Raw Radiofrequency Research Articles

Study of classification of breast lesions using texture GLCM features obtained from the raw ultrasound signal

Most of the methods of classification of breast lesions in ultrasound (US) images have been tested on B-mode images from the commercial equipment. The new possibility of further analysis of this problem showed up with the availability of a public database containing original raw radio frequency (RF) signals. In particular, it appeared that the original texture might contain diagnostic information which could be modified in the typical image processing and which is more difficult to perceive than the details of lesion shape/contour. In this paper a detailed analysis of the lesion texture is conducted by means of the decision trees and logistic regression. The decision trees turned out useful mainly for selecting texture features to be used in the stepwise logistic regression. The RF signals database of 200 breast lesions was used for testing the performance of the benign vs malignant lesion classifier. The Gray Level Cooccurrence Matrix (GLCM) was calculated with the vertical/horizontal offset of up to five pixels. For each of these matrices six features were calculated resulting in a total of 210 features. Using these features a sufficient number of decision trees were generated to calculate pseudo-Receiver Operating Characteristics (ROCs). The outcome of this process is a collection of generated trees for which the employed features are known. These features were then used for generating generalized linear model by means of stepwise logistic regression. The analyzed regression models included the coefficients of up-to-the second degree terms. The texture features were further completed by a single shape feature, that is tumor circularity (TC). The automatic procedure for finding the exact mask of a lesion is also provided for the conditions when the acoustic shadowing makes it impossible to obtain the entire contour reliably and a half-contour has to be used. The selected logistic regression models gave ROCs with the Area Under Curve (AUC) of up to 0.83 and the 95 \% confidence region (0.63 0.96). Analyzing classification results one comes to the conclusion that the tumor circularity, which is the most informative among shape/contour features, is not essential against the background of textural features. The reported study shows that a relatively straightforward procedure can be employed to obtain benign vs malignant classifier comparable with that originally used for the database of the raw RF signals and based on the more complicated segmentation of the parameter maps of homodyned K distribution.

Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networks

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.

Tissue classification in intercostal and paravertebral ultrasound using spectral analysis of radiofrequency backscatter.

Paravertebral and intercostal nerve blocks have experienced a resurgence in popularity. Ultrasound has become the gold standard for visualization of the needle during injection of the analgesic, but the intercostal artery and vein can be difficult to visualize. We investigated the use of spectral analysis of raw radiofrequency (RF) ultrasound signals for identification of the intercostal vessels and six other tissue types in the intercostal and paravertebral spaces. Features derived from the one-dimensional spectrum, two-dimensional spectrum, and cepstrum were used to train four different machine learning algorithms. In addition, the use of the average normalized spectrum as the feature set was compared with the derived feature set. Compared to a support vector machine (SVM) (74.2%), an artificial neural network (ANN) (68.2%), and multinomial analysis (64.1%), a random forest (84.9%) resulted in the most accurate classification. The accuracy using a random forest trained with the first 15 principal components of the average normalized spectrum was 87.0%. These results demonstrate that using a machine learning algorithm with spectral analysis of raw RF ultrasound signals has the potential to provide tissue characterization in intercostal and paravertebral ultrasound.

The contribution of research platforms to the pathway from off-line to real-time high-frame-rate imaging

Ultrasound research platforms are characterized by a great flexibility of parameters that are usually fixed on clinical scanners. They allow the transmission of arbitrary waveforms in arbitrary scan sequences, and to test novel methods by processing off-line the raw radiofrequency channel data received after each transmission (TX) event. In particular, the availability of research platforms has boosted the development of high-frame-rate (HFR) imaging methods. These are typically based on the TX of multi-line focused beams, plane waves or diverging waves, which can be optimally produced by linear power amplifiers. The echoes received after each TX event should thus be beamformed along multiple lines in parallel, which is feasible, on line, only when high processing power is available. In this talk, the characteristics of “hardware-based” research platforms, like the ULA-OP 256, are discussed. The presence of powerful FPGA devices, on board such systems, allows implementing efficient parallel beamforming methods, which is the key-requirement for HFR imaging. Sample results obtained in real-time at rates of hundreds frames/s will be presented for both multi-line and plane wave compounding scan sequences. Finally, the main challenges to be faced to extend such modalities to volumetric (3-D) imaging will be discussed.

