Freehand 3D Ultrasound Imaging Based on Probe-mounted Vision and IMU System.

OBJECTIVES: Freehand three-dimensional (3D) ultrasound (US) is of great significance for clinical diagnosis and treatment, it is often achieved with the aid of external devices (optical and/or electromagnetic, etc.) that monitor the location and orientation of the US probe. However, this external monitoring is often impacted by imaging environment such as optical occlusions and/or electromagnetic (EM) interference. METHODS: To address the above issues, we integrated a binocular camera and an inertial measurement unit (IMU) on a US probe. Subsequently, we built a tight coupling model utilizing the unscented Kalman algorithm based on Lie groups (UKF-LG), combining vision and inertial information to infer the probe's movement, through which the position and orientation of the US image frame are calculated. Finally, the volume data was reconstructed with the voxel-based hole-filling method. RESULTS: The experiments including calibration experiments, tracking performance evaluation, phantom scans, and real scenarios scans have been conducted. The results show that the proposed system achieved the accumulated frame position error of 3.78 mm and the orientation error of 0.36° and reconstructed 3D US images with high quality in both phantom and real scenarios. CONCLUSIONS: The proposed method has been demonstrated to enhance the robustness and effectiveness of freehand 3D US. Follow-up research will focus on improving the accuracy and stability of multi-sensor fusion to make the system more practical in clinical environments.

A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set.

Background: In low-dose computed tomography (LDCT) lung cancer screening, soft tissue is hardly appreciable due to high noise levels. While deep learning-based LDCT denoising methods have shown promise, they typically rely on structurally aligned synthesized paired data, which lack consideration of the clinical reality that there are no aligned LDCT and normal-dose CT (NDCT) images available. This study introduces an LDCT denoising method using clinically structure-unaligned but paired data sets (LDCT and NDCT scans from the same patients) to improve lesion detection during LDCT lung cancer screening. Methods: A cohort of 64 patients undergoing both LDCT and NDCT was randomly divided into training (n=46) and testing (n=18) sets. A two-stage training approach was adopted. First, Gaussian noise was added to NDCT data to create simulated LDCT data for generator training. Then, the model was trained on a clinically structure-unaligned paired data set using a Wasserstein generative adversarial network (WGAN) framework with the initial generator weights obtained during the first stage of training. An attention mechanism was also incorporated into the network. Results: Validated on a clinical CT data set, our proposed method outperformed other available methods [CycleGAN, Pixel2Pixel, block-matching and three-dimensional filtering (BM3D)] in noise removal and detail retention tasks in terms of the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root mean square error (RMSE) metrics. Compared with the results produced by BM3D, our method yielded an average improvement of approximately 7% in terms of the three evaluation indicators. The probability density profile of the denoised CT output produced using our method best fit the reference NDCT scan. Additionally, our two-stage model outperformed the one-stage WGAN-based model in both objective and subjective evaluations, further demonstrating the higher effectiveness of our two-stage training approach. Conclusions: The proposed method performed the best in removing noise from LDCT scans and exhibited good detail retention, which could potentially enhance the lesion detection and characterization effects obtained for soft tissues in the scanning scope of LDCT lung cancer screening.

Imidazolium-Based Main-Chain Copolymers With Alternating Sequences for Broad-Spectrum Bactericidal Activity and Eradication of Bacterial Biofilms.

In response to the escalating challenge of bacterial drug resistance, the imperative to counteract planktonic cell proliferation and eliminate entrenched biofilms underscores the necessity for cationic polymeric antibacterials. However, limited efficacy and cytotoxicity challenge their practical use. Here, novel imidazolium-based main-chain copolymers with imidazolium (PIm+) as the cationic component are introduced. By adjusting precursor molecules, hydrophobicity and cationic density of each unit are fine-tuned, resulting in broad-spectrum bactericidal activity against clinically relevant pathogens. PIm+1 stands out for its potent antibacterial performance, with a minimum inhibitory concentration of 32 µg mL-1 against Methicillin-resistant Staphylococcus aureus (MRSA), and substantial biofilm reduction in Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) biofilms. The bactericidal mechanism involves disrupting the outer and cytoplasmic membranes, depolarizing the cytoplasmic membrane, and triggering intracellular reactive oxygen species (ROS) generation. Collectively, this study postulates the potential of imidazolium-based main-chain copolymers, systematically tailored in their sequences, to serve as a promising candidate in combatting drug-resistant bacterial infections.

MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks.

Low-dose CT techniques attempt to minimize the radiation exposure of patients by estimating the high-resolution normal-dose CT images to reduce the risk of radiation-induced cancer. In recent years, many deep learning methods have been proposed to solve this problem by building a mapping function between low-dose CT images and their high-dose counterparts. However, most of these methods ignore the effect of different radiation doses on the final CT images, which results in large differences in the intensity of the noise observable in CT images. What'more, the noise intensity of low-dose CT images exists significantly differences under different medical devices manufacturers. In this paper, we propose a multi-level noise-aware network (MLNAN) implemented with constrained cycle Wasserstein generative adversarial networks to recovery the low-dose CT images under uncertain noise levels. Particularly, the noise-level classification is predicted and reused as a prior pattern in generator networks. Moreover, the discriminator network introduces noise-level determination. Under two dose-reduction strategies, experiments to evaluate the performance of proposed method are conducted on two datasets, including the simulated clinical AAPM challenge datasets and commercial CT datasets from United Imaging Healthcare (UIH). The experimental results illustrate the effectiveness of our proposed method in terms of noise suppression and structural detail preservation compared with several other deep-learning based methods. Ablation studies validate the effectiveness of the individual components regarding the afforded performance improvement. Further research for practical clinical applications and other medical modalities is required in future works.

Automatic brain structure segmentation for 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance images via deep learning.

Background: Brain structure segmentation is of great value in diagnosing brain disorders, allowing radiologists to quickly acquire regions of interest and assist in subsequent analyses, diagnoses and treatment. Current brain structure segmentation methods are usually applied to magnetic resonance (MR) images, which provide higher soft tissue contrast and better spatial resolution. However, fewer segmentation methods are conducted on a positron emission tomography/magnetic resonance imaging (PET/MRI) system that combines functional and structural information to improve analysis accuracy. Methods: In this paper, we explore a dual-modality image segmentation model to segment brain 18F-fluorodeoxyglucose (18F-FDG) PET/MR images based on the U-Net architecture. This model takes registered PET and MR images as parallel inputs, and four evaluation metrics (Dice score, Jaccard coefficient, precision and sensitivity) are used to evaluate segmentation performance. Moreover, we also compared the proposed approach with other single-modality segmentation strategies, including PET-only segmentation and MRI-only segmentation. Results: The experiments were conducted on the clinical head data of 120 patients, and the results show that the proposed algorithm accurately delineates brain volumes of interest (VOIs), achieving superior performance with 84.24%±1.44% Dice score, 74.36%±2.40% Jaccard, 84.33%±1.56% precision and 84.73%±1.56% sensitivity. Furthermore, compared with directly using the FreeSurfer toolkit, the proposed method reduced the segmentation time, which only needs 20 seconds to segment the whole brain for each patient. Conclusions: We present a deep learning-based method for the joint segmentation of anatomical and functional PET/MR images. Compared with other single-modality methods, our method greatly improved the accuracy of brain structure delineation, which shows great potential for brain analysis.

[Clinical characteristics and etiology of temporomandibular joint herniation into external ear canal].

Objective:To summarize the cases of temporomandibular joint herniation into external auditory canal found and treated in our hospital, to improve the understanding of oral and maxillofacial diseases and otological diseases, and to explore the potential long-term effects of local radiotherapy on temporomandibular joint function. Method:Analyzed the causes of temporomandibular joint herniation into external auditory canal comprehensively through combining history, clinical manifestations and imaging examination. Result:All otoscope results showed soft tissue mass in the deep anterior wall of the external auditory canal. The soft tissue mass moved inside and outside along with the opening and closing of the mouth. CT examination revealed obvious bone defects in the anterior wall of the ear canal. Conclusion:Delayed radiotherapy injury may be a inducing factor of temporomandibular joint herniation into external auditory canal. CT and MRI examination have guiding significance on the disease treatment selection. The specific signs found by otoscope can confirm the diagnosis.

The arch bridge sign: a newly described CT feature of the coronavirus disease-19 (COVID-19) pneumonia.

BACKGROUND: To investigate the distribution of CT features and also to introduce a novel described CT feature of coronavirus disease-19 (COVID-19) pneumonia. METHODS: A series of radiologic signs in 11 COVID-19 patients were summarized and made morphometric analysis. RESULTS: A special sign termed as "the arch bridge sign" owing to its morphological mimicking an arch bridge was firstly introduced. Statistical analyze showed that the subpleural area is the priority distribution location (14/14) and the sign inclined to perform in those patients in a relatively early stage (6/8) and with moderate clinical severity (8/8). Segment VI in lower lobe involved most (6/14). In this retrospective study, other characteristic radiologic signs of COVID-19 pneumonia were analyzed synchronously. A series of radiologic signs were identified in bilateral lungs with a bias towards segment VI, I + II and X. Segment VI had the largest number of each sign. Ground-glass opacities (GGOs), subpleural distribution pattern and vessels dilatation were the top three most common signs among them. CONCLUSIONS: The recognition of the arch bridge sign may benefit patient care by earlier definitive diagnosis of COVID-19 pneumonia. The lesions of COVID-19 pneumonia distributed mainly in the back-lung segments, which characteristic may light new ideas in clinical treatment and nursing strategy.

Computed Tomographic Imaging of 3 Patients With Coronavirus Disease 2019 Pneumonia With Negative Virus Real-time Reverse-Transcription Polymerase Chain Reaction Test.

We reported computed tomographic (CT) imaging findings of 3 patients with coronavirus disease 2019 (COVID-19) pneumonia with initially negative results before CT examination and finally confirmed positive for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by real-time reverse-transcription polymerase chain reaction assay.

BRD4 blockage alleviates pathological cardiac hypertrophy through the suppression of fibrosis and inflammation via reducing ROS generation.

Hypertension is an essential regulator of cardiac injury and remodeling. However, the pathogenesis that contributes to cardiac hypertrophy remains to be fully explored. BRD4, as a bromodomain and extra-terminal (BET) family member, plays an important role in critical biological processes. In the study, our results showed that BRD4 expression was up-regulated in human and mouse hypertrophied hearts, and importantly these effects were modulated by reactive oxygen species (ROS) generation. In angiotensin II (Ang II)-treated cardiomyocytes, BRD4 decrease markedly blunted the prohypertrophic effect, which was further promoted by the combinational treatment of ROS scavenger (N-acetyl-cysteine, NAC). In addition, NAC pre-treatment markedly elevated the anti-fibrotic role of BRD4 suppression in Ang II-incubated cardiomyocytes by repressing transforming growth factor ß1 (TGF-ß1)/SMADs signaling pathway. NAC combined with BRD4 reduction further alleviated inflammation and oxidative stress in Ang II-exposed cardiomyocytes, which was partly through inhibiting nuclear factor-κB (NF-κB) signaling and improving nuclear erythroid factor 2-related factor 2 (Nrf-2)/heme oxygenase-1 (HO-1) pathway, respectively. Furthermore, the in vivo results confirmed the protective effects of BRD4 suppression on mice against aortic banding (AB)-induced cardiac hypertrophy, as evidenced by the reduced cross sectional area and fibrotic area using H&E and Masson trichrome staining. What's more, the degree of cardiac hypertrophy (ANP and BNP), the expression of pro-fibrotic genes (TGF-ß1, Collagen I, Collagen III and CTGF), the levels of inflammation and oxidative stress were all significantly attenuated by the blockage of BRD4 in AB-operated mice. Taken together, repressing BRD4 expression was found to confer a protective effect against experimental cardiac hypertrophy in mice, demonstrating its potential as an effective therapeutic target for pathological cardiac hypertrophy.