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

Abstract

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.

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

“…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].…”

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

“…The proposed separation network was then cascaded with FBP-Net [ 42 ], a deep learning implementation of the FBP algorithm, generating a direct reconstruction model named FBPnet-Sep. The FBPnet-Sep model was verified by simulated dynamic PET data, and compared to Multi-task CNN [ 34 ]. Moreover, experiments were performed to verify the superiority of image separation over sinogram separation, the effectiveness of using global spatial information and channel attention, as well as the application to low-dose data and to different tracer combinations, which will be illustrated in the following sections.…”

“…The other category is the direct reconstruction methods that reconstruct the singletracer images from the dual-tracer sinogram by usually the convolutional neural networks (CNNs), like FBP-CNN [33] and Multi-task CNN [34,35]. The reconstruction part of FBP-CNN adopted a two-dimensional convolution layer to approximate the spatial filter, and a fully-connected layer to approximate the back-projection in the traditional filtered back-projection (FBP) algorithm.…”

Deep learned triple-tracer multiplexed PET myocardial image separation

“…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).…”