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Purpose: In the treatment of cervical cancer, large anatomical deformations, caused by, e.g., tumor shrinkage, bladder and rectum filling changes, organ sliding, and the presence of the brachytherapy (BT) applicator, prohibit the accumulation of external beam radiotherapy (EBRT) and BT dose distributions. This work proposes a structure-wise registration with vector field integration (SW+VF) to map the largely deformed anatomies between EBRT and BT, paving the way for 3D dose accumulation between EBRT and BT. Methods: T2w-MRIs acquired before EBRT and as a part of the MRI-guided BT procedure for 12 cervical cancer patients, along with the manual delineations of the bladder, cervix-uterus, and rectum-sigmoid, were used for this study. A rigid transformation was used to align the bony anatomy in the MRIs. The proposed SW+VF method starts by automatically segmenting features in the area surrounding the delineated organs. Then, each organ and feature pair is registered independently using a feature-based nonrigid registration algorithm developed in-house. Additionally, a background transformation is calculated to account for areas far from all organs and features. In order to obtain one transformation that can be used for dose accumulation, the organ-based, feature-based, and the background transformations are combined into one vector field using a weighted sum, where the contribution of each transformation can be directly controlled by its extent of influence (scope size). The optimal scope sizes for organ-based and feature-based transformations were found by an exhaustive analysis. The anatomical correctness of the mapping was independently validated by measuring the residual distances after transformation for delineated structures inside the cervix-uterus (inner anatomical correctness), and for anatomical landmarks outside the organs in the surrounding region (outer anatomical correctness). The results of the proposed method were compared with the results of the rigid transformation and nonrigid registration of all structures together (AST). Results: The rigid transformation achieved a good global alignment (mean outer anatomical correctness of 4.3 mm) but failed to align the deformed organs (mean inner anatomical correctness of 22.4 mm). Conversely, the AST registration produced a reasonable alignment for the organs (6.3 mm) but not for the surrounding region (16.9 mm). SW+VF registration achieved the best results for both regions (3.5 and 3.4 mm for the inner and outer anatomical correctness, respectively). All differences were significant (p < 0.02, Wilcoxon rank sum test). Additionally, optimization of the scope sizes determined that the method was robust for a large range of scope size values. Conclusions: The novel SW+VF method improved the mapping of large and complex deformations observed between EBRT and BT for cervical cancer patients. Future studies that quantify the mapping error in terms of dose errors are required to test the clinical applicability of dose accumulation by the SW+VF method. (c) 2015 American Association of Physicists in Medicine.

Purpose: 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. Methods: 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 the MATLAB 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. Results: 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. Conclusions: 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. (C) 2014 American Association of Physicists in Medicine.

Purpose: Computer tomography (CT) scans are used for designing radiotherapy treatment plans. However, the tumor is often better visible in magnetic resonance (MR) images. For liver stereotactic body radiation therapy (SBRT), the planning CT scan is acquired while abdominal compression is applied to reduce tumor motion induced by breathing. However, diagnostic MR scans are acquired under voluntary breath-hold without the compression device. The resulting large differences in liver shape hinder the alignment of CT and MR image sets, which severely limits the integration of the information provided by these images. The purpose of the current study is to develop and validate a nonrigid registration method to align breath-hold MR images with abdominal-compressed CT images, using vessels that are automatically segmented within the liver. Methods: Contrast-enhanced MR and CT images of seven patients with liver cancer were used for this study. The registration method combines automatic vessel segmentation with an adapted version of thin-plate spline robust point matching. The vessel segmentation uses a multiscale vesselness measure, which allows vessels of various thicknesses to be segmented. The nonrigid registration is point-based, and progressively improves the correspondence and transformation between two point sets. Moreover, Results: Liver deformations in the range of 2.8-10.7 mm were found after rigid registration of the CT and MR scans. Low residual distances for vessel bifurcations (average 1.6, range 1.3-1.9 mm) and additional anatomical landmarks (1.5, 1.1-2.4 mm) were found after nonrigid registration. A large amount of outliers were identified (25%-55%) caused by vessels present in only one of the image sets and false positives in the vesselness measure. The nonrigid registration was capable of handling these Conclusions: An accurate CT/MR vessel-guided nonrigid registration for largely deformed livers was developed, tested, and validated. The method, combining vessel segmentation and point matching, was robust against differences in the segmented vessels. The authors conclude that nonrigid registration is required for accurate alignment of abdominal-compressed and uncompressed liver anatomy. Alignment of breath-hold MR and abdominal-compressed CT images can be used to improve tumor localization for

Purpose: Modern radiotherapy requires assessment of patient anatomical changes. By using unidirectional registration methods, the quantified anatomical changes are asymmetric, i.e., depend on the direction of the registration. Moreover, the registration is challenged by the large and complex organ deformations that can occur in, e. g., cervical cancer patients. The aim of this work was to develop, test, and validate a symmetric feature-based nonrigid registration method that can handle organs with large-scale deformations. Methods: A symmetric version of the unidirectional thin plate spline robust point matching (TPS-RPM) algorithm was developed, implemented, tested, and validated. Tests were performed by using the delineated cervix and uterus and bladder in CT scans of five cervical cancer patients. For each patient, five CT scans with a large variability in organ shape, volume, and deformations were acquired. Both the symmetric and the unidirectional algorithm were employed to calculate the registration geometric accuracy (surface distance and surface coverage errors), the inverse consistency, the residual distances after transforming anatomical landmarks, and the registration time. Additionally, to facilitate the further use of our symmetric method, a large set of input parameters was tested. Results: The developed symmetric algorithm handled successfully the registration of bladders with extreme volume change for which TPS-RPM failed. Compared to the unidirectional algorithm the symmetric algorithm improved, for the registration of organs with large volume change, the inverse consistency by 78% and the surface coverage by 46%. Similarly, for organs with small volume change, the symmetric algorithm improved the inverse consistency by 69% and the surface coverage by 13%. The method allowed for anatomically coherent registration in only 35 s for cervixuterus and 151 s for bladder, while keeping the inverse consistency errors around 1 mm and the surface matching errors below 1 mm. Compared to rigid alignment the symmetric method reduced the residual distances between anatomical landmarks from a range of 5.8 +/- 2-70.1 +/- 20.1 mm to a range of 1.9 +/- 0.2-8.5 +/- 5.2 mm. Conclusions: The developed symmetric method could be employed to perform fast, accurate, consistent, and anatomically coherent registration of organs with large and complex deformations. Therefore, the method is a useful tool that could support further developments in high precision image guided radiotherapy. (C) 2010 American Association of Physicists in Medicine. [DOI: 10.1118/1.3443436]

Technical improvements in planning and dose delivery and in verification of patient positioning have substantially widened the therapeutic window for radiation treatment of cancer. However, changes in patient anatomy during the treatment limit the exploitation of these new techniques. To further improve radiation treatments, anatomical changes need to be modeled and accounted for. Nonrigid registration can be used for this purpose. This article describes the design, the implementation, and the validation of a new framework for nonrigid registration for radiotherapy applications. The core of this framework is an improved version of the thin plate spline robust point matching (TPS-RPM) algorithm. The TPS-RPM algorithm estimates a global correspondence and a transformation between the points that represent organs of interest belonging to two image sets. However, the algorithm does not allow for the inclusion of prior knowledge on the correspondence of subset of points, and therefore, it can lead to inconsistent anatomical solutions. In this article TPS-RPM was improved by employing a novel correspondence filter that supports simultaneous registration of multiple structures. The improved method allows for coherent organ registration and for the inclusion of user-defined landmarks, lines, and surfaces inside and outside of structures of interest. A procedure to generate control points from segmented organs is described. The framework parameters r and lambda, which control the number of points and the nonrigidness of the transformation, respectively, were optimized for three sites with different degrees of deformation (head and neck, prostate, and cervix) using two cases per site. For the head and neck cases, the salivary glands were manually contoured on CT scans, for the prostate cases the prostate and the vesicles, and for the cervix cases the cervix uterus, the bladder, and the rectum. The transformation error obtained using the best set of parameters was below 1 mm for all the studied cases. The lengths of the deformation vectors were on average (+/- 1 standard deviation) 5.8 +/- 2.5 and 2.6 +/- 1.1 mm for the head and neck cases, 7.2 +/- 4.5 and 8.6 +/- 1.9 mm for the prostate cases, and 19.0 +/- 11.6 and 14.5 +/- 9.3 mm for the cervix cases. Distinguishable anatomical features were identified for each case and were used to validate the registration by calculating residual distances after transformation: 1.5 +/- 0.8, 2.3 +/- 1.0, and 6.3 +/- 2.9 mm for the head and neck, prostate, and cervix sites, respectively. Finally, the authors demonstrated how the inclusion of these anatomical features in the registration process reduced the residual distances to 0.8 +/- 0.5, 0.6 +/- 0.5, and 1.3 +/- 0.7 mm for the head and neck, prostate, and cervix sites, respectively. The inclusion of additional anatomical features produced more anatomically coherent transformations without compromising the transformation error. The authors concluded that the presented nonrigid registration framework is a powerful tool to simultaneously register multiple segmented organs with very different complexities.