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Kidney transplantation is the preferred long-term treatment modality for patients with end-stage renal disease as it confers improved survival and quality of life devoid of dialysis constraints. However, graft loss is still a concern in transplant recipients, necessitating close monitoring. The estimated glomerular filtration rate (eGFR) is often used as a surrogate marker of kidney function. Many transplant centers use a decline in eGFR to identify injury to the allograft, but this can miss early injury before there is substantial nephron damage. The use of quantitative ultrasound (QUS) parameters could aid in increasing the sensitivity for determining rejection particularly at early stages when management strategies may be implemented to avoid graft loss. In this prospective study, two parameters derived from ultrasonic backscatter coefficients (i.e., the slope and the intercept) were estimated. The cohort consisted of 16 renal transplant patients presenting for protocol renal biopsies. Seven volunteers had mild or moderate interstitial fibrosis with tubular atrophy (IFTA) and nine exhibited no signs of rejection. Even though both QUS parameters increased between the No IFTA and the IFTA groups, with the intercept parameter exhibiting statistically significant differences between the No IFTA and IFTA groups (p

The backscatter coefficient (BSC) quantifies the frequency-dependent reflectivity of tissues. Accurate estimation of the BSC requires knowledge of the attenuation coefficient slope (ACS) of tissues in the beam path between the transducer and the insonified region of interest, namely, the total attenuation. In this study, the total attenuation is calculated as the cumulative sum of values of a local attenuation map devised using full angular spatial compounding (FASC). The BSC was parameterized through the integrated backscatter coefficient (iBSC) obtaining iBSC maps. Experimental validation of the proposed approach consisted of scanning two cylindrical physical phantoms with off-centered inclusions having different ACS and BSC values than the background. Additional iBSC maps were computed assuming an uniform ACS map of 0.5 dB/cm/MHz (which is a value assumed for soft tissues) instead of the FASC-ACS map. Finally a iBSC map was obtained using an ideal ACS map formed with ground truth ACS values and knowledge of inclusion true position. The results were comparable when using the FASC-ACS map or the ideal ACS map in term of inclusion detectability and estimation accuracy. The use of the uniform ACS map resulted in some cases with very high fractional error (>;9 dB), which highlights the relevance of accurate compensation for total attenuation. These results suggest that BSCs can be reliably estimated using total attenuation compensation from FASC-ACS maps.

The backscatter coefficient (BSC) quantifies the frequency-dependent reflectivity of tissues. Accurate estimation of the BSC is only possible with the knowledge of the attenuation coefficient slope (ACS) of the tissues under examination. In this study, the use of attenuation maps constructed using full angular spatial compounding (FASC) is proposed for attenuation compensation when imaging integrated BSCs. Experimental validation of the proposed approach was obtained using two cylindrical physical phantoms with off-centered inclusions having different ACS and BSC values than the background, and in a phantom containing an ex vivo chicken breast sample embedded in an agar matrix. With the phantom data, three different ACS maps were employed for attenuation compensation: (1) a ground truth ACS map constructed using insertion loss techniques, (2) the estimated ACS map using FASC attenuation imaging, and (3) a uniform ACS map with a value of 0.5 dBcm\protect \relax \special {t4ht=−}1MHz\protect \relax \special {t4ht=−}1, which is commonly used to represent attenuation in soft tissues. Comparable results were obtained when using the ground truth and FASC-estimated ACS maps in term of inclusion detectability and estimation accuracy, with averaged fractional error below 2.8 dB in both phantoms. Conversely, the use of the homogeneous ACS map resulted in higher levels of fractional error ( > 10 dB), which demonstrates the importance of an accurate attenuation compensation. The results with the ex vivo tissue sample were consistent with the observations using the physical phantoms, with the FASC-derived ACS map providing comparable BSC images to those formed using the ground truth ACS map and more accurate than those BSC images formed using a uniform ACS. These results suggest that BSCs can be reliably estimated using FASC when a self-consistent attenuation compensation stemming from prior estimation of an accurate ACS map is used.

This work proposes an algorithm aimed at recognizing and accounting Koch bacilli in digital images of microbiological sputum samples stained with auramine, in order to determine the degree of concentration and the state of the disease (tuberculosis). The algorithm was developed with the main objective of maximizing the sensitivity and specificity of the analysis of microbiological samples (recognition and counting of bacilli) according to each preparation method (direct and diluted pellets) in order to reduce the subjectivity of the visual inspection applied by the specialist at the time of analyzing the samples. The proposed algorithm consists of a background removal, an image improvement stage based on consecutive morphological closing operations, a segmentation stage of objects of interest based on thresholdization and a classification stage based on SVM. Each algorithmic stage was developed taking into account the method of preparation of the sample to be processed, being this aspect the main contribution of the proposed work, since it was possible to achieve very satisfactory results in terms of specificity and sensitivity. In this context, sensitivity levels of 91.24% and 93.79% were obtained. Specificity levels of 90.33% and 94.85% were also achieved for direct and diluted pellet methods respectively.

Quantitative ultrasound (QUS) is a set of techniques for tissue characterization which allows to model tissue properties, such as ultrasonic attenuation and backscatter coefficients. Nevertheless, the challenge of QUS parameters estimation still remains as the trade-off between estimates variance and spatial resolution limits the applicability of the estimations. This work aims at providing an effective framework for QUS parameters estimation. Based on this framework, an estimation algorithm is proposed and tested using simulated phantoms and an in vivo assessment of breast fibroadenoma. The proposed algorithm was validated using simulated phantoms, while the in vivo experiment showed consistent results with the literature (i.e. attenuation for fibroadenoma: 1.97 ± 0.33 dB. MHz−1. cm-1, and normal tissue: 0.59 ± 0.13 dB. MHz−1. cm-1). These results suggest the proposed framework has the potential to improve the estimation of QUS parameters.

Phase aberration is a phenomenon caused by heterogeneity of the speed of sound in tissue, in which the actual speed of sound of the tissue is different than the assumed speed of sound used for beamforming. It reduces the quality and resolution of ultrasonic images and impairs clinical diagnostic capabilities. Although phase aberration correction (PAC) methods can reduce these detrimental effects, most practical implementations of PAC methods are based on the near field phase screen model, which have limited ability to represent the true aberration induced by inhomogeneous tissue. Accordingly, we propose a locally adaptive phase aberration correction (LAPAC) method that is applied through the use of synthetic aperture. The method is tested using full-wave simulations of models of human abdominal wall, experiments with tissue aberrators, and in vivo carotid images. LAPAC is compared with conventional phase aberration correction (cPAC) where aberration profiles are computed at a preselected depth and applied to the beamformer’s time delays. For all experiments, LAPAC shows an average of 1 to 2 dB higher contrast than cPAC, and enhancements of 3 to 7 dB with respect to the uncorrected cases. We conclude that LAPAC may be a viable option to enhance ultrasound image quality images even in the presence of clinically relevant aberrating conditions.

Cutaneous leishmaniasis (CL) is an ulcerative skin parasitic disease common in people living in tropical regions and travelers(1) . Lesions characteristics (e.g., self-healing and treatment response) are extremely variable. Diagnosis can be challenging when the lesion is in an advanced stage and can be confused with other pathologies. On histology, epidermal and dermal abnormalities have been described(2) . Panniculitis has been reported as an important feature to distinguish CL from other dermatological lesions(3) . High-frequency ultrasound is a non-invasive imaging tool that depicts tissues with high definition(4) . We aim to describe the ultrasound morphology of confirmed cases of CL and correlation with the reported histopathological characteristics.

Skin characterization is of importance for disease diagnosis and treatment follow-up. In particular, estimation of the biomechanical properties is useful for wound healing monitoring. This study proposes and validates for the first time the use of multi-wave quantitative ultrasound (MWQUS) as a skin characterization tool via the estimation of attenuation coefficient slope (ACS) and shear wave speed (SWS) in vivo based on the regularized spectral-log difference technique (RSLD) and crawling wave sonoelastography (CrW). Nine volunteers without skin injuries (age range of 19-30 years) were scanned in the middle of the dorsal side of the forearm. The median of the ACS and SWS were 1.93 dB•cm-1 •MHz-1 and 3.24 m/s, respectively, which are in good agreement with previous reports in the literature. Results showed a decreasing trend of the SWS with increasing ACS with a statistically significant (p = 0.01) Pearson correlation coefficient of -0.79. The importance of the results presented here lies in that the observed variability in the estimates appears to stem from biological variability jointly explained by both ACS and SWS parameters. These results suggest that both methodologies (RSLD and CrW) can be applied together for a more complete, comprehensive multi-wave QUS characterization of the skin and provide estimates that are sensitive to changes in dermal composition.

The attenuation coefficient slope (ACS) has the potential to be used for tissue characterization and as a diagnostic ultrasound tool, hence complementing B-mode images. The ACS can be valuable for the estimation of other ultrasound parameters such as the backscatter coefficient. There is a well-known tradeoff between the precision of the estimated ACS values and the data block size used in the spectral-based techniques such as the spectral-log difference (SLD). This tradeoff limits the practical usefulness of the spectral-based attenuation imaging techniques. In this paper, the regularized SLD (RSLD) technique is presented in detail, and evaluated with simulations and experiments with physical phantoms, ex vivo and in vivo . The RSLD technique allowed decreasing estimation variance when using small data block sizes, i.e., fivefold reduction in the standard deviation of percentage error when using data block sizes larger than $20\lambda \times 20\lambda $ and more than a tenfold reduction when using $10\lambda \times 10\lambda $ data blocks. The precision improvement was obtained without sacrificing estimation accuracy (i.e., estimation bias improved in 70% of the cases by 10% of the ground truth-value on average while degraded in 30% of the cases by 3% of the ground truth-value on average). The improvements in precision allowed for better differentiation of inclusions especially when using small data blocks (i.e., smaller than $20\lambda \times 20\lambda $ ) where the contrast-to-noise ratio improved by an order of magnitude on average. The results suggest that the RSLD allows for the reconstruction of attenuation coefficient images with an improved tradeoff between spatial resolution and estimation precision.

Cutaneous Leishmaniasis is a skin infection which is commonly present in underdeveloped countries. The incidence is particularly high in amazonic countries of Latin-America like Brazil, Colombia and Perú and is usually reported as endemic. In Perú, more than one million people are at risk of infection and approximately 6,000 new cases are detected each year. The present work proposes to integrate a set of control, monitoring and disease quantification procedures in: (1) an automated tool to expedite the analysis in laboratories studying parasiticidal agents and (2) a non-invasive treatment monitoring protocol. The first, consists in the adaptation of an optical microscope KRUSS MBL3100 to perform a fast image capture and automated promastigotes identification. This may be of value to evaluate the effectiveness of various parasiticidal agents. The counting process is performed by an automatic segmentation in the RGB green color space discriminating elements by their area. The second, proposes a protocol for monitoring the evolution of the disease treatment divided into three stages: supra-skin modeling and reconstruction, subcutaneous exploration by textural characteristics and volumetric segmentation. This protocol is performed using a Next Engine HD 3D scanner and a Vevo Visualsonix 2100 ultrasonic scanner. The results show improvements in sample processing time, accuracy and inter- and intra-operational variability. The sensitivity and accuracy of the microscopic identification system was of 97% and 92% respectively. The exactitude and precision error was up to 2% and the sensitivity and specificity went as high as 71% in the 3D reconstruction and texture analysis respectively.

The minimum variance (MV) beamformer has the potential to enhance the resolution and contrast of ultrasound images but is sensitive to steering vector errors. Robust MV beamformers have been proposed but mainly evaluated in the presence of gross sound speed mismatches, and the impact of phase aberration correction (PAC) methods in mitigating the effects of phase aberration in MV beamformed images has not been explored. In this study, an analysis of the effects of aberration on conventional MV and eigenspace MV (ESMV) beamformers is carried out. In addition, the impact of three PAC algorithms on the performance of MV beamforming is analyzed. The different beamformers were tested on simulated data and on experimental data corrupted with electronic and tissue-based aberration. It is shown that all gains in performance of the MV beamformer with respect to delay-and-sum (DAS) are lost at high aberration strengths. For instance, with an electronic aberration of 60 ns, the lateral resolution of DAS degrades by 17% while MV degrades by 73% with respect to the images with no aberration. Moreover, although ESMV shows robustness at low aberration levels, its degradation at higher aberrations is approximately the same as that of regular MV. It is also shown that basic PAC methods improve the aberrated MV beamformer. For example, in the case of electronic aberration, multi-lag reduces degradation in lateral resolution from 73% to 28% and contrast loss from 85% to 25%. These enhancements allow the combination of MV and PAC to outperform DAS and PAC and ESMV in moderate and strong aberrations. We conclude that the effect of aberration on the MV beamformer is stronger than previously reported in the literature and that PAC is needed to improve its clinical potential.

Lymph nodes characterization plays an important role in cancer staging and detecting local or distant recurrences. Previous studies have demonstrated the usefulness of ultrasound backscatter coefficients (BSCs) at high frequencies (16.4 - 33.6 MHz) for detecting micro-metastasis of colorectal, gastric and breast cancers in lymph nodes. However, these studies were conducted ex vivo. In this work, the feasibility of diagnosing metastatic cervical lymph nodes in vivo using BSCs within a clinical frequency range (3.5 - 10 MHz) was evaluated. The reference phantom method was used to estimate the BSC curves from 24 patients examined in a local oncology center for suspicious cervical nodes (metastatic spread at diagnosis or tumor recurrence). For all cases, the diagnosis was provided by a trained histopathologist through confirmatory biopsies. Classification was performed using linear logistic regression with leave-one-out cross-validation and operating points chosen to maximize Youden's index in the corresponding ROC curves. Overall, 12 out of 24 cases had malignant infiltration (50%). The BSC slope and intercept values for the malignant cases were higher and lower than the ones of the benign cases, respectively. Classification using both parameters resulted in a sensitivity of 91.7% and a specificity of 75%. This study presents the first report of in vivo assessment of lymph nodes for metastatic infiltration using BSCs. The preliminary results suggest that it may be possible to discriminate between benign and malignant nodes using BSCs in vivo.

Quantitative ultrasound parameters such as backscatter and attenuation coefficients can be used to characterize tissues along with B-mode image ultrasound. However, these coefficients must be accurately found, and can be hard to measure. Simultaneous estimation of both backscatter coefficient and the total attenuation reduces the necessity of rely on previous studies to accurate know the attenuation of each separate tissue. Furthermore, the total attenuation up to the region of analysis can be also found on heterogeneous media, such as gynecological tissues. A technique for this estimation, the least square fitting (LSF) method, has been proposed to simultaneously estimate the total attenuation coefficient slope $\alpha$ , the magnitude $\mathbf{b}$ and frequency dependence $\mathbf{n}$ of the backscatter coefficient. The aim of the present study is to analyze the precision and accuracy of the LSF method. Simulated data, physical calibrated phantoms and six healthy thyroids cases have been used to this purpose. The estimation bias was used to quantify the accuracy of the method with the simulated and physical phantom data, whereas the coefficient of variation (CV) was used to quantify the precision of the method with simulated, physical phantom and in vivo data. The analysis was performed with regions of $12\lambda$ axially and varying lateral size. In both simulations and physical phantom experiments, the CV ranged between 0.003 and 1.29 for all estimated variables. With the in vivo data, the maximum CV values were 4.25 for $\mathbf{b}$ , 2.15 for $\mathbf{n}$ and 0.86 for the ACS. This increase may be partially explained by the reduced number of effective lines per region (3–6 lines) for the in vivo data when compared to the simulated and physical phantom data (down to 20–30 lines) The bias of $\mathbf{n}$ and the total attenuation was less of 10% for the simulated and physical phantoms, but increased to 50-100% for $\mathbf{b}$ , The results suggest that for regions of analysis with clinically relevant sizes the precision of the method is compromised but accurate estimates of $\alpha$ and $\mathbf{n}$ may be obtained.

Pulse Inversion (PI) is a transmission technique used for harmonic imaging. By transmitting two phase inverted signals, which allow cancelling out the fundamental component, PI is able to achieve better image quality thanks to the reduced clutter/reverberation and to the improved bandwidth of the second harmonic. Here, a technique based on Resolution Enhancement Compression (REC) that improves the performance of PI is proposed. It consists in boosting the energy of the excitation signal at the frequencies where the probe is less efficient, which further improves the bandwidth of the second harmonic signal. The proposed approach (REC-PI) is combined with coherent plane wave compounding and implemented on the UlaOp-256 ultrasound scanner. Promising results are obtained from in-vitro experiments using an excitation signal designed to provide a 35% increase in axial resolution over the one obtained with a broadband pulse excitation. The experimental measured axial resolution from the echoes was improved by 25.4% for the wire phantom, when using the REC-PI technique compared to classical PI. Further, the axial speckle size evaluated on the in-vitro phantom B-mode image was enhanced by 20%. The CNR was also improved by 18.2 dB. These results demonstrate the feasibility of our approach and its implementation on the research scanner UlaOp-256.

Few studies of in vivo normal human dermis characterization have been reported. In this study, the regularized spectral-log difference (RSLD) method was used for the estimation of the attenuation coefficient slope (ACS) in the forearm and thigh dermis with the objective of complementing previous studies on characterization of the forearm dermis and exploring the variations of ACS at different anatomic sites. Ten volunteers without skin injuries between 19–27 years old were scanned in the middle of the ventral side of the forearm and the anterior side of the thigh. The acquisition was performed using a high-frequency ultrasound scanner. The ACS was estimated within an analysis band spanning from 8 to 27 MHz. The average ACS in the forearm dermis was 2.07 dB. cm−1. MHz−1, which is in agreement with the slope found in the literature. In addition, a significant difference (p < 0.05) was found between the ACS in the forearm dermis and the thigh dermis (average ACS of 2.54 dB. cm−1. MHz−1), which is consistent with the expected reduced collagen content in the forearm dermis compared with the thigh dermis due to increased sun exposure. Therefore, these results suggest that ACS has the potential to be used as a tool for in vivo skin characterization.

Elastography provides tissue stiffness information that attempts to characterize the elastic properties of tissue. However, there is still limited literature comparing elastographic modalities for tissue characterization. This study focuses on two quantitative techniques using different vibration sources that have not been compared to date: crawling wave sonoelastography (CWS) and single tracking location shear wave elasticity imaging (STL-SWEI). To understand each technique’s performance, shear wave speed (SWS) was measured in homogeneous phantoms and ex vivo beef liver tissue. Then, the contrast, contrast-to-noise ratio (CNR), and lateral resolution were measured in an inclusion and two-layer phantoms. The SWS values obtained with both modalities were validated with mechanical measurements (MM) which serve as ground truth. The SWS results for the three different homogeneous phantoms (10%, 13%, and 16% gelatin concentrations) and ex vivo beef liver tissue showed good agreement between CWS, STL-SWEI, and MM as a function of frequency. For all gelatin phantoms, the maximum accuracy errors were 2.52% and 2.35% using CWS and STL-SWEI, respectively. For the ex vivo beef liver, the maximum accuracy errors were 9.40% and 7.93% using CWS and STL-SWEI, respectively. For lateral resolution, contrast, and CNR, both techniques obtained comparable measurements for vibration frequencies less than 300 Hz (CWS) and distances between the push beams ( $\Delta x$ ) between 3 mm and 5.31 mm (STL-SWEI). The results obtained in this study agree over an SWS range of 1–6 m/s. They are expected to agree in perfectly linear, homogeneous, and isotropic materials, but the SWS overlap is not guaranteed in all materials because each of the three methods have unique features.

Fine-needle aspiration (FNA) remains the gold standard for the diagnosis of thyroid cancer. However, currently, a large number of FNA biopsies result in negative or undetermined diagnosis, which suggests that better noninvasive tools are needed for the clinical management of thyroid cancer. Spectral-based quantitative ultrasound (QUS) characterizations may offer a better diagnostic management as previously demonstrated in mouse cancer models ex vivo . As a first step toward understanding the potential of QUS markers for thyroid disease management, this paper deals with the spectral-based QUS estimation of healthy human thyroids in vivo . Twenty volunteers were inspected by a trained radiologist using two ultrasonic imaging systems, which allowed them to acquire radio-frequency data spanning the 3–16-MHz frequency range. Estimates of attenuation coefficient slope (ACS) using the spectral logarithmic difference method had an average value of $1.69\;\text{dB}/(\text{cm}\cdot \text{MHz}$ ) with a standard deviation of $0.28\;\text{dB}/(\text{cm}\cdot \text{MHz}$ . Estimates of backscatter coefficient (BSC) using the reference-phantom method had an average value of $0.18\;\text{sr}^{-\text{1}} \cdot \text{cm}^{-\text{1}}$ over the useful frequency range. The intersubject variability when estimating BSCs was less than 1.5 dB over the analysis frequency range. Further, the effectiveness of three scattering models (i.e., fluid sphere, Gaussian, and exponential form factors) when fitting the experimentally estimated BSCs was assessed. The exponential form factor was found to provide the best overall goodness of fit ( ${R}^{2} = 0.917$ ), followed by the Gaussian ( ${{{R}}^2} = 0.807$ ) and the fluid-sphere models ( ${{{R}}^2} = 0.752$ ). For all scattering models used in this study, average estimates of the effective scatterer diameter were between 44 and $56\;{\mu {\rm m}}$ . Overall, an excellent agreement in the estimated attenuation and BSCs with both scanners was exhibited.

Skin elastic properties change during a cutaneous disorder or in the aging process. Deep knowledge of skin layers helps monitoring and diagnosing structural changes. High frequency ultrasound (HF-US) has been recently introduced to diagnose and evaluate some dermatological disorders in the clinical practice. US elastography adds elasticity information of the analyzed tissue. In particular, harmonic elastography estimates the speed of shear waves produced by external vibration sources, in order to relate the shear wave speed to the Young's modulus. In the epidermis and dermis layers, shear waves are not generated; in contrast, surface acoustic waves (SAWs) exist as they propagate in the top of the tissue. This study uses crawling wave sonoelastography for the estimation of SAWs in human thigh dermis in vivo. Experiments were performed in ten volunteers in the range of 200 - 500 Hz. As other studies suggest, SAW speed needs to be compensated to reach shear wave speed, for calculating the Young's modulus. Thus, the SAW speed estimated was corrected when it propagates in solidUS gel interface. Specifically, the elasticity modulus found was $18.35 \pm 1.04$ KPa for a vibration frequency of 200 Hz. Results suggest that the elasticity modulus can be estimated in vivo using crawling wave HF-US for skin application and shows potential for future application in skin disorders.

Conventional echographic imaging depicts anatomical structure by displaying the magnitude of backscattered ultrasound echoes. However, ultrasonic radiofrequency data contains a richer information content that can be exploited for constructing images of intrinsic tissue properties. In particular, spectral-based ultrasonic tissue characterization techniques allow imaging parameters such as the backscatter (BSC) and attenuation (AC) coefficients. Even though this type of analysis has been explored for decades, several challenges ranging from technical algorithmic issues to the lack of widely validated, successful clinical applications have limited efforts directed towards these imaging modalities. The formulation of BSC and AC estimation as a 2-D inverse problem in combination with regularization methods is discussed, and this approach is shown to significantly extend the trade-off between estimation precision and spatial resolution. Recent applications of BSC and AC estimation in vivo are also discussed, including the assessment of interstitial fibrosis and tubular atrophy (IFTA) and inflammation in renal allografts, the diagnosis of cervical lymph nodes, and the high frequency characterization of skin.Conventional echographic imaging depicts anatomical structure by displaying the magnitude of backscattered ultrasound echoes. However, ultrasonic radiofrequency data contains a richer information content that can be exploited for constructing images of intrinsic tissue properties. In particular, spectral-based ultrasonic tissue characterization techniques allow imaging parameters such as the backscatter (BSC) and attenuation (AC) coefficients. Even though this type of analysis has been explored for decades, several challenges ranging from technical algorithmic issues to the lack of widely validated, successful clinical applications have limited efforts directed towards these imaging modalities. The formulation of BSC and AC estimation as a 2-D inverse problem in combination with regularization methods is discussed, and this approach is shown to significantly extend the trade-off between estimation precision and spatial resolution. Recent applications of BSC and AC estimation in vivo are also discussed, in...

International audience; The axial resolution of an ultrasound imaging system is inversely proportional to the bandwidth of the emitted signal. When conventional pulsing (CP) is used, the impulse response of the transducer and the excitation signal determine together the shape of the emitted pulse and its bandwidth. A way to increase the ultrasound image resolution is to increase the transducer's limited passband. The resolution enhancement compression (REC) is a coding technique that boosts the signal energy in the transition frequency bands, where the energy transduction of the ultrasound probe is less efficient. Consequently , image quality metrics including axial resolution, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) can be improved. In this paper, the objective is to combine REC with coherent plane-wave compounding (CPWC) in order to achieve better image quality at an ultrafast acquisition rate. Promising results are obtained from both wire and cyst phantoms using an excitation signal designed to provide a 54% increase in bandwidth over the one obtained with a broadband pulse excitation at −6 dB. The experimental bandwidth measured from the backscattered echoes was improved by 49% for the wire phantom, when using the CPWC-REC technique compared to CPWC-CP. Furthermore, the axial resolution as derived from the modulation transfer function of the envelope of the wire target was enhanced by 29%. The CNR and SNR were improved up to 9 and up to 4 dB, respectively, in the cyst phantom. These results reveal that CPWC-REC is able to achieve higher spatial resolution, compared to CPWC-CP, with better SNR and CNR. Moreover, experimental results show that an effective implementation on a research scanner of REC using plane-wave imaging is possible. Consistent in vivo acquisition results on rabbit are presented and discussed.

Recent studies suggest that Resolution Enhancement Compression (REC) can provide significant improvements in terms of imaging quality over Classical Pulsed (CP) ultrasonic imaging techniques, by employing frequency and amplitude modulated transmitted signals. However the performance of coded excitations methods degrades drastically deeper into the tissue where the attenuation effects become more significant. In this work, a technique that allows overcoming the effects of attenuation on REC imaging is proposed (REC-Opt). It consists in compensating the attenuation effects at each depth in reception. Combined with coherent plane wave compounding (CPWC), REC-Opt was compared to the performance of conventional REC (without attenuation compensation) and CP. With experimental data at 3.25 cm depth in a phantom with an attenuation coefficient slope of 0.5 dB/MHz/cm and using an 8.5 MHz probe, REC-Opt enhanced the bandwidth by 40.6% compared to CP, against an enhancement of only 6% between REC and CP using the same excitation signal designed to provide a 42% increase in bandwidth. The bandwidth enhancements translated into axial resolution improvements of 30% and 3% for REC-Opt vs. CP and REC vs. CP, respectively. This study suggests that REC-Opt is an efficient method to overcome attenuation effects in soft tissues, knowing their attenuation coefficient.

Breast cancer is a public health problem with ~ 1.7 million new cases per year worldwide and with several limitations in the state-of-art screening techniques. Ultrasound elastography involves a set of techniques intended to facilitate the noninvasive diagnosis of cancer. Among these, Vibro-elastography is an ultrasound-based technique that employs external mechanical excitation to infer the elastic properties of soft tissue. In this paper, we evaluate the Vibro-elastography performance in the differentiation of benign and malignant breast lesions. For this study, a group of 18 women with clinically confirmed tumors or suspected malignant breast lesions were invited to participate. For each volunteer, an elastogram was obtained, and the mean elasticity of the lesion and the adjacent healthy tissue were calculated. After the acquisition, the volunteers underwent core-needle biopsy. The histopathological results allowed to validate the Vibro-elastography diagnosis, which ranged from benign to malignant lesions. Results indicate that the mean elasticity value of the benign lesions, malignant lesions and healthy breast tissue were 39.4 ± 12 KPa, 55.4 ± 7.02 KPa and 23.91 ± 4.57 kPa, respectively. The classification between benign and malignant breast cancer was performed using Support Vector Machine based on the measured lesion stiffness. A ROC curve permitted to quantify the accuracy of the differentiation and to define a suitable cutoff value of stiffness, obtaining an AUC of 0.90 and a cutoff value of 44.75 KPa. The results obtained suggest that Vibro-elastography allows differentiating between benign and malignant lesions. Furthermore, the elasticity values obtained for benign, malignant and healthy tissue are consistent with previous reports.

Pneumonia is one of the most common acute respiratory infections among pediatric populations worldwide. Ultrasound is becoming increasingly important in the diagnosis of lung diseases as a more portable and safer alternative to X-ray imaging. In the current work, we present a new automatic system for detection of B-lines, one of the distinctive features of pneumonic ultrasound scans, using amplitude modulation-frequency modulation (AM-FM). Features are evaluated on 109 videos obtained from 100 pediatric patients using a Verasonics V1 scanner. Further, the results were compared to the ones obtained with a previously published spectral feature (SF) method. Sensitivities of 92% and 83% and specificities of 91% and 70% were obtained on zone-1 and zone-2 of the lungs, respectively. In contrast, the SF method provided sensitivities of 72% and 68% in zone-1 and zone-2, respectively, and specificities of 68% and 46% in zone-1 and zone-2, respectively. In addition, the AM-FM method allowed increasing the F1-score when compared to the SF method from 70% to 87% and from 61% to 78% in zone-1 and zone-2, respectively. The results suggest the proposed method may be useful for the computer assisted diagnosis of pneumonia.

The H-scan analysis of ultrasound images is a matched-filter approach derived from analysis of scattering from incident pulses in the form of Gaussian-weighted Hermite polynomial functions. This framework is applied in a preliminary study of thyroid lesions to examine the H-scan outputs for three categories: normal thyroid, benign lesions, and cancerous lesions within a total group size of 46 patients. In addition, phantoms comprised of spherical scatterers are analyzed to establish independent reference values for comparison. The results demonstrate a small but significant difference in some measures of the H-scan channel outputs between the different groups.

Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition. The approach presented here is based on the analysis of brightness distribution patterns present in rectangular segments (here called "characteristic vectors") from the ultrasound digital images. In a first step we identified and eliminated the skin and subcutaneous tissue (fat and muscle) in lung ultrasound frames, and the "characteristic vectors"were analyzed using standard neural networks using artificial intelligence methods. We analyzed 60 lung ultrasound frames corresponding to 21 children under age 5 years (15 children with confirmed pneumonia by clinical examination and X-rays, and 6 children with no pulmonary disease) from a hospital based population in Lima, Peru. Lung ultrasound images were obtained using an Ultrasonix ultrasound device. A total of 1450 positive (pneumonia) and 1605 negative (normal lung) vectors were analyzed with standard neural networks, and used to create an algorithm to differentiate lung infiltrates from healthy lung. A neural network was trained using the algorithm and it was able to correctly identify pneumonia infiltrates, with 90.9% sensitivity and 100% specificity. This approach may be used to develop operator-independent computer algorithms for pneumonia diagnosis using ultrasound in young children.

Skin lesions change elastic properties near the surface. In the last decades, several non-invasive elastography techniques have been developed for detecting the mechanical properties of tissue. In particular, harmonic elastography is characterized for inducing shear wave propagation by an external vibrator in order to estimate shear modulus. However, near the boundary region, propagation is governed by surface acoustic waves (SAW). This paper combines crawling waves elastography with a high-frequency ultrasound (HFUS) system for the estimation of the SAW-to-shear compensation factor when ultrasound (US) gel is used as coupling interface. Experiments explore the SAWspeed in a homogeneous phantom with a solid-water interface in order to corroborate theoretical findings. Subsequently, experiments in a solid-US gel interface are conducted in order to find the correct compensation factor. Preliminary results suggest that SAW propagation can be detected using HFUS, and shear velocity maps can be generated by applying the estimated empirical correction factor. This study will potentially avoid the underestimation of shear modulus when using SAW-based HFUS elastography which is promising for the better diagnosis of skin diseases.

Quantitative ultrasound (QUS) based on backscatter coefficient (BSC) estimation has shown potential for tissue characterization. Beamforming using plane wave compounding has advantages for echographic, Doppler, and elastographic imaging; however, to date, plane wave compounding has not been experimentally evaluated for the purpose of BSC estimation. In this study, two BSC-derived parameters (i.e., the BSC midband fit and intercept) were estimated from experimental data obtained using compound plane wave beamforming. For comparison, QUS parameters were also estimated from data obtained using both fixed focus and dynamic receive beamforming. An ultrasound imaging system equipped with a 9-MHz center frequency, 64-element array was used to collect data up to a depth of 45 mm. Two gelatin phantoms with randomly distributed 20-μm inclusions with a homogeneous scatterer concentration and a two-region scatterer concentration were used for assessing the precision and lateral resolution of QUS imaging, respectively. The use of plane wave compounding resulted in accurate QUS estimation (i.e., bias in the BSC parameters of less than 2 dB) and relatively constant lateral resolution (i.e., BSC midband fit 10% to 90% rise distance ranging between 1.0 and 1.5 mm) throughout a 45 mm field of view. Although both fixed focus and dynamic receive beamforming provided the same performance around the focal depth, the reduction in SNR away from the focus resulted in a reduced field of view in the homogeneous phantom (i.e., only 28 mm). The lateral resolution also degraded away from the focus, with up to a 2-fold and 10-fold increase in the rise distance at 20 mm beyond the focal depth for dynamic receive and fixed focus beamforming, respectively. These results suggest that plane wave compounding has the potential to improve the performance of spectral-based quantitative ultrasound over other conventional beamforming strategies.

International audience; Background, Motivation and ObjectiveIn ultrafast imaging using pulse compression, the image quality is limited by the bandwidth of the ultrasonic (US) transducer. Thus extending the effective bandwidth of the US transducers could improve the performance of the current US imaging methods. Resolution Enhancement Compression (REC) allows bandwidth enhancement by exciting theultrasound transducer with frequency and amplitude modulated signals. By compressing these signals in reception with a Modified Wiener Filter (MWF), REC boosts the energy of the backscattered echoes in the frequency bands where the transducer operates inefficiently. This technique has been successfully applied on piezoelectric US probes. However, noreal study was conducted on Capacitive Micromachined Ultrasonic Transducers (CMUT). This work is focussed on the implementation of the REC on a CMUT probe in order to evaluate the resolution improvement.Statement of Contribution/MethodsThe pre-enhanced chirp, Vpre, used as the excitation for the US transducer in REC is obtained using Eq1. Vlin is a 4μs frequency modulated chirp tapered with a 20% Tukey cosine window. h1 is the Impulse Response (IR) of the transducer and h2 is the desired IR, of a bandwidth larger than the one of h1. The compression of the received signals is performedusing the MWF as in Eq2 which represents the emitted signal adjusted with regard to transducer’s bandwidth and acquisition noise. REC was implemented in simulation, by considering the experimental IR of the CMUT probe, measured using a hydrophone. Experimental results were obtained using the research platform UlaOP. The performance of theMWF compression was evaluated on a single target and an anechoic cyst centred at 22mm depth. The media was insonified using 13 plane waves with steering angles distributed between ±12°.Results/DiscussionThe objective was achieved by boosting the bandwidth of the CMUT probe by 22 % (at -6 dB and -10 dB) using the REC approach, compared to a Conventional Pulse (CP) emission (E). A better axial resolution is noticed (+ 15 % at -6 dB): respectively 210 μm and 242 μm for REC and CP (A, B). The CNR is also improved using the REC technique 7.45 dBagainst 6.36 dB for CP (C, D).

In vivo estimation of attenuation coefficients is useful because of its potential for tissue characterization and relevance in accurate backscatter coefficient estimation. Recently, the regularized spectral log difference (RSLD) technique was proposed to extend the trade-off between precision and spatial resolution in attenuation coefficient slope (ACS) estimation. The aim of this pilot study is to validate the feasibility of in vivo estimation of ACSs from thyroid nodules using the RSLD technique. In vivo data from thyroid nodules was acquired in an oncology clinic right before fine needle aspiration biopsy (FNAB) procedure. Six nodules were analyzed in this study having FNAB results reported as either Bethesda II (benign) or VI (cancer). The nodules had an average diameter of 1.56 cm and their biopsy results were adenomatoid nodules (three cases), Hashimoto's thyroiditis (two cases), and papillary carcinoma (one case). In addition, healthy thyroid regions in three additional patients were also evaluated. The ACS estimation was performed with 16 wavelength axially by 16 wavelengths laterally data blocks. The average ACS values in healthy thyroid tissues and Hashimoto's thyroiditis nodules were found to be 1.62 ± 0.27 and 0.97 ± 0.08 dB.cm−1.MHz−1, which are consistent with previous reports in this subject in the literature. The ACS values for the adenomatoid nodules and the papillary carcinoma were 0.94 ± 0.07 and 0.87 dB.cm−1.MHz−1, respectively. All nodules had lower ACS values than the ones in healthy thyroid tissues. These results suggest that the RSLD method has potential for producing accurate and precise estimates of ACS values in vivo.

Dispositional optimism is the variable within Positive Psychology that has been studied in different environments, as it is presented as related and predictor of psychological variables and health environments. The present study investigated whether there were significant differences in Optimism levels measured through the Life Orientation Test (LOT-R) according to the country of residence of the people, in an accidental sample of 464 people from Argentina, Mexico and Chile. No significant differences were found in the Optimism dimension, but in the Pessimism dimension, where Argentina presents the highest levels. In addition, significant differences were detected in 2 of the items in the Optimism dimension, where Mexican residents presented themselves with better levels, and in an item of the dimension Pessimism. The results are limited to the study that was carried out but they emphasize the importance of carrying out more studies in subjects that the present governments are working to improve the quality of life of the people that can be directly influenced by positive variables like Optimism.

Pneumonia is a disease which causes high mortality in children under five years old, particularly in developing countries. This paper proposes a novel application of ultrasound video analysis for the detection of pneumonia. This application is based on the processing of small video chunks, in which an image processing algorithm analyzes each frame to get some overall video statistics. Then, based on these quantities, the likeness of presence of pneumonia in the video is determined. The algorithm exploits different geometrical properties of typical anatomical and pathological features that commonly appear in lung sonography and which are already clinically typified in the literature. Our technique has been tested on different transverse thoracic scanning protocols and probe's maneuvers, thus, under a variety of clinical and usage protocols. Then, it can be targeted towards screening applications. We present encouraging results (AUC measure between 0.7851 and 0.9177) based on the analysis of 346 videos with an average duration of eight seconds. The analyzed videos were taken from children who were between three and five years old. Finally, our algorithm can be used directly as a classifier, but we detail how its performance may be enhanced if used as a first stage of a larger pipeline of other complementary pneumonia detection processes.

The minimum variance (MV) beamformer is a method that has the potential to enhance the resolution and contrast of ultrasound images. However, it suffers from sensitivity to speed of sound errors and aberration. Although there have been several studies on the application of phase aberration correction (PAC) methods to conventional delay-and-sum (DAS) beamforming, the benefits of PAC methods in mitigating the effects of phase aberration in MV beamformed images are not well understood. A study of this type would be helpful in designing a robust beamformer based on PAC knowledge present in the literature. This study analyzes three PAC algorithms (multi-lag cross-correlation, Rigby's beamsum and scaled covariance matrix) and their impact on the performance of the MV beamformer in the presence of second order phase aberrations. The PAC methods in combination with the MV beamformer were tested on simulated and experimental data corrupted with an electronically created near field phase aberrator. It is shown that all gains in performance of the MV beamformer with respect to DAS is lost at high aberration strengths. For instance, at 60 ns of aberration the lateral resolution of DAS degrades by 22% while MV degrades by 600%. It is also shown that basic PAC methods improve the aberrated MV beamformer. PAC methods reduces degradation in lateral resolution from 600% to 5%. Similar improvements are observed in peak sidelobe level (96% to 27%), contrast (88% to 49% for the simulations and 43% to 15% for experiments) and contrast-to-noise ratio (86% to 42% for the simulations and 68% to 55% for experiments). These enhancements allow the MV beamformer to outperform DAS even in the strongest aberration case.

Tissue characterization using quantitative ultrasound (QUS) parameters has received significant attention in recent years due to its potential to improve the detection and diagnosis of diseased states. However, the vast majority of studies in QUS tissue typing have used parameters derived from either longitudinal or shear waves in isolation, thereby discarding potentially useful complementary information these parameters may carry. In this study, the simultaneous estimation of backscatter coefficients (derived from longitudinal waves) and shear modulus (derived from shear waves) was implemented on data from a clinical scanner. Both parameters were estimated from five ex vivo porcine kidney samples and used to calculate the anisotropy ratio in the parameters when analyzing the middle and pole regions of the kidneys. For all samples, the estimated parameters were higher in the pole regions than in the middle region, with anisotropy ratios of 1.42±0.11 and 3.07±0.70 for the shear modulus and the backscatter coefficient, respectively. Therefore, these results demonstrate that QUS parameters derived from both longitudinal and shear waves can be estimated simultaneously and may be used in conjunction to track changes in tissue structure and composition.

Pediatric Pneumonia is one of the principal causes of death by year on children under the age of five worldwide. The diagnosis is commonly made by clinical criteria with support from imaging tools like radiography. Lung ultrasound has been considered a low-cost and portable alternative for pneumonia imaging; however, interpretation is subjective and requires adequate training. In the present work, a pneumonia detection algorithm based on the measurement of the fundamental bandwidth downshift over depth of ultrasound radiofrequency (RF) signals is presented. RF-data was obtained from lung ultrasound samples of children aged between six months and five years. Sampling was performed using a 6.6 MHz linear transducer. The sample consisted of 10 positive-and 10 negative-diagnosed RF cine-loops selected by a medical expert and captured in a local pediatric health institute. For each frame, several regions of interest were outlined starting from the pleural line. Corresponding functions for each RF-line of the maximum frequency decrement rate over depth from the fundamental spectra at a fixed bandwidth were estimated and linearly fitted. Finally, a descriptor function was build concatenating all fitted values from the RF-lines for each frame respectively. Each descriptor function was later thresholded to differentiate between healthy and pneumonic regions frame-wise. The optimal threshold was found to be 0.46 MHz/cm and was selected based on a receiver operator characteristic (ROC) curve analysis. The results revealed an accuracy rate higher than 90% on the sample.

Crawling wave sonoelastography (CWS) provides quantitative stiffness information from an examined tissue by the application of two mechanical vibrations that generates an interference pattern which is analyzed to reconstruct a shear wave speed (SWS) image. While CWS performance using parallel excitation setup (PES) has been widely studied in the literature, its implementation is complicated to apply in tissue (i.e. breast, liver) of the technique in human tissue often requires a normal excitation setup. The aim of this study is to validate the SWS estimation using a normal excitation setup with two different types of coupling attachments for the surface -i.e. surface plate (SP), rounded head (RH)- and perform a quantitative comparison among the three techniques (PES,SP,RH) in homogeneous and heterogeneous gelatin phantoms. The comparison of the three excitation methods was performed using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), coefficient of variation(CV) and bias over a range of excitation frequencies. (PES:3.74 ±0.14 m/s, SP: 3.43 ±0.14 m/s and RH: 3.32 ± 0.07 m/s) and in the inclusion (PES:4.96 ± 0.18 m/s, SP: 4.88 ± 0.32 m/s and RH: 4.64 ± 0.29 m/s) show that both normal excitation setups are comparable to the PES to estimate the SWS. This suggests that this type of setup could be used for ex vivo and in vivo analysis.

Pneumonia is one of the major causes of child mortality, but it is curable if one can achieves early diagnostics. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. The use of ultrasound to detect pneumonia is limited by the image analysis for interpretation, which is carried by human experts. Pattern recognition and image analysis is a potential tool to facilitate recognition of pneumonia consolidates in absence of medical experts for automatic diagnostics. To perform an automatic analysis of lung ultrasound images for pneumonia detection, the noise introduced by the image portion of the skin, notably complicates the processing and interpretation. This paper presents a methodology to recognize and eliminate the portion of the skin in lung ultrasound images.

International audience; Pneumonia is one of the major causes of child mortality. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. This paper presents a method for automatic diagnostics of pneumonia using ultrasound imaging of the lungs. The approach presented here is based on the analysis of patterns present in rectangular segments from the ultrasound digital images. Specific features from the characteristic vectors were obtained and classified with standard neural networks. A training and testing set of positive and negative vectors were compiled. Vectors obtained from a single patient were included only in the testing or in the training set, but never in both. Our approach was able to correctly classify vectors with evidence of pneumonia, with 91.5% sensitivity and 100% specificity.

This work proposes an algorithm oriented to the detection of tuberculosis bacilli in digital images of sputum samples, inked with the Ziehl Neelsen method and prepared with the direct, pellet and diluted pellet methods. The algorithm aims at automating the optical analysis of bacilli count and the calculation of the concentration level. Several algorithms have been proposed in the literature with the same objective, however, in no case is the performance in sensitivity and specificity evaluated for the 3 preparation methods. The proposed algorithm improves the contrast of the colors of interest, then thresholds the image and segments by labeling the objects of interest (bacilli). Each object then has its geometrical descriptors and photometric descriptors. With all this, a characteristic vector is formed, which are used in the training and classification process of an SVM. For the training 225 images obtained by the 3 preparation methods were used. The proposed algorithm reached, for the direct method, a sensitivity level of 93.67% and a specificity level of 89.23%. In the case of the Pellet method, a sensitivity of 92.13% and a specificity of 82.58% was obtained, while for diluted Pellet the sensitivity was 92.81% and the specificity 83.61%.

Currently, the evaluation of thyroid cancer relies on the use of fine needle aspiration biopsy as non-invasive imaging methods do not provide sufficient levels of accuracy for the diagnosis of this disease. In this study, the potential of quantitative ultrasound methods for characterizing thyroid tissues was studied using a rodent model ex vivo. A high-frequency ultrasonic scanning system (40 MHz) was used to scan thyroids extracted from mice that had spontaneously developed thyroid lesions (cancerous or benign). Three sets of mice were acquired having different predispositions to developing thyroid anomalies (a C-cell adenoma, a papillary thyroid carcinoma (PTC), and a follicular variant papillary thyroid carcinoma (FV-PTC)). A fourth set of mice did not develop thyroid anomalies (normal mice) and were used as controls. The backscatter coefficient was estimated from excised thyroid lobes for the different mice. From the backscatter coefficient versus frequency (25 to 45 MHz), the effective scatterer diameter (ESD) and effective acoustic concentration (EAC) were estimated. From the envelope of the backscattered signal, the homodyned K distribution was used to estimate the k parameter (ratio of coherent to incoherent signal energy) and the μ parameter (number of scatterers per resolution cell). Statistically significant differences were observed between the malignant thyroids and the normal thyroids based on the ESD, EAC and μ parameters. The mean values of the ESDs were 18.0 ± 0.92, 15.9 ± 0.81, and 21.5 ± 1.80 µm for the PTC, FV-PTC and the normal thyroids, respectively. The mean values of the EACs were 59.4 ± 1.74, 62.7 ± 1.61, and 52.9 ± 3.42 dB (mm−3) for the PTC, FV-PTC and the normal thyroids, respectively. The mean values of the μ parameters were 2.55 ± 0.37, 2.59 ± 0.43, and 1.56 ± 0.99 for the PTC, FV-PTC and the normal thyroids, respectively. Statistically significant differences were observed between the malignant thyroids and the C-cell adenomas based on the ESD and EAC parameters with estimated values for the ESD of 21.3 ± 1.50 µm and EAC of 54.7 ± 2.24 dB (mm−3) for the C-cell adenomas. These results suggest that high frequency quantitative ultrasound may enhance the ability to detect and classify diseased thyroid tissues.

The minimum variance (MV) beamformer is an adaptive beamforming technique that can convey enhancements in resolution and contrast and that can be applied to various ultrasonic sequences, including plane-wave imaging. Despite its improvements, it is still susceptible to clutter. The present work uses the short-lag spatial coherence (SLSC) value as a weighting factor in order to improve over conventional MV beamformed images. The proposed SLSC weighted MV beamformer (SLSCw-MV) coherently compounds the different emissions of a plane wave sequence in order to synthesize a tight transmission focus and then computes the SLSC value of signal. This SLSC value is used as a weigthing factor for the beamformed value obtained from the application of the frequency-domain version of the MV beamformer. The proposed method is tested on the 75 plane-wave dataset of the 2016 International Ultrasonics Symposium (IUS) beamforming challenge. Results on simulated and experimental data reveal that SLSCw-MV can convey improvements of 7%–55% in lateral resolution and of 2.5%–13% in contrast with respect to conventional delay-and-sum coherent plane-wave images.

Although the minimum variance (MV) beamformer can provide enhancement in both resolution and contrast of ultrasound images when compared with conventional delay-and-sum (DAS) beamforming, its clinical application is limited by its sensitivity to phase aberrations. Several robust MV beamformers have been proposed, but present limitations when faced with second order phase aberration. Additionally, the information that can be obtained from phase aberration correction (PAC) methods is not utilized and the gain in robustness is usually accompanied by a loss in resolution or contrast. In the current work, we propose a new robust MV beamformer that uses a locally adaptive PAC (LAPAC-MV) in order to cope with the aformentioned difficulties. The method is based on a PAC estimation at every point in the image domain and a profile decision logic is included in the algorithm to take into account regions with low signal power. Preliminary results in full wave simulations and experimental phantom data are reported. In the resulting images, LAPAC-MV achieved enhancement in resolution of 0.03 mm, and reduction in sidelobe levels of around 8 dB when compared with precorrected DAS (DAS-PC) or precorrected MV (MV-PC). Contrast of LAPAC-MV is equivalent to MV-PC in cyst target simulations but the former showed improvements of approximately 1 dB in contrast in experiments with a 20 mm thick tissue aberrator.