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This study addresses a limitation of prior research on pectoralis major (PMaj) thickness changes during the pectoralis fly exercise using a wearable ultrasound imaging setup. Although previous studies used manual measurement and subjective evaluation, it is important to acknowledge the subsequent limitations of automating widespread applications. We then employed a deep learning model for image segmentation and automated measurement to solve the problem and study the additional quantitative supplementary information that could be provided. Our results revealed increased PMaj thickness changes in the coronal plane within the probe detection region when real-time ultrasound imaging (RUSI) visual biofeedback was incorporated, regardless of load intensity (50% or 80% of one-repetition maximum). Additionally, participants showed uniform thickness changes in the PMaj in response to enhanced RUSI biofeedback. Notably, the differences in PMaj thickness changes between load intensities were reduced by RUSI biofeedback, suggesting altered muscle activation strategies. We identified the optimal measurement location for the maximal PMaj thickness close to the rib end and emphasized the lightweight applicability of our model for fitness training and muscle assessment. Further studies can refine load intensities, investigate diverse parameters, and employ different network models to enhance accuracy. This study contributes to our understanding of the effects of muscle physiology and exercise training. [ABSTRACT FROM AUTHOR]

Scoliosis is a 3D spinal deformation where the spine takes a lateral curvature, forming an angle in the coronal plane. Diagnosis of scoliosis requires periodic detection, and frequent exposure to radiative imaging may cause cancer. A safer and more economical alternative imaging, i.e., 3D ultrasound imaging modality, is being explored. However, unlike other radiative modalities, an ultrasound image is noisy, which often suppresses the image's useful information. Through this research, a novel hybridized CNN architecture, multi-scale feature fusion Skip-Inception U-Net (SIU-Net), is proposed for a fully automatic bony feature detection, which can be further used to assess the severity of scoliosis safely and automatically. The proposed architecture, SIU-Net, incorporates two novel features into the basic U-Net architecture: (a) an improvised Inception block and (b) newly designed decoder-side dense skip pathways. The proposed model is tested on 109 spine ultrasound image datasets. The architecture is evaluated using the popular (i) Jaccard Index (ii) Dice Coefficient and (iii) Euclidean distance, and compared with (a) the basic U-net segmentation model, (b) a more evolved UNet++ model, and (c) a newly developed MultiResUNet model. The results show that SIU-Net gives the clearest segmentation output, especially in the important regions of interest such as thoracic and lumbar bony features. The method also gives the highest average Jaccard score of 0.781 and Dice score of 0.883 and the lowest histogram Euclidean distance of 0.011 than the other three models. SIU-Net looks promising to meet the objectives of a fully automatic scoliosis detection system. [ABSTRACT FROM AUTHOR]

