Efficient metal free organic radical scintillators.

The development of high-performance metal-free organic X-ray scintillators (OXSTs), characterized by a synergistic combination of robust X-ray absorption, efficient exciton utilization, and short luminescence lifetimes, poses a considerable challenge. Here we present an effective strategy for achieving augmented X-ray scintillation through the utilization of halogenated open-shell organic radical scintillators. Our experimental results demonstrate that the synthesized scintillators exhibit strong X-ray absorption derived from halogen atoms, display efficacious X-ray stability, and theoretically achieve 100% exciton utilization efficiency with a short lifetime (∼18 ns) due to spin-allowed doublet transitions.

HEAL: High-Frequency Enhanced and Attention-Guided Learning Network for Sparse-View CT Reconstruction.

X-ray computed tomography (CT) imaging technology has become an indispensable diagnostic tool in clinical examination. However, it poses a risk of ionizing radiation, making the reduction of radiation dose one of the current research hotspots in CT imaging. Sparse-view imaging, as one of the main methods for reducing radiation dose, has made significant progress in recent years.

Feasibility study on the introduction of Micro-CT technology for the identification of and its adulterants.

, a kind of Chinese herbal medicine with great clinical use, is often confused with its adulterants, and it is difficult to identify it without certain knowledge. The existing identification methods have their own drawbacks, so a new method is needed to realize the identification of and its adulterants. We used Micro Computed Tomography (Micro-CT) to perform tomography scans on and its adulterants, performed data screening and data correction on the obtained DICOM images, and then applied 3D reconstruction, data augmentation, and ResNext deep learning model for the classification study.

PIDNET: Polar Transformation Based Implicit Disentanglement Network for Truncation Artifacts.

The interior problem, a persistent ill-posed challenge in CT imaging, gives rise to truncation artifacts capable of distorting CT values, thereby significantly impacting clinical diagnoses. Traditional methods have long struggled to effectively solve this issue until the advent of supervised models built on deep neural networks. However, supervised models are constrained by the need for paired data, limiting their practical application.

MARGANVAC: metal artifact reduction method based on generative adversarial network with variable constraints.

Metal artifact reduction (MAR) has been a key issue in CT imaging. Recently, MAR methods based on deep learning have achieved promising results. However, when deploying deep learning-based MAR in real-world clinical scenarios, two prominent challenges arise.

Bilateral extended FDK: An improved weighting method for static CT imaging.

Background: Improving imaging speed has always been the focus of research in CT technology, which is related to the radiation dose and imaging quality of moving organs, including heart and blood vessels. However, it is difficult to achieve further improvement by increasing the rotation speed of the gantry due to its structural strength limitation. Differing from the conventional CTs, the static CT employs dozens of ray sources to acquire projection data from different angular ranges, and each source only needs to be rotated in a small range to finish a full 360° scan, thus greatly increasing the scanning speed.

A Novel Contrastive Self-Supervised Learning Framework for Solving Data Imbalance in Solder Joint Defect Detection.

Poor chip solder joints can severely affect the quality of the finished printed circuit boards (PCBs). Due to the diversity of solder joint defects and the scarcity of anomaly data, it is a challenging task to automatically and accurately detect all types of solder joint defects in the production process in real time. To address this issue, we propose a flexible framework based on contrastive self-supervised learning (CSSL).

A nonconvex model-based combined geometric calibration scheme for micro cone-beam CT with irregular trajectories.

Background: Many dedicated cone-beam CT (CBCT) systems have irregular scanning trajectories. Compared with the standard CBCT calibration, accurate calibration for CBCT systems with irregular trajectories is a more complex task, since the geometric parameters for each scanning view are variable. Most of the existing calibration methods assume that the intrinsic geometric relationship of the fiducials in the phantom is precisely known, and rarely delve deeper into the issue of whether the phantom accuracy is adapted to the calibration model.

Analysis and fabrication of a small-scale radio-frequency balun for magnetic resonance imaging amplifier.

In this study, the authors report the design and fabrication of a small mixed-integrated balun for magnetic resonance imaging (MRI). The device was designed by using the positive anti-symmetric coupling method, which applies the lump surface-mount technology capacitors as well as mirror-symmetric coupling strips that were etched on the top and bottom layers of a printed circuit board. The capacitors reduced the length of the coupling strips and compensated for imbalances in the phase and gain due to errors in the fabrication process.

TIME-Net: Transformer-Integrated Multi-Encoder Network for limited-angle artifact removal in dual-energy CBCT.

Dual-energy cone-beam computed tomography (DE-CBCT) is a promising imaging technique with foreseeable clinical applications. DE-CBCT images acquired with two different spectra can provide material-specific information. Meanwhile, the anatomical consistency and energy-domain correlation result in significant information redundancy, which could be exploited to improve image quality.

DIOR: Deep Iterative Optimization-Based Residual-Learning for Limited-Angle CT Reconstruction.

Limited-angle CT is a challenging problem in real applications. Incomplete projection data will lead to severe artifacts and distortions in reconstruction images. To tackle this problem, we propose a novel reconstruction framework termed Deep Iterative Optimization-based Residual-learning (DIOR) for limited-angle CT.

Micro-CT image denoising with an asymmetric perceptual convolutional network.

Micro-CT has important applications in biomedical research due to its ability to perform high-precision 3D imaging of micro-architecture in a non-invasive way. Because of the limited power of the radiation source, it is difficult to obtain a high signal-to-noise image under the requirement of temporal resolution. Therefore, low-dose CT image denoising has attracted considerable attention to improve the image quality of micro-CT while maintaining time resolution.

