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Background Obesity increases and surgical weight reduction decreases the risk of atrial fibrillation (AF) and heart failure (HF). We hypothesized that surgically induced weight loss may favorably affect left atrial (LA) mechanical function measured by longitudinal strain, which has recently emerged as an independent imaging biomarker of increased AF and HF risk. Methods We retrospectively evaluated echocardiograms performed before and 12.2 ± 2.2 months after bariatric surgery in 65 patients with severe obesity (mean age 39 [36; 47] years, 72% of females) with no known cardiac disease or arrhythmia. The LA mechanical function was measured by the longitudinal strain using the semi-automatic speckle tracking method. Results After surgery, body mass index decreased from 43.72 ± 4.34 to 30.04 ± 4.33 kg/m2. We observed a significant improvement in all components of the LA strain. LA reservoir strain (LASR) and LA conduit strain (LASCD) significantly increased (35.7% vs 38.95%, p = 0.0005 and − 19.6% vs − 24.4%, p p = 0.0075). There was a significant correlation between an increase in LASR and LASCD and the improvement in parameters of left ventricular diastolic and longitudinal systolic function (increase in E’ and MAPSE). Another significant correlation was identified between the decrease in LASCT and an improvement in LA function (decrease in A’). Conclusions The left atrial mechanical function improves after bariatric surgery. It is partially explained by the beneficial effect of weight reduction on the left ventricular diastolic and longitudinal systolic function. This effect may contribute to decreased risk of AF and HF after bariatric surgery. Graphical abstract

OBJECTIVES—: To develop and evaluate deep learning models devised for liver fat assessment based on ultrasound (US) images acquired from four different liver views: transverse plane (hepatic veins at the confluence with the inferior vena cava, right portal vein, right posterior portal vein) and sagittal plane (liver/kidney). METHODS—: US images (four separate views) were acquired from 135 participants with known or suspected nonalcoholic fatty liver disease. Proton density fat fraction (PDFF) values derived from chemical shift-encoded magnetic resonance imaging served as ground truth. Transfer learning with a deep convolutional neural network (CNN) was applied to develop models for diagnosis of fatty liver (PDFF ≥ 5%), diagnosis of advanced steatosis (PDFF ≥ 10%), and PDFF quantification for each liver view separately. In addition, an ensemble model based on all four liver view models was investigated. Diagnostic performance was assessed using the area under the receiver operating characteristics curve (AUC), and quantification was assessed using the Spearman correlation coefficient (SCC). RESULTS—: The most accurate single view was the right posterior portal vein, with an SCC of 0.78 for quantifying PDFF and AUC values of 0.90 (PDFF ≥ 5%) and 0.79 (PDFF ≥ 10%). The ensemble of models achieved an SCC of 0.81 and AUCs of 0.91 (PDFF ≥ 5%) and 0.86 (PDFF ≥ 10%). CONCLUSION—: Deep learning-based analysis of US images from different liver views can help assess liver fat.

Objective. Prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer is important for patient outcomes. In this work, we propose a deep learning based approach to NAC response prediction in ultrasound (US) imaging. Approach. We develop recurrent neural networks that can process serial US imaging data to predict chemotherapy outcomes. We present models that can process either raw radio-frequency (RF) US data or regular US images. The proposed approach is evaluated based on 204 sequences of US data from 51 breast cancers. Each sequence included US data collected before the chemotherapy and after each subsequent dose, up to the 4th course. We investigate three pre-trained convolutional neural networks (CNNs) as back-bone feature extractors for the recurrent network. The CNNs were pre-trained using raw US RF data, US b-mode images and RGB images from the ImageNet dataset. The first two networks were developed using US data collected from malignant and benign breast masses. Main results. For the pre-treatment data, the better performing network, with back-bone CNN pre-trained on US images, achieved area under the receiver operating curve (AUC) of 0.81 (±0.04). Performance of the recurrent networks improved with each course of the chemotherapy. For the 4th course, the better performing model, based on the CNN pre-trained with RGB images, achieved AUC value of 0.93 (±0.03). Statistical analysis based on the DeLong test presented that there were no significant differences in AUC values between the pre-trained networks at each stage of the chemotherapy (p-values > 0.05). Significance. Our study demonstrates the feasibility of using recurrent neural networks for the NAC response prediction in breast cancer US.

