Suppression of Grating Lobe Artifacts in Ultrasound Images Formed from Diverging Transmitting Beams by Modulation of Receiving Beams

In linear array transducers, owing to regular spacing of the array elements, grating lobes exist in transmission and reception. In ultrasonic imaging involving the use of diverging (unfocused) transmitting beams and steered receiving beams by linear transducer arrays, aperture apodization and spatial combination of steered receiving beams from multiple transmissions are not sufficient to suppress receive-grating lobe artifacts. To further suppress receive-grating lobe artifacts in reconstructed B-mode images, we propose a technique of modulating the receiving beams by a factor that is governed by the envelope of a corresponding signal, which is formed by filtering the receiving beam with a zero-phase low-pass filter with a cut-off frequency that is determined by the receiving beam steering angle. This technique suppressed receive-grating lobe artifacts without significant loss in spatial resolution in offline reconstructed B-mode images from simulation, phantom and in vivo imaging of the carotid artery. In a simulation of point scatterers, a relative reduction in grating lobe artifacts of 40 dB was realized in images from diverging beam scanning.     

Fast Scan Conversion Algorithms for Displaying Ultrasound Sector Images

Two fast algorithms for interpolation of ultrasonic sector-scans were developed. Both algorithms are based on line-drawing algorithms and are free from multiplications in the innermost loops. The algorithms were compared to the following conventional interpolators: 2-D windowed sinc, bicubic spline, 4 × 4 point bicubic spline, bilinear, and nearest neighbor. The most accurate of the two new algorithms is about eight times faster than nearest neighbor interpolation. The quantitative errors are of the same order as the errors of the nearest neighbor interpolator. The subjective image quality is between nearest neighbor and bilinear interpolation.     

Statistical Region-Based Segmentation of Ultrasound Images

Segmentation of ultrasound images is a challenging problem due to speckle, which corrupts the image and can result in weak or missing image boundaries, poor signal to noise ratio and diminished contrast resolution. Speckle is a random interference pattern that is characterized by an asymmetric distribution as well as significant spatial correlation. These attributes of speckle are challenging to model in a segmentation approach, so many previous ultrasound segmentation methods simplify the problem by assuming that the speckle is white and/or Gaussian distributed. Unlike these methods, in this article we present an ultrasound-specific segmentation approach that addresses both the spatial correlation of the data as well as its intensity distribution. We first decorrelate the image and then apply a region-based active contour whose motion is derived from an appropriate parametric distribution for maximum likelihood image segmentation. We consider zero-mean complex Gaussian, Rayleigh, and Fisher-Tippett flows, which are designed to model fully formed speckle in the in-phase/quadrature (IQ), envelope detected, and display (log compressed) images, respectively. We present experimental results demonstrating the effectiveness of our method and compare the results with other parametric and nonparametric active contours. (E-mail:greg.slabaugh@gmail.com)     

A Novel Model of Thyroid Nodule Segmentation for Ultrasound Images

Ultrasonography is regarded as an effective technique for the detection, diagnosis and monitoring of thyroid nodules. Segmentation of thyroid nodules on ultrasound images is important in clinical practice. However, because in ultrasound images there is an unclear boundary between thyroid nodules and surrounding tissues, the accuracy of segmentation remains a challenge. Although the deep learning model provides an accurate and convenient method for thyroid nodule segmentation, it is unsatisfactory of the existing model in segmenting the margin of thyroid nodules. In this study, we developed boundary attention transformer net (BTNet), a novel segmentation network with a boundary attention mechanism combining the advantages of a convolutional neural network and transformer, which could fuse the features of both long and short ranges. Boundary attention is improved to focus on learning the boundary information, and this module enhances the segmentation ability of the network boundary. For features of different scales, we also incorporate a deep supervision mechanism to blend the outputs of different levels to enhance the segmentation effect. As the BTNet model incorporates the long range–short range connectivity effect and the boundary–regional cooperation capability, our model has excellent segmentation performance in thyroid nodule segmentation. The development of BTNet was based on the data set from Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital and the public data set. BTNet achieved good performance in the segmentation of thyroid nodules with an intersection-over-union of 0.810 and Dice coefficient of 0.892 Moreover, our work revealed great improvement in the boundary metrics; for example, the boundary distance was 7.308, the boundary overlap 0.201 and the boundary Dice 0.194, all with p values <0.05.     

