Scott M. Mcolash
GE Healthcare
4 Papers
37 Citations
Scott M. Mcolash is an academic researcher from GE Healthcare. The author has contributed to research in topics: Iterative reconstruction & Projection (set theory). The author has an hindex of 3, co-authored 4 publications.
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Papers
CatSim: a new computer assisted tomography simulation environment
Bruno De Man,Samit Kumar Basu,Naveen Chandra,Bruce Dunham,Peter Michael Edic,Maria Iatrou,Scott M. Mcolash,Paavana Sainath,Charlie Shaughnessy,Brendon D. Tower,Eugene Clifford Williams +10 more
- 08 Mar 2007
TL;DR: CatSim as discussed by the authors is a simulation environment for X-ray computed tomography, which allows simulating complex analytic phantoms, such as the FORBILD PHANTOM, including boxes, ellipsoids, elliptical cylinders, cones, and cut planes.
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Enhancement of in-plane spatial resolution in volumetric Computed Tomography with focal spot wobbling - overcoming the constraint on number of projection views per gantry rotation.
Xiangyang Tang,Suresh Narayanan,Jiang Hsieh,Jed Douglas Pack,Scott M. Mcolash,Paavana Sainath,Roy A. Nilsen,Basel Hasan Taha +7 more
TL;DR: A method to accommodate focal spot wobbling at an arbitrary number of projection views per gantry rotation in CT is presented and evaluated here and shows that the row-wise fan-to-parallel rebinning with the beta-correction can increase the quantitative in-plane spatial resolution substantially, while the visual spatial resolution can be enhanced significantly.
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Extending Three-Dimensional Weighted Cone Beam Filtered Backprojection (CB-FBP) Algorithm for Image Reconstruction in Volumetric CT at Low Helical Pitches
TL;DR: The extended 3D weighted helical CB-FBP algorithm is extended to handle helical pitches that are smaller than 1: 1 and it is believed that, such an efficient CB reconstruction algorithm that can provide superior noise characteristics or dose efficiency at low helical pitched may find its extensive applications in CT medical imaging.
Cone beam filtered backprojection (CB-FBP) image reconstruction by tracking re-sampled projection data
TL;DR: It has been experimentally found that, to obtain a thick image with the reconstruction accuracy comparable to that of a thin image, the CB-FBP reconstruction algorithm has to be applied by tracking adaptively up-sampled cone beam projection data, which is the novelty of the proposed algorithm.
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