Scoliosis is a widespread medical condition where the spine becomes severely deformed and bends over time. It mostly affects young adults and may have a permanent impact on them. A periodic assessment, using a suitable modality, is necessary for its early detection. Conventionally, the usually employed modalities include X-ray and MRI, which employ ionising radiation and are expensive. Hence, a non-radiating 3D ultrasound imaging technique has been developed as a safe and economic alternative. However, ultrasound produces low-contrast images that are full of speckle noise, and skilled intervention is necessary for their processing. Given the prevalent occurrence of scoliosis and the limitations of scalability of human expert interventions, an automatic, fast, and low-computation assessment technique is being developed for mass scoliosis diagnosis. In this paper, a novel hybridized light-weight convolutional neural network architecture is presented for automatic lateral bony feature identification, which can help to develop a fully-fledged automatic scoliosis detection system. The proposed architecture, Light-convolution Dense Selection U-Net (LDS U-Net), can accurately segment ultrasound spine lateral bony features, from noisy images, thanks to its capabilities of smartly selecting only the useful information and extracting rich deep layer features from the input image. The proposed model is tested using a dataset of 109 spine ultrasound images. The segmentation result of the proposed network is compared with basic U-Net, Attention U-Net, and MultiResUNet using various popular segmentation indices. The results show that LDS U-Net provides a better segmentation performance compared to the other models. Additionally, LDS U-Net requires a smaller number of parameters and less memory, making it suitable for a large-batch screening process of scoliosis without a high computational requirement. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Muscle thickness is a widely used parameter for quantifying muscle function in ultrasound imaging. However, current measurement techniques generally rely on manual digitization, which is subjective, time consuming, and prone to error. The primary purposes of this study were to develop an automated muscle boundary tracking algorithm to overcome these limitations and to report its intraexaminer reliability on pectoralis major muscle. Methods: Real-time B-mode ultrasound images of the pectoralis major muscles were acquired by an integrated data acquisition system. A transducer placement protocol was developed to facilitate better repositioning of an ultrasound transducer. Intraexaminer reliability of the tracking algorithm for static measurements was studied using intraclass correlation coefficient based on the thickness data from 11 healthy subjects recruited from a chiropractic college measured at 3 independent sessions. Standard error of measurement and minimal detectable change were calculated. Feasibility of using the tracking algorithm for dynamic measurements was also evaluated. Results: All calculated intraclass correlation coefficients were larger than 0.96, indicating excellent reliability of the sonomyographic measurements. Minimal detectable changes were 9.7%, 6.7%, and 6.8% of the muscle thickness at the lateral, central, and medial aspects, respectively. For a 400-frame image stack with 3 pairs of 40 × 40 pixels tracking windows, the tracking took about 80 seconds to complete. Conclusions: The tracking algorithm offers precise and reliable measurements of muscle thickness changes in clinical settings with potential to quantify the effects of a wide variety of chiropractic techniques on muscle function. [Copyright &y& Elsevier]

(1) Background: Ultrasound provides a radiation-free and portable method for assessing swallowing. Hyoid bone locations and displacements are often used as important indicators for the evaluation of swallowing disorders. However, this requires clinicians to spend a great deal of time reviewing the ultrasound images. (2) Methods: In this study, we applied tracking algorithms based on deep learning and correlation filters to detect hyoid locations in ultrasound videos collected during swallowing. Fifty videos were collected from 10 young, healthy subjects for training, evaluation, and testing of the trackers. (3) Results: The best performing deep learning algorithm, Fully-Convolutional Siamese Networks (SiamFC), proved to have reliable performance in getting accurate hyoid bone locations from each frame of the swallowing ultrasound videos. While having a real-time frame rate (175 fps) when running on an RTX 2060, SiamFC also achieved a precision of 98.9% at the threshold of 10 pixels (3.25 mm) and 80.5% at the threshold of 5 pixels (1.63 mm). The tracker's root-mean-square error and average error were 3.9 pixels (1.27 mm) and 3.3 pixels (1.07 mm), respectively. (4) Conclusions: Our results pave the way for real-time automatic tracking of the hyoid bone in ultrasound videos for swallowing assessment. [ABSTRACT FROM AUTHOR]

Scoliosis is a spinal deformation in which the spine takes a lateral curvature, generating an angle in the coronal plane. The conventional method for detecting scoliosis is measurement of the Cobb angle in spine images obtained by anterior X-ray scanning. Ultrasound imaging of the spine is found to be less ionising than traditional radiographic modalities. For posterior ultrasound scanning, alternate indices of the spinous process angle (SPA) and ultrasound curve angle (UCA) were developed and have proven comparable to those of the traditional Cobb angle. In SPA, the measurements are made using the spinous processes as an anatomical reference, leading to an underestimation of the traditionally used Cobb angles. Alternatively, in UCA, more lateral features of the spine are employed for measurement of the main thoracic and thoracolumbar angles; however, clear identification of bony features is required. The current practice of UCA angle measurement is manual. This research attempts to automate the process so that the errors related to human intervention can be avoided and the scalability of ultrasound scoliosis diagnosis can be improved. The key objective is to develop an automatic scoliosis diagnosis system using 3-D ultrasound imaging. The novel diagnosis system is a three-step process: (i) finding the ultrasound spine image with the most visible lateral features using the convolutional RankNet algorithm; (ii) segmenting the bony features from the noisy ultrasound images using joint spine segmentation and noise removal; and (iii) calculating the UCA automatically using a newly developed centroid pairing and inscribed rectangle slope method. The proposed method was evaluated on 109 patients with scoliosis of different severity. The results obtained had a good correlation with manually measured UCAs ( R 2 = 0.9784 for the main thoracic angle and R 2 = 0.9671 for the thoracolumbar angle) and a clinically acceptable mean absolute difference of the main thoracic angle (2.82 ± 2.67°) and thoracolumbar angle (3.34 ± 2.83°). The proposed method establishes a very promising approach for enabling the applications of economic 3-D ultrasound volume projection imaging for mass screening of scoliosis. [ABSTRACT FROM AUTHOR]

