TU-AB-303-08: GPU-Based Software Platform for Efficient Image-Guided Adaptive Radiation Therapy

TomoTherapy involves image-guided radiation therapy (IGRT) using Mega-voltage CT (MVCT) for each treatment session. The acquired MVCT images can be utilized for the retrospective assessment of dose distribution. The TomoTherapy provides 18 distinct imaging conditions that can be selected based on a combination of algorithms, acquisition pitch, and slice interval. We investigated the accuracy of dose calculation and deformable image registration (DIR) depending on MVCT scan parameters and their effects on adaptive radiation therapy (ART). We acquired image values for density calibration tables (IVDTs) under 18 different MVCT conditions and compared them. The planning CT (pCT) was performed using a thoracic phantom, and an esophageal intensity-modulated radiation therapy (IMRT) plan was created. MVCT images of the thoracic phantom were acquired under each of the 18 conditions, and dose recalculation was performed. DIR was performed on the MVCT images acquired under each condition. The accuracy of DIR, depending on the MVCT scan parameters, was compared using the mean distance to agreement (MDA) and Dice similarity coefficient (DSC). The dose distribution calculated on the MVCT images was deformed using deformed vector fields (DVF). No significant differences were observed in the results of the 18 IVDTs. The esophageal IMRT plan also showed a small dose difference. Regarding verifying the DIR accuracy, the MDA increased, and the DSC decreased as the acquisition pitch and slice interval increased. The difference between the dose distributions after dose mapping was comparable to that before DIR. The MVCT scan parameters had little effect on ART.

Deformable image registration (DIR) is a crucial step in adaptive radiation therapy (ART) to deform the planning CT to the current CBCT for dose calculation and for contour propagation. CBCT images are sometimes truncated in the axial plane due to limited field of view (FOV). DIR between CT and truncated CBCT often leads to unphysical results, especially in and near missing regions, which may result in significant errors in dose calculation afterwards. The purpose of this work is to develop and evaluate a method to improve existing DIR algorithms to solve the truncation problem. The radius of FOV of CBCT is first estimated. A recently developed robust CT-CBCT DIR algorithm, called Deformation with Intensity Simultaneously Corrected (DISC), is used for deformation field calculation. At each iteration of DISC, the calculated deformation vector field outside the FOV is replaced by a smooth propagation of the deformation field inside the FOV. Six head-and-neck cancer cases and two prostate cancer cases are used for evaluation. Dose calculation is performed to test the impacts on the resulting dose distribution. In terms of DIR accuracy, it is found that the average normalized mutual information (NMI), normalized cross correlation (NCC) and feature similarity index (FSIM) increase from 0.638, 0.948 and 0.917, to 0.641, 0.951 and 0.919, respectively, when compared with DISC without propagation. In terms of dose distribution, the relative L2 distance of dose (inside the FOV) between the ground truth and that calculated on deformed CT with propagation reduces from 9.25%to 1.41%, compared with those without propagation. We have developed a deformation field propagation method for DIR to register the planning CT and the CBCT image with small FOV. Tests on head-and-neck and prostate cancer cases have demonstrated that our algorithm can generate more accurate registration results for dose calculation. This work is supported in part by the University of California Lab Fees Research Program, the Master Research Agreement from Varian Medical Systems, Inc., and the grants from the National Natural Science Foundation of China (No.30970866).

PurposeKilo‐voltage cone‐beam computed tomography (CBCT) is widely used for patient alignment, contour propagation, and adaptive treatment planning in radiation therapy. In this study, we evaluated the accuracy of deformable image registration (DIR) for CBCT under various imaging protocols with different noise and patient dose levels.MethodsA physical phantom previously developed to facilitate end‐to‐end testing of the DIR accuracy was used with Varian Velocity v4.0 software to evaluate the performance of image registration from CT to CT, CBCT to CT, and CBCT to CBCT. The phantom is acrylic and includes several inserts that simulate different tissue shapes and properties. Deformations and anatomic changes were simulated by changing the rotations of both the phantom and the inserts. CT images (from a head and neck protocol) and CBCT images (from pelvis, head and “Image Gently” protocols) were obtained with different image noise and dose levels. Large inserts were filled with Mobil DTE oil to simulate soft tissue, and small inserts were filled with bone materials. All inserts were contoured before the DIR process to provide a ground truth contour size and shape for comparison. After the DIR process, all deformed contours were compared with the originals using Dice similarity coefficient (DSC) and mean distance to agreement (MDA). Both large and small volume of interests (VOIs) for DIR volume selection were tested by simulating a DIR process that included whole patient image volume and clinical target volumes (CTV) only (for CTVs propagation).ResultsFor cross‐modality DIR registration (CT to CBCT), the DSC were >0.8 and the MDA were <3 mm for CBCT pelvis, and CBCT head protocols. For CBCT to CBCT and CT to CT, the DIR accuracy was improved relative to the cross‐modality tests. For smaller VOIs, the DSC were >0.8 and MDA <2 mm for all modalities.ConclusionsThe accuracy of DIR depends on the quality of the CBCT image at different dose and noise levels.

