two-dimensional-super-resolution-visualization-rat-brain

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

The cerebral microvasculature forms a complex network of vessels essential for maintaining brain function. Diseases such as stroke, Alzheimer's disease, ...

Fudan University

The ultrasonic backscatter technique holds the promise of characterizing bone density and microstructure. This paper conducts ultrasonic backscatter parametric imaging based on measurements of apparent integrated backscatter (AIB), spectral centroid shift (SCS), frequency slope of apparent backscatter (FSAB), and frequency intercept of apparent backscatter (FIAB) for representing trabecular bone mass and microstructure. We scanned 33 bovine trabecular bone samples using a 7.5 MHz focused transducer in a 20 mm × 20 mm region of interest (ROI) with a step interval of 0.05 mm. Images based on the ultrasonic backscatter parameters (i.e., AIB, SCS, FSAB, and FIAB) were constructed to compare with photographic images of the specimens as well as two-dimensional (2D) μ-CT images from approximately the same depth and location of the specimen. Similar structures and trabecular alignments can be observed among these images. Statistical analyses demonstrated that the means and standard deviations of the ultrasonic backscatter parameters exhibited significant correlations with bone density (|R| = 0.45-0.78,  < 0.01) and bone microstructure (|R| = 0.44-0.87,  < 0.001). Some bovine trabecular bone microstructure parameters were independently associated with the ultrasonic backscatter parameters (Δ = 4.18%-44.45%,  < 0.05) after adjustment for bone apparent density (BAD). The results show that ultrasonic backscatter parametric imaging can provide a direct view of the trabecular microstructure and can reflect information about the density and microstructure of trabecular bone.

The Ability of Ultrasonic Backscatter Parametric Imaging to Characterize Bovine Trabecular Bone.

Follicular thyroid carcinoma (FTC) is more aggressive than the most common papillary thyroid carcinoma (PTC). However, the current research on FTC is less than PTC. Here, we investigated the effects of long noncoding RNA (lncRNA) GAS5 and miR-221-3p in FTC.

Long Noncoding RNA GAS5 Targeting miR-221-3p/Cyclin-Dependent Kinase Inhibitor 2B Axis Regulates Follicular Thyroid Carcinoma Cell Cycle and Proliferation.

Due to its multimode and dispersive nature, ultrasonic guided waves (UGWs) usually consist of overlapped wave packets, which challenge accurate bone characterization. To overcome this obstacle, a classic idea is to separate individual modes and to extract the corresponding dispersion curves. Reported single-channel mode separation algorithms mainly focused on offering a time-frequency representation (TFR) where the energy distributions of individual modes were apart from each other. However, such approaches are still limited to identifying the modes without significant overlapping in time-frequency domain. In this study, a spectrogram decomposition technique was developed based on a combination strategy of generalized separable nonnegative matrix factorization (GS-NMF) and adaptive basis learning, towards the automatic mode extraction under severe overlapping and low signal-to-noise ratio (SNR). The extracted modes were further used for cortical thickness estimation. The method was verified using broadband simulated and experimental datasets. Experiments were conducted on a bone-mimicking plate and bovine cortical bone plates. For simulated data, the relative errors between extracted and theoretical dispersion curves are 1.33% (SNR = ∞), 1.43% (SNR = 10 dB) and 0.88% (SNR = 5 dB). The root-mean-square errors of the estimated thickness for 3.10 mm-thick bone-mimicking plate, 3.83 mm- and 4.00 mm-thick bovine cortical bone plates are 0.039 mm, 0.049 mm, and 0.052 mm, respectively. It is demonstrated that the proposed method is capable of separating multimodal UGWs even under significantly overlapping and low SNR conditions, further facilitating the UGW-based cortical thickness assessment.

Spectrogram decomposition of ultrasonic guided waves for cortical thickness assessment using basis learning.

Accurate segmentation of lesions in medical images is of great significance for clinical diagnosis and evaluation. The low contrast between lesions and surrounding tissues increases the difficulty of automatic segmentation, while the efficiency of manual segmentation is low. In order to increase the generalization performance of segmentation model, we proposed a deep learning-based automatic segmentation model called CM-SegNet for segmenting medical images of different modalities. It was designed according to the multiscale input and encoding-decoding thoughts, and composed of multilayer perceptron and convolution modules. This model achieved communication of different channels and different spatial locations of each patch, and considers the edge related feature information between adjacent patches. Thus, it could fully extract global and local image information for the segmentation task. Meanwhile, this model met the effective segmentation of different structural lesion regions in different slices of three-dimensional medical images. In this experiment, the proposed CM-SegNet was trained, validated, and tested using six medical image datasets of different modalities and 5-fold cross validation method. The results showed that the CM-SegNet model had better segmentation performance and shorter training time for different medical images than the previous methods, suggesting it is faster and more accurate in automatic segmentation and has great potential application in clinic.

