scholarly journals 3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Zichun Zhong ◽  
Xiaohu Guo ◽  
Yiqi Cai ◽  
Yin Yang ◽  
Jing Wang ◽  
...  
Keyword(s):  
Vector Fields ◽  
Displacement Vector ◽  
Ct Images ◽  
Image Features ◽  
Density Field ◽  
Tetrahedral Meshes ◽  
Nonuniform Meshes ◽  
Body Boundary ◽  
Feature Based ◽  
Planning Ct

By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT) scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs) are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs) of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes.

Correlation of displacement vector fields calculated by different deformable image registration algorithms with motion parameters in helical, axial and cone beam CT imaging

2019 ◽  
Vol 19 (3) ◽  
pp. 219-225
Author(s):  
Nesreen Alsbou ◽  
Salahuddin Ahmad ◽  
Imad Ali
Keyword(s):  
Computed Tomography ◽  
Image Registration ◽  
Vector Fields ◽  
Displacement Vector ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Ct Imaging ◽  
Cone Beam ◽  
Helical Computed Tomography ◽  
Motion Parameters

AbstractAim:The purpose of this study is to investigate quantitatively the correlation of displacement vector fields (DVFs) from different deformable image registration (DIR) algorithms to register images from helical computed tomography (HCT), axial computed tomography (ACT) and cone beam computed tomography (CBCT) with motion parameters.Materials and methods:CT images obtained from scanning of the mobile phantom were registered with the stationary CT images using four DIR algorithms from the DIRART software: Demons, Fast-Demons, Horn–Schunck and Lucas–Kanade. HCT, ACT and CBCT imaging techniques were used to image a mobile phantom, which included three targets with different sizes (small, medium and large) that were manufactured from a water-equivalent material and embedded in low-density foam to simulate lung lesions. The phantom was moved with controlled cyclic motion patterns where a range of motion amplitudes (0–20 mm) and frequencies (0·125–0·5 Hz) were used.Results:The DVF obtained from different algorithms correlated well with motion amplitudes applied on the mobile phantom for CBCT and HCT, where the maximal DVF increased linearly with the motion amplitudes of the mobile phantom. In ACT, the DVF correlated less with motion amplitudes where motion-induced strong image artefacts and the DIR algorithms were not able to deform the ACT image of the mobile targets to the stationary targets. Three DIR algorithms produce comparable values and patterns of the DVF for certain CT imaging modality. However, DVF from Fast-Demons deviated strongly from other algorithms at large motion amplitudes.Conclusions:The local DVFs provide direct quantitative values for the actual internal tumour shifts that can be used to determine margins for the internal target volume that consider tumour motion during treatment planning. Furthermore, the DVF distributions can be used to extract motion parameters such as motion amplitude that can be extracted from the maximal or minimal DVF calculated by the different DIR algorithms and used in the management of the patient motion.

scholarly journals The impact of tumour regression in locally advanced carcinoma cervix during external beam radiotherapy and the need for adaptive planning

2016 ◽  
Keyword(s):  
Ct Scan ◽  
Locally Advanced ◽  
Target Volume ◽  
Ct Images ◽  
Tumour Regression ◽  
Target Coverage ◽  
Carcinoma Cervix ◽  
Advanced Carcinoma ◽  
The Impact ◽  
Planning Ct

Aim: To study the impact of tumour regression occurring during IMRT for locally advanced carcinoma cervix and study dose distribution to target volume and OARs and hence the need for any replanning. Materials and Methods: 40 patients undergoing IM-IGRT and weekly chemotherapy were included in the study. After 36 Gy, a second planning CT-scan was done and target volume and OARs were recontoured. First plan (non-adaptive) was compared with second plan (adaptive plan) to evaluate whether it would still offer sufficient target coverage to the CTV and spare the OARs after having delivered 36 Gy. Finally new plan was created based on CT-images to investigate whether creating a new treatment plan would optimize target coverage and critical organ sparing. To measure the response of the primary tumour and pathologic nodes to EBRT, the differences in the volumes of the primary GTV and nodal GTV between the pretreatment and intratreatment CT images was calculated. Second intratreatment IMRT plans was generated, using the delineations of the intratreatment CT images. The first IMRT plan (based on the first CT-scan or non adaptive plan) was compared with second IMRT plan (based on the second CT-scan or adaptive plan). Results: 35% patients had regression in GTV in the range of 4.1% to 5%, 20% in the range of 1.1%-2%, 15% in the range of 2.1%-3% and 20% in the range of 6%-15%. There was significant mean decrease in GTV of 4.63 cc (p=0.000). There was a significant decrease in CTV on repeat CT done after 36 Gy by 23.31 cc (p=0.000) and in PTV by 23.31 cc (p=0.000). There was a statistically significant increase in CTV D98, CTV D95, CTV D50 and CTV D2 in repeat planning CT done after 36 Gy. There was no significant alteration in OARs doses. Conclusion: Despite tumour regression and increased target coverage in locally advanced carcinoma cervix after a delivery of 36 Gy there was no sparing of OARs. Primary advantage of adaptive RT seems to be in greater target coverage with non-significant normal tissue sparing.