We propose a novel approach to breast mass classification based on deep learning models that utilize raw radio-frequency (RF) ultrasound (US) signals. US images, typically displayed by US scanners and used to develop computer-aided diagnosis systems, are reconstructed using raw RF data. However, information related to physical properties of tissues present in RF signals is partially lost due to the irreversible compression necessary to make raw data readable to the human eye. To utilize the information present in raw US data, we develop deep learning models that can automatically process small 2D patches of RF signals and their amplitude samples. We compare our approach with classification method based on the Nakagami parameter, a widely used quantitative US technique utilizing RF data amplitude samples. Our better performing deep learning model, trained using RF signals and their envelope samples, achieved good classification performance, with the area under the receiver attaining operating characteristic curve (AUC) and balanced accuracy of 0.772 and 0.710, respectively. The proposed method significantly outperformed the Nakagami parameter-based classifier, which achieved AUC and accuracy of 0.64 and 0.611, respectively. The developed deep learning models were used to generate parametric maps illustrating the level of mass malignancy. Our study presents the feasibility of using RF data for the development of deep learning breast mass classification models.

Ultrasound (US) imaging is widely used for the tissue characterization. However, US images commonly suffer from speckle noise, which degrades perceived image quality. Various deep learning approaches have been proposed for US image denoising, but most of them lack the interpretability of how the network is processing the US image (black box problem). In this work, we utilize a deep reinforcement learning (RL) approach, the pixelRL, to US image denoising. The technique utilizes a set of easily interpretable and commonly used filtering operations applied in a pixel-wise manner. In RL, software agents act in an unknown environment and receive appropriate numerical rewards. In our case, each pixel of the input US image has an agent and state of the environment is the current US image. Agents iteratively denoise the US image by executing the following pixel-wise pre-defined actions: Gaussian, bilateral, median and box filtering, pixel value increment/decrement and no action. The proposed approach can be used to generate action maps depicting operations applied to process different parts of the US image. Agents were pre-trained on natural gray-scale images and evaluated on the breast mass US images. To enable the evaluation, we artificially corrupted the US images with noise. Compared with the reference (noise free US images), filtration of the images with the proposed method increased the average peak signal-to-noise ratio (PSNR) score from 14 dB to 26 dB and increased the structure similarity index score from 0.22 to 0.54. Our work confirms that it is feasible to use pixel-wise RL techniques for the US image denoising.

To assess the feasibility of using ultrasound (US) image features related to the median nerve echogenicity and shape for carpal tunnel syndrome (CTS) diagnosis.In 31 participants (21 healthy participants and 10 patients with CTS), US images were collected with a 30-MHz transducer from median nerves at the wrist crease in 2 configurations: a neutral position and with wrist extension. Various morphologic features, including the cross-sectional area (CSA), were calculated to assess the nerve shape. Carpal tunnel syndrome commonly results in loss of visualization of the nerve fascicular pattern on US images. To assess this phenomenon, we developed a nerve-tissue contrast index (NTI) method. The NTI is a ratio of average brightness levels of surrounding tissue and the median nerve, both calculated on the basis of a US image. The area under the curve (AUC) from a receiver operating characteristic curve analysis and t test were used to assess the usefulness of the features for differentiation of patients with CTS from control participants.We obtained significant differences in the CSA and NTI parameters between the patients with CTS and control participants (P .01), with the corresponding highest AUC values equal to 0.885 and 0.938, respectively. For the remaining investigated morphologic features, the AUC values were less than 0.685, and the differences in means between the patients and control participants were not statistically significant (P .10). The wrist configuration had no impact on differences in average parameter values (P .09).Patients with CTS can be differentiated from healthy individuals on the basis of the median nerve CSA and echogenicity. Carpal tunnel syndrome is not manifested in a change of the median nerve shape that could be related to circularity or contour variability.

