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Background/Objectives: Preclinical chest computed tomography (CT) imaging in small animals is often limited by low resolution due to scan time and dose constraints, which hinders accurate detection of subtle lesions. Traditional super-resolution (SR) metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), may not adequately reflect clinical interpretability. We aimed to evaluate whether deep learning-based SR models could enhance image quality and lesion detectability in rat chest CT, balancing quantitative metrics with radiologist assessment. Methods: We retrospectively analyzed 222 chest CT scans acquired from polyhexamethylene guanidine phosphate (PHMG-p) exposure studies in Sprague Dawley rats. Three SR models were implemented and compared: single-image SR (SinSR), segmentation-guided SinSR with lung cropping (SinSR3), and omni-super-resolution (OmniSR). Models were trained on rat CT data and evaluated using PSNR and SSIM. Two board-certified thoracic radiologists independently performed blinded evaluations of lesion margin clarity, nodule detectability, image noise, artifacts, and overall image quality. Results: SinSR1 achieved the highest PSNR (33.64 ± 1.30 dB), while SinSR3 showed the highest SSIM (0.72 ± 0.08). Despite lower PSNR (29.21 ± 1.46 dB), OmniSR received the highest radiologist ratings for lesion margin clarity, nodule detectability, and overall image quality (mean score 4.32 ± 0.41, κ = 0.74). Reader assessments diverged from PSNR and SSIM, highlighting the limited correlation between conventional metrics and clinical interpretability. Conclusions: Deep learning-based SR improved visualization of rat chest CT images, with OmniSR providing the most clinically interpretable results despite modest numerical scores. These findings underscore the need for reader-centered evaluation when applying SR techniques to preclinical imaging.

We consider the recovery of an underlying signal$\\mathbf{x}\\in\\mathbb{C}^m$based on projection measurements of the form$\\mathbf{y}=\\mathbf{M}\\mathbf{x}+\\mathbf{w}$ , where$\\mathbf{y}\\in\\mathbb{C}^\\ell$and$\\mathbf{w}$is measurement noise; we are interested in the case$\\ell\\ll m$ . It is assumed that the signal model$p(\\mathbf{x})$is known and that$\\mathbf{w}\\sim\\mathcal{CN}(\\mathbf{w};\\boldsymbol{0},\\bf \\Sigma_w)$for known$\\bf \\Sigma_w$ . The objective is to design a projection matrix$\\mathbf{M}\\in\\mathbb{C}^{\\ell\\times m}$to maximize key information-theoretic quantities with operational significance, including the mutual information between the signal and the projections$\\mathcal{I}(\\mathbf{x};\\mathbf{y})$or the Renyi entropy of the projections$\\mbox{h}_\\alpha \\left( \\mathbf{y} \\right)$(Shannon entropy is a special case). By capitalizing on explicit characterizations of the gradients of the information measures with respect to the projection matrix, where we also partially extend the well-known results of Palomar and Verdu from the mutual information to the Renyi entropy domain, we reveal the key operations carried out by the optimal projection designs: mode exposure and mode alignment. Experiments are considered for the case of compressive sensing (CS) applied to imagery. In this context, we provide a demonstration of the performance improvement possible through the application of the novel projection designs in relation to conventional ones, as well as justification for a fast online projection design method with which state-of-the-art adaptive CS signal recovery is achieved. [PUBLICATION ABSTRACT]

This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 × 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics.

In recent years, thermal imaging cameras are widely used in the field of intelligent surveillance because of their special imaging characteristics and better privacy protection properties. However, due to the low resolution and fixed location for current thermal imaging cameras, it is difficult to effectively identify human behavior using a single detection method based on skeletal keypoints. Therefore, a self-update learning method is proposed for fixed thermal imaging camera scenes, called the behavioral parameter field (BPF). This method can express the regularity of human behavior patterns concisely and directly. Firstly, the detection accuracy of small targets under low-resolution video is improved by optimizing the YOLOv4 network to obtain a human detection model under thermal imaging video. Secondly, the BPF model is designed to learn the human normal behavior features at each position. Finally, based on the learned BPF model, we propose to use metric modules, such as cosine similarity and intersection over union matching, to accomplish the classification of human abnormal behaviors. In the experimental stage, the living scene of the indoor elderly living alone is applied as our experimental case, and a variety of detection models are compared to the proposed method for verifying the effectiveness and practicability of the proposed behavioral parameter field in the self-collected thermal imaging dataset for the indoor elderly living alone.

Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex（the implant area） is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine（LSVM） as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern.

Frequency-following responses (FFRs) are neurophonic potentials that provide a window into the encoding of complex sounds (e.g., speech/music), auditory disorders, and neuroplasticity. While the neural origins of the FFR remain debated, renewed controversy has reemerged after demonstration that FFRs recorded via magnetoencephalography (MEG) are dominated by cortical rather than brainstem structures as previously assumed. Here, we recorded high-density (64 ch) FFRs via EEG and applied state-of-the art source imaging techniques to multichannel data (discrete dipole modeling, distributed imaging, independent component analysis, computational simulations). Our data confirm a mixture of generators localized to bilateral auditory nerve (AN), brainstem inferior colliculus (BS), and bilateral primary auditory cortex (PAC). However, frequency-specific scrutiny of source waveforms showed the relative contribution of these nuclei to the aggregate FFR varied across stimulus frequencies. Whereas AN and BS sources produced robust FFRs up to ∼700 Hz, PAC showed weak phase-locking with little FFR energy above the speech fundamental (100 Hz). Notably, CLARA imaging further showed PAC activation was eradicated for FFRs >150 Hz, above which only subcortical sources remained active. Our results show (i) the site of FFR generation varies critically with stimulus frequency; and (ii) opposite the pattern observed in MEG, subcortical structures make the largest contribution to electrically recorded FFRs (AN ≥ BS > PAC). We infer that cortical dominance observed in previous neuromagnetic data is likely due to the bias of MEG to superficial brain tissue, underestimating subcortical structures that drive most of the speech-FFR. Cleanly separating subcortical from cortical FFRs can be achieved by ensuring stimulus frequencies are >150–200 Hz, above the phase-locking limit of cortical neurons.

High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution image into a corresponding high-resolution image by a specific algorithm. With the emergence and swift advancement of generative adversarial networks (GANs), image super-resolution reconstruction is experiencing a new era of progress. Unfortunately, there has been a lack of comprehensive efforts to bring together the advancements made in the field of super-resolution reconstruction using generative adversarial networks. Hence, this paper presents a comprehensive overview of the super-resolution image reconstruction technique that utilizes generative adversarial networks. Initially, we examine the operational principles of generative adversarial networks, followed by an overview of the relevant research and background information on reconstructing remote sensing images through super-resolution techniques. Next, we discuss significant research on generative adversarial networks in high-resolution image reconstruction. We cover various aspects, such as datasets, evaluation criteria, and conventional models used for image reconstruction. Subsequently, the super-resolution reconstruction models based on generative adversarial networks are categorized based on whether the kernel blurring function is recognized and utilized during training. We provide a brief overview of the utilization of generative adversarial network models in analyzing remote sensing imagery. In conclusion, we present a prospective analysis of forthcoming research directions pertaining to super-resolution reconstruction methods that rely on generative adversarial networks.

Whole-body bone scan (WBS) is usually used as the effective diagnostic method for early-stage and comprehensive bone metastases of breast cancer. WBS images with breast cancer bone metastasis have the characteristics of low resolution, small foreground, and multiple lesions, hindering the widespread application of deep learning-based models. Automatically detecting a large number of densely small lesions on low-resolution WBS images remains a challenge. We aim to develop a unified framework for detecting multiple densely bone metastases based on low-resolution WBS images. We propose a novel unified detection framework to detect multiple bone metastases based on WBS images. Considering the difficulties of feature extraction caused by low resolution and multiple lesions, we innovatively propose the plug-and-play position auxiliary extraction module and feature fusion module to enhance the ability of global information extraction. In order to accurately detect small metastases in WBS, we designed the self-attention transformer-based target detection head. This retrospective study included 512 patients with breast cancer bone metastases from Peking Union Medical College Hospital. The data type is whole-body bone scan image. For our study, the ratio of training set, validation set and test set is about 6:2:2. The benchmarks are four representative baselines, SSD, YOLOR, Faster_RCNN_R and Scaled-YOLOv4. The performance metrics are Average Precision (AP), Precision and Recall. The detection results obtained through the proposed method were assessed using the Bonferroni-adjusted Wilcoxon rank test. The significant level is adjusted according to different multiple comparisons. We conducted extensive experiments and ablation studies on a private dataset of breast cancer WBS and a public dataset of bone scans from West China Hospital to validate the effectiveness and generalization. Experiments were conducted to evaluate the effectiveness of our method. First, compared to different network architectures, our method obtained AP of 55.0 ± 6.4% (95% confidence intervals (CI) 49.9–60.1%, ), which improved AP by 45.2% for the SSD baseline with AP 9.8 ± 2% (95% CI 8.1–11.4%). For the metric of recall, our method achieved the average of 54.3 ± 4.2% (95% CI 50.9–57.6%, ), which has improved the recall values by 49.01% for the SSD model with 5.2 ± 12.7% (95% CI 10–21.3%). Second, we conducted ablation studies. On the private dataset, adding the detection head module and position auxiliary extraction module will increase the AP values by 14.03% (from 33.3 ± 2% to 47.6 ± 4.4%) and 19.3% (from 33.3 ± 2% to 52.6 ± 6.1%), respectively. In addition, the generalization of the method was also verified on the public dataset BS-80K from West China Hospital. Extensive experimental results have demonstrated the superiority and effectiveness of our method. To the best of our knowledge, our work is the first attempt for developing automatic detector considering the unique characteristics of low resolution, small foreground and multiple lesions of breast cancer WBS images. Our framework is tailored for whole-body WBS and can be used as a clinical decision support tool for early decision-making for breast cancer bone metastases.

