Partial volume effect (PVE) arises from the limited spatial resolution of positron emission tomography (PET) scanners, causing significant quantitative biases that hinder accurate metabolic activity assessment. To address these problems, we proposed an unsupervised deep residual compensation model (U-DRCM) for PET partial volume correction (PVC). U-DRCM first predicted an initial blur kernel for the PVE-affected PET image based on a conditional blind deconvolution module (CBD module). Then, a conditional residual compensation module (CRC module) was introduced to compensate for the error caused by inaccurate blur kernel prediction. The whole model is unsupervised which only needs a single patient's PET image as the training label and the corresponding MR image as the network input. The performance of U-DRCM was evaluated against several established PVC approaches, including Richardson-Lucy (RL), reblurred Van-Cittert (RVC), iterative Yang (IY), neural blind deconvolution (NBD), and deep convolutional neural network (DeepPVC) using both simulated BrainWeb phantom and real clinical datasets. In the simulation study, U-DRCM consistently outperformed competing methods across multiple quantitative metrics, achieved a higher peak signal-to-noise ratio (PSNR), an improved structural similarity index (SSIM), and a lower root mean square error (RMSE). For the real clinical study, U-DRCM delivered substantial improvements in standardized uptake value (SUV) and standardized uptake value ratio (SUVR) across various brain volumes of interest (VOIs). Experimental results show that U-DRCM effectively mitigates the impact of PVE, resulting in high-quality PVC PET images with enhanced brain visualization.

This work develops a novel framework that learns a microstructural codebook to enable accurate, rapid, and multi-parameter microstructure imaging on diffusion MRI, thereby resolving the limited generalization across protocols and inextensibility to new microstructural indices. By integrating the spherical mean technique with a hybrid Mamba-CNN network and learnable tissue-compartment kernels, our approach effectively captures multi-scale spatial dependencies and links spherical mean signals to biophysical microstructure models, enhancing both interpretability and adaptability. The model supports robust estimation of 24 microstructural metrics from 8 biophysical diffusion models, even under undersampled acquisition settings. Furthermore, it generalizes across varying acquisition protocols and enables seamless adaptation to new microstructural indices with minimal fine-tuning. Extensive experiments on multiple datasets validate the effectiveness of our method, demonstrating its excellence in accuracy, generalization, and transferability on microstructure estimation. This work contributes to the development of a foundation model for microstructure imaging, offering a unified framework that bridges biophysical modeling and deep learning for more interpretable and adaptable dMRI analysis. Code is available at https://github.com/1nlandempire/dMRI_codebook_imaging.

The parcellation of Cranial Nerves (CNs) serves as a crucial quantitative methodology for evaluating the morphological characteristics and anatomical pathways of specific CNs. Multi-modal CNs parcellation networks have achieved promising segmentation performance, which combine structural Magnetic Resonance Imaging (MRI) and diffusion MRI. However, insufficient exploration of diffusion MRI information has led to low performance of existing multi-modal fusion. In this work, we propose a tractography-guided Dual-label Collaborative Learning Network (DCLNet) for multi-modal CNs parcellation. The key contribution of our DCLNet is the introduction of coarse labels of CNs obtained from fiber tractography through CN atlas, and collaborative learning with precise labels annotated by experts. Meanwhile, we introduce a Modality-adaptive Encoder Module (MEM) to achieve soft information swapping between structural MRI and diffusion MRI. Extensive experiments conducted on the publicly available Human Connectome Project (HCP) dataset demonstrate performance improvements compared to single-label network. This systematic validation underscores the effectiveness of dual-label strategies in addressing inherent ambiguities in CNs parcellation tasks.

Optical Coherence Tomography (OCT) is a high-resolution, non-invasive imaging technology increasingly used for biometric data collection from fingertips. OCT captures volume data up to 3mm below the skin surface in the form of a series of B-scan images, enabling the reconstruction of internal fingerprints (IF) and internal sweat pores (ISP), thereby enhancing the security of biometric recognition. Despite the advantages, OCT images suffer from speckle noise and tissue discontinuity, making the extraction of subcutaneous biometric features challenging. Traditional hardware and software-based enhancement methods often result in over-smoothing and structural loss. Recent advancements in deep learning (DL) offer promising alternatives, with supervised DL methods showing efficacy when trained with high-quality paired datasets. However, the absence of ground-truth (GT) data makes it impossible to apply these models. This study proposes a novel supervised enhancement method for fingertip OCT images, with a paired dataset generation strategy. An OCT few-shot GAN and a Quality Estimation Module are proposed and incorporated into the strategy to realize translation from minimal GT manual augmentation to high-quality paired dataset, effectively addressing the challenge of data scarcity. A Fast Supervised Enhancement GAN (FSE-GAN) is proposed thereafter to perform simultaneous speckle noise reduction and tissue structure restoration, facilitating accurate extraction of internal fingerprints and sweat pores. Experiments demonstrate that the enhanced images significantly simplify IF and ISP extraction while achieving outstanding result quality.

