Ultrasound is widely used as an inexpensive, real-time method for imaging vascular tissue. However, sonographs often lack automatic or semi-automatic software for measuring vascular diameter precisely, especially in low- and mid-income countries or institutions. Tools can be developed to perform this task, but they must be validated before being accepted for clinic use. For that purpose, in this work we present low-cost phantoms that resemble vascular tissue when subjected to ultrasound. Several materials are analysed and a step-by-step recipe for building a simple phantom is presented. Qualitatively, models were imaged by an ultrasound expert physician, and several characteristic are assessed. Quantitatively, a comparison between ultrasound and caliper measurements of the phantoms is presented. Finally, a discussion about the results and the recommended materials for low-cost vascular phantoms is carried out.
Background: Intravascular ultrasound (IVUS) provides axial gray-scale images, allowing the assessment of vessel morphology and tissues. Automated segmentation of lumen-intima and media-adventitia interfaces is valuable to identify artery occlusion.
Purpose: Bifurcations, shadows and echogenic plaques usually affect proper segmentation of the vessel wall. Thus, identification of these morphological structures is an advisable step when developing segmentation techniques, which have been dealing with this issue by using different features and methods in the past. The aim of this work is to develop a simultaneous classification method for IVUS image sectors into bifurcations, shadows, echogenic plaques and normal, as an intermediate step for the arterial wall segmentation.
Methods: A 22-dimensional feature vector, mainly composed by current existing methods, is computed for each column in the polar image. To deal with this multiclass classification problem, Random Forest (RF) is used as classifier. Due to the high skewness of the problem, RFs are successively trained by resampling the training data, specifically the majority class.
Results: Fscore reaches 0.62, when the RF is trained with 15% of the normal samples of the training set. Thresholds found in the RF are close to the previously reported values for the features in the literature.
Conclusion: Random Forest demonstrates good performance to classify morphological structures in IVUS. Random undersampling for training was useful to deal with the imbalanced data, and to manage the trade-off between precision and recall of minority classes. However, better features must be developed to improve the classification of the structures, specially in the case of the echogenic plaque.
Background and motivation: Real-time ultrasound simulation refers to the process of computationally creating fully synthetic ultrasound images instantly. Due to the high value of specialized low cost training for healthcare professionals, there is a growing interest in the use of this technology and the development of high fidelity systems that simulate the acquisitions of echographic images. The objective is to create an efficient and reproducible simulator that can run either on notebooks or desktops using low cost devices. Materials and methods: We present an interactive ultrasound simulator based on CT data. This simulator is based on ray-casting and provides real-time interaction capabilities. The simulation of scattering that is coherent with the transducer position in real time is also introduced. Such noise is produced using a simplified model of multiplicative noise and convolution with point spread functions (PSF) tailored for this purpose. Results: The computational efficiency of scattering maps generation was revised with an improved performance. This allowed a more efficient simulation of coherent scattering in the synthetic echographic images while providing highly realistic result. We describe some quality and performance metrics to validate these results, where a performance of up to 55fps was achieved. Conclusion: The proposed technique for real-time scattering modeling provides realistic yet computationally efficient scatter distributions. The error between the original image and the simulated scattering image was compared for the proposed method and the state-of-the-art, showing negligible differences in its distribution.
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