Deep Learning in Ultrasound Imaging

In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g., sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage. As some examples, we outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learn compressive encodings for the color Doppler, and provide a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have a considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.

Quantitative Ultrasound Imaging for the Differentiation between Fresh and Decellularized Mouse Kidneys.

Decellularization is a technique that permits the removal of cells from intact organs while preserving the extracellular matrix (ECM). It has many applications in various fields such as regenerative medicine and tissue engineering. This study aims to differentiate between fresh and decellularized kidneys using quantitative ultrasound (QUS) parameters. Spectral parameters were extracted from the linear fit of the power spectrum of raw radio frequency data and parametric maps were generated corresponding to the regions of interest, from which four textural parameters were estimated. The results of this study indicated that decellularization affects both spectral and textural parameters. The Mid Band Fit mean and contrast were found to be the best spectral and textural predictors of kidney decellularization, respectively.

RF-based drone detection and identification using deep learning approaches: An initiative towards a large open source drone database

Abstract The omnipresence of unmanned aerial vehicles, or drones, among civilians can lead to technical, security, and public safety issues that need to be addressed, regulated and prevented. Security agencies are in continuous search for technologies and intelligent systems that are capable of detecting drones. Unfortunately, breakthroughs in relevant technologies are hindered by the lack of open source databases for drone’s Radio Frequency (RF) signals, which are remotely sensed and stored to enable developing the most effective way for detecting and identifying these drones. This paper presents a stepping stone initiative towards the goal of building a database for the RF signals of various drones under different flight modes. We systematically collect, analyze, and record raw RF signals of different drones under different flight modes such as: off, on and connected, hovering, flying, and video recording. In addition, we design intelligent algorithms to detect and identify intruding drones using the developed RF database. Three deep neural networks (DNN) are used to detect the presence of a drone, the presence of a drone and its type, and lastly, the presence of a drone, its type, and flight mode. Performance of each DNN is validated through a 10-fold cross-validation process and evaluated using various metrics. Classification results show a general decline in performance when increasing the number of classes. Averaged accuracy has decreased from 99.7% for the first DNN (2-classes), to 84.5% for the second DNN (4-classes), and lastly, to 46.8% for the third DNN (10-classes). Nevertheless, results of the designed methods confirm the feasibility of the developed drone RF database to be used for detection and identification. The developed drone RF database along with our implementations are made publicly available for students and researchers alike.

Monitoring breast cancer response to neoadjuvant chemotherapy with ultrasound signal statistics and integrated backscatter.

BackgroundNeoadjuvant chemotherapy (NAC) is used in patients with breast cancer to reduce tumor focus, metastatic risk, and patient mortality. Monitoring NAC effects is necessary to capture resistant patients and stop or change treatment. The existing methods for evaluating NAC results have some limitations. The aim of this study was to assess the tumor response at an early stage, after the first doses of the NAC, based on the variability of the backscattered ultrasound energy, and backscatter statistics. The backscatter statistics has not previously been used to monitor NAC effects.MethodsThe B-mode ultrasound images and raw radio frequency data from breast tumors were obtained using an ultrasound scanner before chemotherapy and 1 week after each NAC cycle. The study included twenty-four malignant breast cancers diagnosed in sixteen patients and qualified for neoadjuvant treatment before surgery. The shape parameter of the homodyned K distribution and integrated backscatter, along with the tumor size in the longest dimension, were determined based on ultrasound data and used as markers for NAC response. Cancer tumors were assigned to responding and non-responding groups, according to histopathological evaluation, which was a reference in assessing the utility of markers. Statistical analysis was performed to rate the ability of markers to predict the final NAC response based on data obtained after subsequent therapeutic doses.ResultsStatistically significant differences (p<0.05) between groups were obtained after 2, 3, 4, and 5 doses of NAC for quantitative ultrasound markers and after 5 doses for the assessment based on maximum tumor dimension. Statistical analysis showed that, after the second and third NAC courses the classification based on integrated backscatter marker was characterized by an AUC of 0.69 and 0.82, respectively. The introduction of the second quantitative marker describing the statistical properties of scattering increased the corresponding AUC values to 0.82 and 0.91.ConclusionsQuantitative ultrasound information can characterize the tumor's pathological response better and at an earlier stage of therapy than the assessment of the reduction of its dimensions. The introduction of statistical parameters of ultrasonic backscatter to monitor the effects of chemotherapy can increase the effectiveness of monitoring and contribute to a better personalization of NAC therapy.

