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Robust principle component analysis based four-dimensional computed tomography

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If you have a question about this talk, please contact Mustapha Amrani.

Inverse Problems

We present a new spatiotemporal model for 4D-CT from matrix perspective, Robust PCA based 4DCT model. Instead of viewing 4D object as a temporal collection of three-dimensional (3D) images and looking for local coherence in time or space independently, we explore the maximum temporal coherence of spatial structure among phases. This Robust PCA based 4DCT model can be applicable in other imaging problems for motion reduction or/and change detection. A dynamic data acquisition procedure, i.e., a temporally spiral scheme, is proposed that can potentially maintain the similar reconstruction accuracy while using fewer projections of the data. The key point of this dynamic scheme is to reduce the total number of measurements and hence the radiation dose by acquiring complementary data in different phases without redundant measurements of the common background structure.

This talk is part of the Isaac Newton Institute Seminar Series series.

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