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Image registration using radial basis functions with adaptive radius.

ABSTRACT: Deformable registration of medical images often requires initial rigid alignment. Because of variations in the articulation of bony structures, rigid alignment can capture only limited regions of the image. We propose a method that allows us to compensate for misalignment of mobile parts, which leads to improved accuracy of deformable registration. The method is based on matching landmarks using radial basis functions (RBF) with adaptive radius.Based on the assumption that the compactly positioned landmarks likely delineate an anatomic structure whose position needs to be corrected, the algorithm incorporates unsupervised clustering of landmarks based on their positions within the reference image. It calculates an appropriate RBF radius based on the set of pairwise distances between landmarks within the cluster. The algorithm distinguishes between clusters of different size and between clusters of spherical and elongated shape, and assigns the optimal RBF radius for each cluster in order to restrict the deformation field to the closest vicinity of the structure of interest.Experiments with synthetic images demonstrate sensitivity of registration results to the choice of the radius of RBF support. We have statistically validated the methods on a large set of pulmonary landmarks. We also tested the method on medical use cases that show that it is potentially advantageous for initial registration of images with large spatial dislocations.The results of registration of CT images demonstrate that an automated selection of the RBF radius simplifies the registration routine and improves the registration quality. The selection is based on two criteria of preserving diffeomorphism of deformation and localization of the deformation within a desired area of the image.

SUBMITTER: Shusharina N

PROVIDER: S-EPMC3482254 | biostudies-other | 2012 Nov

REPOSITORIES: biostudies-other

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Project description:The quality of adaptive treatment planning depends on the accuracy of its underlying deformable image registration (DIR). The purpose of this study is to evaluate the performance of two DIR algorithms, B?spline?based deformable multipass (DMP) and deformable demons (Demons), implemented in a commercial software package. Evaluations were conducted using both computational and physical deformable phantoms. Based on a finite element method (FEM), a total of 11 computational models were developed from a set of CT images acquired from four lung and one prostate cancer patients. FEM generated displacement vector fields (DVF) were used to construct the lung and prostate image phantoms. Based on a fast?Fourier transform technique, image noise power spectrum was incorporated into the prostate image phantoms to create simulated CBCT images. The FEM?DVF served as a gold standard for verification of the two registration algorithms performed on these phantoms. The registration algorithms were also evaluated at the homologous points quantified in the CT images of a physical lung phantom. The results indicated that the mean errors of the DMP algorithm were in the range of 1.0~3.1mm for the computational phantoms and 1.9 mm for the physical lung phantom. For the computational prostate phantoms, the corresponding mean error was 1.0–1.9 mm in the prostate, 1.9–2.4 mm in the rectum, and 1.8–2.1 mm over the entire patient body. Sinusoidal errors induced by B?spline interpolations were observed in all the displacement profiles of the DMP registrations. Regions of large displacements were observed to have more registration errors. Patient?specific FEM models have been developed to evaluate the DIR algorithms implemented in the commercial software package. It has been found that the accuracy of these algorithms is patient?dependent and related to various factors including tissue deformation magnitudes and image intensity gradients across the regions of interest. This may suggest that DIR algorithms need to be verified for each registration instance when implementing adaptive radiation therapy. PACS numbers: 87.10.Kn, 87.55.km, 87.55.Qr, 87.57.nj

Project description:The quality of adaptive treatment planning depends on the accuracy of its underlying deformable image registration (DIR). The purpose of this study is to evaluate the performance of two DIR algorithms, B-spline-based deformable multipass (DMP) and deformable demons (Demons), implemented in a commercial software package. Evaluations were conducted using both computational and physical deformable phantoms. Based on a finite element method (FEM), a total of 11 computational models were developed from a set of CT images acquired from four lung and one prostate cancer patients. FEM generated displacement vector fields (DVF) were used to construct the lung and prostate image phantoms. Based on a fast-Fourier transform technique, image noise power spectrum was incorporated into the prostate image phantoms to create simulated CBCT images. The FEM-DVF served as a gold standard for verification of the two registration algorithms performed on these phantoms. The registration algorithms were also evaluated at the homologous points quantified in the CT images of a physical lung phantom. The results indicated that the mean errors of the DMP algorithm were in the range of 1.0 ~ 3.1 mm for the computational phantoms and 1.9 mm for the physical lung phantom. For the computational prostate phantoms, the corresponding mean error was 1.0-1.9 mm in the prostate, 1.9-2.4mm in the rectum, and 1.8-2.1 mm over the entire patient body. Sinusoidal errors induced by B-spline interpolations were observed in all the displacement profiles of the DMP registrations. Regions of large displacements were observed to have more registration errors. Patient-specific FEM models have been developed to evaluate the DIR algorithms implemented in the commercial software package. It has been found that the accuracy of these algorithms is patient dependent and related to various factors including tissue deformation magnitudes and image intensity gradients across the regions of interest. This may suggest that DIR algorithms need to be verified for each registration instance when implementing adaptive radiation therapy.

