SU-E-J-115: Correlation of Displacement Vector Fields Calculated by Deformable Image Registration Algorithms with Motion Parameters of CT Images with Well-Defined Targets and Controlled-Motion

2015 ◽  
Vol 42 (6Part9) ◽  
pp. 3291-3291
Author(s):  
J Jaskowiak ◽  
N Alsbou ◽  
S Ahmad ◽  
I Ali
Keyword(s):  
Image Registration ◽  
Vector Fields ◽  
Displacement Vector ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Motion Parameters ◽  
Controlled Motion

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 [P232] Deformable image registration for CT-images of the thorax region

2018 ◽  
Vol 52 ◽  
pp. 166-167
Author(s):  
Sebastian Sarudis ◽  
Anna Karlsson ◽  
Dan Bibac ◽  
Jan Nyman ◽  
Anna Bäck
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Ct Images

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 Biomechanical deformable image registration of longitudinal lung CT images using vessel information

2016 ◽  
Vol 61 (13) ◽  
pp. 4826-4839 ◽  
Author(s):  
Guillaume Cazoulat ◽  
Dawn Owen ◽  
Martha M Matuszak ◽  
James M Balter ◽  
Kristy K Brock
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Lung Ct

scholarly journals A Voxel-by-Voxel Comparison of Deformable Vector Fields Obtained by Three Deformable Image Registration Algorithms Applied to 4DCT Lung Studies

2015 ◽  
Vol 5 ◽  
Author(s):  
Mirek Fatyga ◽  
Nesrin Dogan ◽  
Elizabeth Weiss ◽  
William C. Sleeman ◽  
Baoshe Zhang ◽  
...  
Keyword(s):  
Image Registration ◽  
Vector Fields ◽  
Deformable Image Registration
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