Accurate identification of the prostatic urethra and bladder can help determine dosing and evaluate urinary toxicity during intensity-modulated radiation therapy (IMRT) planning in patients with localized prostate cancer. However, it is challenging to locate the prostatic urethra in planning computed tomography (pCT). In the present study, we developed a multiatlas-based auto-segmentation method for prostatic urethra identification using deformable image registration accuracy prediction with machine learning (ML) and assessed its feasibility. We examined 120 patients with prostate cancer treated with IMRT. All patients underwent temporary urinary catheter placement for identification and contouring of the prostatic urethra in pCT images (ground truth). Our method comprises the following three steps: (a) select four atlas datasets from the atlas datasets using the deformable image registration (DIR) accuracy prediction model, (b) deform them by structure-based DIR, (3) and propagate urethra contour using displacement vector field calculated by the DIR. In (a), for identifying suitable datasets, we used the trained support vector machine regression (SVR) model and five feature descriptors (e.g., prostate volume) to increase DIR accuracy. This method was trained/validated using 100 patients and performance was evaluated within an independent test set of 20 patients. Fivefold cross-validation was used to optimize the hype parameters of the DIR accuracy prediction model. We assessed the accuracy of our method by comparing it with those of two others: Acostas method-based patient selection (previous study method, by Acosta et al.), and the Waterman's method (defines the prostatic urethra based on the center of the prostate, by Waterman et al.). We used the centerlines distance (CLD) between the ground truth and the predicted prostatic urethra as the evaluation index. The CLD in the entire prostatic urethra was 2.09±0.89mm (our proposed method), 2.77±0.99mm (Acosta et al., P=0.022), and 3.47±1.19mm (Waterman et al., P<0.001); our proposed method showed the highest accuracy. In segmented CLD, CLD in the top 1/3 segment was highly improved from that of Waterman et.al. and was slightly improved from that of Acosta et.al., with results of 2.49±1.78mm (our proposed method), 2.95±1.75mm (Acosta et al., P=0.42), and 5.76±3.09mm (Waterman et al., P<0.001). We developed a DIR accuracy prediction model-based multiatlas-based auto-segmentation method for prostatic urethra identification. Our method identified prostatic urethra with mean error of 2.09mm, likely due to combined effects of SVR model employment in patient selection, modified atlas dataset characteristics and DIR algorithm. Our method has potential utility in prostate cancer IMRT and can replace use of temporary indwelling urinary catheters.

Benchmarking of Deformable Image Registration for Multiple Anatomic Sites Using Digital Data Sets With Ground-Truth Deformation Vector Fields

To design and construct a three-dimensional (3D) anthropomorphic abdominal phantom for geometric accuracy and dose summation accuracy evaluations of deformable image registration (DIR) algorithms for adaptive radiation therapy (ART). Organ molds, including liver, kidney, spleen, stomach, vertebra, and two metastasis tumors, were 3D printed using contours from an ovarian cancer patient. The organ molds were molded with deformable gels made of different mixtures of polyvinyl chloride (PVC) and the softener dioctyl terephthalate. Gels with different densities were obtained by a polynomial fitting curve that described the relation between the Hounsfield unit (HU) and PVC-softener blending ratio. The rigid vertebras were constructed by molding of white cement and cellulose pulp. The final abdominal phantom was assembled by arranging all the fabricated organs inside a hollow dummy according to their anatomies, and sealed by deformable gel with averaged HU of muscle and fat. Fiducial landmarks were embedded inside the phantom for spatial accuracy and dose accumulation accuracy studies. Two channels were excavated to facilitate ionization chamber insertion for dosimetric measurements. Phantom properties such as deformable gel elasticity and HU stability were studied. The dosimetric measurement accuracy in the phantom was performed, and the DIR accuracies of three DIR algorithms available in the open source DIR toolkit-DIRART were also validated. The constructed deformable gel showed elastic behavior and was stable in HU values over times, proving to be a practical material for the deformable phantom. The constructed abdominal phantom consisted of realistic anatomies in terms of both anatomical shapes and densities when compared with its reference patient. The dosimetric measurements showed a good agreement with the calculated doses from the treatment planning system. Fiducial-based accuracy analysis conducted on the constructed phantom demonstrated the feasibility of applying the phantom for organ-wise DIR accuracy assessment. We have designed and constructed an anthropomorphic abdominal deformable phantom with satisfactory elastic property, realistic organ density, and anatomy. This physical phantom can be used for routine validations of DIR geometric accuracy and dose accumulation accuracy in ART.

