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Temporal information‐guided dynamic dual‐tracer PET signal separation network.

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Title: Temporal information‐guided dynamic dual‐tracer PET signal separation network.
Authors: Tong, Junyi1 (AUTHOR), Wang, Chunxia1 (AUTHOR), Liu, Huafeng1 (AUTHOR) liuhf@zju.edu.cn
Source: Medical Physics. Jul2022, Vol. 49 Issue 7, p4585-4598. 14p.
Subjects: Deep learning, Imaging phantoms, Signal separation, Signal-to-noise ratio, Positron emission tomography
Abstract: Purpose: The difficulty of dynamic dual‐tracer positron emission tomography (PET) technology is to separate the complete single‐tracer information from mixed dual‐tracer. Traditional methods cannot separate single‐injection single‐scan dynamic dual‐tracer PET images. In this paper, we propose a deep learning framework based on gated recurrent unit (GRU) network and evaluate its performance with simulation experiments and realistic monkey data. Methods: The proposed single‐scan dynamic dual‐tracer PET image separation network consists of three parts, including encoder, separation, and decoder module. Encoder part is to map the mixed time activity curves (TACs) from the low‐dimensional space to the high‐dimensional space to get mixed weight vector matrix. Separation part is to capture the temporal information of mixed weight vector matrix using bi‐directional GRU (bi‐GRU) layer to obtain the single‐tracer masks, and the decoding part remaps the high‐dimensional single‐tracer weight vector matrix to the low‐dimensional space to obtain two separated single tracers. Results: In the simulation experiments under different tracers, phantoms, noise levels, arterial input function (AIF), and k‐parameter with Gaussian random, compared to the stacked auto encoder network and traditional background subtraction method, GRU‐based network has better performance with low bias and mean squared error. In the realistic study, the image results of GRU network have higher mean structural similarity and peak signal to noise ratio. Conclusions: This study demonstrates the feasibility of temporal information‐guided neural network in single‐injection single‐scan dynamic dual‐tracer PET images separation. The GRU‐based network uses TAC temporal information without AIFs to make the separation results more robust and accurate, which significantly outperforms state‐of‐the‐art method qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Temporal information‐guided dynamic dual‐tracer PET signal separation network.
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  Data: <searchLink fieldCode="AR" term="%22Tong%2C+Junyi%22">Tong, Junyi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Chunxia%22">Wang, Chunxia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Huafeng%22">Liu, Huafeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liuhf@zju.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Jul2022, Vol. 49 Issue 7, p4585-4598. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Imaging+phantoms%22">Imaging phantoms</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+separation%22">Signal separation</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Positron+emission+tomography%22">Positron emission tomography</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Purpose: The difficulty of dynamic dual‐tracer positron emission tomography (PET) technology is to separate the complete single‐tracer information from mixed dual‐tracer. Traditional methods cannot separate single‐injection single‐scan dynamic dual‐tracer PET images. In this paper, we propose a deep learning framework based on gated recurrent unit (GRU) network and evaluate its performance with simulation experiments and realistic monkey data. Methods: The proposed single‐scan dynamic dual‐tracer PET image separation network consists of three parts, including encoder, separation, and decoder module. Encoder part is to map the mixed time activity curves (TACs) from the low‐dimensional space to the high‐dimensional space to get mixed weight vector matrix. Separation part is to capture the temporal information of mixed weight vector matrix using bi‐directional GRU (bi‐GRU) layer to obtain the single‐tracer masks, and the decoding part remaps the high‐dimensional single‐tracer weight vector matrix to the low‐dimensional space to obtain two separated single tracers. Results: In the simulation experiments under different tracers, phantoms, noise levels, arterial input function (AIF), and k‐parameter with Gaussian random, compared to the stacked auto encoder network and traditional background subtraction method, GRU‐based network has better performance with low bias and mean squared error. In the realistic study, the image results of GRU network have higher mean structural similarity and peak signal to noise ratio. Conclusions: This study demonstrates the feasibility of temporal information‐guided neural network in single‐injection single‐scan dynamic dual‐tracer PET images separation. The GRU‐based network uses TAC temporal information without AIFs to make the separation results more robust and accurate, which significantly outperforms state‐of‐the‐art method qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/mp.15566
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        Text: English
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        PageCount: 14
        StartPage: 4585
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      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Imaging phantoms
        Type: general
      – SubjectFull: Signal separation
        Type: general
      – SubjectFull: Signal-to-noise ratio
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      – SubjectFull: Positron emission tomography
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      – TitleFull: Temporal information‐guided dynamic dual‐tracer PET signal separation network.
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            NameFull: Tong, Junyi
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            NameFull: Wang, Chunxia
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            NameFull: Liu, Huafeng
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            – D: 01
              M: 07
              Text: Jul2022
              Type: published
              Y: 2022
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