To develop a fully automated full-thickness cartilage segmentation and mapping of T1, T1ρ, and T2*, as well as macromolecular fraction (MMF) by combining a series of quantitative 3D ultrashort echo time (UTE) cones MR imaging with a transfer learning–based U-Net convolutional neural networks (CNN) model. Sixty-five participants (20 normal, 29 doubtful-minimal osteoarthritis (OA), and 16 moderate-severe OA) were scanned using 3D UTE cones T1 (Cones-T1), adiabatic T1ρ (Cones-AdiabT1ρ), T2* (Cones-T2*), and magnetization transfer (Cones-MT) sequences at 3 T. Manual segmentation was performed by two experienced radiologists, and automatic segmentation was completed using the proposed U-Net CNN model. The accuracy of cartilage segmentation was evaluated using the Dice score and volumetric overlap error (VOE). Pearson correlation coefficient and intraclass correlation coefficient (ICC) were calculated to evaluate the consistency of quantitative MR parameters extracted from automatic and manual segmentations. UTE biomarkers were compared among different subject groups using one-way ANOVA. The U-Net CNN model provided reliable cartilage segmentation with a mean Dice score of 0.82 and a mean VOE of 29.86%. The consistency of Cones-T1, Cones-AdiabT1ρ, Cones-T2*, and MMF calculated using automatic and manual segmentations ranged from 0.91 to 0.99 for Pearson correlation coefficients, and from 0.91 to 0.96 for ICCs, respectively. Significant increases in Cones-T1, Cones-AdiabT1ρ, and Cones-T2* (p

In this paper, we propose a novel deep learning method for joint classification and segmentation of breast masses based on radio-frequency (RF) ultrasound (US) data. In comparison to commonly used classification and segmentation techniques, utilizing B-mode US images, we train the network with RF data (data before envelope detection and dynamic compression), which are considered to include more information on tissue's physical properties than standard B-mode US images. Our multi-task network, based on the Y-Net architecture, can effectively process large matrices of RF data by mixing 1D and 2D convolutional filters. We use data collected from 273 breast masses to compare the performance of networks trained with RF data and US images. The multi-task model developed based on the RF data achieved good classification performance, with area under the receiver operating characteristic curve (AUC) of 0.90. The network based on the US images achieved AUC of 0.87. In the case of the segmentation, we obtained mean Dice scores of 0.64 and 0.60 for the approaches utilizing US images and RF data, respectively. Moreover, the interpretability of the networks was studied using class activation mapping technique and by filter weights visualizations.

Temperature monitoring in ultrasound (US) imaging is important for various medical treatments, such as high-intensity focused US (HIFU) therapy or hyperthermia. In this work, we present a deep learning based approach to temperature monitoring based on radio-frequency (RF) US data. We used Siamese neural networks in an unsupervised way to spatially compare RF data collected at different time points of the heating process. The Siamese model consisted of two identical networks initially trained on a large set of simulated RF data to assess tissue backscattering properties. To illustrate our approach, we experimented with a tissue-mimicking phantom and an ex-vivo tissue sample, which were both heated with a HIFU transducer. During the experiments, we collected RF data with a regular US scanner. To determine spatiotemporal variations in temperature distribution within the samples, we extracted small 2D patches of RF data and compared them with the Siamese network. Our method achieved good performance in determining the spatiotemporal distribution of temperature during heating. Compared with the temperature monitoring based on the change in radio-frequency signal backscattered energy parameter, our method provided more smooth spatial parametric maps and did not generate ripple artifacts. The proposed approach, when fully developed, might be used for US based temperature monitoring of tissues.

Convolutional neural networks (CNNs) have achieved remarkable success in medical image analysis tasks. In ultrasound (US) imaging, CNNs have been applied to object classification, image reconstruction and tissue characterization. However, CNNs can be vulnerable to adversarial attacks, even small perturbations applied to input data may significantly affect model performance and result in wrong output. In this work, we devise a novel adversarial attack, specific to ultrasound (US) imaging. US images are reconstructed based on radio-frequency signals. Since the appearance of US images depends on the applied image reconstruction method, we explore the possibility of fooling deep learning model by perturbing US B-mode image reconstruction method. We apply zeroth order optimization to find small perturbations of image reconstruction parameters, related to attenuation compensation and amplitude compression, which can result in wrong output. We illustrate our approach using a deep learning model developed for fatty liver disease diagnosis, where the proposed adversarial attack achieved success rate of 48%., Comment: 4 pages, 3 figures