Multiple Resolution Bayesian Segmentation of Ultrasound Images

We propose a novel method for obtaining the maximum a posteriori (MAP) probabilistic segmentation of speckle-laden ultrasound images. Our technique is multiple-resolution based, and relies on the conversion of speckle images with Rayleigh statistics to subsampled images with Gaussian statistics. This conversion reduces computation time, as well as allowing accurate parameter estimation for a probabilistic segmentation algorithm. Results appear to provide improvements over previous techniques in terms of low-contrast detail and accuracy.     

Deconvolution of In Vivo Ultrasound B-Mode Images

An algorithm for deconvolution of medical ultrasound images is presented. The procedure involves estimation of the basic one-dimensional ultrasound pulse, determining the ratio of the covariance of the noise to the covariance of the reflection signal, and finally deconvolution of the rf signal from the transducer. Using pulse and covariance estimators makes the approach self-calibrating, as all parameters for the procedure are estimated from the patient under investigation. An example of use on a clinical, in-vivo image is given. A 2 × 2 cm region of the portal vein in a liver is deconvolved. An increase in axial resolution by a factor of 2.4 is obtained. The procedure can also be applied to whole images, when it is ensured that the rf signal is properly measured. A method for doing that is outlined.     

Tracking Dynamic Tongue Motion in Ultrasound Images for Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a breathing disorder characterized by repetitive collapse of the pharyngeal airway during sleep, can cause intermittent hypoxemia and frequent arousal. The evaluation of dynamic tongue motion not only provides the biomechanics and pathophysiology for OSA diagnosis, but also helps doctors to determine treatment strategies for these patients with OSA. The purpose of this study was to develop and verify a dedicated tracking algorithm, called the modified optical flow (OF)-based method, for monitoring the dynamic motion of the tongue base in ultrasound image sequences derived from controls and patients with OSA. The performance of the proposed method was verified by phantom and synthetic data. A common tracking method, the normalized cross-correlation method, was included for comparison. The efficacy of the algorithms was evaluated by calculating the estimated displacement error. All results indicated that the modified OF-based method exhibited higher accuracy in verification experiments. In the human subject experiment, all participants performed the Müller maneuver (MM) to simulate the contour changes of the tongue base with a negative pharyngeal airway pressure in sleep apnea. Ultrasound image sequences of the tongue were obtained during 10 s of a transition from normal breathing to the MM, and these were measured using the modified OF-based method. The results indicated that the displacement of the tongue base during the MM was larger in the controls than in the patients with OSA (p < 0.05); the calculated areas of the tongue in the controls and patients with OSA were 24.9 ± 3.0 and 27.6 ± 3.3 cm², respectively, during normal breathing (p < 0.05), and 24.7 ± 3.6 and 27.3 ± 3.8 cm², respectively, at the end of the MM. The percentage changes in the tongue area were 2.2% and 1.3% in the controls and patients with OSA, respectively. We found that quantitative assessment of tongue motion by ultrasound imaging is suitable for evaluating pharyngeal airway behavior in OSA patients with minimal invasiveness and easy accessibility.     

