Analysis of free breathing motion using artifact reduced 4D CT image data

2007 ◽  
Author(s):  
Jan Ehrhardt ◽  
Rene Werner ◽  
Thorsten Frenzel ◽  
Wei Lu ◽  
Daniel Low ◽  
...  
Keyword(s):  
Image Data ◽  
Free Breathing ◽  
Ct Image ◽  
4D Ct ◽  
Breathing Motion

SU-GG-T-585: The Impact of Free Breathing versus Average 4D CT Image Data on External Beam Radiotherapy Planning for Liver Tumors

2010 ◽  
Vol 37 (6Part25) ◽  
pp. 3322-3322
Author(s):  
H Li ◽  
M Delclos ◽  
T Briere ◽  
S Beddar ◽  
P Das ◽  
...  
Keyword(s):  
Liver Tumors ◽  
External Beam Radiotherapy ◽  
Image Data ◽  
Free Breathing ◽  
Radiotherapy Planning ◽  
External Beam ◽  
Ct Image ◽  
4D Ct ◽  
The Impact

EP-1450 IMPLICATIONS OF FREE BREATHING MOTION ASSESSED BY 4D-CT ON THE DELIVERED DOSE IN ESOPHAGEAL CANCER

2012 ◽  
Vol 103 ◽  
pp. S552-S553
Author(s):  
M.N. Duma ◽  
M. Oechsner ◽  
M. Geier ◽  
H. Hammami ◽  
H. Geinitz
Keyword(s):  
Esophageal Cancer ◽  
Free Breathing ◽  
4D Ct ◽  
Breathing Motion ◽  
Delivered Dose

Motion Artifact Reducing Reconstruction of 4D CT Image Data for the Analysis of Respiratory Dynamics

2007 ◽  
Vol 46 (03) ◽  
pp. 254-260 ◽  
Author(s):  
J. Ehrhardt ◽  
T. Frenzel ◽  
D. Säring ◽  
W. Lu ◽  
D. Low ◽  
...  
Keyword(s):  
Optical Flow ◽  
Motion Analysis ◽  
Respiratory Motion ◽  
Image Data ◽  
Motion Artifacts ◽  
Data Sets ◽  
Ct Image ◽  
Ct Reconstruction ◽  
4D Ct ◽  
Ct Data

Summary Objectives: Respiratory motion represents a major problem in radiotherapy of thoracic and abdominal tumors. Methods for compensation require comprehensive knowledge of underlying dynamics. Therefore, 4D (= 3D + t) CT data can be helpful. But modern CT scanners cannot scan a large region of interest simultaneously. So patients have to be scanned in segments. Commonly used approaches for reconstructing the data segments into 4D CT images cause motion artifacts. In orderto reduce the artifacts, a new method for 4D CT reconstruction is presented. The resulting data sets are used to analyze respiratory motion. Methods: Spatiotemporal CT image sequences of lung cancer patients were acquired using a multi-slice CT in cine mode during free breathing. 4D CT reconstruction was done by optical flow based temporal interpolation. The resulting 4D image data were compared with data generated bythe commonly used nearest neighbor reconstruction. Subsequent motion analysis is mainly concerned with tumor mobility. Results: The presented optical flow-based method enables the reconstruction of 3D CT images at arbitrarily chosen points of the patient’s breathing cycle. A considerable reduction of motion artifacts has been proven in eight patient data sets. Motion analysis showed that tumor mobility differs strongly between the patients. Conclusions: Due to the proved reduction of motion artifacts, the optical flow-based 4D CT reconstruction offers the possibility of high-quality motion analysis. Because the method is based on an interpolation scheme, it additionally has the potential to enable the reconstruction of 4D CT data from a lesser number of scans.

Patient-specific finite element modeling of respiratory lung motion using 4D CT image data

2009 ◽  
Vol 36 (5) ◽  
pp. 1500-1511 ◽  
Author(s):  
René Werner ◽  
Jan Ehrhardt ◽  
Rainer Schmidt ◽  
Heinz Handels
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Image Data ◽  
Patient Specific ◽  
Ct Image ◽  
4D Ct ◽  
Element Modeling ◽  
Lung Motion

Estimation of motion fields by non-linear registration for local lung motion analysis in 4D CT image data

2010 ◽  
Vol 5 (6) ◽  
pp. 595-605 ◽  
Author(s):  
René Werner ◽  
Jan Ehrhardt ◽  
Alexander Schmidt-Richberg ◽  
Anabell Heiß ◽  
Heinz Handels
Keyword(s):  
Motion Analysis ◽  
Image Data ◽  
Ct Image ◽  
4D Ct ◽  
Lung Motion ◽  
Non Linear

Fast CT-FFR Analysis Method for the Coronary Artery Based on 4D-CT Image Analysis and Structural and Fluid Analysis

2015 ◽  
Author(s):  
Mitsuaki Kato ◽  
Kenji Hirohata ◽  
Akira Kano ◽  
Shinya Higashi ◽  
Akihiro Goryu ◽  
...  
Keyword(s):  
Blood Flow ◽  
Coronary Artery ◽  
Boundary Conditions ◽  
Coronary Arteries ◽  
Computational Time ◽  
Ct Image ◽  
Analysis Method ◽  
Fractional Flow ◽  
4D Ct ◽  
Fluid Analysis

Non invasive fractional flow reserve derived from CT coronary angiography (CT-FFR) has to date been typically performed using the principles of computational fluid analysis in which a lumped parameter coronary vascular bed model is assigned to represent the impedance of the downstream coronary vascular networks absent in the computational domain for each coronary outlet. This approach may have a number of limitations. It may not account for the impact of the myocardial contraction and relaxation during the cardiac cycle, patient-specific boundary conditions for coronary artery outlets and vessel stiffness. We have developed a novel approach based on 4D-CT image tracking (registration) and structural and fluid analysis based on one dimensional mechanical model, to address these issues. In our approach, we analyzed the deformation variation of vessels and the volume variation of vessels to better define boundary conditions and stiffness of vessels. We focused on the blood flow and vessel deformation of coronary arteries and aorta near coronary arteries in the diastolic cardiac phase from 70% to 100 %. The blood flow variation of coronary arteries relates to the deformation of vessels, such as expansion and contraction of the cross-sectional area, during this period where resistance is stable, pressure loss is approximately proportional to flow. We used a statistical estimation method based on a hierarchical Bayes model to integrate 4D-CT measurements and structural and fluid analysis data. Under these analysis conditions, we performed structural and fluid analysis to determine pressure, flow rate and CT-FFR. Furthermore, the reduced-order model based on fluid analysis was studied in order to shorten the computational time for 4D-CT-FFR analysis. The consistency of this method has been verified by a comparison of 4D-CT-FFR analysis results derived from five clinical 4D-CT datasets with invasive measurements of FFR. Additionally, phantom experiments of flexible tubes with and without stenosis using pulsating pumps, flow sensors and pressure sensors were performed. Our results show that the proposed 4D-CT-FFR analysis method has the potential to accurately estimate the effect of coronary artery stenosis on blood flow.

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