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Distance between Slices Investigation on Siemens CT Phantom Images

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This study aims to develop an algorithm for automatic measurement of distance between slices on a Siemens CT phantom with variations of slice thickness and field of view (FOV). The Siemens 64-slice Somatom Perspective CT scanner was utilized to acquire the Siemens phantom image data, with slice thickness variation (i.e., 1, 2, 3, 4, and 5 mm) and FOV variation (i.e., 220, 240, and 300 mm). The distance between slices was calculated by comparing two images from different slice positions. The first stage was opening the images. The second stage was segmentation of the ramp object from image slice-1. The third stage was determination of the centroid coordinates of the ramp object from image slice-1. The fourth stage repeated the processes at second and third stages for slice-2. The fifth stage was calculating the distance between slices. The measured distances were compared with the set distances extracting from DICOM header for every slice position. The developed algorithm can measure distance between slices for variations of slice thickness and FOV. The results of automatic measurement of distance between slices have strong linear correlation ( > 0.99) with the set distances. The differences between automated measurement results and set distances are more than 2 mm for distances up to 50 mm or the differences are around 23% for both slice thickness and FOV variations.
Title: Distance between Slices Investigation on Siemens CT Phantom Images
Description:
This study aims to develop an algorithm for automatic measurement of distance between slices on a Siemens CT phantom with variations of slice thickness and field of view (FOV).
The Siemens 64-slice Somatom Perspective CT scanner was utilized to acquire the Siemens phantom image data, with slice thickness variation (i.
e.
, 1, 2, 3, 4, and 5 mm) and FOV variation (i.
e.
, 220, 240, and 300 mm).
The distance between slices was calculated by comparing two images from different slice positions.
The first stage was opening the images.
The second stage was segmentation of the ramp object from image slice-1.
The third stage was determination of the centroid coordinates of the ramp object from image slice-1.
The fourth stage repeated the processes at second and third stages for slice-2.
The fifth stage was calculating the distance between slices.
The measured distances were compared with the set distances extracting from DICOM header for every slice position.
The developed algorithm can measure distance between slices for variations of slice thickness and FOV.
The results of automatic measurement of distance between slices have strong linear correlation ( > 0.
99) with the set distances.
The differences between automated measurement results and set distances are more than 2 mm for distances up to 50 mm or the differences are around 23% for both slice thickness and FOV variations.

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