Dual-Tracer PET Image Separation by Deep Learning: A Simulation Study

Pan, Bailin; Marsden, Paul; Reader, Andrew J.

doi:10.3390/app13074089

Cited by 10 publications

(7 citation statements)

References 60 publications

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Section: Deep Learning For Dynamic Pet Image Reconstructionmentioning

confidence: 99%

“…Dual-tracer PET imaging can measure two PET tracers in a single scan, which may be useful for diagnosing and tracking diseases as another application of dynamic PET [227,228]. Deep learning has been reported to be useful for these approaches [229][230][231][232][233][234]. One of the recent breakthroughs in PET hardware is totalbody PET geometry [235][236][237] that obtains high-sensitivity PET data and can provide extremely less noisy training datasets for deep learning-based PET image reconstruction [238].…”

Section: Deep Learning For Dynamic Pet Image Reconstructionmentioning

confidence: 99%

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Deep learning-based PET image denoising and reconstruction: a review

Hashimoto,

Onishi,

Ote

et al. 2024

Radiol Phys Technol

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This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection through to recent iterative PET image reconstruction algorithms, and then review deep learning methods for PET data up to the latest innovations within three main categories. The first category involves post-processing methods for PET image denoising. The second category comprises direct image reconstruction methods that learn mappings from sinograms to the reconstructed images in an end-to-end manner. The third category comprises iterative reconstruction methods that combine conventional iterative image reconstruction with neural-network enhancement. We discuss future perspectives on PET imaging and deep learning technology.

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Section: Deep Learning For Dynamic Pet Image Reconstructionmentioning

confidence: 99%

Section: Deep Learning For Dynamic Pet Image Reconstructionmentioning

confidence: 99%

Deep learning-based PET image denoising and reconstruction: a review

Hashimoto,

Onishi,

Ote

et al. 2024

Radiol Phys Technol

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“…We employed an architecture based on the CED, which has been widely used in the DL-based mPET image separation (31,33,(38)(39)(40). In the proposed network, the encoder branches consist of the repeated application of two 3 Â 3 2D convolution layers, each followed by a batch normalisation (BN) and a parametric rectified linear unit (PReLU), in addition to a max-pooling layer for downsampling, followed by the BN and the PReLU.…”

Section: Deep Learned Triple-tracer Pet Image Separationmentioning

confidence: 99%

“…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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“…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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Dual-Tracer PET Image Separation by Deep Learning: A Simulation Study

Cited by 10 publications

References 60 publications

Deep learning-based PET image denoising and reconstruction: a review

Deep learning-based PET image denoising and reconstruction: a review

Deep learned triple-tracer multiplexed PET myocardial image separation

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

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