Purpose:Multi‐modality deformable image registration (DIR) for head &amp; neck (HN) radiotherapy is difficult, particularly when matching computed tomography (CT) scans with magnetic resonance imaging (MRI) scans. We hypothesized that the ‘shared information’ between images of different modalities was to be found in some form of edge‐based transformation, and that novel edge‐based DIR methods might outperform standard DIR methods.Methods:We propose a novel method that combines gray‐scale edge‐based morphology and mutual information (MI) in two stages. In the first step, we applied a modification of a previously published mathematical morphology method as an efficient gray scale edge estimator, with denoising function. The results were fed into a MI‐based solver (plastimatch). The method was tested on 5 HN patients with pretreatment CT and MR datasets and associated follow‐up weekly MR scans. The followup MRs showed significant regression in tumor and normal structure volumes as compared to the pretreatment MRs. The MR images used in this study were obtained using fast spin echo based T2w images with a 1 mm isotropic resolution and FOV matching the CT scan.Results:In all cases, the novel edge‐based registration method provided better registration quality than MI‐based DIR using the original CT and MRI images. For example, the mismatch in carotid arteries was reduced from 3–5 mm to within 2 mm. The novel edge‐based method with different registration regulation parameters did not show any distorted deformations as compared to the non‐realistic deformations resulting from MI on the original images. Processing time was 1.3 to 2 times shorter (edge vs. non‐edge). In general, we observed quality improvement and significant calculation time reduction with the new method.Conclusion:Transforming images to an ‘edge‐space,’ if designed appropriately, greatly increases the speed and accuracy of DIR.

PurposeKilovoltage cone beam computed tomography (kVCBCT)‐guided adaptive radiation therapy (ART) uses daily deformed CT (dCT), which is generated automatically through deformable registration methods. These registration methods may perform poorly in reproducing volumes of the target organ, rectum, and bladder during treatment. We analyzed the registration errors between the daily kVCBCTs and corresponding dCTs for these organs using the default optical flow algorithm and two registration procedures. We validated the effectiveness of these registration methods in replicating the geometry for dose calculation on kVCBCT for ART.MethodsWe evaluated three deformable image registration (DIR) methods to assess their registration accuracy and dose calculation effeciency in mapping target and critical organs. The DIR methods include (1) default intensity‐based deformable registration, (2) hybrid deformable registration, and (3) a two‐step deformable registration process. Each technique was applied to a computerized imaging reference system (CIRS) phantom (Model 062 M) and to five patients who received volumetric modulated arc therapy to the prostate. Registration accuracy was assessed using the 95% Hausdorff distance (HD95) and Dice similarity coefficient (DSC), and each method was compared with the intensity‐based registration method. The improvement in the dCT image quality of the CIRS phantom and five patients was assessed by comparing dCT with kVCBCT. Image quality quantitative metrics for the phantom included the signal‐to‐noise ratio (SNR), uniformity, and contrast‐to‐noise ratio (CNR), whereas those for the patients included the mean absolute error (MAE), mean error, peak signal‐to‐noise ratio (PSNR), and structural similarity index measure (SSIM). To determine dose metric differences, we used a dose‐volume histogram (DVH) and 3.0%/0.3 mm gamma analysis to compare planning computed tomography (pCT) and kVCBCT recalculations with restimulated CT images used as a reference.ResultsThe dCT images generated by the hybrid (dCTH) and two‐step (dCTC) registration methods resulted in significant improvements compared to kVCBCT in the phantom model. Specifically, the SNR improved by 107% and 107.2%, the uniformity improved by 90% and 75%, and the CNR improved by 212.2% and 225.6 for dCTH and dCTC methods, respectively. For the patient images, the MAEs improved by 98% and 94%, the PSNRs improved by 16.3% and 22.9%, and the SSIMs improved by 1% and 1% in the dCTH and dCTC methods, respectively. For the geometric evaluation, only the two‐step registration method improved registration accuracy. The dCTH method yielded an average HD95 of 12 mm and average DSC of 0.73, whereas dCTC yielded an average HD95 of 2.9 mm and average DSC of 0.902. The DVH showed that the dCTC‐based dose calculations differed by <2% from the expected results for treatment targets and volumes of organs at risk. Additionally, gamma indices for dCTC‐based treatment plans were >95% at all points, whereas they were <95% for kVCBCT‐based treatment plans.ConclusionThe two‐step registration method outperforms the intensity‐based and hybrid registration methods. While the hybrid and two‐step‐based methods improved the image quality of kVCBCT in a linear accelerator, only the two‐step method improved the registration accuracy of the corresponding structures among the pCT and kVCBCT datasets. A two‐step registration process is recommended for applying kVCBCT to ART, which achieves better registration accuracy for local and global image structures. This method appears to be beneficial for radiotherapy dose calculation in patients with pelvic cancer.