•We introduce an alternate implementation of low-rank fMRI reconstruction by using alternating minimization, which allows for easy integration of the subspace-specific L2 constraints.•We use the alternating minimization approach to accelerate FMRI by exploiting coil sensitivity, low-rank structures, and additional L2 constraints.•We found Tikhonov and temporal subspace smoothness constraints show improved performance over other methods for R = 15–30.•Tikhonov constraints were the most robust of the constrained-subspace methods, with the shortest reconstruction time.•Temporal subspace smoothness produced the highest reconstruction scores in the prospectively under-sampled data. Acceleration methods in fMRI aim to reconstruct high fidelity images from under-sampled k-space, allowing fMRI datasets to achieve higher temporal resolution, reduced physiological noise aliasing, and increased statistical degrees of freedom. While low levels of acceleration are typically part of standard fMRI protocols through parallel imaging, there exists the potential for approaches that allow much greater acceleration. One such existing approach is k-t FASTER, which exploits the inherent low-rank nature of fMRI. In this paper, we present a reformulated version of k-t FASTER which includes additional L2 constraints within a low-rank framework. We evaluated the effect of three different constraints against existing low-rank approaches to fMRI reconstruction: Tikhonov constraints, low-resolution priors, and temporal subspace smoothness. The different approaches are separately tested for robustness to under-sampling and thermal noise levels, in both retrospectively and prospectively-undersampled finger-tapping task fMRI data. Reconstruction quality is evaluated by accurate reconstruction of low-rank subspaces and activation maps. The use of L2 constraints was found to achieve consistently improved results, producing high fidelity reconstructions of statistical parameter maps at higher acceleration factors and lower SNR values than existing methods, but at a cost of longer computation time. In particular, the Tikhonov constraint proved very robust across all tested datasets, and the temporal subspace smoothness constraint provided the best reconstruction scores in the prospectively-undersampled dataset. These results demonstrate that regularized low-rank reconstruction of fMRI data can recover functional information at high acceleration factors without the use of any model-based spatial constraints.

Person images captured by public surveillance cameras often have low resolutions (LRs), along with uncontrolled pose variations, background clutter and occlusion. These issues cause the resolution mismatch problem when matched with high-resolution (HR) gallery images (typically available during collection), harming the person re-identification (re-id) performance. While a number of methods have been introduced based on the joint learning of super-resolution and person re-id, they ignore specific discriminant identity information encoded in LR person images, leading to ineffective model performance. In this work, we propose a novel joint bilateral-resolution identity modeling method that concurrently performs HR-specific identity feature learning with super-resolution, LR-specific identity feature learning, and person re-id optimization. We also introduce an adaptive ensemble algorithm for handling different low resolutions. Extensive evaluations validate the advantages of our method over related state-of-the-art re-id and super-resolution methods on cross-resolution re-id benchmarks. An important discovery is that leveraging LR-specific identity information enables a simple cascade of super-resolution and person re-id learning to achieve state-of-the-art performance, without elaborate model design nor bells and whistles, which has not been investigated before.