Abstract: In three-dimensional (3D) freehand ultrasound (US), reconstructing a set of B-scans into a regular voxel array is the key procedure for consequent visualization and analysis. This paper presents a new adaptive interpolation algorithm for computing the voxel array to suppress speckle noises and enhance contrast. The local statistics of homogeneous regions including mean and variance were measured and the ratio of variance to mean was used as homogeneity criteria. For the computation of each voxel, the interpolation method was adaptively determined with respect to its local statistics. If the neighbouring pixels of a voxel satisfied the homogeneity criterion, its value was computed with an arithmetic mean filter. Otherwise, the voxel was probably locating in an inhomogeneous region and an adaptive distance-weighted (ADW) interpolation method was employed to compute its value. A resolution phantom and a subject’s forearm were reconstructed using the proposed algorithm and two other well-known methods – conventional distance-weighted (DW) and voxel nearest neighbourhood (VNN) interpolations. The comparison results demonstrated that the adaptive interpolation algorithm was able to suppress speckles, preserve edges and enhance contrast effectively for the volume reconstruction. [Copyright &y& Elsevier]

Early screening and frequent monitoring effectively decrease the risk of severe scoliosis, but radiation exposure is a consequence of traditional radiograph examinations. Additionally, traditional X-ray images on the coronal or sagittal plane have difficulty providing three-dimensional (3-D) information on spinal deformities. The Scolioscan system provides an innovative 3-D spine imaging approach via ultrasonic scanning, and its feasibility has been demonstrated in numerous studies. In this paper, to further examine the potential of spinal ultrasonic data for describing 3-D spinal deformities, we propose a novel deep-learning tracker named Si-MSPDNet for extracting widely employed landmarks (spinous process (SP)) in ultrasonic images of spines and establish a 3-D spinal profile to measure 3-D spinal deformities. Si-MSPDNet has a Siamese architecture. First, we employ two efficient two-stage encoders to extract features from the uncropped ultrasonic image and the patch centered on the SP cut from the image. Then, a fusion block is designed to strengthen the communication between encoded features and further refine them from channel and spatial perspectives. The SP is a very small target in ultrasonic images, so its representation is weak in the highest-level feature maps. To overcome this, we ignore the highest-level feature maps and introduce parallel partial decoders to localize the SP. The correlation evaluation in the traditional Siamese network is also expanded to multiple scales to enhance cooperation. Furthermore, we propose a binary guided mask based on vertebral anatomical prior knowledge, which can further improve the performance of our tracker by highlighting the potential region with SP. The binary-guided mask is also utilized for fully automatic initialization in tracking. We collected spinal ultrasonic data and corresponding radiographs on the coronal and sagittal planes from 150 patients to evaluate the tracking precision of Si-MSPDNet and the performance of the generated 3-D spinal profile. Experimental results revealed that our tracker achieved a tracking success rate of 100% and a mean IoU of 0.882, outperforming some commonly used tracking and real-time detection models. Furthermore, a high correlation existed on both the coronal and sagittal planes between our projected spinal curve and that extracted from the spinal annotation in X-ray images. The correlation between the tracking results of the SP and their ground truths on other projected planes was also satisfactory. More importantly, the difference in mean curvatures was slight on all projected planes between tracking results and ground truths. Thus, this study effectively demonstrates the promising potential of our 3-D spinal profile extraction method for the 3-D measurement of spinal deformities using 3-D ultrasound data. • A novel tracker is proposed to real-time extracting spinous processes in 3D ultrasonic images. • Vertebral anatomical prior and spinal continuity are introduced into our tracker. • Experimental results indicate that our tracker can effectively describe 3-D spinal deformities. [ABSTRACT FROM AUTHOR]

