Non-Rigid Image Registration based on Parameterized Surfaces: Application to 3D Cardiac Motion Image Analysis

2021 ◽  
pp. 2250028
Author(s):  
Said Khalid Shah
Keyword(s):  
Image Registration ◽  
Motion Tracking ◽  
Salient Feature ◽  
Ct Images ◽  
Heart Beat ◽  
Cardiac Motion ◽  
Free Form ◽  
3D Ct ◽  
Registration Accuracy ◽  
Whole Heart

This paper describes the Fast Radial Basis Function (RBF) method for cardiac motion tracking in 3D CT using non-rigid medical image registration based on parameterized (regular) surfaces. The technique is a point-based registration evaluation algorithm which does register 3D MR or CT images in real time. We first extract the surface of the whole heart 3D CT and its contrast enhanced part (left ventricle (LV) blood cavity) of each dataset with a semiautomatic contouring and a fully-automatic triangulation method followed by a global surface parameterization and optimization algorithm. In second step, a set of registration experiments are run to calculate the deformation field at various phases of cardiac motion or cycle from CT images, which results into significant deformation during each phase of a cycle. The surface points of the whole heart and LV are used to register the source systole image to various diastole target images taken at different phases during a heart beat. Our registration accuracy improves with the increase in number of salient feature points (i.e. optimized parameterized surfaces) and it has no effect on the speed of the algorithm (i.e. still less than a second). The results show that the target registration error is less than 3[Formula: see text]mm (2.53) and the performance of the Fast RBF algorithm is less than a second using a whole heart CT dataset of a single patient taken over the course of the entire cardiac cycle. At the end, the results for recovery (or analysis) of bigger deformation in heart CT images using the Fast RBF algorithm is compared to the state-of-the-art Free Form Deformation (FFD) registration technique. It is proved that the Fast RBF method is performing better in speed and slightly less accurate than the FFD (when measured in terms of NMI) due to iterative nature of the latter.

scholarly journals Evaluation of Intensity- and Contour-Based Deformable Image Registration Accuracy in Pancreatic Cancer Patients

Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1447 ◽  
Author(s):  
Yoshiki Kubota ◽  
Masahiko Okamoto ◽  
Yang Li ◽  
Shintaro Shiba ◽  
Shohei Okazaki ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Image Registration ◽  
Prone Position ◽  
Supine Position ◽  
Deformable Image Registration ◽  
Pancreatic Cancer Patient ◽  
Target Volume ◽  
Ct Images ◽  
Registration Accuracy ◽  
Computed Tomography Images

We aimed to clarify the accuracy of rigid image registration and deformable image registration (DIR) in carbon-ion radiotherapy (CIRT) for pancreatic cancer. Six patients with pancreatic cancer who were treated with passive irradiation CIRT were enrolled. Three registration patterns were evaluated: treatment planning computed tomography images (TPCT) to CT images acquired in the treatment room (IRCT) in the supine position, TPCT to IRCT in the prone position, and TPCT in the supine position to the prone position. After warping the contours of the original CT images to the destination CT images using deformation matrices from the registration, the warped delineated contours on the destination CT images were compared with the original ones using mean displacement to agreement (MDA). Four contours (clinical target volume (CTV), gross tumor volume (GTV), stomach, duodenum) and four registration algorithms (rigid image registration [RIR], intensity-based DIR [iDIR], contour-based DIR [cDIR], and a hybrid iDIR-cDIR ([hDIR]) were evaluated. The means ± standard deviation of the MDAs of all contours for RIR, iDIR, cDIR, and hDIR were 3.40 ± 3.30, 2.2 1± 2.48, 1.46 ± 1.49, and 1.46 ± 1.37 mm, respectively. There were significant differences between RIR and iDIR, and between RIR/iDIR and cDIR/hDIR. For the pancreatic cancer patient images, cDIR and hDIR had better accuracy than RIR and iDIR.

scholarly journals Interactive Multigrid Refinement for Deformable Image Registration

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Wu Zhou ◽  
Yaoqin Xie
Keyword(s):  
Image Registration ◽  
Medical Images ◽  
Salient Feature ◽  
Deformable Image Registration ◽  
Free Form ◽  
Reference Image ◽  
Free Form Deformation ◽  
Feature Based ◽  
Automatic Algorithm ◽  
Overlapping Region