Near Real-Time Robust Non-rigid Registration of Volumetric Ultrasound Images for Neurosurgery

Ultrasound images are acquired before and after the resection of brain tumors to help the surgeon to localize the tumor and its extent and to minimize the amount of residual tumor after the resection. Because the brain undergoes large deformation between these two acquisitions, deformable image-based registration of these data sets is of substantial clinical importance. In this work, we present an algorithm for non-rigid registration of ultrasound images (RESOUND) that models the deformation with free-form cubic B-splines. We formulate a regularized cost function that uses normalized cross-correlation as the similarity metric. To optimize the cost function, we calculate its analytic derivative and use the stochastic gradient descent technique to achieve near real-time performance. We further propose a robust technique to minimize the effect of non-corresponding regions such as the resected tumor and possible hemorrhage in the post-resection image. Using manually labeled corresponding landmarks in the pre- and post-resection ultrasound volumes, we illustrate that our registration algorithm reduces the mean target registration error from an initial value of 3.7 to 1.5 mm. We also compare RESOUND with the previous work of Mercier et al. (2013) and illustrate that it has three important advantages: (i) it is fully automatic and does not require a manual segmentation of the tumor, (ii) it produces smaller registration errors and (iii) it is about 30 times faster. The clinical data set is available online on the BITE database website.     

Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images

Incorporating human domain knowledge for breast tumor diagnosis is challenging because shape, boundary, curvature, intensity or other common medical priors vary significantly across patients and cannot be employed. This work proposes a new approach to integrating visual saliency into a deep learning model for breast tumor segmentation in ultrasound images. Visual saliency refers to image maps containing regions that are more likely to attract radiologists’ visual attention. The proposed approach introduces attention blocks into a U-Net architecture and learns feature representations that prioritize spatial regions with high saliency levels. The validation results indicate increased accuracy for tumor segmentation relative to models without salient attention layers. The approach achieved a Dice similarity coefficient (DSC) of 90.5% on a data set of 510 images. The salient attention model has the potential to enhance accuracy and robustness in processing medical images of other organs, by providing a means to incorporate task-specific knowledge into deep learning architectures.     

Visual Screening of Muscle Ultrasound Images in Children

In children, non-invasive muscle ultrasound (MU) imaging has become increasingly important for the detection of neuromuscular pathology, by either quantitative or visual assessment. MU quantification requires time, expertise and equipment. If application of visual MU screening provides reliable results, ubiquitous application could be advocated. Previously, we found that visual MU screening can reliably detect segmental neuromuscular alterations within a patient. Analogously, we reasoned that visual MU screening could discern pathologic MU images from healthy controls. We therefore investigated visual screening results by 20 clinical observers (involving 100 MU images, with [n = 53] and without [n = 47] neuromuscular pathology). MU screening revealed adequate sensitivity, specificity and negative predictive value (85%, 75% and 82%, respectively). MU-experienced observers revealed higher specificity than MU-inexperienced observers (86% vs. 69%, p = 0.005). We conclude that clinical observers can identify neuromuscular pathology by visual screening. To enhance specificity, a secondary view by an expert appears advisory.     

Diagnosis of Metacarpophalangeal Synovitis with Musculoskeletal Ultrasound Images

Rheumatoid arthritis (RA) is a chronic autoimmune disease that can result in considerable disability and pain. The metacarpophalangeal (MCP) joint is the most common diseased joint in RA. In clinical practice, MCP synovitis is commonly diagnosed on the basis of musculoskeletal ultrasound (MSUS) images. However, because of the vague criteria, the consistency in grading MCP synovitis based on MSUS images fluctuates between ultrasound imaging practitioners. Therefore, a new method for diagnosis of MCP synovitis is needed. Deep learning has developed rapidly in the medical area, which often requires a large-scale data set. However, the total number of MCP-MSUS images fell far short of the demand, and the distribution of different medical grades of images was unbalanced. With use of the traditional image augmentation methods, the diversity of the data remains insufficient. In this study, a high-resolution generative adversarial network (HRGAN) method that generates enough images for network training and enriches the diversity of the training data set is described. In comparison experiments, our proposed diagnostic system based on MSUS images provided more consistent results than those provided by clinical physicians. As the proposed method is image relevant, this study might provide a reference for other medical image classification research with insufficient data sets.     

