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1. A content-adaptive unstructured grid based regularized CT reconstruction method with a SART-type preconditioned fixed-point proximity algorithm

   https://doi.org/10.1088/1361-6420/ac490f  

   Chen, Yun; Lu, Yao; Ma, Xiangyuan; Xu, Yuesheng (January 2022, Inverse Problems)

   Abstract The goal of this study is to develop a new computed tomography (CT) image reconstruction method, aiming at improving the quality of the reconstructed images of existing methods while reducing computational costs. Existing CT reconstruction is modeled by pixel-based piecewise constant approximations of the integral equation that describes the CT projection data acquisition process. Using these approximations imposes a bottleneck model error and results in a discrete system of a large size. We propose to develop a content-adaptive unstructured grid (CAUG) based regularized CT reconstruction method to address these issues. Specifically, we design a CAUG of the image domain to sparsely represent the underlying image, and introduce a CAUG-based piecewise linear approximation of the integral equation by employing a collocation method. We further apply a regularization defined on the CAUG for the resulting ill-posed linear system, which may lead to a sparse linear representation for the underlying solution. The regularized CT reconstruction is formulated as a convex optimization problem, whose objective function consists of a weighted least square norm based fidelity term, a regularization term and a constraint term. Here, the corresponding weighted matrix is derived from the simultaneous algebraic reconstruction technique (SART). We then develop a SART-type preconditioned fixed-point proximity algorithm to solve the optimization problem. Convergence analysis is provided for the resulting iterative algorithm. Numerical experiments demonstrate the superiority of the proposed method over several existing methods in terms of both suppressing noise and reducing computational costs. These methods include the SART without regularization and with the quadratic regularization, the traditional total variation (TV) regularized reconstruction method and the TV superiorized conjugate gradient method on the pixel grid. 

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2. 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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3. 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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4. Dynamic low-count PET image reconstruction using spatio-temporal primal dual network

   Hu R.; Cui J.; Li C.; Yu C.; Chen Y.; Liu H. (October 2023, Physics in medicine and biology)

   Objective. Dynamic positron emission tomography (PET) imaging, which can provide information on dynamic changes in physiological metabolism, is now widely used in clinical diagnosis and cancer treatment. However, the reconstruction from dynamic data is extremely challenging due to the limited counts received in individual frame, especially in ultra short frames. Recently, the unrolled modelbased deep learning methods have shown inspiring results for low-count PET image reconstruction with good interpretability. Nevertheless, the existing model-based deep learning methods mainly focus on the spatial correlations while ignore the temporal domain. Approach. In this paper, inspired by the learned primal dual (LPD) algorithm, we propose the spatio-temporal primal dual network (STPDnet) for dynamic low-count PET image reconstruction. Both spatial and temporal correlations are encoded by 3D convolution operators. The physical projection of PET is embedded in the iterative learning process of the network, which provides the physical constraints and enhances interpretability. Main results. The experiments of both simulation data and real rat scan data have shown that the proposed method can achieve substantial noise reduction in both temporal and spatial domains and outperform the maximum likelihood expectation maximization, spatio-temporal kernel method, LPD and FBPnet. Significance. Experimental results show STPDnet better reconstruction performance in the low count situation, which makes the proposed method particularly suitable in whole-body dynamic imaging and parametric PET imaging that require extreme short frames and usually suffer from high level of noise. 

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5. Reduced Order Model Hessian Approximations in Newton Methods for Optimal Control

   https://doi.org/10.1007/978-3-030-95157-3_18  

   Heinkenschloss, Matthias; Magruder, Caleb (July 2022, Realization and Model Reduction of Dynamical Systems - A Festschrift in Honor of the 70th Birthday of Thanos Antoulas)

   Beattie, C.A.; Benner, P.; Embree, M.; Gugercin, S.; Lefteriu, S. (Ed.)

   This paper introduces reduced order model (ROM) based Hessian approximations for use in inexact Newton methods for the solution of optimization problems implicitly constrained by a large-scale system, typically a discretization of a partial differential equation (PDE). The direct application of an inexact Newton method to this problem requires the solution of many PDEs per optimization iteration. To reduce the computational complexity, a ROM Hessian approximation is proposed. Since only the Hessian is approximated, but the original objective function and its gradient is used, the resulting inexact Newton method maintains the first-order global convergence property, under suitable assumptions. Thus even computationally inexpensive lower fidelity ROMs can be used, which is different from ROM approaches that replace the original optimization problem by a sequence of ROM optimization problem and typically need to accurately approximate function and gradient information of the original problem. In the proposed approach, the quality of the ROM Hessian approximation determines the rate of convergence, but not whether the method converges. The projection based ROM is constructed from state and adjoint snapshots, and is relatively inexpensive to compute. Numerical examples on semilinear parabolic optimal control problems demonstrate that the proposed approach can lead to substantial savings in terms of overall PDE solves required. 

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