Modelling the Siemens SOMATOM Sensation 64 Multi-Slice CT (MSCT) Scanner

Reconstructing large volumetric 3D images with minimal radiation dosage exposure with reduced scanning time has been one of the main objectives in the advancement of CT development. One of its advancement is the introduction of multi-slice arc detector geometry from a cone-beam source in third gener...

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Bibliographic Details
Published in:Journal of physics. Conference series 2017-05, Vol.851 (1), p.12012
Main Authors: Amin, A T Mohd, Rahni, A A Abd
Format: Article
Language:English
Subjects:
Algorithms
Computed tomography
Field of view
Geometry
Image reconstruction
Inverse problems
Iterative methods
Linear equations
Physics
Radiation dosage
Scanners
Sensors
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Summary:Reconstructing large volumetric 3D images with minimal radiation dosage exposure with reduced scanning time has been one of the main objectives in the advancement of CT development. One of its advancement is the introduction of multi-slice arc detector geometry from a cone-beam source in third generation scanners. In solving this complex geometry, apart from the known vast computations in CT image reconstruction due to large CT images, iterative reconstruction methods are preferred compared to analytic methods due to its flexibility in image reconstruction. A scanner of interest that has this type of geometry is the Siemens SOMATOM Sensation 64 Multi-Slice CT (MSCT) Scanner, which has a total of 32 slices with 672 detector elements on each slice. In this paper, the scanner projection is modelled via the intersecting lengths between each ray (exhibited from the source to the detector elements) with the scanned image voxels, which are evaluated using the classical Siddon's algorithm to generate the system matrix, H. This is a prerequisite to perform various iterative reconstruction methods, which involves solving the inverse problem arising from the linear equation: S = H·I; where S is the projections produced from the image, I. Due to the 'cone-beam geometry' along the z-axis, the effective field-of-view (FOV) with voxel dimensions (0.4×0.4×0.4) mm3 is 512×512×32 voxels. The scanner model is demonstrated by reconstructing an image from simulated projections using the analytic Feldkamp-Davis-Kress (FDK) method against basic iterative image reconstruction methods.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/851/1/012012