SU-E-J-91: Biomechanical Deformable Image Registration of Longitudinal Lung CT Images

2015 ◽  
Vol 42 (6Part8) ◽  
pp. 3285-3285
Author(s):  
G Cazoulat ◽  
D Owen ◽  
M Matuszak ◽  
J Balter ◽  
K Brock
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Lung Ct

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 Inelastic Deformable Image Registration (i-DIR): Capturing Sliding Motion through Automatic Detection of Discontinuities

Mathematics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 97
Author(s):  
Carlos I. Andrade ◽  
Daniel E. Hurtado
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Synthetic Image ◽  
Analysis Method ◽  
Biomedical Sciences ◽  
Sliding Motion ◽  
Lung Strain ◽  
Inelastic Shear ◽  
Lung Ct

Deformable image registration (DIR) is an image-analysis method with a broad range of applications in biomedical sciences. Current applications of DIR on computed-tomography (CT) images of the lung and other organs under deformation suffer from large errors and artifacts due to the inability of standard DIR methods to capture sliding between interfaces, as standard transformation models cannot adequately handle discontinuities. In this work, we aim at creating a novel inelastic deformable image registration (i-DIR) method that automatically detects sliding surfaces and that is capable of handling sliding discontinuous motion. Our method relies on the introduction of an inelastic regularization term in the DIR formulation, where sliding is characterized as an inelastic shear strain. We validate the i-DIR by studying synthetic image datasets with strong sliding motion, and compare its results against two other elastic DIR formulations using landmark analysis. Further, we demonstrate the applicability of the i-DIR method to medical CT images by registering lung CT images. Our results show that the i-DIR method delivers accurate estimates of a local lung strain that are similar to fields reported in the literature, and that do not exhibit spurious oscillatory patterns typically observed in elastic DIR methods. We conclude that the i-DIR method automatically locates regions of sliding that arise in the dorsal pleural cavity, delivering significantly smaller errors than traditional elastic DIR methods.

SU-E-J-226: Propagation of Pancreas Target Contours On Respiratory Correlated CT Images Using Deformable Image Registration

2014 ◽  
Vol 41 (6Part10) ◽  
pp. 209-209 ◽  
Author(s):  
F Liu ◽  
E Yorke ◽  
G Mageras ◽  
K Goodman
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Ct Images

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 Simulation of pseudo-CT images based on deformable image registration of ultrasound images: A proof of concept for transabdominal ultrasound imaging of the prostate during radiotherapy

2016 ◽  
Vol 43 (4) ◽  
pp. 1913-1920 ◽  
Author(s):  
Skadi van der Meer ◽  
Saskia M. Camps ◽  
Wouter J. C. van Elmpt ◽  
Mark Podesta ◽  
Pedro Gomes Sanches ◽  
...  
Keyword(s):  
Image Registration ◽  
Ultrasound Imaging ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Ultrasound Images ◽  
Transabdominal Ultrasound ◽  
Proof Of Concept

SU-FF-J-82: Improving Soft Tissue Contrast in 4D CT Images of Liver Cancer Patients Using Deformable Image Registration Method

2006 ◽  
Vol 33 (6Part6) ◽  
pp. 2039-2039
Author(s):  
H Wang ◽  
S Krishnan ◽  
X Wang ◽  
A Beddar ◽  
T Briere ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Image Registration ◽  
Liver Cancer ◽  
Cancer Patients ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Registration Method ◽  
4D Ct ◽  
Soft Tissue Contrast ◽  
Tissue Contrast
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