Michał Byra

Correcting the influence of tissue attenuation on Nakagami distribution shape parameter estimation

Nakagami distribution is used to model the statistical properties of backscattered echoes in tissue. The proper estimate requires the compensation of attenuation along each scanning line. Attenuation of the wave results in decreasing of the envelope mean intensity with depth what modifies the Nakagami scale parameter. This phenomenon violates the assumption that envelope samples within region of interest are identically distributed and disrupts estimation. Here, we investigate the influence of wave attenuation on Nakagami shape parameter estimators for various scattering scenarios, attenuation coefficients and region of interest size. Three methods are proposed to solve this issue. Scans of a thyroid and of a breast lesion are analyzed. It was found that proposed methods improved the estimation, especially when larger regions were used to collect envelope samples.

Annular phased array transducer for preclinical testing of anti-cancer drug efficacy on small animals

HighlightsUltrasound spherical annular phased array transducer for preclinical studies on small animals.Relationship between exposure parameters and location and extent of necrotic lesions.Effective noninvasive technique for testing anti‐cancer drug efficacy. Abstract A technique using pulsed High Intensity Focused Ultrasound (HIFU) to destroy deep‐seated solid tumors is a promising noninvasive therapeutic approach. A main purpose of this study was to design and test a HIFU transducer suitable for preclinical studies of efficacy of tested, anti‐cancer drugs, activated by HIFU beams, in the treatment of a variety of solid tumors implanted to various organs of small animals at the depth of the order of 1–2 cm under the skin. To allow focusing of the beam, generated by such transducer, within treated tissue at different depths, a spherical, 2‐MHz, 29‐mm diameter annular phased array transducer was designed and built. To prove its potential for preclinical studies on small animals, multiple thermal lesions were induced in a pork loin ex vivo by heating beams of the same: 6 W, or 12 W, or 18 W acoustic power and 25 mm, 30 mm, and 35 mm focal lengths. Time delay for each annulus was controlled electronically to provide beam focusing within tissue at the depths of 10 mm, 15 mm, and 20 mm. The exposure time required to induce local necrosis was determined at different depths using thermocouples. Location and extent of thermal lesions determined from numerical simulations were compared with those measured using ultrasound and magnetic resonance imaging techniques and verified by a digital caliper after cutting the tested tissue samples. Quantitative analysis of the results showed that the location and extent of necrotic lesions on the magnetic resonance images are consistent with those predicted numerically and measured by caliper. The edges of lesions were clearly outlined although on ultrasound images they were fuzzy. This allows to conclude that the use of the transducer designed offers an effective noninvasive tool not only to induce local necrotic lesions within treated tissue without damaging the surrounding tissue structures but also to test various chemotherapeutics activated by the HIFU beams in preclinical studies on small animals.

Differentiation of normal tissue and tissue lesions using statistical properties of backscattered ultrasound in breast

The aim of the study was finding the relationship between BIRADS classification combined with envelope K and Nakagami statistics of the echoes backscattered in the breast tissue in vivo and the histological data. 107 breast lesions were examined. Both, the RF echo-signal and B-mode images from the lesions and surrounding tissue were recorded. The analysis method was based on the combining data from BIRADS classifications and both distributions parameters. 107 breasts lesions - 32 malignant and 75 benign - were examined. When only BIRADS classification was used all malignant lesions were diagnosed correctly, however 34 benign lesions were sent for the biopsy unnecessarily. For K distribution the sensitivity and specificity were 78.13%, and 86.67% while for Nakagami statistics the sensitivity and specificity were 62.50% and 93.33%, respectively. Combined K and BIRADS resulted in sensitivity of 96.67% and specificity 60%. Combined BIRADS (3/4a cut-off) plus Nakagami statistics showed 100% of sensitivity with specificity equal 57.33%, decreasing the number of lesions which were biopsied from 34 to 28.

A multiparametric approach integrating vessel diameter, wall shear rate and physiologic signals for optimized Flow Mediated Dilation studies

Flow Mediated Dilation (FMD) is a technique widely used to assess the endothelial function by ultrasound. Ideally, both the brachial artery wall shear stress (stimulus) and the diameter change (effect) shall be estimated and monitored for up to 10 minutes, while blood flow is restricted by a cuff and then suddenly released. An inherent methods difficulty is maintaining the linear array probe aligned with the artery for such a long time. The problem is here faced by an integrated hardware/software approach that displays in real-time both the spatial velocity profiles and the diameter changes, and acquires raw data all over the exam.

Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images

PurposeThe nonalcoholic fatty liver disease is the most common liver abnormality. Up to date, liver biopsy is the reference standard for direct liver steatosis quantification in hepatic tissue samples. In this paper we propose a neural network-based approach for nonalcoholic fatty liver disease assessment in ultrasound.MethodsWe used the Inception-ResNet-v2 deep convolutional neural network pre-trained on the ImageNet dataset to extract high-level features in liver B-mode ultrasound image sequences. The steatosis level of each liver was graded by wedge biopsy. The proposed approach was compared with the hepatorenal index technique and the gray-level co-occurrence matrix algorithm. After the feature extraction, we applied the support vector machine algorithm to classify images containing fatty liver. Based on liver biopsy, the fatty liver was defined to have more than 5% of hepatocytes with steatosis. Next, we used the features and the Lasso regression method to assess the steatosis level.ResultsThe area under the receiver operating characteristics curve obtained using the proposed approach was equal to 0.977, being higher than the one obtained with the hepatorenal index method, 0.959, and much higher than in the case of the gray-level co-occurrence matrix algorithm, 0.893. For regression the Spearman correlation coefficients between the steatosis level and the proposed approach, the hepatorenal index and the gray-level co-occurrence matrix algorithm were equal to 0.78, 0.80 and 0.39, respectively.ConclusionsThe proposed approach may help the sonographers automatically diagnose the amount of fat in the liver. The presented approach is efficient and in comparison with other methods does not require the sonographers to select the region of interest.

Combining Nakagami imaging and convolutional neural networks for breast lesion classification

The quantitative ultrasound (QUS) imaging provides additional information on tissue properties in comparison to standard ultrasonography. In this paper we use the Nakagami imaging to address the problem of breast lesion classification. Images of breast lesions may contain areas with calcifications or/and necrosis. These areas are visible on Nakagami maps and their presence make the classification more difficult; efficient texture features for classification are harder to estimate. As a remedy, we propose to use a convolutional neural network which automatically learn task dependent patterns from images. We train the network based on Nakagami maps of breast lesions.

Two frequencies push-pull differential imaging

Nowadays there are new modalities in ultrasound imaging allowing better characterization of tissue regions with different stiffness. We are proposing an approach based on simultaneous propagation of two waves being a combination of two pulses differing in pressure and frequency: a low frequency pulse is expected to change the local scattering properties of the tissue due to compression/rarefaction while a high frequency pulse is used for imaging. Two transmissions are performed for each scanning line. First, with the imaging pulse that propagates on maximum compression caused by a low frequency wave. Next, the low frequency wave is inverted and the imaging pulse propagates over the maximum rarefaction. After the processing of the subtracted echoes from subsequent transmissions including wavelet transform and band-pass filtering, differential images were reconstructed. The low frequency wave has a visible impact on the scattering properties of the tissue which can be observed on a differential image.