigid registration alone. Dice similarity coefficients (DSC) in the WG, CG and PZ were 88.2 ± 5.3, 85.6 ± 7.6 and 68.7 ± 6.9 percent, respectively. Furthermore, the mean absolute distances (MAD) between surfaces was 1.26 ± 0.56 mm and 1.27 ± 0.55 mm in the WG and CG, after deformable registration. These results indicate that the proposed registration technique has sufficient accuracy for localizing prostate tumors in MRI-guided targeted biopsy or focal therapy of clinically localized prostate cancer.

Aligning 3D time-of-flight MRA datasets for quantitative longitudinal studies: evaluation of rigid registration techniques

Objective3D Time-of-flight (TOF) magnetic resonance angiography is commonly used for vascular analyses. A quantification of longitudinal morphological changes usually requires the registration of TOF image sequences acquired at different time points. The aim of this study was to evaluate the precision of different 3D rigid registration setups such that an optimal quantification of morphological changes can be achieved. MethodsEight different rigid registration techniques were implemented and evaluated in this study using the target registration error (TRE) calculated based on 554 landmarks defined in twenty TOF datasets. The registration techniques differed in integration of brain and vessel segmentation masks and usage of a multi-resolution framework. Furthermore, the benefit of a prior volume-of-interest definition for registration accuracy was evaluated. ResultsThe results revealed that the highest registration accuracies can be achieved using a multi-resolution framework and a cerebrovascular segmentation as mask. Numerically, a mean TRE of 1.1mm was calculated. If applicable, a prior definition of a volume-of-interest allows a reduction of the TRE to only 0.6mm. ConclusionTOF datasets should be registered using vessel segmentations as mask, multi-resolution framework and previous volume-of-interest definition if possible to obtain the highest registration precision. This is especially the case for longitudinal datasets that are separated by several months while the registration technique seems less important for datasets that are only separated by a few days.

The effectiveness of gadolinium MRI to improve target delineation for radiotherapy in hepatocellular carcinoma: A comparative study of rigid image registration techniques

To achieve consistent target delineation in radiotherapy for hepatocellular carcinoma (HCC), image registration between simulation CT and diagnostic MRI was explored.Twenty patients with advanced HCC were included. The median interval between MRI and CT was 11 days. CT was obtained with shallow free breathing and MRI at exhale phase. On each CT and MRI, the liver and the gross target volume (GTV) were drawn. A rigid image registration was taken according to point information of vascular bifurcation (Method[A]) and pixel information of volume of interest only including the periphery of the liver (Method[B]) and manually drawn liver (Method[C]). In nine cases with an indefinite GTV on CT, a virtual sphere was generated at the epicenter of the GTV. The GTV from CT (VGTV[CT]) and MRI (VGTV[MR]) and the expanded GTV from MRI (V+GTV[MR]) considering geometrical registration error were defined. The underestimation (uncovered V[CT] by V[MR]) and the overestimation (excessive V[MR] by V[CT]) were calculated. Through a paired T-test, the difference between image registration techniques was analyzed.For method[A], the underestimation rates of VGTV[MR] and V+GTV[MR] were 16.4 ± 8.9% and 3.2 ± 3.7%, and the overestimation rates were 16.6 ± 8.7% and 28.4 ± 10.3%, respectively. For VGTV[MR] and V+GTV[MR], the underestimation rates and overestimation rates of method[A] were better than method[C]. The underestimation rates and overestimation rates of the VGTV[MR] were better in method[B] than method[C]. By image registration and additional margin, about 97% of HCC could be covered. Method[A] or method[B] could be recommended according to physician preference.