Deformable image registration is the spatial mapping of corresponding locations between images and can be used for important applications in radiotherapy. Although numerous methods have attempted to register deformable medical images automatically, such as salient-feature-based registration (SFBR), free-form deformation (FFD), and demons, no automatic method for registration is perfect, and no generic automatic algorithm has shown to work properly for clinical applications due to the fact that the deformation field is often complex and cannot be estimated well by current automatic deformable registration methods. This paper focuses on how to revise registration results interactively for deformable image registration. We can manually revise the transformed image locally in a hierarchical multigrid manner to make the transformed image register well with the reference image. The proposed method is based on multilevel B-spline to interactively revise the deformable transformation in the overlapping region between the reference image and the transformed image. The resulting deformation controls the shape of the transformed image and produces a nice registration or improves the registration results of other registration methods. Experimental results in clinical medical images for adaptive radiotherapy demonstrated the effectiveness of the proposed method.

scholarly journals Evaluation of Deformable Image Registration Methods for Dose Monitoring in Head and Neck Radiotherapy

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Bastien Rigaud ◽  
Antoine Simon ◽  
Joël Castelli ◽  
Maxime Gobeli ◽  
Juan-David Ospina Arango ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Image Registration ◽  
Head And Neck ◽  
Intensity Modulated Radiation Therapy ◽  
Deformable Image Registration ◽  
Free Form ◽  
Registration Accuracy ◽  
Dose Accumulation ◽  
Dose Monitoring ◽  
Dose Accuracy

In the context of head and neck cancer (HNC) adaptive radiation therapy (ART), the two purposes of the study were to compare the performance of multiple deformable image registration (DIR) methods and to quantify their impact for dose accumulation, in healthy structures. Fifteen HNC patients had a planning computed tomography (CT0) and weekly CTs during the 7 weeks of intensity-modulated radiation therapy (IMRT). Ten DIR approaches using different registration methods (demons or B-spline free form deformation (FFD)), preprocessing, and similarity metrics were tested. Two observers identified 14 landmarks (LM) on each CT-scan to compute LM registration error. The cumulated doses estimated by each method were compared. The two most effective DIR methods were the demons and the FFD, with both the mutual information (MI) metric and the filtered CTs. The corresponding LM registration accuracy (precision) was 2.44 mm (1.30 mm) and 2.54 mm (1.33 mm), respectively. The corresponding LM estimated cumulated dose accuracy (dose precision) was 0.85 Gy (0.93 Gy) and 0.88 Gy (0.95 Gy), respectively. The mean uncertainty (difference between maximal and minimal dose considering all the 10 methods) to estimate the cumulated mean dose to the parotid gland (PG) was 4.03 Gy (SD = 2.27 Gy, range: 1.06–8.91 Gy).

scholarly journals Symmetric and Transitive Registration of Image Sequences

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Oskar Škrinjar ◽  
Arnaud Bistoquet ◽  
Hemant Tagare
Keyword(s):  
Image Registration ◽  
Motion Tracking ◽  
Magnetic Resonance Images ◽  
Image Sequences ◽  
Pixel Size ◽  
Machine Precision ◽  
Registration Accuracy ◽  
Nonrigid Image Registration ◽  
Registration Algorithm ◽  
Image Type

This paper presents a method for constructing symmetric and transitive algorithms for registration of image sequences from image registration algorithms that do not have these two properties. The method is applicable to both rigid and nonrigid registration and it can be used with linear or periodic image sequences. The symmetry and transitivity properties are satisfied exactly (up to the machine precision), that is, they always hold regardless of the image type, quality, and the registration algorithm as long as the computed transformations are invertable. These two properties are especially important in motion tracking applications since physically incorrect deformations might be obtained if the registration algorithm is not symmetric and transitive. The method was tested on two sequences of cardiac magnetic resonance images using two different nonrigid image registration algorithms. It was demonstrated that the transitivity and symmetry errors of the symmetric and transitive modification of the algorithms could be made arbitrary small when the computed transformations are invertable, whereas the corresponding errors for the nonmodified algorithms were on the order of the pixel size. Furthermore, the symmetric and transitive modification of the algorithms had higher registration accuracy than the nonmodified algorithms for both image sequences.

Evaluation of deformable image registration accuracy for CT images of the thorax region

2019 ◽  
Vol 57 ◽  
pp. 191-199 ◽  
Author(s):  
Sebastian Sarudis ◽  
Anna Karlsson ◽  
Dan Bibac ◽  
Jan Nyman ◽  
Anna Bäck
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Registration Accuracy

scholarly journals Multi-planar whole heart segmentation of 3D CT images using 2D spatial propagation CNN

2020 ◽  
Author(s):  
Josefine Vilsboell Sundgaard ◽  
Kristine Aavild Juhl ◽  
Klaus Fuglsang Kofoed ◽  
Rasmus Reinhold Paulsen
Keyword(s):  
Ct Images ◽  
3D Ct ◽  
Spatial Propagation ◽  
Whole Heart
Sign in / Sign up

Export Citation Format

Share Document