LINC00887 regulates malignant progression and T-cell chemotaxis in clear cell renal cell carcinoma by activating CD70 via recruitment of SPI1.

BACKGROUND: LINC00887 has been mentioned in several articles regarding its involvement in various cancers like nasopharyngeal carcinoma, lung cancer and glioma. However, the mechanism of LINC00887 in the malignant progression of clear cell renal cell carcinoma (ccRCC) is still unclear. The topic of our study is mainly centered on exploring how LINC00887 exactly affects ccRCC malignant progression. METHODS: The bioinformatics method predicted the downstream TF and target genes of LINC00887 by the "LncRNA-transcription factor (TF)-Gene" triplet model. RNA immunoprecipitation, chromatin immunoprecipitation analysis, and Dual-luciferase reporter assay determined the regulatory relationship between LINC00887 and its downstream genes. The LINC00887 expression and its downstream gene expression in ccRCC cells were examined by qRT-PCR and Western blot. The effect of LINC00887-SPI1-CD70 modulation axis on proliferative transfer, cell stemness and T cell chemotaxis of ccRCC cells was examined in cellular and animal experiments. RESULTS: Our research demonstrated an upregulation of LINC00887 in ccRCC, which facilitated tumor growth and stemness in vivo. In addition, LINC00887 could upregulate the CD70 expression by recruiting transcriptional factor SPI1. The results of in vitro experiments illustrated that the LINC00887-SPI1-CD70 regulatory axis facilitated ccRCC malignant progression by promoting cell stemness and hindering T-cell chemotaxis. CONCLUSION: LINC00887, by recruiting SPI1, activated CD70 transcription, thereby propelling malignant progression and cell stemness and suppressing T cell chemotaxis in ccRCC. Based on our findings, we believed that the LINC00887-SPI1-CD70 regulatory axis had the potential to be a critical breakthrough for treating ccRCC.

Deep Fusion Network Based Sparse View CT Reconstructions for Clinical Diagnostic Scanners.

Sparse view CT scan has the advantage of reducing radiation exposure and scanning time in clinical diagnosis. However, the limited number of x-ray projections can make the reconstruction problem ill posed and result in image artifacts. To tackle the problem, we propose a novel model-based deep fusion network(DFN) satisfying the clinical set-up. It extracts fused features encoded from both the sinogram and the preliminary reconstructed image generated by filtered back projection (FBP) to improve the quality of reconstruction. The preliminary reconstructed image endows fused features with prior knowledge that facilitate the convergence of neural network to high-quality reconstruction images. We design a custom loss for training that enforces the network to learn both the pixel value and the integrity of the tissue structure. A synthetic sparse view breast CT dataset from American Association of Physicists in Medicine(AAPM) is used for training, validation and testing. The qualitative and quantitative evaluations show that the DFN reconstruction algorithm significantly improves in balancing between the image quality and reconstruction speed, hence enables fast and high quality CT reconstruction despite the sparse view limitations.

A joint method of coherence factor and nonlinear beamforming for synthetic aperture imaging with a ring array.

Ultrasound computed tomography (USCT) with a ring array is an emerging diagnostic method for breast cancer. In the literature, synthetic aperture (SA) imaging has employed the delay-and-sum (DAS) beamforming technique for ring-array USCT to obtain isotropic resolution reflection images. However, the images obtained by the conventional DAS beamformer suffer from off-axis clutter and low resolution due to inhomogeneity of the medium and phase distortion. To address these issues, researchers have developed adaptive beamforming methods, such as coherence factor (CF) and convolutional beamforming algorithm (COBA), that improve image quality. In this study, we propose a joint method that combines CF with short-lag COBA (SLCOBA). First, we estimate the average sound speed using CF to address tissue inhomogeneity. Based on the corrected sound speed map, SLCOBA effectively suppresses side lobes and enhances image quality. Numerical results show that the proposed method reduces clutter and noise, improving resolution performance. These findings suggest that the proposed method may be a practical option for breast imaging in inhomogeneous mediums in the future.

Pulse Stretching Correction for Improving Ultrasound Image Resolution.

Recent advances in ultrasound technology have led to the development of wide band large-aperture transducer arrays that can provide high-resolution images with deeper imaging depth using delay-and-sum synthetic aperture (SA) imaging techniques. However, imaging with long array signals may result in resolution degradation and image aliasing due to pulse stretching at the near field where large angle of reflection often occurs. To address this issue, this paper proposes a solution known as pulse stretching correction (PSC). The PSC method involves mathematically developing a pulse stretching model and reformulating the delay-and-sum SA equation into a common-angle form. Pulse stretching is then corrected in the frequency domain to reduce or eliminate it in the reflection angle domain. The effectiveness of this method is demonstrated through simulations and experimental results, which show that it can effectively suppress shallow noise and improve image resolution.

Auto-tuning numerical method for acoustic wave simulation using analytical solution.

Numerical wavefield simulation such as commercial simulation software enables an optimal design of an ultrasound computed tomography (USCT) system for clinical purpose of before prototyping. Such simulator, not developed for optimal design though, can provide rapid implementation for acoustic wave propagation but may lead to unexpected errors during the establishment of numerical model. Here, we propose an auto-tuning numerical method (ATNM), aiming to optimize physical parameters (e.g. grid size, Courant-Friedrichs-Lewy (CFL) number, perfectly matched layer (PML) absorption coefficient, etc) such that the enumerated wavefield computed on those converges to the corresponding analytical solution derived from acoustic scattering theory. We use genetic algorithm (GA) to automatically calibrate numerical wavefield. Our preliminary test is to investigate the best design of PML absorption coefficient for USCT to minimize mean relative error (MRE) between the k-Wave simulation and the analytic model and show its efficacy. The experimental results verify our hypothesis that this calibrated numerical simulator on a simple physical domain is generalizable to any other domains.