Accurate tracking of anatomical changes and computation of actually delivered dose to the patient are critical for successful adaptive radiation therapy (ART). Additionally, efficient data management and fast processing are practically important for the adoption in clinic as ART involves a large amount of image and treatment data. The purpose of this study was to develop an accurate and efficient Software platform for CUmulative Dose Assessment (scuda) that can be seamlessly integrated into the clinical workflow. scuda consists of deformable image registration (DIR), segmentation, dose computation modules, and a graphical user interface. It is connected to our image PACS and radiotherapy informatics databases from which it automatically queries/retrieves patient images, radiotherapy plan, beam data, and daily treatment information, thus providing an efficient and unified workflow. For accurate registration of the planning CT and daily CBCTs, the authors iteratively correct CBCT intensities by matching local intensity histograms during the DIR process. Contours of the target tumor and critical structures are then propagated from the planning CT to daily CBCTs using the computed deformations. The actual delivered daily dose is computed using the registered CT and patient setup information by a superposition/convolution algorithm, and accumulated using the computed deformation fields. Both DIR and dose computation modules are accelerated by a graphics processing unit. The cumulative dose computation process has been validated on 30 head and neck (HN) cancer cases, showing 3.5 ± 5.0 Gy (mean±STD) absolute mean dose differences between the planned and the actually delivered doses in the parotid glands. On average, DIR, dose computation, and segmentation take 20 s/fraction and 17 min for a 35-fraction treatment including additional computation for dose accumulation. The authors developed a unified software platform that provides accurate and efficient monitoring of anatomical changes and computation of actually delivered dose to the patient, thus realizing an efficient cumulative dose computation workflow. Evaluation on HN cases demonstrated the utility of our platform for monitoring the treatment quality and detecting significant dosimetric variations that are keys to successful ART.

Evaluation of the effect of user-guided deformable image registration of thoracic images on registration accuracy among users

Background. To present a new method that determines an optimised IGRT couch correction vector from a displacement vector field (DVF). The DVF is computed by a deformable image registration (DIR) method. The proposed method can improve the quality of volume-of-interest (VOI) alignment in image guided radiation therapy (IGRT), and can serve as a decision-making aid for re-planning. Material and methods. The proposed method was demonstrated using the CT data sets of 11 head-and-neck cancer patients with daily kilovoltage control-CTs. A DVF was computed for each control-CT using a DIR method. The DVF was used for voxel tracking and re-contouring of the VOIs in the control-CTs. Then a rigid body transformation, which could be used as couch correction vector, was optimised. The aim of the optimisation process was to find a vector and rotations that map the deformed VOIs into a specified territory. This territory was defined by a margin extension of the VOIs at the time of the planning process. Within this extension, VOI motion and deformation was tolerated. The objective function in the optimisation process was the sum of all volume fractions outside the defined territories. Results. The proposed method was able to find a correction vector, which resulted in a coverage of the target volumes of at least 98% in 52.3% of all fractions. In contrast, a standard IGRT correction using a rigid registration method only fulfilled this criterion in 22.6% of all fractions. The optimisation process took an average of 1.5 minutes per fraction. Conclusion. The knowledge of the deformation of the anatomy allows the determination of an optimised rigid correction vector using our method. The method ensures controlled mapping of the VOIs despite small deformations. If no optimised vector can be determined, re-planning should be considered. Thus, our method can also serve as a decision-making aid for re-planning.

