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Proceedings Paper

Low dose CT perfusion using k-means clustering
Author(s): Francesco Pisana; Thomas Henzler; Stefan Schönberg; Ernst Klotz; Bernhard Schmidt; Marc Kachelrieß
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Paper Abstract

We aim at improving low dose CT perfusion functional parameters maps and CT images quality, preserving quantitative information. In a dynamic CT perfusion dataset, each voxel is measured T times, where T is the number of acquired time points. In this sense, we can think about a voxel as a point in a T-dimensional space, where the coordinates of the voxels would be the values of its time attenuation curve (TAC). Starting from this idea, a k-means algorithm was designed to group voxels in K classes. A modified guided time-intensity profile similarity (gTIPS) filter was implemented and applied only for those voxels belonging to the same class. The approach was tested on a digital brain perfusion phantom as well as on clinical brain and body perfusion datasets, and compared to the original TIPS implementation. The TIPS filter showed the highest CNR improvement, but lowest spatial resolution. gTIPS proved to have the best combination of spatial resolution and CNR improvement for CT images, while k-gTIPS was superior to both gTIPS and TIPS in terms of perfusion maps image quality. We demonstrate k-means clustering analysis can be applied to denoise dynamic CT perfusion data and to improve functional maps. Beside the promising results, this approach has the major benefit of being independent from the perfusion model employed for functional parameters calculation. No similar approaches were found in literature.

Paper Details

Date Published: 22 March 2016
PDF: 11 pages
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97833M (22 March 2016); doi: 10.1117/12.2214709
Show Author Affiliations
Francesco Pisana, German Cancer Research Ctr. (Germany)
Siemens Healthcare GmbH (Germany)
Thomas Henzler, Institute of Clinical Radiology and Nuclear Medicine (Germany)
Stefan Schönberg, Institute of Clinical Radiology and Nuclear Medicine (Germany)
Ernst Klotz, Siemens Healthcare GmbH (Germany)
Bernhard Schmidt, Siemens Healthcare GmbH (Germany)
Marc Kachelrieß, German Cancer Research Ctr. (Germany)

Published in SPIE Proceedings Vol. 9783:
Medical Imaging 2016: Physics of Medical Imaging
Despina Kontos; Thomas G. Flohr, Editor(s)

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