Early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer is crucial for guiding therapy decisions. In this work, we propose a deep learning based approach for the early NAC response prediction in ultrasound (US) imaging. We used transfer learning with deep convolutional neural networks (CNNs) to develop the response prediction models. The usefulness of two transfer learning techniques was examined. First, a CNN pre-trained on the ImageNet dataset was utilized. Second, we applied double transfer learning, the CNN pre-trained on the ImageNet dataset was additionally fine-tuned with breast mass US images to differentiate malignant and benign lesions. Two prediction tasks were investigated. First, a L1 regularized logistic regression prediction model was developed based on generic neural features extracted from US images collected before the chemotherapy ( a priori prediction). Second, Siamese CNNs were used to quantify differences between US images collected before the treatment and after the first and second course of NAC. The proposed methods were evaluated using US data collected from 39 tumors. The better performing deep learning models achieved areas under the receiver operating characteristic curve of 0.797 and 0.847 in the case of the a priori prediction and the Siamese model, respectively. The proposed approach was compared with a method based on handcrafted morphological features. Our study presents the feasibility of using transfer learning with CNNs for the NAC response prediction in US imaging.

BACKGROUND: Radiofrequency ultrasound data from the liver contain rich information about liver microstructure and composition. Deep learning might exploit such information to assess nonalcoholic fatty liver disease (NAFLD). PURPOSE: To develop and evaluate deep learning algorithms that use radiofrequency data for NAFLD assessment, with MRI-derived proton density fat fraction (PDFF) as the reference. MATERIALS AND METHODS: A HIPAA-compliant secondary analysis of a single-center prospective study was performed for adult participants with NAFLD and control participants without liver disease. Participants in the parent study were recruited between February 2012 and March 2014 and underwent same-day US and MRI of the liver. Participants were randomly divided into an equal number of training and test groups. The training group was used to develop two algorithms via cross-validation: a classifier to diagnose NAFLD (MRI PDFF ≥ 5%) and a fat fraction estimator to predict MRI PDFF. Both algorithms used one-dimensional convolutional neural networks. The test group was used to evaluate the classifier for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy and to evaluate the estimator for correlation, bias, limits of agreements, and linearity between predicted fat fraction and MRI PDFF. RESULTS: A total of 204 participants were analyzed, 140 had NAFLD (mean age, 52 years ± 14 [standard deviation]; 82 women) and 64 were control participants (mean age, 46 years ± 21; 42 women). In the test group, the classifier provided 96% (95% confidence interval [CI]: 90%, 99%) (98 of 102) accuracy for NAFLD diagnosis (sensitivity, 97% [95% CI: 90%, 100%], 68 of 70; specificity, 94% [95% CI: 79%, 99%], 30 of 32; positive predictive value, 97% [95% CI: 90%, 99%], 68 of 70; negative predictive value, 94% [95% CI: 79%, 98%], 30 of 32). The estimator-predicted fat fraction correlated with MRI PDFF (Pearson r = 0.85). The mean bias was 0.8% (P = .08), and 95% limits of agreement were -7.6% to 9.1%. The predicted fat fraction was linear with an MRI PDFF of 18% or less (r = 0.89, slope = 1.1, intercept = 1.3) and nonlinear with an MRI PDFF greater than 18%. CONCLUSION: Deep learning algorithms using radiofrequency ultrasound data are accurate for diagnosis of nonalcoholic fatty liver disease and hepatic fat fraction quantification when other causes of steatosis are excluded. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lockhart and Smith in this issue.

Ultrasound imaging is widely used for breast lesion differentiation. In this paper we propose a neural transfer learning method for breast lesion classification in ultrasound. As reported in several papers, the content and the style of a particular image can be separated with a convolutional neural network. The style, coded by the Gram matrix, can be used to perform neural transfer of artistic style. In this paper we extract the neural style representations of malignant and benign breast lesions using the VGG19 neural network. Next, the Fisher discriminant analysis is used to separate those neural style representations and perform classification. The proposed approach achieves good classification performance (AUC of 0.847). Our method is compared with another transfer learning technique based on extracting pooling layer features (AUC of 0.826). Moreover, we apply the Fisher discriminant analysis to differentiate breast lesions using ultrasound images (AUC of 0.758). Additionally, we extract the eigenimages related to malignant and benign breast lesions and show that these eigenimages present features commonly associated with lesion type, such as contour attributes or shadowing. The proposed techniques may be useful for the researchers interested in ultrasound breast lesion characterization.