Purpose: To evaluate the accuracy of deformable image registration (DIR) between the planning kVCT (pCT) and the daily MVCT combined with the histogram matching (HM) algorithm, and evaluate the deformable dose accumulation using a suggested method for adaptive radiotherapy with Helical Tomotharapy (HT). Methods: For five prostate cancer patients (76 Gy/38 Fr) treated with HT in our institution, seven MVCT series (a total of 35 series) acquired weekly were investigated. First, to minimize the effect of different HU values between pCT and MVCT, this image-processing method adjusts HU values between pCT and MVCT images by using image cumulative histograms of HU values, generating an HM-MVCT. Then, the DIR of the pCT to the HM-MVCT was performed, generating a deformed pCT. Finally, deformable dose accumulation was performed toward the pCT image. Results: The accuracy of DIR was significantly improved by using the HM algorithm, compared with non-HM method for several structures (p < 0.05). The mean dice similarity coefficient of the non-HM method was 0.75 ± 0.05, 0.83 ± 0.06, and 0.90 ± 0.04 for the CTV, rectum, and bladder, respectively, while that of the HM method was 0.81 ± 0.06, 0.81 ± 0.04, and 0.92 ± 0.06, respectively. For the deformable dose accumulation, some difference was observed between the two methods, particularly for the small calculated regions, such as rectum V60 and V70. Conclusion: Adapting the HM method can improve the accuracy of DIR. Furthermore, dose calculation using the deformed pCT using HM methods can be an effective tool for adaptive radiotherapy.

The spatial accuracy of deformable image registration (DIR) is important in the implementation of image guided adaptive radiotherapy techniques for cancer in the pelvic region. Validation of algorithms is best performed on phantoms with fiducial markers undergoing controlled large deformations. Excised porcine bladders, exhibiting similar filling and voiding behavior as human bladders, provide such an environment. The aim of this study was to determine the spatial accuracy of different DIR algorithms on CT images of ex vivo porcine bladders with radiopaque fiducial markers applied to the outer surface, for a range of bladder volumes, using various accuracy metrics. Five excised porcine bladders with a grid of 30-40 radiopaque fiducial markers attached to the outer wall were suspended inside a water-filled phantom. The bladder was filled with a controlled amount of water with added contrast medium for a range of filling volumes (100-400 ml in steps of 50 ml) using a luer lock syringe, and CT scans were acquired at each filling volume. DIR was performed for each data set, with the 100 ml bladder as the reference image. Six intensity-based algorithms (optical flow or demons-based) implemented in theMATLAB platform DIRART, a b-spline algorithm implemented in the commercial software package VelocityAI, and a structure-based algorithm (Symmetric Thin Plate Spline Robust Point Matching) were validated, using adequate parameter settings according to values previously published. The resulting deformation vector field from each registration was applied to the contoured bladder structures and to the marker coordinates for spatial error calculation. The quality of the algorithms was assessed by comparing the different error metrics across the different algorithms, and by comparing the effect of deformation magnitude (bladder volume difference) per algorithm, using the Independent Samples Kruskal-Wallis test. The authors found good structure accuracy without dependency on bladder volume difference for all but one algorithm, and with the best result for the structure-based algorithm. Spatial accuracy as assessed from marker errors was disappointing for all algorithms, especially for large volume differences, implying that the deformations described by the registration did not represent anatomically correct deformations. The structure-based algorithm performed the best in terms of marker error for the large volume difference (100-400 ml). In general, for the small volume difference (100-150 ml) the algorithms performed relatively similarly. The structure-based algorithm exhibited the best balance in performance between small and large volume differences, and among the intensity-based algorithms, the algorithm implemented in VelocityAI exhibited the best balance. Validation of multiple DIR algorithms on a novel physiological bladder phantom revealed that the structure accuracy was good for most algorithms, but that the spatial accuracy as assessed from markers was low for all algorithms, especially for large deformations. Hence, many of the available algorithms exhibit sufficient accuracy for contour propagation purposes, but possibly not for accurate dose accumulation.

