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

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.

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

International Journal of Biomedical Imaging ◽

10.1155/2015/125648 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 1

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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An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing

Medical Physics ◽

10.1118/1.2431245 ◽

2007 ◽

Vol 34 (2) ◽

pp. 711-721 ◽

Cited By ~ 100

Author(s):

Jan Ehrhardt ◽

René Werner ◽

Dennis Säring ◽

Thorsten Frenzel ◽

Wei Lu ◽

...

Keyword(s):

Optical Flow ◽

Free Breathing ◽

Data Sets ◽

4D Ct ◽

Ct Data

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Reconstruction of 4D-CT data sets acquired during free breathing for the analysis of respiratory motion

10.1117/12.651730 ◽

2006 ◽

Cited By ~ 8

Author(s):

Jan Ehrhardt ◽

Rene Werner ◽

Thorsten Frenzel ◽

Dennis Säring ◽

Wei Lu ◽

...

Keyword(s):

Respiratory Motion ◽

Free Breathing ◽

Data Sets ◽

4D Ct ◽

Ct Data

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

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-010-0418-7 ◽

2010 ◽

Vol 5 (6) ◽

pp. 595-605 ◽

Cited By ~ 8

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

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Evaluation and Comparison of Force Terms for the Estimation of Lung Motion by Non-linear Registration of 4D-CT Image Data

IFMBE Proceedings - World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany ◽

10.1007/978-3-642-03882-2_565 ◽

2009 ◽

pp. 2128-2131 ◽

Cited By ~ 2

Author(s):

A. Schmidt-Richberg ◽

J. Ehrhardt ◽

R. Werner ◽

H. Handels

Keyword(s):

Image Data ◽

Ct Image ◽

4D Ct ◽

Lung Motion ◽

Non Linear

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Adaptive Fuzzy Segmentation of Tumors in Three-Dimensional Computed Tomography (CT) Image Data

Volume 2: 27th Computers and Information in Engineering Conference, Parts A and B ◽

10.1115/detc2007-35413 ◽

2007 ◽

Author(s):

Jung Leng Foo ◽

Go Miyano ◽

Thom Lobe ◽

Eliot Winer

Keyword(s):

Computed Tomography ◽

Fuzzy Inference ◽

Three Dimensional ◽

Fuzzy Rule ◽

Region Of Interest ◽

Image Data ◽

Ct Image ◽

Inference System ◽

Ct Data ◽

Fuzzy Segmentation

The continuing advancement of computed tomography (CT) technology has improved the analysis and visualization of tumor data. As imaging technology continues to accommodate the need for high quality medical image data, this encourages the research for more efficient ways of extracting crucial information from these vast amounts of data. A new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data has been developed. To initialize the segmentation process, the user selects the region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROI’s spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy inference system. From a set of predefined fuzzy rules, the system generates a defuzzified output for every pixel in terms of similarity to the object. Pixels with the highest similarity values are selected to be the tumor. This process is repeated for every subsequent slice in the CT set, and the segmented region from the previous slice is used as the ROI for the current slice. This creates a propagation of information from the previous slices, to be used to segment the current slice. The membership functions used during the fuzzification and defuzzification processes are adaptive to the changes in the size and pixel intensities of the current ROI. The proposed method is highly customizable to suit different needs of a user, requiring information from only a single two-dimensional image. Implementing the fuzzy segmentation on two distinct CT sets, the fuzzy segmentation algorithm was able to successfully extract the tumor from the CT image data. Based on the results statistics, the developed segmentation technique is approximately 96% accurate when compared to the results of manual segmentations performed.

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