Purpose The aim of this paper is to provide access to a database consisting of the raw radio-frequency ultrasonic echoes acquired from malignant and benign breast lesions. The database is freely available for study and signal analysis. Acquisition and Validation Methods The ultrasonic radio-frequency echoes were recorded from breast focal lesions of patients of the Institute of Oncology in Warsaw. The data were collected between 11/2013 and 10/2015. Patients were examined by a radiologist with 18 years’ experience in the ultrasonic examination of breast lesions. The set of data includes scans from 52 malignant and 48 benign breast lesions recorded in a group of 78 women. For each lesion, two individual orthogonal scans from the pathological region were acquired with the Ultrasonix SonixTouch Research ultrasound scanner using the L14-5/38 linear array transducer. All malignant lesions were histologically assessed by core needle biopsy. In the case of benign lesions, part of them was histologically assessed and another part was observed over a two-year period. Data Format and Usage Notes The radio-frequency echoes were stored in Matlab file format. For each scan, the region of interest was provided to correctly indicate the lesion area. Moreover, for each lesion, the BI-RADS category and the lesion class were included. Two code examples of data manipulation are presented. The data can be downloaded via the Zenodo repository (DOI: 10.5281/zenodo.545928) or the website h t t p ://bluebox.ippt. gov. p l/~hpiotrzk. Potential Applications The database can be used to test quantitative ultrasound techniques and ultrasound image processing algorithms, or to develop computer-aided diagnosis systems. This article is protected by copyright. All rights reserved.

Author(s): Byra, Michal; Wu, Mei; Zhang, Xiaodong; Jang, Hyungseok; Ma, Ya-Jun; Chang, Eric Y; Shah, Sameer; Du, Jiang | Abstract: PurposeTo develop a deep learning-based method for knee menisci segmentation in 3D ultrashort echo time (UTE) cones MR imaging, and to automatically determine MR relaxation times, namely the T1, T1ρ , and T2∗ parameters, which can be used to assess knee osteoarthritis (OA).MethodsWhole knee joint imaging was performed using 3D UTE cones sequences to collect data from 61 human subjects. Regions of interest (ROIs) were outlined by 2 experienced radiologists based on subtracted T1ρ -weighted MR images. Transfer learning was applied to develop 2D attention U-Net convolutional neural networks for the menisci segmentation based on each radiologist's ROIs separately. Dice scores were calculated to assess segmentation performance. Next, the T1, T1ρ , T2∗ relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared.ResultsThe models developed using ROIs provided by 2 radiologists achieved high Dice scores of 0.860 and 0.833, while the radiologists' manual segmentations achieved a Dice score of 0.820. Linear correlation coefficients for the T1, T1ρ , and T2∗ relaxations calculated using the automatic and manual segmentations ranged between 0.90 and 0.97, and there were no associated differences between the estimated average meniscal relaxation parameters. The deep learning models achieved segmentation performance equivalent to the inter-observer variability of 2 radiologists.ConclusionThe proposed deep learning-based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times. The method has the potential to help radiologists with the assessment of meniscal diseases, such as OA.

In this work, we propose a deep learning method for breast mass segmentation in ultrasound (US). Variations in breast mass size and image characteristics make the automatic segmentation difficult. To address this issue, we developed a selective kernel (SK) U-Net convolutional neural network. The aim of the SKs was to adjust network's receptive fields via an attention mechanism, and fuse feature maps extracted with dilated and conventional convolutions. The proposed method was developed and evaluated using US images collected from 882 breast masses. Moreover, we used three datasets of US images collected at different medical centers for testing (893 US images). On our test set of 150 US images, the SK-U-Net achieved mean Dice score of 0.826, and outperformed regular U-Net, Dice score of 0.778. When evaluated on three separate datasets, the proposed method yielded mean Dice scores ranging from 0.646 to 0.780. Additional fine-tuning of our better-performing model with data collected at different centers improved mean Dice scores by ∼6%. SK-U-Net utilized both dilated and regular convolutions to process US images. We found strong correlation, Spearman's rank coefficient of 0.7, between the utilization of dilated convolutions and breast mass size in the case of network's expansion path. Our study shows the usefulness of deep learning methods for breast mass segmentation. SK-U-Net implementation and pre-trained weights can be found at github.com/mbyr/bus_seg .