scholarly journals Robustness of radiomic features in CT images with different slice thickness, comparing liver tumour and muscle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lorena Escudero Sanchez ◽  
Leonardo Rundo ◽  
Andrew B. Gill ◽  
Matthew Hoare ◽  
Eva Mendes Serrao ◽  
...  
Keyword(s):  
Large Fraction ◽  
Therapy Response ◽  
Response Assessment ◽  
Ct Images ◽  
Optimal Number ◽  
Image Features ◽  
Liver Tumour ◽  
Slice Thickness ◽  
Invasive Method ◽  
Feature Values

AbstractRadiomic image features are becoming a promising non-invasive method to obtain quantitative measurements for tumour classification and therapy response assessment in oncological research. However, despite its increasingly established application, there is a need for standardisation criteria and further validation of feature robustness with respect to imaging acquisition parameters. In this paper, the robustness of radiomic features extracted from computed tomography (CT) images is evaluated for liver tumour and muscle, comparing the values of the features in images reconstructed with two different slice thicknesses of 2.0 mm and 5.0 mm. Novel approaches are presented to address the intrinsic dependencies of texture radiomic features, choosing the optimal number of grey levels and correcting for the dependency on volume. With the optimal values and corrections, feature values are compared across thicknesses to identify reproducible features. Normalisation using muscle regions is also described as an alternative approach. With either method, a large fraction of features (75–90%) was found to be highly robust (< 25% difference). The analyses were performed on a homogeneous CT dataset of 43 patients with hepatocellular carcinoma, and consistent results were obtained for both tumour and muscle tissue. Finally, recommended guidelines are included for radiomic studies using variable slice thickness.

Micro and Macromechanical Study of Stress-Induced Martensitic Transformation in a Cu-Al-Be Polycrystalline Shape Memory Alloy

2006 ◽  
Vol 509 ◽  
pp. 87-92 ◽  
Author(s):  
F.M. Sánchez ◽  
G. Pulos
Keyword(s):  
Martensitic Transformation ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Vector Fields ◽  
Displacement Vector ◽  
Optical Microscope ◽  
Image Sequences ◽  
Tension Test ◽  
Loading Device ◽  
Stress States

An experimental investigation of the micro and macromechanical stress-induced martensitic transformation in a Cu-Al-Be polycrystalline shape memory alloy is undertaken using a uniaxial tension test. Digital images are acquired at different stress states. The image sequences are analyzed to estimate the optical flow to get displacement vector fields. The experiments are carried out on a miniature hydraulic loading device mounted under an optical microscope. The stress-strain curves and associated images show stress-induced martensitic transformation in specific grains. Displacement vector fields for the polycrystalline shape memory alloy are obtained. They are inhomogeneous due to the martensitic transformation and inter-granular interactions.

Algorithm of digital image preprocessing for constructing displacement vector fields

2018 ◽  
Author(s):  
V. V. Titkov ◽  
S. V. Panin ◽  
P. S. Lyubutin ◽  
A. V. Eremin
Keyword(s):  
Digital Image ◽  
Vector Fields ◽  
Displacement Vector ◽  
Image Preprocessing

scholarly journals Fully Convolutional Network for Consistent Voxel-Wise Correspondence

2020 ◽  
Vol 34 (07) ◽  
pp. 12935-12942 ◽  
Author(s):  
Yungeng Zhang ◽  
Yuru Pei ◽  
Yuke Guo ◽  
Gengyu Ma ◽  
Tianmin Xu ◽  
...  
Keyword(s):  
Vector Fields ◽  
State Of The Art ◽  
Displacement Vector ◽  
Cone Beam Ct ◽  
Deformable Registration ◽  
Cone Beam ◽  
Nonlinear Mapping ◽  
Convolutional Network ◽  
Mapping Functions ◽  
Fully Convolutional Network

In this paper, we propose a fully convolutional network-based dense map from voxels to invertible pair of displacement vector fields regarding a template grid for the consistent voxel-wise correspondence. We parameterize the volumetric mapping using a convolutional network and train it in an unsupervised way by leveraging the spatial transformer to minimize the gap between the warped volumetric image and the template grid. Instead of learning the unidirectional map, we learn the nonlinear mapping functions for both forward and backward transformations. We introduce the combinational inverse constraints for the volumetric one-to-one maps, where the pairwise and triple constraints are utilized to learn the cycle-consistent correspondence maps between volumes. Experiments on both synthetic and clinically captured volumetric cone-beam CT (CBCT) images show that the proposed framework is effective and competitive against state-of-the-art deformable registration techniques.

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