banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing
Rent this article for


Image of FIG. 1.
FIG. 1.

The process used to reconstruct a 4D CT data set. The left section illustrates the CT scan acquisition using a multislice CT scanner. The scanner is operated in cine mode while the patient undergoes digital spirometry measurement. Thus, each acquired data segment is assigned to a couch position and a tidal volume (shown in the bottom left table). To reconstruct a 3D data set at a user-defined tidal volume , for each couch position an interpolated data segment is generated. For a given couch position, the nearest data segments and with and are selected. The right side shows the interpolation process. First the optical flow between segment and is estimated. Following an interpolated data segment is generated. The last row of the right side shows the full 3D CT scan created by stacking the interpolated scans from each couch position.

Image of FIG. 2.
FIG. 2.

Two examples for the reconstruction of a 4D CT data set using the nearest-neighbor approach (left column) and the optical flow based reconstruction method (right column). The top row shows a coronal section of the reconstructed 4D CT data set of patient 1 at a predefined tidal volume of . The bottom row presents a more apparent example. A coronal section of the 4D CT data set of patient 4 at a predefined tidal volume of is shown. In the images of the left column artifacts at the diaphragm caused by free breathing motion are visible. These artifacts are reduced by the optical flow based interpolation, as shown on the right side.

Image of FIG. 3.
FIG. 3.

Probability map for the occurrence of tumorous tissue during the respiratory cycle. The estimated appearance probabilities are shown color-coded. A large motion in cranio-caudal direction is visible for the tumor on the left. In contrast, the tumor on the right shows only a small cranio-caudal motion.

Image of FIG. 4.
FIG. 4.

3D models of segmented tumor and lung for a respiratory cycle. Only four out of ten reconstructed time steps are shown: mid inhale (a), peak inhale (b), mid exhale (c), and peak exhale (d).

Image of FIG. 5.
FIG. 5.

Visualization of the maximum displacement of the skin surface during the respiratory cycle of four patients. Black surface points indicate a maximum displacement (scale: ).


Generic image for table

Quantitative evaluation of the reconstruction artifacts for nearest-neighbor (NN) and optical flow based reconstruction method (OF). The mean squared gray value differences of adjacent slices inside the original CT data segments and of adjacent slices at the border of two segments for the two reconstruction methods ( and ) are shown. The presented values are average values for ten reconstructed tidal volumes. The standard deviation is shown in brackets. The statistical relevance values are used to compare the optical flow based reconstruction method and the nearest-neighbor reconstruction. Positive values indicate the optical flow method performed better.


Article metrics loading...


Full text loading...

This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing