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1. List‐mode TOF‐PET 3D image reconstruction using stochastic primal‐dual network

   https://doi.org/10.1002/mp.70324  

   Tian, Kun; Hu, Rui; Wan, Yiming; Zheng, Zhenrong; Chen, Yunmei; Liu, Huafeng (February 2026, Medical Physics)

   Abstract BackgroundAs an imaging modality in nuclear medicine characterized by its notable sensitivity, positron emission tomography (PET) facilitates the visualization of physiological activities occurring within biological tissues. However, conventional PET images are frequently challenged by poor spatial resolution and a low signal‐to‐noise ratio (SNR) due to inherent detector physics and constraints on radiotracer dosage. The integration of time‐of‐flight (TOF) data into PET image reconstruction has, in recent years, led to enhanced image quality. This enhancement is achieved by using the timing information from detected pairs of annihilation photons, allowing for more accurate determination of the locations where positron annihilation occurs. However, significant challenges persist in TOF‐PET reconstruction. The inclusion of TOF information drastically increases the size of sinogram data (often by tens of folds), severely escalating computational time and memory requirements for reconstruction. PurposeTo address these challenges, this work introduces a new deep learning approach that reconstructs PET images directly from list‐mode data, which avoids the above problems while enhancing image quality. MethodsSpecifically, we propose LM‐SPD‐Net, a list‐mode TOF‐PET reconstruction framework based on a stochastic primal‐dual network architecture. LM‐SPD‐Net comprises two main components as follows: a primal module, constructed using Convolutional Neural Networks (CNNs), to process information in the image domain; and a dual module, built with fully connected neural networks (FCNNs), to handle data‐domain features. These modules are coupled via a projection model and are alternately updated over multiple iterations to obtain the final reconstructed image. By introducing the FCNN and projection model, LM‐SPD‐Net overcomes the conventional limitation of CNNs in processing list‐mode data. Furthermore, the inclusion of a physics‐informed projection process in the training pipeline enhances the interpretability and generalizability of the model. Moreover, to reduce memory usage during training and facilitate 3D PET image reconstruction, a subset partitioning strategy is employed. ResultsQuantitative and qualitative comparisons with list‐mode ordered subset expectation maximization (LM‐OSEM), list‐mode stochastic primal‐dual hybrid gradient (LM‐SPDHG), and Fast‐PET across both simulated and semi‐real clinical data show that LM‐SPD‐Net outperforms these methods by 5%–20% improvements in PSNR and SSIM metrics, while also demonstrating visibly enhanced image quality. ConclusionsComprehensive experiments in this study demonstrate that the proposed method effectively reconstructs the overall subject structure with high fidelity, while maintaining excellent performance in clinically relevant regions such as tumors and the thalamus, particularly under low‐count conditions. 

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2. 3D Tensor Based Nonlocal Low Rank Approximation in Dynamic PET Reconstruction

   https://doi.org/10.3390/s19235299  

   Xie, Nuobei; Chen, Yunmei; Liu, Huafeng (January 2019, Sensors)

   Reconstructing images from multi-view projections is a crucial task both in the computer vision community and in the medical imaging community, and dynamic positron emission tomography (PET) is no exception. Unfortunately, image quality is inevitably degraded by the limitations of photon emissions and the trade-off between temporal and spatial resolution. In this paper, we develop a novel tensor based nonlocal low-rank framework for dynamic PET reconstruction. Spatial structures are effectively enhanced not only by nonlocal and sparse features, but momentarily by tensor-formed low-rank approximations in the temporal realm. Moreover, the total variation is well regularized as a complementation for denoising. These regularizations are efficiently combined into a Poisson PET model and jointly solved by distributed optimization. The experiments demonstrated in this paper validate the excellent performance of the proposed method in dynamic PET. 

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3. Accelerated primal-dual methods for convex-strongly-concave saddle point problems

   Khalafi, Mohammad; Boob, Digvijay (July 2023, Proceedings of Machine Learning Research)

   We investigate a primal-dual (PD) method for the saddle point problem (SPP) that uses a linear approximation of the primal function instead of the standard proximal step, resulting in a linearized PD (LPD) method. For convex-strongly concave SPP, we observe that the LPD method has a suboptimal dependence on the Lipschitz constant of the primal function. To fix this issue, we combine features of Accelerated Gradient Descent with the LPD method resulting in a single-loop Accelerated Linearized Primal-Dual (ALPD) method. ALPD method achieves the optimal gradient complexity when the SPP has a semi-linear coupling function. We also present an inexact ALPD method for SPPs with a general nonlinear coupling function that maintains the optimal gradient evaluations of the primal parts and significantly improves the gradient evaluations of the coupling term compared to the ALPD method. We verify our findings with numerical experiments. 

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4. DULDA: Dual-Domain Unsupervised Learned Descent Algorithm for PET Image Reconstruction

   Hu, R.; Chen, Y.; Kim, K.; Rockenbach, M.A.B.C.; Li, Q.; Liu, H. (October 2023, Lecture Notes in Computer Science, vol 14229)

   Deep learning based PET image reconstruction methods have achieved promising results recently. However, most of these methods follow a supervised learning paradigm, which rely heavily on the availability of high-quality training labels. In particular, the long scanning time required and high radiation exposure associated with PET scans make obtaining these labels impractical. In this paper, we propose a dual-domain unsupervised PET image reconstruction method based on learned descent algorithm, which reconstructs high-quality PET images from sinograms without the need for image labels. Specifically, we unroll the proximal gradient method with a learnable norm for PET image reconstruction problem. The training is unsupervised, using measurement domain loss based on deep image prior as well as image domain loss based on rotation equivariance property. The experimental results demonstrate the superior performance of proposed method compared with maximum-likelihood expectation-maximization (MLEM), total-variation regularized EM (EM-TV) and deep image prior based method (DIP). 

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5. Single-Photon Camera Guided Extreme Dynamic Range Imaging

   Liu, Yuhao; Gutierrez-Barragan, Felipe; Ingle, Atul; Gupta, Mohit; Velten, Andreas (January 2022, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV))

   Reconstruction of high-resolution extreme dynamic range images from a small number of low dynamic range (LDR) images is crucial for many computer vision applications. Current high dynamic range (HDR) cameras based on CMOS image sensor technology rely on multiexposure bracketing which suffers from motion artifacts and signal-to-noise (SNR) dip artifacts in extreme dynamic range scenes. Recently, single-photon cameras (SPCs) have been shown to achieve orders of magnitude higher dynamic range for passive imaging than conventional CMOS sensors. SPCs are becoming increasingly available commercially, even in some consumer devices. Unfortunately, current SPCs suffer from low spatial resolution. To overcome the limitations of CMOS and SPC sensors, we propose a learning-based CMOS-SPC fusion method to recover high-resolution extreme dynamic range images. We compare the performance of our method against various traditional and state-of-the-art baselines using both synthetic and experimental data. Our method outperforms these baselines, both in terms of visual quality and quantitative metrics. 

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