Spatiotemporal Attention Constrained Deep Learning Framework for Dual-Tracer PET Imaging

Lian, Dankun; Li, Yue; Liu, Huafeng

doi:10.1007/978-3-031-12053-4_7

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…Only temporal features were learned from the TACs and used for separation. There also existed a method separating from the image domain, using both spatial and temporal features [ 28 ]. However, these two types of methods were both influenced by the quality of reconstructed images.…”

Section: Discussionmentioning

confidence: 99%

“…There are two kinds of deep learning approaches for dual-tracer reconstruction: the indirect ones and direct ones. Indirect methods firstly reconstruct the dual-tracer dynamic images by traditional reconstruction algorithms, then separate the dual-tracer images by deep neural networks to obtain single-tracer images [24][25][26][27][28]. These networks separate signals either from the voxel time-activity curves (TACs) [24][25][26][27] or from the entire dynamic image [28], and are easily influenced by the quality of reconstructed images.…”

Section: Introductionmentioning

confidence: 99%

“…Indirect methods firstly reconstruct the dual-tracer dynamic images by traditional reconstruction algorithms, then separate the dual-tracer images by deep neural networks to obtain single-tracer images [24][25][26][27][28]. These networks separate signals either from the voxel time-activity curves (TACs) [24][25][26][27] or from the entire dynamic image [28], and are easily influenced by the quality of reconstructed images. Direct methods reconstruct images of two tracers directly from the dual-tracer sinogram, which means the network learns to solve both the separation task and reconstruction task.…”

Section: Introductionmentioning

confidence: 99%

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Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

et al. 2023

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Background Simultaneous dual-tracer positron emission tomography (PET) imaging can observe two molecular targets in a single scan, which is conducive to disease diagnosis and tracking. Since the signals emitted by different tracers are the same, it is crucial to separate each single tracer from the mixed signals. The current study proposed a novel deep learning-based method to reconstruct single-tracer activity distributions from the dual-tracer sinogram. Methods We proposed the Multi-task CNN, a three-dimensional convolutional neural network (CNN) based on a framework of multi-task learning. One common encoder extracted features from the dual-tracer dynamic sinogram, followed by two distinct and parallel decoders which reconstructed the single-tracer dynamic images of two tracers separately. The model was evaluated by mean squared error (MSE), multiscale structural similarity (MS-SSIM) index and peak signal-to-noise ratio (PSNR) on simulated data and real animal data, and compared to the filtered back-projection method based on deep learning (FBP-CNN). Results In the simulation experiments, the Multi-task CNN reconstructed single-tracer images with lower MSE, higher MS-SSIM and PSNR than FBP-CNN, and was more robust to the changes in individual difference, tracer combination and scanning protocol. In the experiment of rats with an orthotopic xenograft glioma model, the Multi-task CNN reconstructions also showed higher qualities than FBP-CNN reconstructions. Conclusions The proposed Multi-task CNN could effectively reconstruct the dynamic activity images of two single tracers from the dual-tracer dynamic sinogram, which was potential in the direct reconstruction for real simultaneous dual-tracer PET imaging data in future.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

et al. 2023

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“…These methods can be divided into two categories. One is the indirect reconstruction methods, which firstly reconstruct the dualtracer images by traditional algorithms, then separate the images by neural networks [26][27][28][29][30][31][32]. The separation can be performed to either the voxel TACs [26][27][28][29][30] or the dynamic image as a whole [31,32], with the latter utilizing the spatial information in addition to the temporal information.…”

Section: Introductionmentioning

confidence: 99%

Signal separation of simultaneous dual-tracer PET imaging based on global spatial information and channel attention