ARFI and shear wave imaging of the prostate to delineate clinically-significant cancers

Current prostate cancer screening methods involve non-targeted needle biopsies and the detection of clinically-insignificant lesions that receive excessive treatments, exposing patients to unnecessary adverse side effects and placing a burden on our health care systems. There is a strong clinical need for improved prostate imaging methods that are sensitive and specific for clinically-significant prostate cancer lesions to guide needle biopsies, target focal treatments, and improve overall patient outcomes. We have developed a 3D, in vivo Acoustic Radiation Force Impulse (ARFI) imaging and Shear Wave Elasticity Imaging (SWEI) system using a side-fire endorectal linear array with a custom rotation stage to collect prostate imaging volumes to identify regions of suspicion that should be targeted during biopsy procedures. We have validated that ARFI imaging can reliability identify regions of clinically-significant prostate cancer by comparing our imaging volumes with whole-mount histology in patients undergoing radical prostatectomy; however, there was opportunity to improve the diagnostic sensitivity and specificity by combining ARFI imaging data with SWEI. While ARFI images contain regions of relative contrast (decreased displacement) to delineate regions of suspicion, we hypothesized that SWEI could provide absolute shear wave speeds that could quantify the degree of suspicion of an identified lesion, with more aggressive lesions being stiffer and having higher shear wave speeds. In an ongoing clinical study of over 30 patients, we have demonstrated that the combination of ARFI and SWEI data can improve the diagnostic accuracy of clinically-significant lesion identification, while the inclusion of B-mode echogenicity was not found to improve diagnostic accuracy. More complex classification models to identify clinically-significant prostate cancer using ARFI and SWEI metrics are being developed using Linear Discriminant Analysis (LDA) and Support-Vector Machines (SVMs). These classification models will be further extended to explore the diagnostic value of additional quantitative ultrasound metrics derived from the raw radiofrequency (RF) data and other imaging methods commonly used in the diagnostic workup of prostate cancer patients, such as multiparametric magnetic resonance imaging (mp-MRI).

Real-time H-scan ultrasound imaging using a Verasonics research scanner

H-scan ultrasound (US) is a new imaging technique that relies on matching a model that describes US image formation to the mathematics of a class of Gaussian-weighted Hermite polynomials (GH). In short, H-scan US (where the ‘H’ denotes Hermite or hue) is a tissue classification technique that images the relative size of acoustic scatterers. Herein, we detail development of a real-time H-scan US imaging technology that was implemented on a programmable US research scanner (Vantage 256, Verasonics Inc, Kirkland, WA). This custom US imaging system has a dual display for real-time visualization of both the H-scan and B-scan US images. This MATLAB-based (Mathworks Inc, Natick, MA) system includes a graphical user interface (GUI) for controlling the entire US scan sequence including the raw radio frequency (RF) data acquisition parameters, image processing, variable control of a parallel set of convolution filters used to derive the H-scan US signal, and data (cine loop) save. The system-level structure used for software-based image reconstruction and display is detailed. Imaging studies were conducted using a series of homogeneous and heterogeneous tissue-mimicking phantom materials embedded with monodisperse spherical US scatterers of size 15–40 µm in diameter. Relative to H-scan US image measurements from a phantom with 15 µm-sized scatterers, data from phantoms with the 30 and 40 µm-sized scatterers exhibited mean intensity increases of 5.2% and 11.6%, respectively. Overall, real-time H-scan US imaging is a promising approach for visualizing the relative size and distribution of acoustic scattering objects.

Radiofrequency spectral analysis of EBUS for peripheral pulmonary lesions.