Project description:OBJECTIVE:To compare the accuracy of different methods for image registration in image-guided adaptive brachytherapy (IGABT) for cervical cancer. METHODS:The last treatment planning CT images (CT1) and the first treatment planning CT images (CT2) were acquired from 15 patients with cervical cancer and registered with different match image qualities (retained/removed catheter source in images) and different match regions [target only (S Group)/ interested organ structure (M Group)/body (L Group)] in Velocity3.2 software. The dice similarity coefficient (DSC) between the clinical target volumes (CTV) of the CT1 and CT2 images (CTVCT1 and CTVCT2, respectively) and between the organs-at-risk (OAR) of the two imaging datasets (OARCT1 and OARCT2, respectively) were used to evaluate the image registration accuracy. RESULTS:The auto-segmentation volume of the catheter source using Velocity software based on the CT threshold was the closest to the actual volume within the CT value range of 1700-1800 HU. In the retained group, the DSC for the OARs of was better than or equal to that of the removed group, and the DSC value of the rectum was significantly improved (P < 0.05). For comparison of different match regions, the high-risk target volume (HRCTV) and the low-risk target volume (IRCTV) had the best precision for registration of the target area, which was significantly greater than that of M group and L group (P < 0.05). The M group had better registration accuracy of the target area and the best accuracy for the OARs. The DSC values of the bladder and rectum were significantly better than those of the other two groups (P < 0.05). CONCLUSIONS:The CT value range of 1700-1800 HU is optimal for automatic image segmentation using Velocity software. Automatic segmentation and shielding the volume of the catheter source can improve the image quality. We recommend the use of interested organ structures regions for image registration in image-guided adaptive brachytherapy for cervical cancer.

Project description:For accurate analysis of bone formation and resorption during fracture healing, correct registration of follow-up onto baseline image is required. A per-fragment approach could improve alignment compared to standard registration based on the whole fractured region. In this exploratory study, we tested the effect of fragment size and displacement on a per-fragment registration, and compared the results of this per-fragment registration to the results of the standard registration in two stable fractures and one unstable fracture. To test the effect of fragment size and displacement, high-resolution peripheral quantitative computed tomography (HR-pQCT) scans of three unfractured radii were divided into subvolumes. Different displacements in x-, y, or z-direction or rotations around each axis were applied, and each subvolume was registered onto the initial volume to realign it. Next, registration of follow-up onto baseline scan was performed in two stable and one unstable fracture. After coarsely aligning the follow-up onto the baseline scan, a more accurate registration was performed of the whole fracture, i.e. the standard registration, and of each fracture fragment separately, i.e. per-fragment registration. Alignment was checked using overlay images showing baseline, follow-up and overlap between these scans, and by comparing correlation coefficients between the standard and per-fragment registration. Generally, subvolumes as small as 300 mm3 that were displaced up to 0.82 mm in x- or y-, or up to 1.64 mm in z-direction could be realigned correctly. For the fragments of all fractures, correlation coefficients were higher after per-fragment registration compared to standard registration. Most improvement was found in the unstable fracture and one fragment of the unstable fracture did not align correctly. This exploratory study showed that image registration of individual subvolumes, such as fracture fragments, is feasible in both stable and unstable fractures, and leads to better alignment of these fragments compared to an approach that is based on registration using the whole fractured region. This result is promising for additional analysis of bone formation and resorption in HR-pQCT studies on fracture healing.

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Image registration using radial basis functions with adaptive radius.

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