A novel image reconstruction method based on exponent-based anisotropic variational partial differential equation (PDE) for digital tomosynthesis (DT) is proposed. An exponent map instead of fixed values 1 or 2 is used as the order of total variation (TV), which can balance staircase artifact and over smoothing. The smoothed structure tensor (SST) capable of characterizing of different image features is used to build the exponent map, which has good ability of noise suppressing as well as detail preserving. Numerical experiment demonstrates that our method achieves better performance in many aspects (such as noise level, structure similarity) than existing methods, including adaptive steepest descent-projection onto convex sets (ASD-POCS) and selective diffusion regularized simultaneous algebraic reconstruction technique (SART).
Optical coherence tomography (OCT) technology has significant potential value in the application of early gastrointestinal tumor screening and intraoperative guidance. In the application of diagnosing gastrointestinal diseases, a key step of OCT image intelligent analysis system is to segment the tissues and layers accurately. In this paper, we propose a new encoder-decoder network named PDTANet, which contains a global context-guided PDFF module and a lightweight attention-aware triplet attention (TA) mechanism. Moreover, during the model training stage, we adopt a region-aware and boundary-aware hybrid loss function to learn and update model parameters. The proposed PDTANet model has been applied for automatic tumor segmentation of guinea pig colorectal OCT images. The experimental results show that our proposed PDTANet model has the ability to focus on and connect global context and important feature information for OCT images. Compared with the prediction results of the model trained by the traditional Unet model and Dice loss function, the PDTANet model and a combination of dice and boundary related loss function proposed as the hybrid loss function proposed in this paper have significantly improved the accuracy of the segmentation of tissue boundaries, especially the surface Dice metric, which is improved by about 3%.
The diversity of bacteria and their ability to acquire drug resistance lead to many challenges in traditional antibacterial methods. Photothermal therapies that convert light energy into localized physical heat to kill target microorganisms do not induce resistance and provide an alternative for antibacterial treatment. However, many photothermal materials cannot specifically target bacteria, which can lead to thermal damage to normal tissues, thus seriously affecting their biological applications. Here, we designed and synthesized bacteria-affinitive photothermal carbon dots (BAPTCDs) targeting MurD ligase that catalyzes the synthesis of peptidoglycan (PG) in bacteria. BAPTCDs presented specific recognition ability and excellent photothermal properties. BAPTCDs can bind to bacteria very tightly due to their chiral structure and inhibit enzyme activity by competing with D-glutamic acid to bind to MurD ligases, thus inhibiting the synthesis of bacterial walls. It also improves the accuracy of bacteria treatment by laser irradiation. Through the synergy of biochemical and physical effects, the material offers outstanding antibacterial effects and potentially contributes to tackling the spread of antibiotic resistance and facilitation of antibiotic stewardship.
In conventional microscopic imaging of chromosomes, the use of high numerical aperture (NA) oil-immersion objectives is essential. However, the use of oil-immersion objectives poses a significant challenge to automated imaging systems because it increases the risk of sample contamination and instrumental damage. The shallow depth of field (DOF) of oil-immersion objectives also demands more sophisticated mechanical focusing. Here, we introduce a chromosome oil-free microscopic imaging system based on Fourier Ptychographic Microscopy (FPM) technology. The system employs a 100×, 0.8 NA dry objective to achieve a half-pitch resolution of 194 nm at an incident wavelength of 524 nm. The reconstructed images of chromosomes surpassed the conventional imaging with a 100×, 1.25 NA oil-immersion objective. We also utilized digital refocusing methods to extend the effective DOF to ±2.4 μm. This study preliminarily validates the possibility of developing a new generation of chromosome scanners without using an oil-immersion objective.
The sensory neocortex has been suggested to be a substrate for long-term memory storage, yet which exact single cells could be specific candidates underlying such long-term memory storage remained neither known nor visible for over a century. Here, using a combination of day-by-day two-photon Ca2+ imaging and targeted single-cell loose-patch recording in an auditory associative learning paradigm with composite sounds in male mice, we reveal sparsely distributed neurons in layer 2/3 of auditory cortex emerged step-wise from quiescence into bursting mode, which then invariably expressed holistic information of the learned composite sounds, referred to as holistic bursting (HB) cells. Notably, it was not shuffled populations but the same sparse HB cells that embodied the behavioral relevance of the learned composite sounds, pinpointing HB cells as physiologically-defined single-cell candidates of an engram underlying long-term memory storage in auditory cortex.
BackgroundAccurate lymph nodes (LNs) assessment is important for rectal cancer (RC) staging in multiparametric magnetic resonance imaging (mpMRI). However, it is incredibly time-consumming to identify all the LNs in scan region. This study aims to develop and validate a deep-learning-based, fully-automated lymph node detection and segmentation (auto-LNDS) model based on mpMRI.MethodsIn total, 5789 annotated LNs (diameter ≥ 3 mm) in mpMRI from 293 patients with RC in a single center were enrolled. Fused T2-weighted images (T2WI) and diffusion-weighted images (DWI) provided input for the deep learning framework Mask R-CNN through transfer learning to generate the auto-LNDS model. The model was then validated both on the internal and external datasets consisting of 935 LNs and 1198 LNs, respectively. The performance for LNs detection was evaluated using sensitivity, positive predictive value (PPV), and false positive rate per case (FP/vol), and segmentation performance was evaluated using the Dice similarity coefficient (DSC).FindingsFor LNs detection, auto-LNDS achieved sensitivity, PPV, and FP/vol of 80.0%, 73.5% and 8.6 in internal testing, and 62.6%, 64.5%, and 8.2 in external testing, respectively, significantly better than the performance of junior radiologists. The time taken for model detection and segmentation was 1.3 s/case, compared with 200 s/case for the radiologists. For LNs segmentation, the DSC of the model was in the range of 0.81–0.82.InterpretationThis deep learning–based auto-LNDS model can achieve pelvic LNseffectively based on mpMRI for RC, and holds great potential for facilitating N-staging in clinical practice.
In this paper, a high-speed swept frequency laser light source is used, combined with FPGA and GPU acceleration technology to design a high-speed endoscopic Swept-source optical coherence tomography (SS-OCT) system. Several key parameters that affect the performance of the system are tested. The results show that the system's imaging depth is 4. 5mm, imaging resolution is $7.3\mu \mathrm{m}$, and system sensitivity is 110dB in air. The image of finger tissue is stable and the structure is clear. When the rotation speed reaches 9000RPM, the real-time frame rate of the system can reach 141 frames per second.
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