Fang,

Zeng,

Liu

2024

EJNMMI Phys

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Background Simultaneous dual-tracer positron emission tomography (PET) imaging efficiently provides more complete information for disease diagnosis. The signal separation has long been a challenge of dual-tracer PET imaging. To predict the single-tracer images, we proposed a separation network based on global spatial information and channel attention, and connected it to FBP-Net to form the FBPnet-Sep model. Results Experiments using simulated dynamic PET data were conducted to: (1) compare the proposed FBPnet-Sep model to Sep-FBPnet model and currently existing Multi-task CNN, (2) verify the effectiveness of modules incorporated in FBPnet-Sep model, (3) investigate the generalization of FBPnet-Sep model to low-dose data, and (4) investigate the application of FBPnet-Sep model to multiple tracer combinations with decay corrections. Compared to the Sep-FBPnet model and Multi-task CNN, the FBPnet-Sep model reconstructed single-tracer images with higher structural similarity, peak signal-to-noise ratio and lower mean squared error, and reconstructed time-activity curves with lower bias and variation in most regions. Excluding the Inception or channel attention module resulted in degraded image qualities. The FBPnet-Sep model showed acceptable performance when applied to low-dose data. Additionally, it could deal with multiple tracer combinations. The qualities of predicted images, as well as the accuracy of derived time-activity curves and macro-parameters were slightly improved by incorporating a decay correction module. Conclusions The proposed FBPnet-Sep model was considered a potential method for the reconstruction and signal separation of simultaneous dual-tracer PET imaging.

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“…In comparison to the MTCM method, the supervised DL-based methods (i) separate the mPET signals without explicitly knowing the AIF of each tracer; (ii) have the ability to separate mPET signals using staggered or even simultaneous injection protocols; and (iii) sufficiently reduce the influence of noise in the separation process. DL-based methods for mPET imaging mainly fall into one of two categories: (i) learned post-separation of an mPET reconstruction, such as filtered back projection (FBP) (31,32), maximum likelihood expectation maximisation (MLEM) (32)(33)(34)(35), and alternating direction method of multipliers (ADMM) (32,34,36,37), (ii) direct-learned mPET image separation from sinogram (38,39). The direct-learned method has also been extended to the separation of simultaneous triple-tracers ([ 11 C] FMZ+[ 11 C]MET+[ 18 F]FDG) PET imaging based on simulated data (40).…”

Section: Introductionmentioning

confidence: 99%

Deep learned triple-tracer multiplexed PET myocardial image separation

Pan,

Marsden,

Reader

2024

Front. Nucl. Med.

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IntroductionIn multiplexed positron emission tomography (mPET) imaging, physiological and pathological information from different radiotracers can be observed simultaneously in a single dynamic PET scan. The separation of mPET signals within a single PET scan is challenging due to the fact that the PET scanner measures the sum of the PET signals of all the tracers. The conventional multi-tracer compartment modeling method (MTCM) requires staggered injections and assumes that the arterial input functions (AIFs) of each tracer are known.MethodsIn this work, we propose a deep learning-based method to separate triple-tracer PET images without explicitly knowing the AIFs. Dynamic triple-tracer noisy MLEM reconstruction was used as the network input and dynamic single-tracer noisy MLEM reconstructions were used as the training labels.ResultsA simulation study was performed to evaluate the performance of the proposed framework on triple-tracer ([18F]FDG+82Rb+[94mTc]sestamibi) PET myocardial imaging. The results show that the proposed methodology substantially reduced the noise level compared to the results obtained from single-tracer imaging. Additionally, it achieved lower bias and standard deviation in the separated single-tracer images compared to the MTCM-based method at both the voxel and ROI levels.DiscussionAs compared to the MTCM separation, the proposed method uses spatiotemporal information for separation, which enhances the separation performance at both the voxel and ROI levels. The simulation study also indicates the feasibility and potential of the proposed DL-based method for the application to pre-clinical and clinical studies.

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Spatiotemporal Attention Constrained Deep Learning Framework for Dual-Tracer PET Imaging

Cited by 6 publications

References 17 publications

Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

Signal separation of simultaneous dual-tracer PET imaging based on global spatial information and channel attention

Deep learned triple-tracer multiplexed PET myocardial image separation

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