The most common algorithm for clinical PET image reconstruction is MLEM, in which higher frequencies are the slowest to converge. This is exploited to control noise by early stopping. If noise is to be controlled using a more accurate method, e.g.: regularization, convergent reconstructions require thousands of iterations through the whole datasets. In this work, we revise an old, preconditioned conjugate gradient strategy. An ideal preconditioner would involve the backprojection of the inverse of the estimated sinogram, which is unknown before the reconstruction. The measured sinogram cannot be used as an approximation, due to the presence of zeros, especially when using Time Of Flight. We overcome this limit by introducing approximations, specifically tailored to this problem. The method was validated using different digital phantoms to study the algorithm behaviour with different contrasts, object sizes and different amounts of random and scattered coincidences In all the simulated conditions the proposed algorithm reached almost full convergence using less than 10 cycles over the whole datasets, with most of the recovery happening already in the first 2 iterations. This is achieved without the help of accelerating strategies like ordered subsets. The proposed algorithm has the potential to allow accurate, convergent, PET image reconstruction using a very limited number of operations.

Presotto, L. (2020). Ultra-fast ToF-PET Reconstruction with an Optimized Preconditioned Gradient Descent. In 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020. Institute of Electrical and Electronics Engineers Inc. [10.1109/NSS/MIC42677.2020.9507868].

Ultra-fast ToF-PET Reconstruction with an Optimized Preconditioned Gradient Descent

Presotto L.
2020

Abstract

The most common algorithm for clinical PET image reconstruction is MLEM, in which higher frequencies are the slowest to converge. This is exploited to control noise by early stopping. If noise is to be controlled using a more accurate method, e.g.: regularization, convergent reconstructions require thousands of iterations through the whole datasets. In this work, we revise an old, preconditioned conjugate gradient strategy. An ideal preconditioner would involve the backprojection of the inverse of the estimated sinogram, which is unknown before the reconstruction. The measured sinogram cannot be used as an approximation, due to the presence of zeros, especially when using Time Of Flight. We overcome this limit by introducing approximations, specifically tailored to this problem. The method was validated using different digital phantoms to study the algorithm behaviour with different contrasts, object sizes and different amounts of random and scattered coincidences In all the simulated conditions the proposed algorithm reached almost full convergence using less than 10 cycles over the whole datasets, with most of the recovery happening already in the first 2 iterations. This is achieved without the help of accelerating strategies like ordered subsets. The proposed algorithm has the potential to allow accurate, convergent, PET image reconstruction using a very limited number of operations.
No
poster + paper
image reconstruction; emission tomography; regularization;
English
2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020 - 31 October 2020 through 7 November 2020
9781728176932
Presotto, L. (2020). Ultra-fast ToF-PET Reconstruction with an Optimized Preconditioned Gradient Descent. In 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020. Institute of Electrical and Electronics Engineers Inc. [10.1109/NSS/MIC42677.2020.9507868].
Presotto, L
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/380403
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