UltraSound Elastography (USE) has been widely used to obtain mechanical properties of tissues. Radio frequency (RF) data is usually used in USE to estimate the displacement. However, RF data is not available in all ultrasound imaging devices. B-mode images which are basically the envelope of the RF data are the most well-known output of the common ultrasound imaging devices. In B-mode images, the phase information of RF data is lost. Consequently, USE can be more challenging and the strain image quality would be degraded. The aim of this paper is to employ Demons algorithm, which is a powerful non-rigid image registration algorithm, to estimate displacement using B-mode images. In USE, the post-compression image may have large deformations in axial direction which deteriorates the Demons algorithm performance. In order to compensate the large deformations, an optimization algorithm is proposed to find and compensate the mean value axial deformation. Experimental and numerical phantoms are used to verify the algorithm performance in normal and severe situations. The results are compared with the common normalized cross correlation (NCC) algorithm. The results confirm that Demons algorithm is an appropriate algorithm for USE for B-mode images considering the fact that phase information are not available.
In photoacoustic (PA) imaging using commercial ultrasound transducers, two dimensional PA images are pro- duced by commonly used delay-and-sum (DAS) as the reconstruction method. However, the reconstructed image is affected by noise and artifacts. Here, we investigate the performance of a nonlinear (NL) beamforming method for linear-array PA image formation. The proposed algorithm uses the pth root of the recorded signals and imposes a computational complexity in the order of DAS (O(M )). We have evaluated the performance of the proposed method numerically, having a signal-to-noise ratio of 30 dB and -10 dB. It is shown that at the depth of 15 mm, the NL 3 (NL beamformer with a p=3) outperforms DAS, DMAS and NL 2 for about 26 dB, 11 dB and 11 dB, in terms of level of sidelobe, respectively. In addition, DAS, DMAS, NL 2 and NL 3 lead to a full-width-half-maximum of about 1.4 mm, 1.04 mm, 1.04 mm and 0.86 mm, respectively. The proposed method can be a great choice for sentinel lymph node imaging.
KEYWORDS: Signal detection, Phased arrays, Signal to noise ratio, Image quality, Lymphatic system, Interference (communication), Ultrasonography, Transducers, Acquisition tracking and pointing, In vivo imaging
In linear-array transducer-based photoacoustic (PA) imaging, B-scan PA images are formed using the raw channel PA signals. Delay-and-sum (DAS) is the most prevalent algorithm due to its simple implementation, but it leads to low-quality images. Delay-multiply-and-sum (DMAS) provides a higher image quality in comparison with DAS while it imposes a computational burden of O ( M2 ) . We introduce a nonlinear (NL) beamformer for linear-array PA imaging, which uses the p’th root of the detected signals and imposes the complexity of DAS [O ( M ) ]. The proposed algorithm is evaluated numerically and experimentally [wire-target and in-vivo sentinel lymph node (SLN) imaging], and the effects of the parameter p are investigated. The results show that the NL algorithm, using a root of p (NL_p), leads to lower sidelobes and higher signal-to-noise ratio compared with DAS and DMAS, for (p > 2). The sidelobes level (for the wire-target phantom), at the depth of 11.4 mm, are about −31, −52, −52, −67, −88, and −109 dB, for DAS, DMAS, NL_2, NL_3, NL_4, and NL_5, respectively, indicating the superiority of the NL_p algorithm. In addition, the best value of p for SLN imaging is reported to be 12.
Non-invasive vascular elastography is an emerging technique in vascular tissue imaging. During the past decades, several techniques have been suggested to estimate the tissue elasticity by measuring the displacement of the Carotid vessel wall. Cross correlation-based methods are the most prevalent approaches to measure the strain exerted in the wall vessel by the blood pressure. In the case of a low pressure, the displacement is too small to be apparent in ultrasound imaging, especially in the regions far from the center of the vessel, causing a high error of displacement measurement. On the other hand, increasing the compression leads to a relatively large displacement in the regions near the center, which reduces the performance of the cross correlation-based methods. In this study, a non-rigid image registration-based technique is proposed to measure the tissue displacement for a relatively large compression. The results show that the error of the displacement measurement obtained by the proposed method is reduced by increasing the amount of compression while the error of the cross correlationbased method rises for a relatively large compression. We also used the synthetic aperture imaging method, benefiting the directivity diagram, to improve the image quality, especially in the superficial regions. The best relative root-mean-square error (RMSE) of the proposed method and the adaptive cross correlation method were 4.5% and 6%, respectively. Consequently, the proposed algorithm outperforms the conventional method and reduces the relative RMSE by 25%.
Photoacoustic imaging (PAI) is a promising medical imaging modality providing the spatial resolution of ultrasound imaging and the contrast of optical imaging. For linear-array PAI, a beamformer can be used as the reconstruction algorithm. Delay-and-sum (DAS) is the most prevalent beamforming algorithm in PAI. However, using DAS beamformer leads to low-resolution images as well as high sidelobes due to nondesired contribution of off-axis signals. Coherence factor (CF) is a weighting method in which each pixel of the reconstructed image is weighted, based on the spatial spectrum of the aperture, to mainly improve the contrast. We demonstrate that the numerator of the formula of CF contains a DAS algebra and propose the use of a delay-multiply-and-sum beamformer instead of the available DAS on the numerator. The proposed weighting technique, modified CF (MCF), has been evaluated numerically and experimentally compared to CF. It was shown that MCF leads to lower sidelobes and better detectable targets. The quantitative results of the experiment (using wire targets) show that MCF leads to for about 45% and 40% improvement, in comparison with CF, in the terms of signal-to-noise ratio and full-width-half-maximum, respectively.