The ultimate goal of this work is a real-time processing framework for ultrasound image reconstruction augmented with machine learning. To attain this, we have implemented WaveFlow-a set of ultrasound data acquisition and processing tools for TensorFlow. WaveFlow includes: ultrasound Environments (connection points between the input raw ultrasound data source and TensorFlow) and signal processing Operators (ops) library. Raw data can be processed in real-time using algorithms available both in TensorFlow and WaveFlow. Currently, WaveFlow provides ops for B-mode image reconstruction (beamforming), signal processing and quantitative ultrasound. The ops were implemented both for the CPU and GPU, as well as for built-in automated tests and benchmarks. To demonstrate WaveFlow's performance, ultrasound data were acquired from wire and cyst phantoms and elaborated using selected sequences of the ops. We implemented and evaluated: Delay-and-Sum beamformer, synthetic transmit aperture imaging (STAI), planewave imaging (PWI), envelope detection algorithm and dynamic range clipping. The benchmarks were executed on the NVidia@ Titan X GPU integrated in the USPlatform research scanner (us4us Ltd., Poland). We achieved B-mode image reconstruction frame rates of 55 fps, 17 fps for the STAI and the PWI algorithms, respectively. The results showed the feasibility of realtime ultrasound image reconstruction using WaveFlow operators in the TensorFlow framework. WaveFlow source code can be found at !!ithub.com/waveflow-teamlwaveflow.

Purpose We propose a deep learning-based approach to breast mass classification in sonography and compare it with the assessment of four experienced radiologists employing breast imaging reporting and data system 4th edition lexicon and assessment protocol. Methods Several transfer learning techniques are employed to develop classifiers based on a set of 882 ultrasound images of breast masses. Additionally, we introduce the concept of a matching layer. The aim of this layer is to rescale pixel intensities of the grayscale ultrasound images and convert those images to red, green, blue (RGB) to more efficiently utilize the discriminative power of the convolutional neural network pretrained on the ImageNet dataset. We present how this conversion can be determined during fine-tuning using back-propagation. Next, we compare the performance of the transfer learning techniques with and without the color conversion. To show the usefulness of our approach, we additionally evaluate it using two publicly available datasets. Results Color conversion increased the areas under the receiver operating curve for each transfer learning method. For the better-performing approach utilizing the fine-tuning and the matching layer, the area under the curve was equal to 0.936 on a test set of 150 cases. The areas under the curves for the radiologists reading the same set of cases ranged from 0.806 to 0.882. In the case of the two separate datasets, utilizing the proposed approach we achieved areas under the curve of around 0.890. Conclusions The concept of the matching layer is generalizable and can be used to improve the overall performance of the transfer learning techniques using deep convolutional neural networks. When fully developed as a clinical tool, the methods proposed in this paper have the potential to help radiologists with breast mass classification in ultrasound.

In this paper we propose a computer-aided diagnosis system for the breast lesion classification. Our approach is based on quantitative ultrasound and deep learning. We used the Nakagami imaging to create parametric maps of breast lesions that illustrate tissue scattering properties. For this task the sliding window technique was applied. The Nakagami parameter was calculated using the maximum likelihood estimator. Next, we used the Nakagami parameter maps to train a convolutional neural network. Classification performance was evaluated by 5-fold cross-validation. We obtained the area under the receiver operating characteristic curve equal to 0.91. The results showed that our approach is useful to distinguishing between malignant and benign breast lesions. The proposed method serves as a general approach for tissue characterization and differentiation. The Nakagami parameter used in this study can be replaced with other QUS parameters and the neural network can be trained in a similar fashion.