Complex Transformer Network for Single-Angle Plane-Wave Imaging.

OBJECTIVE: Plane-wave imaging (PWI) is a high-frame-rate imaging technique that sacrifices image quality. Deep learning can potentially enhance plane-wave image quality, but processing complex in-phase and quadrature (IQ) data and suppressing incoherent signals pose challenges. To address these challenges, we present a complex transformer network (CTN) that integrates complex convolution and complex self-attention (CSA) modules. METHODS: The CTN operates in a four-step process: delaying complex IQ data from a 0° single-angle plane wave for each pixel as CTN input data; extracting reconstruction features with a complex convolution layer; suppressing irrelevant features derived from incoherent signals with two CSA modules; and forming output images with another complex convolution layer. The training labels are generated by minimum variance (MV). RESULTS: Simulation, phantom and in vivo experiments revealed that CTN produced comparable- or even higher-quality images than MV, but with much shorter computation time. Evaluation metrics included contrast ratio, contrast-to-noise ratio, generalized contrast-to-noise ratio and lateral and axial full width at half-maximum and were -11.59 dB, 1.16, 0.68, 278 µm and 329 µm for simulation, respectively, and 9.87 dB, 0.96, 0.62, 357 µm and 305 µm for the phantom experiment, respectively. In vivo experiments further indicated that CTN could significantly improve details that were previously vague or even invisible in DAS and MV images. And after being accelerated by GPU, the CTN runtime (76.03 ms) was comparable to that of delay-and-sum (DAS, 61.24 ms). CONCLUSION: The proposed CTN significantly improved the image contrast, resolution and some unclear details by the MV beamformer, making it an efficient tool for high-frame-rate imaging.

Low-field magnetic resonance image enhancement via stochastic image quality transfer.

Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher income countries e.g. for small child patients with obesity, claustrophobia, implants, or tattoos. However, low-field MR images commonly have lower resolution and poorer contrast than images from high field (1.5T, 3T, and above). Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field. Our approach uses (i) a stochastic low-field image simulator as the forward model to capture uncertainty and variation in the contrast of low-field images corresponding to a particular high-field image, and (ii) an anisotropic U-Net variant specifically designed for the IQT inverse problem. We evaluate the proposed algorithm both in simulation and using multi-contrast (T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR)) clinical low-field MRI data from an LMIC hospital. We show the efficacy of IQT in improving contrast and resolution of low-field MR images. We demonstrate that IQT-enhanced images have potential for enhancing visualisation of anatomical structures and pathological lesions of clinical relevance from the perspective of radiologists. IQT is proved to have capability of boosting the diagnostic value of low-field MRI, especially in low-resource settings.

Deep-Learning-Based Ultrasound Sound-Speed Tomography Reconstruction with Tikhonov Pseudo-Inverse Priori.

Ultrasound sound-speed tomography (USST) is a promising technology for breast imaging and breast cancer detection. Its reconstruction is a complex non-linear mapping from the projection data to the sound-speed image (SSI). The traditional reconstruction methods include mainly the ray-based methods and the waveform-based methods. The ray-based methods with linear approximation have low computational cost but low reconstruction quality; the full wave-based methods with the complex non-linear model have high quality but high cost. To achieve both high quality and low cost, we introduced traditional linear approximation as prior knowledge into a deep neural network and treated the complex non-linear mapping of USST reconstruction as a combination of linear mapping and non-linear mapping. In the proposed method, the linear mapping was seamlessly implemented with a fully connected layer and initialized using the Tikhonov pseudo-inverse matrix. The non-linear mapping was implemented using a U-shape Net (U-Net). Furthermore, we proposed the Tikhonov U-shape net (TU-Net), in which the linear mapping was done before the non-linear mapping, and the U-shape Tikhonov net (UT-Net), in which the non-linear mapping was done before the linear mapping. Moreover, we conducted simulations and experiments for evaluation. In the numerical simulation, the root-mean-squared error was 6.49 and 4.29 m/s for the UT-Net and TU-Net, the peak signal-to-noise ratio was 49.01 and 52.90 dB, the structural similarity was 0.9436 and 0.9761 and the reconstruction time was 10.8 and 11.3 ms, respectively. In this study, the SSIs obtained with the proposed methods exhibited high sound-speed accuracy. Both the UT-Net and the TU-Net achieved high quality and low computational cost.

HybridCTrm: Bridging CNN and Transformer for Multimodal Brain Image Segmentation.

Multimodal medical image segmentation is always a critical problem in medical image segmentation. Traditional deep learning methods utilize fully CNNs for encoding given images, thus leading to deficiency of long-range dependencies and bad generalization performance. Recently, a sequence of Transformer-based methodologies emerges in the field of image processing, which brings great generalization and performance in various tasks. On the other hand, traditional CNNs have their own advantages, such as rapid convergence and local representations. Therefore, we analyze a hybrid multimodal segmentation method based on Transformers and CNNs and propose a novel architecture, HybridCTrm network. We conduct experiments using HybridCTrm on two benchmark datasets and compare with HyperDenseNet, a network based on fully CNNs. Results show that our HybridCTrm outperforms HyperDenseNet on most of the evaluation metrics. Furthermore, we analyze the influence of the depth of Transformer on the performance. Besides, we visualize the results and carefully explore how our hybrid methods improve on segmentations.