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Computational approaches for parametric imaging of dynamic PET data

S Crisci, M Piana, V Ruggiero, M Scussolini
doi: https://doi.org/10.1101/748806
S Crisci
Dipartimento di Matematica e Informatica, Università di Ferrara, Ferrara, Italy
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M Piana
Dipartimento di Matematica, Università di Genova, and CNR-SPIN, Genova, Italy
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  • For correspondence: piana@dima.unige.it
V Ruggiero
Dipartimento di Matematica e Informatica, Università di Ferrara, Ferrara, Italy
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M Scussolini
Dipartimento di Matematica, Università di Genova, Genova, Italy
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Abstract

Parametric imaging of nuclear medicine data exploits dynamic functional images in order to reconstruct maps of kinetic parameters related to the metabolism of a specific tracer injected in the biological tissue. From a computational viewpoint, the realization of parametric images requires the pixel-wise numerical solution of compartmental inverse problems that are typically ill-posed and nonlinear. In the present paper we introduce a fast numerical optimization scheme for parametric imaging relying on a regularized version of the standard affine-scaling Trust Region method. The validation of this approach is realized in a simulation framework for brain imaging and comparison of performances is made with respect to a regularized Gauss-Newton scheme and a standard nonlinear least-squares algorithm.

Footnotes

  • E-mail: serena.crisci{at}unife.it

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted August 28, 2019.
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Computational approaches for parametric imaging of dynamic PET data
S Crisci, M Piana, V Ruggiero, M Scussolini
bioRxiv 748806; doi: https://doi.org/10.1101/748806
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Computational approaches for parametric imaging of dynamic PET data
S Crisci, M Piana, V Ruggiero, M Scussolini
bioRxiv 748806; doi: https://doi.org/10.1101/748806

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