A Real-Time Intrasubject Elastic Registration Algorithm for Dynamic 2-D Ultrasound Images

A new dynamic registration technique is designed to elastically align a sequence of 2-D ultrasound (US) images. The proposed algorithm tracks features over an image sequence in real-time, as opposed to our previous technique which registers images without utilizing prior information. The registration results were evaluated using a customized validation framework for elastic US registration algorithms. Experiments were performed on 600 simulated images as well as 20 image sequences obtained from 10 volunteer subjects, each sequence containing 50 images. Strategies for qualitative and quantitative evaluation consisted of visual assessment, feature overlap, similarity measures, and simulation experiments. The registration method has comparable performance to our previous registration technique; however, has the advantage of lower computational requirements and hence, is potentially more suitable for clinical applications. Rigorous performance evaluations attest to the fast speed of registration at an average of 5.5 frames per second on a conventional computing platform. (E-mail: purang@cs.queensu.ca)     

宫外孕几种常见声像图的鉴别

目的探讨宫外孕的声图像特征,总结几种常见声像图的鉴别,以提高其诊断水平。方法对286例临床疑诊宫外孕的患者,采用经阴道二维及彩色多普勒超声检查,将超声结果与临床病理及随访资料进行对比分析。结果超声诊断宫外孕196例,经手术或保守治疗后确诊187例,假阳性4.8%,假阴性1.1%,诊断准确率为94.1%。图像不典型需进行鉴别诊断的占44.8%。结论宫外孕仍有一定的规则可循,通过二维及彩色多普勒超声图像分析,可对绝大部分宫外孕进行准确诊断。     

正常甲状旁腺超声影像位置及特点

目的 探讨正常甲状旁腺在高频超声、彩色及能量多普勒超声、超声弹性检查、超声造影中的影像特征及显示位置,为临床甲状腺切除术中正常甲状旁腺的保护提供指导性依据。方法 选取220例正常成人和60例合并甲状腺肿瘤拟甲状腺全切除者,均行甲状旁腺超声检查,以甲状腺为基础作位置标记,行高频超声、彩色及能量多普勒超声检查,其中50例正常成人同时行超声弹性检查,20例正常成人同时进行了超声造影检查,分析总结正常甲状旁腺的超声影像特征及位置。60例甲状腺全切除者分为术中行超声检测30例,未行超声检测30例,比较两者术后甲状旁腺功能低下的发生率。结果 ①280例受检者中甲状旁腺显示217例,高频超声显示率77.5%（217/280）,多表现为边界清晰的椭圆形高回声及稍高回声小结;共显示412枚甲状旁腺,位于甲状腺上极后方36枚（8.7%）、体部后方138枚（33.5%）、下极后方179枚（43.5%）、侧叶下方2 cm内59枚（14.3%）。②彩色及能量多普勒超声可显示血流信号301枚（73.0%）。③50例正常甲状旁腺超声弹性成像测得弹性值为（12.3±2.1）kPa,相邻正常甲状腺弹性值为（27.2±2.5）kPa,差异有统计学意义（P＜0.05）。④20例正常甲状旁腺超声造影显示造影剂灌注持续时间约（4.8±2.4）s,呈整体均匀增强、轮廓清,与相邻正常甲状腺实质比较呈等增强。⑤术中未使用超声检测者有6例术后发生甲状旁腺功能低下,术中使用超声检测者仅2例,差异有统计学意义（P＜0.05）。结论 正常甲状旁腺超声多表现为椭圆形高回声及稍高回声小结,位于甲状腺下极附近居多,其内可探及血流信号,超声造影表现为均匀等灌注,弹性成像表现为较甲状腺硬度稍软。甲状腺全切除者术中使用超声检测可降低术后甲状旁腺功能低下的发生率。