We previously reported that histogram-based quantitative evaluation for endobronchial ultrasonography (EBUS) B-mode images could differentiate between benign and malignant lesions. However, these images were generated from reconstructed raw radiofrequency (RF) signals and had some limitations. Currently, there are no reports on raw RF signal data to quantitatively differentiate ultrasound information for peripheral pulmonary lesions. We prospectively hypothesized that RF spectral analysis from EBUS images could reveal sonographic features of peripheral pulmonary diseases. RF data were imported into a frequency spectral analysis software programme, comparing four parameters: mean frequency (MHz); slope; mid-band fit (dB); and y-intercept (dB), to differentiate between benign and malignant lesions. Furthermore, we compared subgroup analysis within benign and malignant lesions. RF data from EBUS images were obtained in 146 cases, of which, 106 lung cancers and 40 inflammatory diseases were present. Significant differences were observed for three parameters in benign and malignant lesions (mean frequency: P < 0.05, slope: P < 0.05, y-intercept: P < 0.01) with diagnostic accuracy of 61%, 57.5%, 63%, respectively. In subgroup analysis, the acute pneumonia group showed higher mean frequency, higher slope and lower y-intercept patterns compared to mycobacterial and fibrotic diseases (P < 0.05). In malignant lesions, small cell carcinoma showed higher mean frequency, higher slope and lower y-intercept pattern compared to other histopathological lung cancers (P < 0.01). RF analysis might be capable of demonstrating aspects of the lesion's pathological heterogeneity rather than precisely differentiating between benign and malignant lesions.

Auto Segmentation of Breast Tumor in B-Mode Ultrasound Analysis

Segmentation or lesion boundary detection in classification of Benign and Malignant breast tumors from B-Mode ultrasound image analysis is a challenging one. In this study, first a suitable frame is chosen by strain and velocity imaging from a raw radio frequency (RF) echo of clinical cases. The consequent B-Mode ultrasound (US) image is calculated and binarized. Finally, the lesion boundary is well-defined by binary dilation technique using MATLAB.&#x0D; GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 5(1), Dec 2018 P 75-78

DCEUS-based multiparametric perfusion imaging using pulse-inversion Bubblet decorrelation.

This study aimed to clarify the influences of composite dynamic contrast-enhanced ultrasound (DCEUS) on multiparametric perfusion imaging (PPI) and to develop a novel PPI scheme through pulse-inversion Bubblet decorrelation (PIBD) to improve its contrast and detailed discriminability. In invivo perfusion experiments on rabbit kidneys, a pair of phase-inverted "Bubblets" was constructed. Phase-inverted raw radiofrequency echoes were reconstructed by using the maximum coefficients obtained from Bubblet decorrelation analysis and summed to form DCEUS loops. Nine perfusion parameters were estimated from these loops and color coded to create the corresponding PIBD-based PPIs. In addition to time-related PPIs, the contrast and detailed discriminability quantified by the average contrast and information entropy of intensity- and ratio-related PPIs were proportional to the microbubble detection sensitivity and microvascular discriminability evaluated by CTR in DCEUS techniques. Compared with the second harmonic, the CTR of DCEUS and the average contrast and information entropy of PPI were significantly improved by 9.03±5.39dB (P<0.01), 6.39±1.38dB (P<0.01), and 0.57±0.15 (P<0.05) in PIBD technique, respectively. As a multiparametric functional imaging technique, these improvements in the proposed scheme can be beneficial to accurately quantify and depict the hemodynamic perfusion features and details of tumor angiogenesis, and further can also assist clinicians in making a confirmed diagnosis.

A Nonlinear Beamformer Based on p-th Root Compression—Application to Plane Wave Ultrasound Imaging

Ultrafast medical ultrasound imaging is necessary for 3D and 4D ultrasound imaging, and it can also achieve high temporal resolution (thousands of frames per second) for monitoring of transient biological phenomena. However, reaching such frame rates involves reduction of image quality compared with that obtained with conventional ultrasound imaging, since the latter requires each image line to be reconstructed separately with a thin ultrasonic focused beam. There are many techniques to simultaneously acquire several image lines, although at the expense of resolution and contrast, due to interference from echoes from the whole medium. In this paper, a nonlinear beamformer is applied to plane wave imaging to improve resolution and contrast of ultrasound images. The method consists of the introduction of nonlinear operations in the conventional delay-and-sum (DAS) beamforming algorithm. To recover the value of each pixel, the raw radiofrequency signals are first dynamically focused and summed on the plane wave dimension. Then, their amplitudes are compressed using the signed p t h root. After summing on the element dimension, the signed p-power is applied to restore the original dimensionality in volts. Finally, a band-pass filter is used to remove artificial harmonics introduced by these nonlinear operations. The proposed method is referred to as p-DAS, and it has been tested here on numerical and experimental data from the open access platform of the Plane wave Imaging Challenge in Medical UltraSound (PICMUS). This study demonstrates that p-DAS achieves better resolution and artifact rejection than the conventional DAS (for p = 2 with eleven plane wave imaging on experimental phantoms, the lateral resolution is improved by 21 % , and contrast ratio (CR) by 59 % ). However, like many coherence-based beamformers, it tends to distort the conventional speckle structure (contrast-to-noise-ratio (CNR) decreased by 45 % ). It is demonstrated that p-DAS, for p = 2 , is very similar to the nonlinear filtered-delay-multiply-and-sum (FDMAS) beamforming, but also that its impact on image quality can be tuned changing the value of p.

Ultrasound Imaging From Sparse RF Samples Using System Point Spread Functions.

Upcoming phased-array 2-D sensors will soon enable fast high-definition 3-D ultrasound imaging. Currently, the communication of raw radio-frequency (RF) channel data from the probe to the computer for digital beamforming is a bottleneck. For reducing the amount of transferred data samples, this paper investigates the design of an adapted sparse sampling technique for image reconstruction inspired by the compressed sensing framework. Echo responses from isolated points are generated using a physically based simulation of ultrasound wave propagation through tissues. These point spread functions form a dictionary of shift-variant bent waves, which depend on the specific sound excitation and acquisition protocols. Speckled ultrasound images can be approximately decomposed in this dictionary where sparsity is enforced at the system matrix design. The Moore-Penrose pseudoinverse is precomputed and used at the reconstruction stage for fast minimum-norm recovery from nonuniform pseudorandom sampled raw RF data. Results on simulated and acquired phantoms demonstrate the benefits of an optimized basis function design for high-quality B-mode image recovery from few RF channel data samples.

A nonlinear approach to identify pathological change of thyroid nodules based on statistical analysis of ultrasound RF signals

In order to reassure the majority of patients with benign nodules from unnecessary needle biopsy, there is an increasing clinical requirement to identify benign and malignant thyroid nodules during ultrasound diagnosis. A nonlinear approach based on statistical analysis of ultrasound radio-frequency (RF) signals was developed for differential diagnosing the thyroid nodules to improve the diagnostic accuracy. Data from 44 patients with solitary thyroid nodules were collected, following with the ultrasound-guided fine needle aspiration (FNA) as the ground truth. The relative P-value (rP-value) was estimated to quantify the pathophysiologic changes by comparing the region of interest (ROI) with the no pathological change part in the thyroid gland using only one frame of raw RF data. The malignant nodules were distinguished from benign ones with high accuracy and high credibility (sensitivity = 100%, specificity = 80%). Suspicious nodules (rP-value < 0.5) could be picked out for FNA with no additional instruments. This method shows promising in differentiating malignant from benign thyroid nodules, subsequently avoiding unnecessary biopsies.

High-Frequency Ultrasound Imaging of Tidemark In Vitro in Advanced Knee Osteoarthritis

High-frequency ultrasound imaging has been widely adopted for assessment of the degenerative changes of articular cartilage in osteoarthritis (OA). Yet, there are few reports on investigating its capability to evaluate subchondral bone. Here, we employed high-frequency ultrasound imaging (25 MHz) to examine in vitro the tidemark in cylindrical osteochondral disks (n = 33) harvested from advanced OA knees of humans. We found good correspondence in morphology observed by ultrasound imaging and micro-computed tomography. Ultrasound roughness index (URI) of tidemark was derived from the raw radiofrequency signals to compare with bone quality factors, including bone volume fraction (BV/TV) and bone mineral density (BMD) measured by micro-computed tomography, using the Spearman correlation (ρ). URI of the tidemark was negatively associated with the subchondral plate BV/TV (ρ = −0.73, p < 0.001), BMD (ρ = −0.40, p = 0.020), as well as the underneath trabecular bone BV/TV (ρ = −0.39, p = 0.025) and BMD (ρ = −0.43, p = 0.012). In conclusion, this preliminary study demonstrated that morphology measured by high-frequency ultrasound imaging could reflect the quality of the subchondral bone. High-frequency ultrasound is a promising imaging tool to evaluate the changes of the subchondral bone in addition to those of the overlying cartilage in OA.

Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions.

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. 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 yr' 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 2-year period. 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 (https://doi.org/10.5281/zenodo.545928) or the website http://bluebox.ippt.gov.pl/~hpiotrzk. The database can be used to test quantitative ultrasound techniques and ultrasound image processing algorithms, or to develop computer-aided diagnosis systems.

EEMD Domain AR Spectral Method for Mean Scatterer Spacing Estimation of Breast Tumors From Ultrasound Backscattered RF Data

We present a novel method for estimating the mean scatterer spacing (MSS) of breast tumors using ensemble empirical mode decomposition (EEMD) domain analysis of deconvolved backscattered radio frequency (RF) data. The autoregressive (AR) spectrum from which the MSS is estimated is obtained from the intrinsic mode functions (IMFs) due to regular scatterers embedded in RF data corrupted by the diffuse scatterers. The IMFs are chosen by giving priority to the presence of an enhanced fundamental harmonic and the presence of a greater number of higher harmonics in the AR spectrum estimated from the IMFs. The AR model order is chosen by minimizing the mean absolute percentage error (MAPE) criterion. In order to ensure that the backscattered data is indeed from a source of coherent scattering, we begin by performing a non-parametric Kolmogorov-Smirnov (K-S) classification test on the backscattered RF data. Deconvolution of the backscattered RF data, which have been classified by the K-S test as sources of significant coherent scattering, is done to reduce the system effect. EEMD domain analysis is then performed on the deconvolved data. The proposed method is able to recover the harmonics associated with the regular scatterers and overcomes many problems encountered while estimating the MSS from the AR spectrum of raw RF data. Using our technique, a mean absolute percentage error (MAPE) of 5.78% is obtained while estimating the MSS from simulated data, which is lower than that of the existing techniques. Our proposed method is shown to outperform the state of the art techniques, namely, singular spectrum analysis, generalized spectrum (GS), spectral autocorrelation (SAC), and modified SAC for different simulation conditions. The MSS for in vivo normal breast tissue is found to be 0.69 ± 0.04 mm; for benign and malignant tumors it is found to be 0.73 ± 0.03 and 0.79 ± 0.04 mm, respectively. The separation between the MSS values of normal and benign tissues for our proposed method is similar to the separations obtained for the conventional methods, but the separation between the MSS values for benign and malignant tissues for our proposed method is slightly higher than that for the conventional methods. When the MSS is used to classify breast tumors into benign and malignant, for a threshold-based classifier, the increase in specificity, accuracy, and area under curve are 18%, 19%, and 22%, respectively, and that for statistical classifiers are 9%, 13%, and 19%, respectively, from that of the next best existing technique.

Characterization of benign thyroid nodules with HyperSPACE (Hyper Spectral Analysis for Characterization in Echography) before and after percutaneous laser ablation: a pilot study

To evaluate the capability of the HyperSPACE (Hyper SPectral Analysis for Characterization in Echography)method in tissue characterization, in order to provide information for the laser treatment of benign thyroid nodules in respectof conventional B-mode images and elastography. The method, based on the spectral analysis of theraw radiofrequency ultrasonic signal, was applied to characterize the nodule before and after laser treatment. Thirty patients(25 females and 5 males, age between 37 and 81 years) with thyroid benign nodule at cytology (Thyr 2) were evaluated byconventional ultrasonography, elastography, and HyperSPACE, before and after laser ablation. The images processedby HyperSPACE exhibit different color distributions that are referred to different tissue features. By calculating the percentagesof the color coverages, the analysed nodules were subdivided into 3 groups. Each nodule belonging to the same groupexperienced, on average, similar necrosis extension. The nodules exhibit different Configurations (colors) distributions thatcould be indicative of the response of nodular tissue to the laser treatmentConclusions: HyperSPACEcan characterize benignnodules by providing additional information in respect of conventional ultrasound and elastography which is useful for supportin the laser treatment of nodules in order to increase the probability of success.