Robust regression-based estimation of isocenter offset with subpixel precision in tomographic image reconstruction.

Tomographic image reconstruction requires precise geometric measurements and calibration for the scanning system to yield optimal images. The isocenter offset is a very important geometric parameter that directly governs the spatial resolution of reconstructed images. Due to system imperfections such as mechanical misalignment, an accurate isocenter offset is difficult to achieve.

Data sustained misalignment correction in microscopic cone beam CT via optimization under the Grangeat Epipolar consistency condition.

Purpose: The misalignment correction in cone beam computed tomography (CBCT), which is usually carried out in an offline manner, is a difficult and tedious process. It becomes even more challenging in microscopic CBCT due to the much higher requirements on spatial resolution. In practice, however, an offline approach for misalignment correction may not be readily implementable, especially in the situations where either time is of the essence or the process needs to be carried out repetitively.

[Design and implementation of postoperative evaluation pipeline of deep brain stimulation by multimodality imaging].

Deep brain stimulation (DBS) surgery is an important treatment for patients with Parkinson's disease in the middle and late stages. The accuracy of the implantation of electrode at the location of the nuclei directly determines the therapeutic effect of the operation. At present, there is no single imaging method that can obtain images with electrodes, nuclei and their positional relationship.

Single-Irradiation Simultaneous Dual-Modal Bioimaging Using Nanostructure Scintillators as Single Contrast Agent.

The rising demand for clinical diagnosis tools has led to extensive research on multimodal bioimaging systems. Unlike single-modal detection, multimodal imaging not only can provide both function and structure information but also can address the issue of sensitivity, depth, and cost. Despite enormous efforts, conventional step-by-step procedures for obtaining multimodal imaging pose a significant constraint on their practical applications.

A novel calibration method incorporating nonlinear optimization and ball-bearing markers for cone-beam CT with a parameterized trajectory.

Purpose: Cone-beam (CB) CT is a powerful noninvasive imaging modality, and is widely used in many applications. Accurate geometric parameters are essential for high-quality image reconstruction. Usually, a CBCT system with higher spatial resolution, particularly on the order of microns or nanometers, will be more sensitive to the parametric accuracy.

Interior tomography in microscopic CT with image reconstruction constrained by full field of view scan at low spatial resolution.

In high resolution (microscopic) CT applications, the scan field of view should cover the entire specimen or sample to allow complete data acquisition and image reconstruction. However, truncation may occur in projection data and results in artifacts in reconstructed images. In this study, we propose a low resolution image constrained reconstruction algorithm (LRICR) for interior tomography in microscopic CT at high resolution.

Enhancing the anti-multiple myeloma efficiency in a cancer stem cell xenograft model by conjugating the ABCG2 antibody with microbubbles for a targeted delivery of ultrasound mediated epirubicin.

Background: Although multiple myeloma (MM) treatment has improved in the last decade, it remains largely incurable. One of main reasons is that there are cancer stem cells (CSCs) in MM, which are responsible for MM's drug resistance and relapse. In this study, we used the targeting microbubbles (MBs) conjugated with anti-ABCG2 monoclonal antibody (mAb) for ultrasound mediated epirubicin (EPI) delivery to evaluate the therapeutic effectiveness of the novel agent in MM CSC xenograft model.

MiRNA-34a overexpression inhibits multiple myeloma cancer stem cell growth in mice by suppressing TGIF2.

Hematological malignancy originated from B-cell line, multiple myeloma (MM), is a kind of plasma cells in bone marrow hyperplasia and cause of osteoclast-mediated skeletal destruction disease. MiR-34a plays an important epigenetic regulating role in malignant tumors and presents a therapeutic potential. In this study, we investigated the effects of overexpression of miR-34a in MM cancer stem cells (CSCs) on tumor growth and bone lesions.

PEGylated long-circulating liposomes deliver homoharringtonine to suppress multiple myeloma cancer stem cells.

The goal of this investigation was to evaluate the inhibiting effect of high proportion polyethyleneglycol of long-circulating homoharringtonine liposomes on RPMI8226 multiple myeloma cancer stem cells. The CD138CD34 multiple myeloma cancer stem cells isolated from RPMI8226 cell line using magnetic activated cell sorting system were, respectively, incubated with the optimized formulation of polyethyleneglycol of long-circulating homoharringtonine liposomes and the homoharringtonine in vitro, and the multiple myeloma cancer stem cell proliferation, colony formation, and cell cycle were analyzed. The inhibition of the multiple myeloma CD138CD34 cancer stem cell growth was investigated in non-obese-diabetic/severe-combined-immunodeficiency mice that were implanted with multiple myeloma RPMI 8226 cancer stem cells and treated with the LCL-HHT-H-PEG.

A fast beam hardening correction method incorporated in a filtered back-projection based MAP algorithm.

The beam hardening effect can induce strong artifacts in CT images, which result in severely deteriorated image quality with incorrect intensities (CT numbers). This paper develops an effective and efficient beam hardening correction algorithm incorporated in a filtered back-projection based maximum a posteriori (BHC-FMAP). In the proposed algorithm, the beam hardening effect is modeled and incorporated into the forward-projection of the MAP to suppress beam hardening induced artifacts, and the image update process is performed by Feldkamp-Davis-Kress method based back-projection to speed up the convergence.