Cone-beam CT is extensively used in medical diagnosis and treatment. Despite its large longitudinal field of view (FoV), the horizontal FoV of CBCT systems is severely limited due to the detector width. Certain commercial CBCT systems increase the horizontal FoV by employing the offset detector method. However, this method necessitates 360° full circular scanning trajectory which increases the scanning time and is not compatible with specific CBCT system models. In this paper, we investigate the feasibility of large FoV imaging under short scan trajectories with an additional X-ray source. A dual-source CBCT geometry is proposed as well as two corresponding image reconstruction algorithms. The first one is based on cone-parallel rebinning and the subsequent employs a modified Parker weighting scheme. Theoretical calculations demonstrate that the proposed geometry achieves a wider horizontal FoV than the ${90}\%$ detector offset geometry (radius of ${214}.{83}\textit {mm}$ vs. ${198}.{99}\textit {mm}$ ) with a significantly reduced rotation angle (less than 230° vs. 360°). As demonstrated by experiments, the proposed geometry and reconstruction algorithms obtain comparable imaging qualities within the FoV to conventional CBCT imaging techniques. Implementing the proposed geometry is straightforward and does not substantially increase development expenses. It possesses the capacity to expand CBCT applications even further.

Compressed sensing (CS) provides a robust and simple framework for compressing images in resource-constrained environments. However, CS-based image coding schemes often have poor rate-distortion (R-D) performance, particularly due to the quantization process. Our research indicates that leveraging the image prior enables the estimation of most significant bits (MSBs) from least significant bits (LSBs), which provides a quantization strategy to improve R-D performance without increasing coding complexity. That is discarding MSBs of measurements, and only transmitting LSBs to the decoder side. At the decoder side, we reconstruct images by solving an inverse-quantization set-constrained CS optimization problem. Our approach further employs a tailored designed deep denoiser as the proximal operator to enhance the reconstructed image quality. Extensive experimental results demonstrate that the proposed scheme achieves satisfactory performance, with promising R-D results (PSNR gains over 1.71 dB than JPEG at 0.50 bpp compression ratio), and robust bit error and loss resilience (reconstructed 29.98 dB even with 50% bit loss at 0.50 bpp compression ratio), meanwhile having lower encoding complexity (less than half encoding time of CCSDS-IDC).

Accurate segmentation of cerebrovascular structures from Time-of-flight magnetic resonance angiography is vital for treating cerebrovascular diseases. However, existing methods rely on voxel categorization, leading to discontinuities in fine vessel locations. We propose a connectivity-based cerebrovascular segmentation method that considers inter-voxel relationships to alleviate this limitation. By modeling connectivity, we convert voxel classification into inter-voxel connectivity prediction. Given the sparse and widely distributed nature of cerebrovascular structures, we employ a sparse 3D Bi-level routing attention to effectively capture cerebrovascular features. To extract inter-voxel directional information, we utilize a 3D direction excitation block. Additionally, a 3D direction interactive block continuously enhances the directional information within the feature map. This directional enhanced feature map is then concatenated with the feature map from the encoder layer and fed into the decoder layer. We compare our method with current state-of-the-art cerebrovascular segmentation techniques and general medical image segmentation methods. Our method achieved a Dice Similarity Coefficient of 92.413% and 83.197% on the clinical and open cerebrovascular datasets respectively, outperforming existing approaches.

For discrete-time nonlinear systems with uncertainty, this paper presents an interval analysis approach to design controller and compute the estimate of the closed-loop robust domain of attraction (RDOA). The dynamics of the system is modelled using difference inclusions. A robust negative-definite and invariant set (RNIS) in the state-control space is proposed. An RNIS is defined by the combination of a robust negative-definite set (RNS) and a robust controlled invariant set (RCIS), which leads to sufficient conditions for Lyapunov stability of the system. The estimate of RDOA can be obtained by projecting an RNIS along the state space. However, the RNIS is hard to obtain by its definition. Drawing inspiration from the RCIS-computation approach, we define a mapping that utilizes the predecessor operator in the state-control space to compute a set limit. Then, the RNIS can be obtained by finding the limit set for an RNS. The computations of RNS and the limit set are based on interval analysis. An algorithm to estimate the RNIS is introduced with rigorous convergence analysis. Finally, we formulate an optimization problem that is solvable, and enlarges the RNIS and the estimate of RDOA. The method is validated on examples of nonlinear systems subject to actuator saturation.

Video Snapshot compressive imaging (SCI) reconstruction recovers video frames from a compressed 2D measurement. However, frames at each time cannot be observed since the limitation of hardware. To make SCI suitable for more applications, we propose an optical flow-based deep unfolding network for video SCI reconstruction. To extract the optical flow, the feature maps during the iterative process are transformed by the convolution layer into the estimated optical flow. We designed a motion regularizer, which uses voxels of iterative frames and optical flow to update the reconstructed frames. The proposed motion regularizer efficiently captures the temporal correlation between the previous and next frames, which contributes to reconstructing the observed and unobserved frames from input measurement in a SCI reconstruction process. Experiments show that our method achieves state-of-the-art results on PSNR and SSIM.

Abstract This paper proposed an image compression‐encryption scheme based on compressive sensing theory, which achieves high security, strong robustness, and high rate‐distortion performance. First, the denoising preprocessing strategy is applied at the encoder side, which can enhance the rate‐distortion performance without sacrificing security and robustness. Second, the preprocessed image is randomly down‐sampled using scrambled block Bernoulli sampling with diffusion noise (SBBS‐DN), which is generated by combining a hyper‐chaotic system and SHA256 hash of the plain image. Third, a deep‐learned plug‐and‐play is embedded prior for plain image reconstruction at the decoder side. Simulation results show that the proposed scheme has desirable security performance (being resistant to different attacks), high R‐D performance (PSNR gains over 1.3 dB than JPEG at 0.50 bpp compression ratio), and high error resilience (reconstructed 29.92 dB at 0.50 bpp compression ratio even with 50% bit loss).

Remote photoplethysmography (rPPG) has been an active research topic in recent years. While most existing methods are focusing on eliminating motion artifacts in the raw traces obtained from single-scale region-of-interest (ROI), it is worth noting that there are some noise signals that cannot be effectively separated in single-scale space but can be separated more easily in multi-scale space. In this paper, we analyze the distribution of pulse signal and motion artifacts in different layers of a Gaussian pyramid. We propose a method that combines multi-scale analysis and neural network for pulse extraction in different scales, and a layer-wise attention mechanism to adaptively fuse the features according to signal strength. In addition, we propose spatial-temporal joint attention module and channel-temporal joint attention module to learn and exaggerate pulse features in the joint spaces, respectively. The proposed remote pulse extraction network is called Joint Attention and Multi-Scale fusion Network (JAMSNet). Extensive experiments have been conducted on two publicly available datasets and one self-collected dataset. The results show that the proposed JAMSNet shows better performance than state-of-the-art methods.

Using remote photoplethysmography (rPPG) to estimate blood volume pulse in a non-contact way is an active research topic in recent years. Existing methods are mainly based on the single-scale region of interest (ROI). However, some noise signals that are not easily separated in single-scale space can be easily separated in multi-scale space. In addition, existing spatiotemporal networks mainly focus on local spatiotemporal information and lack emphasis on temporal information which is crucial in pulse extraction problems, resulting in insufficient spatiotemporal feature modeling. This paper proposes a multi-scale facial video pulse extraction network based on separable spatiotemporal convolution and dimension separable attention. First, in order to solve the problem of single-scale ROI, we construct a multi-scale feature space for initial signal separation. Secondly, separable spatiotemporal convolution and dimension separable attention are designed for efficient spatiotemporal correlation modeling, which increases the information interaction between long-span time and space dimensions and puts more emphasis on temporal features. The signal-to-noise ratio (SNR) of the proposed network reaches 9.58 dB on the PURE dataset and 6.77 dB on the UBFC-rPPG dataset, which outperforms state-of-the-art algorithms. Results show that fusing multi-scale signals generally obtains better results than methods based on the only single-scale signal. The proposed separable spatiotemporal convolution and dimension separable attention mechanism contributes to more accurate pulse signal extraction.

Deep learning has recently been intensively studied in the context of image compressive sensing (CS) to discover and represent complicated image structures. These approaches, however, either suffer from nonflexibility for an arbitrary sampling ratio or lack an explicit deep-learned regularization term. This paper aims to solve the CS reconstruction problem by combining the deep-learned regularization term and proximal operator. We first introduce a regularization term using a carefully designed residual-regressive net, which can measure the distance between a corrupted image and a clean image set and accurately identify to which subspace the corrupted image belongs. We then address a proximal operator with a tailored dilated residual channel attention net, which enables the learned proximal operator to map the distorted image into the clean image set. We adopt an adaptive proximal selection strategy to embed the network into the loop of the CS image reconstruction algorithm. Moreover, a self-ensemble strategy is presented to improve CS recovery performance. We further utilize state evolution to analyze the effectiveness of the designed networks. Extensive experiments also demonstrate that our method can yield superior accurate reconstruction (PSNR gain over 1 dB) compared to other competing approaches while achieving the current state-of-the-art image CS reconstruction performance. The test code is available at https://github.com/zjut-gwl/CSDRCANet.