Deformable image registration (DIR) enables advanced applications for image-guided and adaptive radiotherapy. However, DIR has not yet been widely adopted in clinical settings. This study addresses two challenges: (1) evaluating DIR practices and identifying barriers through a survey of Swiss radiotherapy institutes, providing Swiss-specific insights, and (2) assessing multi-institutional deformable-based dose accumulation (DDA) uncertainties by analyzing dose discrepancies on the accumulated dose. These findings aim to inform consensus guidelines, quantify DDA uncertainties, and support standardized future DIR integration into routine RT. All Swiss radiotherapy institutes with available contact details were invited to participate in the study's two parts: a survey and a multi-institutional DDA evaluation. The survey consisted of 25 questions and was conducted from August 23 to October 31, 2024. Survey results were compared to surveys from other countries. For the DDA evaluation, participants used shared datasets, including a lung cancer case with two dose distributions, one conventional VMAT dose and one artificially created cuboid dose, to perform DIR and accumulate doses. The cuboid dose was used to have a standardized dose with a "perfect" dose gradient. The level of inter-institutional dose discrepancies was assessed visually and by calculating dose differences on a voxel level. Eighteen out of 26 institutes (69%) completed the survey. All 18 institutes use rigid image registration (RIR) clinically, 11 (61.1%) use DIR. Five of those institutes use DIR in research projects. RIR is primarily applied for image fusion (100% of the institutes), patient positioning (88.9%), and as a preliminary step to DIR or adaptive RT (55.6%). DIR use cases include image fusion, dose accumulation, and adaptive workflows. Barriers to DIR adoption in clinical routine include software limitations and a lack of quality management methods. Standardized quality management of DIRs is largely missing in Swiss radiotherapy institutes so far. Six institutes participated in the dose accumulation part, submitting dose accumulation results for a VMAT and a cuboid dose distribution. Visual inspection revealed inter-institutional differences, particularly in steep dose gradient areas. Quantitative analysis showed average voxel-wise absolute differences of 0.01-0.03 Gy (VMAT), corresponding to 0.1%-0.3%, and 0.18-0.45 Gy (cuboid), corresponding to 1.8%-4.5%, for a total dose of 10 Gy. Differences were more pronounced in steep dose gradients and, consequently, in proximity to the PTV. The voxel-wise maximum-minimum analysis highlighted variability in gradient regions, even for the same algorithms used by different institutes, emphasizing the impact of software, software usage, and workflow choices on dose accumulation outcomes. The cuboid dose showed increased interpretability of the uncertainty compared to VMAT doses. The usage of DIR in Swiss RT institutes is primarily hindered by workflow inefficiencies in commercially available software and insufficient quality management options. Specifically, a lower level of standardized validation, commissioning, and uncertainty handling for DIR was observed in the Swiss RT institutes compared to survey outcomes in other countries. The multi-institutional DDA comparison increased the knowledge on DDA uncertainty areas and magnitude. Cuboid doses might help to evaluate uncertainties on the dose level in a standardized way in the future.

Purpose. To develop an optimization scheme and semi-physical regularization method for accurate and efficient deformable image registration (DIR) between magnetic resonance imaging (MRI) and computed tomography (CT) for breast cancer radiation therapy planning. Methods and Materials. A sequentially-applied semi-physical model regularization method with bending energy limited diffeomorphism (BELD) was developed to regularize transformation in free-form (B-spline) DIR. Bony structures were first segmented to account for their rigidity during deformation. Feature points were identified and filtered to be used as a penalty term. Rigidity, BELD, and feature point distance penalty terms are applied sequentially during the optimization process based on mutual information metric. Sample MRI and CT images of breast cancer patients were used to demonstrate the registration. Agreements of feature point pairs and contour transfer between the two image modalities were measured to illustrate the accuracy and efficiency for the developed DIR method. Results. The newly developed tool can accurately register MRI to CT with minimum user intervention for situations with moderate to large deformations. For the sample cases tested, the average feature point pair distance was reduced from 6.7 to 3.2 mm by simple B-spline registration and to 1.8 mm by the newly-developed registration method. Compared to simultaneous optimization of multiple penalty terms, the computation time was shortened by 30% and the accuracy was improved by 15%. The bone area warping artifact in deformed MRI disappeared in the proposed method. Considering the resolution of MR images is 1.3 mm, the resulting feature point pair distance is satisfactory. Conclusion. The proposed DIR method of sequentially applying physical model regularization terms during the optimization process can reduce the pitfalls of local extremity, weighting factor adjustment and multi-resolution optimization stages, therefore improving the accuracy and efficiency of MRI to CT registration even with moderate to large deformations commonly seen in radiation treatment planning with multi-modality images.