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Register a new userGeometric distortion of area in medical ultrasound images
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Medical ultrasound scanners are typically calibrated to the soft tissue average of 1540 m s$^{-1}$. In regions of different sound speed, for example, organs and tumours, the $B$-scan image then becomes a distortion of the true tissue cross-section, due to the misrepresentation of length and refraction. To quantify this distortion we develop a general geometric ray model for an object with an atypical speed of sound embedded in an ambient medium. We analyse the ensuing area distortion for circular and elliptical objects, mapping it out as a function of the key parameters, including the speed of sound mismatch, the object size and its elongation. We find that the area distortion can become significant, even for small-scale speed of sound mismatches. Our findings are verified by ultrasound imaging of a test object.
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Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice. DCE and similar datasets of dynamic medical data tend to contain redundant information on the spatial and temporal components that may not be relevant for detection of the object of interest and result in unnecessarily complex computer models with long training times that may also under-perform at test time due to the abundance of noisy heterogeneous data. This work attempts to increase the training efficacy and performance of deep networks by determining redundant information in the spatial and spectral components and show that the performance of segmentation accuracy can be maintained and potentially improved. Reported experiments include the evaluation of training/testing efficacy on a heterogeneous dataset composed of abdominal images of pediatric DCE patients, showing that drastic data reduction (higher than 80%) can preserve the dynamic information and performance of the segmentation model, while effectively suppressing noise and unwanted portion of the images.
We present an adaptation of the pixel-reassignment technique from confocal fluorescent microscopy to coherent ultrasound imaging. The method, Ultrasound Pixel-Reassignment (UPR), provides a resolution and signal to noise (SNR) improvement in ultrasound imaging by computationally reassigning off-focus signals acquired using traditional plane-wave compounding ultrasonography. We theoretically analyze the analogy between the optical and ultrasound implementations of pixel reassignment, and experimentally evaluate the imaging quality on tissue-mimicking acoustic phantoms. We demonstrate that UPR provides a $25%$ resolution improvement and a $3dB$ SNR improvement in in-vitro scans, without any change in hardware or acquisition scheme.
Feature matching is an important technique to identify a single object in different images. It helps machines to construct recognition of a specific object from multiple perspectives. For years, feature matching has been commonly used in various computer vision applications, like traffic surveillance, self-driving, and other systems. With the arise of Computer-Aided Diagnosis(CAD), the need for feature matching techniques also emerges in the medical imaging field. In this paper, we present a deep learning-based method specially for ultrasound images. It will be examined against existing methods that have outstanding results on regular images. As the ultrasound images are different from regular images in many fields like texture, noise type, and dimension, traditional methods will be evaluated and optimized to be applied to ultrasound images.
Morphological features of small vessels provide invaluable information regarding underlying tissue, especially in cancerous tumors. This paper introduces methods for obtaining quantitative morphological features from microvasculature images obtained by non-contrast ultrasound imaging. Those images suffer from the artifact that limit quantitative analysis of the vessel morphological features. In this paper we introduce processing steps to increase accuracy of the morphological assessment for quantitative vessel analysis in presence of these artifact. Specifically, artificats are reduced by additional filtering and vessel segments obtained by skeletonization of the regularized microvasculature images are further analyzed to satisfy additional constraints, such as diameter, and length of the vessel segments. Measurement of some morphological metrics, such as tortuosity, depends on preserving large vessel trunks that may be broken down into multiple branches. We propose two methods to address this problem. In the first method, small vessel segments are suppressed in the vessel filtering process via adjusting the size scale of the regularization. Hence, tortuosity of the large trunks can be more accurately estimated by preserving longer vessel segments. In the second approach, small connected vessel segments are removed by a combination of morphological erosion and dilation operations on the segmented vasculature images. These methods are tested on representative in vivo images of breast lesion microvasculature, and the outcomes are discussed. This paper provides a tool for quantification of microvasculature image from non-contrast ultrasound imaging may result in potential biomarkers for diagnosis of some diseases.
The objectives were to develop a novel three-dimensional technology for imaging naturally occurring shear wave (SW) propagation, demonstrate feasibility on human volunteers and quantify SW velocity in different propagation directions. Imaging of natural SWs generated by valve closures has emerged to obtain a direct measurement of cardiac stiffness. Recently, natural SW velocity was assessed in two dimensions on parasternal long axis view under the assumption of a propagation direction along the septum. However, in this approach the source localization and the complex three-dimensional propagation wave path was neglected making the speed estimation unreliable. High volume rate transthoracic acquisitions of the human left ventricle (1100 volume/s) was performed with a 4D ultrafast echocardiographic scanner. Four-dimensional tissue velocity cineloops enabled visualization of aortic and mitral valve closure waves. Energy and time of flight mapping allowed propagation path visualization and source localization, respectively. Velocities were quantified along different directions. Aortic and mitral valve closure SW velocities were assessed for the three volunteers with low standard deviation. Anisotropic propagation was also found suggesting the necessity of using a three-dimensional imaging approach. Different velocities were estimated for the three directions for the aortic (3.4$pm$0.1 m/s, 3.5$pm$0.3 m/s, 5.4$pm$0.7 m/s) and the mitral (2.8$pm$0.5 m/s, 2.9$pm$0.3 m/s, 4.6$pm$0.7 m/s) valve SWs. 4D ultrafast ultrasound alleviates the limitations of 2D ultrafast ultrasound for cardiac SW imaging based on natural SW propagations and enables a comprehensive measurement of cardiac stiffness. This technique could provide stiffness mapping of the left ventricle.
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