Validation and comparison of a biophysical modeling approach and non-linear registration for estimation of lung motion fields in thoracic 4D CT data

2009 ◽  
Author(s):  
Rene Werner ◽  
Jan Ehrhardt ◽  
Alexander Schmidt-Richberg ◽  
Heinz Handels
Keyword(s):  
Biophysical Modeling ◽  
4D Ct ◽  
Modeling Approach ◽  
Lung Motion ◽  
Non Linear ◽  
Ct Data

Estimation of lung motion fields in 4D CT data by variational non-linear intensity-based registration: A comparison and evaluation study

2014 ◽  
Vol 59 (15) ◽  
pp. 4247-4260 ◽  
Author(s):  
René Werner ◽  
Alexander Schmidt-Richberg ◽  
Heinz Handels ◽  
Jan Ehrhardt
Keyword(s):  
Evaluation Study ◽  
4D Ct ◽  
Lung Motion ◽  
Non Linear ◽  
Ct Data ◽  
Linear Intensity

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

4D Computed Tomography for Dynamic Upper Airway Evaluation in Robin Sequence

2021 ◽  
pp. 019459982110021
Author(s):  
Austin S. Lam ◽  
Michael D. Bindschadler ◽  
Kelly N. Evans ◽  
Seth D. Friedman ◽  
Jeffrey P. Otjen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Upper Airway ◽  
Quantitative Information ◽  
4D Ct ◽  
Robin Sequence ◽  
Analysis Tools ◽  
Airway Caliber ◽  
4D Computed Tomography ◽  
Ct Data ◽  
Sample Case

Thorough assessment of dynamic upper airway obstruction (UAO) in Robin sequence (RS) is critical, but traditional evaluation modalities have significant limitations. Four-dimensional computed tomography (4D-CT) is promising in that it enables objective and quantitative evaluation throughout all phases of respiration. However, there exist few protocols or analysis tools to assist in obtaining and interpreting the vast amounts of obtained data. A protocol and set of data analysis tools were developed to enable quantification and visualization of dynamic 4D-CT data. This methodology was applied to a sample case at 2 time points. In the patient with RS, overall increases in normalized airway caliber were observed from 5 weeks to 1 year. There was, however, continued dynamic obstruction at all airway levels, though objective measures of UAO did improve at the nasopharynx and oropharynx. Use of 4D-CT and novel analyses provide additional quantitative information to evaluate UAO in patients with RS.

Planning lung radiotherapy using 4D CT data and a motion model

2008 ◽  
Vol 53 (20) ◽  
pp. 5815-5830 ◽  
Author(s):  
R Colgan ◽  
J McClelland ◽  
D McQuaid ◽  
P M Evans ◽  
D Hawkes ◽  
...  
Keyword(s):  
Motion Model ◽  
4D Ct ◽  
Ct Data ◽  
Lung Radiotherapy

scholarly journals Impact of 4D CT for treatment planning of moving targets: a computer-based biological evaluation

2017 ◽  
Vol 3 (2) ◽  
pp. 665-668
Author(s):  
Eike Helf ◽  
Oliver Waletzko ◽  
Christian Mehrens ◽  
Ralf Rohn ◽  
Andreas Block
Keyword(s):  
Treatment Plan ◽  
Tumour Volume ◽  
Target Volume ◽  
Biological Evaluation ◽  
Internal Target Volume ◽  
4D Ct ◽  
Tumour Control ◽  
Ct Data ◽  
Conventional Ct ◽  
The Impact

AbstractThis study deals with comparison of conventional and 4D CT (GE Lightspeed) planning on the tumour control probability (TCP), using the TCP model of the AAPM-Report Task Group 166. In the first step a VMAT treatment plan was calculated (Varian Eclipse 13.7) on basis of conventional CT data. This treatment plan was transferred to the complete 4D CT, which represents the tumour volume in motion. Due to the increased volume and the resulting decrease of tumour coverage the TCP went down from 97,6% to 91,2%. After adding an internal target volume (ITV, ICRU 62) to the conventional CT according to our clinical protocols (1,0 cm cc and 0,3 cm axial plane) the TCP increased to 98,0% when applying the conventional plan to the 4D CT. This finding demonstrates the need of 4D CT for moving tumours in chest and abdomen region.Average IPs with increasing width have been created to evaluate the impact on the TCP and the non-malignant tissue. Our observations had shown that heart, lung and spinal cord radiation exposure did not correlate to chosen respiration segment. This could be explained by the extremely slight ratio of the planning target volume and the irradiated normal tissue.This procedure enables us to evaluate the efficacy of treatment plans. Furthermore, optimizing trials like the influence of respiration-gated RT, setting individual margins and fitting planning objectives and parameters are still under investigation.

scholarly journals Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Gurman Gill ◽  
Reinhard R. Beichel
Keyword(s):  
Image Data ◽  
Active Shape Model ◽  
Data Sets ◽  
Segmentation Method ◽  
Data Set ◽  
Shape Model ◽  
Lung Segmentation ◽  
4D Ct ◽  
Active Shape ◽  
Ct Data

Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of0.9773±0.0254, which was statistically significantly better (pvalue≪0.001) than the 3D method (0.9659±0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes.

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