Assessment of medium nonlinearity parameter in pulse-echo mode usually demands information on the second harmonic. However, the acquisition of quantitative data on backscattered amplitude at the fundamental and second harmonic components is difficult using the same probe due to its limited bandwidth. In this paper we propose how to assess medium nonlinearity using only the fundamental harmonic and the plane wave imaging. We utilize the fact that the backscattered echo amplitude at the fundamental is not linearly proportional to the initial plane wave amplitude. This relation is modified due to the wave nonlinear propagation.

In this paper we investigate how to assess the ultrasound nonlinearity coefficient using plane wave imaging. We employ the technique based on excitation of the medium with ultrasonic pulses of increasing amplitude level. As the pulse pressure is increased, due to medium nonlinearity, higher fraction of energy is transferred from the fundamental to higher harmonics during the propagation. In this case the amplitude of the backscattered echo is not linear in respect to the initial pulse amplitude at source. This phenomenon can be used for the nonlinearity coefficient assessment and show its implementation for the plane wave imaging. The method was validated experimentally using a wire phantom immersed in water and scanned using the Verasonics scanner. We discuss the usefulness of the proposed technique and its shortcomings. In comparison to other nonlinearity coefficient assessment methods, the presented technique works in the pulse-echo mode and it doesn't require information on second harmonic or using a special wide-band transducer. The method can be implemented directly into a medical scanner.

International audience; 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.

Purpose Quantitative imaging methods could improve diagnosis of rotator cuff degeneration, but the capability of quantitative MR and US imaging parameters to detect alterations in collagen is unknown. The goal of this study was to assess quantitative MR and US imaging measures for detecting abnormalities in collagen using an in vitro model of tendinosis with biochemical and histological correlation. Method 36 pieces of supraspinatus tendons from 6 cadaveric donors were equally distributed into 3 groups (2 subjected to different concentrations of collagenase and a control group). Ultrashort echo time MR and US imaging measures were performed to assess changes at baseline and after 24 h of enzymatic digestion. Biochemical and histological measures, including brightfield, fluorescence, and polarized microscopy, were used to verify the validity of the model and were compared with quantitative imaging parameters. Correlations between the imaging parameters and biochemically measured digestion were analyzed. Results Among the imaging parameters, macromolecular fraction (MMF), adiabatic T1ρ, T2*, and backscatter coefficient (BSC) were useful in differentiating between the extent of degeneration among the 3 groups. MMF strongly correlated with collagen loss (r=-0.81; 95% confidence interval [CI]: -0.90,-0.66), while the adiabatic T1ρ (r = 0.66; CI: 0.42,0.81), T2* (r = 0.58; CI: 0.31,0.76), and BSC (r = 0.51; CI: 0.22,0.72) moderately correlated with collagen loss. Conclusions MMF, adiabatic T1ρ, and T2* measured and US BSC can detect alterations in collagen. Of the quantitative MR and US imaging measures evaluated, MMF showed the highest correlation with collagen loss and can be used to assess rotator cuff degeneration.

We developed breast mass classification methods based on deep convolutional neural networks (CNNs) and morphological features (MF), then compared those to assessment of four experienced radiologists employing BI-RADS protocol. The classification models were developed based on 882 clinical ultrasound B-mode images of masses with confirmed findings and regions of interest indicating mass areas. Various transfer learning techniques, including fine-tuning of a pre-trained CNN, were investigated to develop deep learning models. A matching layer technique was applied to convert gray-scale images to red, green, blue to efficiently utilize discrimination of the pre-trained model. For the classical approach, we calculated MF related to breast mass shape (e.g., height-width ratio, circularity) and then trained binary classifiers. We additionally evaluated both approaches using two publicly available US datasets. Several statistical measures (area under the receiver operating curve [AUC], sensitivity and specificity) were used to assess the classification performance on a test set of 150 cases. The matching layer significantly increased AUC from 0.895 to 0.936 while radiologists’ AUCs ranged from 0.806 to 0.882. This study shows both deep learning and classical models achieve high performance. When developed as a clinical tool, the methods examined in this study have potential to aid radiologists accurate breast mass classification with ultrasound.