• This work is designed to provide registration for spine ultrasound and X-ray radiograph. • An improved FFD registration method based on mutual registration and hierarchical adaptive grid (MRHA-FFD) is first put forward in this paper. • The registration image quality is improved by introducing a Blur-aware Attention Network (BANet) • The feasibility of the registration for clinical spine imaging is verified through qualitative and quantitative evaluation indicators. Ultrasound is a promising imaging method for scoliosis evaluation because it is radiation free and provide real-time images. However, it cannot provide bony details because ultrasound cannot penetrate the bony structure. Therefore, registration of real-time ultrasound images with the previous X-ray radiograph can help physicians understand the spinal deformity of patients. In this study, an improved free-from deformation registration method based on mutual registration and hierarchical adaptive grid (MRHA-FFD) was developed. The method first performed registration grid preprocessing and then optimized control points and conducted mutual registration. Finally, a Blur-aware Attention Network was adopted for image deblurring. The performance of each step was verified by ablation experiments. Comparison experiment between the proposed method and traditional registration methods was also conducted. The qualitative and quantitative results suggested that MRHA-FFD is a promising approach for registering spine ultrasound image and X-ray radiograph. [ABSTRACT FROM AUTHOR]

• This work is designed to provide both ultrasound and X-ray images for scoliosis patients based on ultrasound scanning. • A generative attention network for synthesizing X-ray images from ultrasound images is first put forward in this paper. • The image synthesis quality is improved by designing attention mechanism and introducing adaptive instance normalization. • The feasibility of the generated X-ray images is verified through qualitative and quantitative evaluation indicators. Standing X-ray radiograph with Cobb's method is the gold standard for scoliosis diagnosis. However, radiation hazard restricts its application, especially for close follow-up of adolescent patients. Compared with X-ray, ultrasound imaging has advantages of being radiation-free and real-time. To combine advantages of the above two imaging modalities, an ultrasound to X-ray synthesis generative attentional network (UXGAN) was proposed to synthesize ultrasound images into X-ray-like images. In this network, a cyclically consistent network was adopted and was trained end-to-end. An attention module was added and different residual blocks were designed. The quantitative comparison results demonstrated the superiority of our method to the state-of-the-art CycleGAN methods. We further compared the Cobb angle values measured on synthesized images and the real X-ray images, respectively. A good linear correlation (r = 0.95) was demonstrated between the two methods. The above results proved that the proposed method is of great significance for providing both X-ray images and ultrasound images based on the radiation-free ultrasound scanning. [ABSTRACT FROM AUTHOR]

Abstract: Vascular complication occurrence increases with the duration of diabetes. The present study compared the peripheral blood flow in superficial skin among subjects with a short or long history of diabetes as compared to a healthy control group. Thirty-two subjects with type 2 diabetes were recruited and stratified into a group of those with a short history of diabetes (i.e., shorter than or equal to ten years) and a group of those with a long history of diabetes (i.e., over ten years). Thirty-eight healthy age-matched volunteers were recruited as the control. The blood flow velocity of the superficial small veins in the skin over the base of the 1st metatarsal bone was measured by ultrasound biomicroscopy. The blood flow (flux) of the cutaneous microcirculation over the base of the 1st metatarsal bone (Flux1) and over the distal 1st phalanges bone (Flux2) was measured by Laser Doppler Flowmetry. One-way analysis of variance was used to analyze the differences between the three groups. No significant between-group difference was found in any outcome (all P >0.05). However, the group with a long history of diabetes tended to have a more reduced blood flow than did the healthy control group. A difference was found between the diabetes group and the healthy control in any outcomes. A trend of hemodynamic changes in the three groups was observed, but the difference did not reach significance. Ten years seems to be the time when angiopathy becomes noticeable among people with diabetes. [Copyright &y& Elsevier]