CM-SegNet: A deep learning-based automatic segmentation approach for medical images by combining convolution and multilayer perceptron.

Elastography is a promising diagnostic tool that measures the hardness of tissues, and it has been used in clinics for detecting lesion progress, such as benign and malignant tumors. However, due to the high cost of examination and limited availability of elastic ultrasound devices, elastography is not widely used in primary medical facilities in rural areas. To address this issue, a deep learning approach called the multiscale elastic image synthesis network (MEIS-Net) was proposed, which utilized the multiscale learning to synthesize elastic images from ultrasound data instead of traditional ultrasound elastography in virtue of elastic deformation. The method integrates multi-scale features of the prostate in an innovative way and enhances the elastic synthesis effect through a fusion module. The module obtains B-mode ultrasound and elastography feature maps, which are used to generate local and global elastic ultrasound images through their correspondence. Finally, the two-channel images are synthesized into output elastic images. To evaluate the approach, quantitative assessments and diagnostic tests were conducted, comparing the results of MEIS-Net with several deep learning-based methods. The experiments showed that MEIS-Net was effective in synthesizing elastic images from B-mode ultrasound data acquired from two different devices, with a structural similarity index of 0.74 ± 0.04. This outperformed other methods such as Pix2Pix (0.69 ± 0.09), CycleGAN (0.11 ± 0.27), and StarGANv2 (0.02 ± 0.01). Furthermore, the diagnostic tests demonstrated that the classification performance of the synthetic elastic image was comparable to that of real elastic images, with only a 3 % decrease in the area under the curve (AUC), indicating the clinical effectiveness of the proposed method.

B-mode ultrasound to elastography synthesis using multiscale learning.

Pulmonary cavity lesion is one of the commonly seen lesions in lung caused by a variety of malignant and non-malignant diseases. Diagnosis of a cavity lesion is commonly based on accurate recognition of the typical morphological characteristics. A deep learning-based model to automatically detect, segment, and quantify the region of cavity lesion on CT scans has potential in clinical diagnosis, monitoring, and treatment efficacy assessment.

Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans.

Ultrasound Localization Microscopy (ULM) surpasses the constraints imposed by acoustic diffraction, achieving sub-wavelength resolution visualization of microvasculature through the precise localization of minute microbubbles (MBs). Nonetheless, the analysis of densely populated regions with overlapping MB point spread responses introduces significant localization errors, limiting the use of technique to low-concentration conditions. This raises a trade-off issue between localization efficiency and MB density. In this work, we present a new deep learning framework that combines Transformer and U-Net architectures, termed ULM-TransUNet. As a non-linear model, it is able to learn the complex data patterns of overlapping MBs in dense conditions for accurate localization. To evaluate the performance of ULM-TransUNet, a series of numerical simulations and in vivo experiments are carried out. Numerical simulation results indicate that ULM-TransUNet achieves high-quality ULM imaging, with improvements of 21.93 % in detection rate, 17.36 % in detection precision, and 20.53 % in detection sensitivity, compared to previous state-of-the-art deep learning (DL) method (e.g., ULM-UNet). For the in vivo experiments, ULM-TransUNet achieves the highest spatial resolution (9.4 μm) and rapid inference speed (26.04 ms/frame). Furthermore, it consistently detects more small vessels and resolves closely spaced vessels more effectively. The outcomes of this work imply that ULM-TransUNet can potentially enhance the microvascular imaging performance on high-density MB conditions.

In vivo ultrasound localization microscopy for high-density microbubbles.

Tissue optical and mechanical properties are key indicators of bone health, and influence the generation and propagation of photoacoustic guided waves (PAGWs). The effectiveness of mechanical property characterization based on mode dispersion features of ultrasonic guided waves has been demonstrated in the diagnosis of osteoporosis; however, variation in PAGW characteristics with bone optical properties is less analyzed. It limits the development of ultrasound in precision diagnosis of bone diseases. In this study, the excitation of PAGWs was simulated using bulk thermal flux in the finite element method, taking into account the optical penetration depth in bone. The influence of laser parameters, as well as the absorption and scattering coefficients of bone, on the waveform and mode amplitude of PAGWs was analyzed. Research results indicate the mode components and their amplitude are influenced by the spatial distribution of thermal stress, force source of PAGWs, and wave structure of modes. The signal amplitude of PAGWs increases with the absorption coefficient, the radius and rise time of laser beam, while it decreases with the scattering coefficient. For in-plane and out-of-plane displacements, the optical properties exert different effects on the amplitudes of anti-symmetric and symmetric modes. The amplitude change ratio between A and S modes decreases with increasing optical penetration depth, exhibiting distinct trends with frequency for in-plane and out-of-plane displacements. These results demonstrate the relationship between mode amplitude of PAGWs and the optical properties of 1 mm thick bone plates, thereby enhancing the understanding of the theory of photoacoustic bone assessment.

Yifang Li

Academy for Engineering and Technology

Fudan University

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy