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A Proposal of Spatio-Temporal Reconstruction Method Based on a Fast Block-Iterative Algorithm
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Parametric images can help investigate disease mechanisms and vital functions. To estimate parametric images, it is necessary to obtain the tissue time activity curves (tTACs), which express temporal changes of tracer activity in human tissue. In general, the tTACs are calculated from each voxel's value of the time sequential PET images estimated from dynamic PET data. Recently, spatio-temporal PET reconstruction methods have been proposed in order to take into account the temporal correlation within each tTAC. Such spatio-temporal algorithms are generally quite computationally intensive. On the other hand, typical algorithms such as the preconditioned conjugate gradient (PCG) method still does not provide good accuracy in estimation. To overcome these problems, we propose a new spatio-temporal reconstruction method based on the dynamic row-action maximum-likelihood algorithm (DRAMA). As the original algorithm does, the proposed method takes into account the noise propagation, but it achieves much faster convergence. Performance of the method is evaluated with digital phantom simulations and it is shown that the proposed method requires only a few reconstruction processes, thereby remarkably reducing the computational cost required to estimate the tTACs. The results also show that the tTACs and parametric images from the proposed method have better accuracy.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.4 pp.819-825
- Publication Date
- 2013/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.819
- Type of Manuscript
- Special Section PAPER (Special Section on Medical Imaging)
- Category
- Medical Image Processing
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Tatsuya KON, Takashi OBI, Hideaki TASHIMA, Nagaaki OHYAMA, "A Proposal of Spatio-Temporal Reconstruction Method Based on a Fast Block-Iterative Algorithm" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 4, pp. 819-825, April 2013, doi: 10.1587/transinf.E96.D.819.
Abstract: Parametric images can help investigate disease mechanisms and vital functions. To estimate parametric images, it is necessary to obtain the tissue time activity curves (tTACs), which express temporal changes of tracer activity in human tissue. In general, the tTACs are calculated from each voxel's value of the time sequential PET images estimated from dynamic PET data. Recently, spatio-temporal PET reconstruction methods have been proposed in order to take into account the temporal correlation within each tTAC. Such spatio-temporal algorithms are generally quite computationally intensive. On the other hand, typical algorithms such as the preconditioned conjugate gradient (PCG) method still does not provide good accuracy in estimation. To overcome these problems, we propose a new spatio-temporal reconstruction method based on the dynamic row-action maximum-likelihood algorithm (DRAMA). As the original algorithm does, the proposed method takes into account the noise propagation, but it achieves much faster convergence. Performance of the method is evaluated with digital phantom simulations and it is shown that the proposed method requires only a few reconstruction processes, thereby remarkably reducing the computational cost required to estimate the tTACs. The results also show that the tTACs and parametric images from the proposed method have better accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.819/_p
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@ARTICLE{e96-d_4_819,
author={Tatsuya KON, Takashi OBI, Hideaki TASHIMA, Nagaaki OHYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={A Proposal of Spatio-Temporal Reconstruction Method Based on a Fast Block-Iterative Algorithm},
year={2013},
volume={E96-D},
number={4},
pages={819-825},
abstract={Parametric images can help investigate disease mechanisms and vital functions. To estimate parametric images, it is necessary to obtain the tissue time activity curves (tTACs), which express temporal changes of tracer activity in human tissue. In general, the tTACs are calculated from each voxel's value of the time sequential PET images estimated from dynamic PET data. Recently, spatio-temporal PET reconstruction methods have been proposed in order to take into account the temporal correlation within each tTAC. Such spatio-temporal algorithms are generally quite computationally intensive. On the other hand, typical algorithms such as the preconditioned conjugate gradient (PCG) method still does not provide good accuracy in estimation. To overcome these problems, we propose a new spatio-temporal reconstruction method based on the dynamic row-action maximum-likelihood algorithm (DRAMA). As the original algorithm does, the proposed method takes into account the noise propagation, but it achieves much faster convergence. Performance of the method is evaluated with digital phantom simulations and it is shown that the proposed method requires only a few reconstruction processes, thereby remarkably reducing the computational cost required to estimate the tTACs. The results also show that the tTACs and parametric images from the proposed method have better accuracy.},
keywords={},
doi={10.1587/transinf.E96.D.819},
ISSN={1745-1361},
month={April},}
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TY - JOUR
TI - A Proposal of Spatio-Temporal Reconstruction Method Based on a Fast Block-Iterative Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 819
EP - 825
AU - Tatsuya KON
AU - Takashi OBI
AU - Hideaki TASHIMA
AU - Nagaaki OHYAMA
PY - 2013
DO - 10.1587/transinf.E96.D.819
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2013
AB - Parametric images can help investigate disease mechanisms and vital functions. To estimate parametric images, it is necessary to obtain the tissue time activity curves (tTACs), which express temporal changes of tracer activity in human tissue. In general, the tTACs are calculated from each voxel's value of the time sequential PET images estimated from dynamic PET data. Recently, spatio-temporal PET reconstruction methods have been proposed in order to take into account the temporal correlation within each tTAC. Such spatio-temporal algorithms are generally quite computationally intensive. On the other hand, typical algorithms such as the preconditioned conjugate gradient (PCG) method still does not provide good accuracy in estimation. To overcome these problems, we propose a new spatio-temporal reconstruction method based on the dynamic row-action maximum-likelihood algorithm (DRAMA). As the original algorithm does, the proposed method takes into account the noise propagation, but it achieves much faster convergence. Performance of the method is evaluated with digital phantom simulations and it is shown that the proposed method requires only a few reconstruction processes, thereby remarkably reducing the computational cost required to estimate the tTACs. The results also show that the tTACs and parametric images from the proposed method have better accuracy.
ER -
English
