Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Probability Maps Derived from Quantitative Ultrasound Parametric Images.

OBJECTIVE: Neoadjuvant chemotherapy (NAC) is widely used in the treatment of breast cancer. However, to date, there are no fully reliable, non-invasive methods for monitoring NAC. In this article, we propose a new method for classifying NAC-responsive and unresponsive tumors using quantitative ultrasound. METHODS: The study used ultrasound data collected from breast tumors treated with NAC. The proposed method is based on the hypothesis that areas that characterize the effect of therapy particularly well can be found. For this purpose, parametric images of texture features calculated from tumor images were converted into NAC response probability maps, and areas with a probability above 0.5 were used for classification. RESULTS: The results obtained after the third cycle of NAC show that the classification of tumors using the traditional method (area under the ROC curve AUC = 0.81 - 0.88) can be significantly improved thanks to the proposed new approach (AUC = 0.84-0.94). This improvement is achieved over a wide range of cutoff values (0.2-0.7), and the probability maps obtained from different quantitative parameters correlate well. CONCLUSION: The results suggest that there are tumor areas that are particularly well suited to assessing response to NAC. SIGNIFICANCE: The proposed approach to monitoring the effects of NAC not only leads to a better classification of responses, but also may contribute to a better understanding of the microstructure of neoplastic tumors observed in an ultrasound examination.

Prediction of response to neoadjuvant chemotherapy in breast cancer with recurrent neural networks and raw ultrasound signals.

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

Explaining a Deep Learning Based Breast Ultrasound Image Classifier with Saliency Maps.

Aim of the study: Deep neural networks have achieved good performance in breast mass classification in ultrasound imaging. However, their usage in clinical practice is still limited due to the lack of explainability of decisions conducted by the networks. In this study, to address the explainability problem, we generated saliency maps indicating ultrasound image regions important for the network's classification decisions. Material and methods: Ultrasound images were collected from 272 breast masses, including 123 malignant and 149 benign. Transfer learning was applied to develop a deep network for breast mass classification. Next, the class activation mapping technique was used to generate saliency maps for each image. Breast mass images were divided into three regions: the breast mass region, the peritumoral region surrounding the breast mass, and the region below the breast mass. The pointing game metric was used to quantitatively assess the overlap between the saliency maps and the three selected US image regions. Results: Deep learning classifier achieved the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 0.887, 0.835, 0.801, and 0.868, respectively. In the case of the correctly classified test US images, analysis of the saliency maps revealed that the decisions of the network could be associated with the three selected regions in 71% of cases. Conclusions: Our study is an important step toward better understanding of deep learning models developed for breast mass diagnosis. We demonstrated that the decisions made by the network can be related to the appearance of certain tissue regions in breast mass US images.

Quantitative Ultrasonography as a Tool for the Evaluation of Breast Tumor Response to Neoadjuvant Chemotherapy.

Neoadjuvant chemotherapy is increasingly becoming the first treatment step in breast cancer. Despite the enormous advantages of this therapy, it is a method characterized by a high level of toxicity and thus carries a huge burden for the patient. Therefore, it is highly desirable to begin monitoring the patient's response to treatment at an earlier stage. Currently, apart from traditional imaging methods, a completely new technique (in the context of monitoring the outcomes of chemotherapy), called quantitative ultrasound, is gaining popularity. It is a method based on the exact same ultrasound echoes as in traditional ultrasound imaging. The innovative approach of the method is that these echoes are not used for visualization but to characterize the condition of the tissue by parameterizing it with the aid of ultrasound biomarkers. The biomarkers make it possible to assess the state of the tissue at the microscopic level, and thus evaluate changes occurring in the tissue under the influence of treatment at a very early treatment stage. The present paper aims to familiarize the reader with the physical foundations of this method as well as present the latest results of related research.

Joint segmentation and classification of breast masses based on ultrasound radio-frequency data and convolutional neural networks.

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

Assessment of breast cancer response to neoadjuvant chemotherapy based on ultrasound backscattering envelope statistics.

PURPOSE: Neo-adjuvant chemotherapy (NAC) is used in breast cancer before tumor surgery to reduce the size of the tumor and the risk of spreading. Monitoring the effects of NAC is important because in a number of cases the response to therapy is poor and requires a change in treatment. A new method that uses quantitative ultrasound to assess tumor response to NAC has been presented. The aim was to detect NAC unresponsive tumors at an early stage of treatment. METHODS: The method assumes that ultrasound scattering is different for responsive and nonresponsive tumors. The assessment of the NAC effects was based on the differences between the histograms of the ultrasound echo amplitude recorded from the tumor after each NAC dose and from the tissue phantom, estimated using the Kolmogorov-Smirnov statistics (KSS) and the symmetrical Kullback-Leibler divergence (KLD). After therapy, tumors were resected and histopathologically evaluated. The percentage of residual malignant cells was determined and was the basis for assessing the tumor response. The data set included ultrasound data obtained from 37 tumors. The performance of the methods was assessed by means of the area under the receiver operating characteristic curve (AUC). RESULTS: For responding tumors, a decrease in the mean KLD and KSS values was observed after subsequent doses of NAC. In nonresponding tumors, the KLD was higher and did not change in subsequent NAC courses. Classification based on the KSS or KLD parameters allowed to detect tumors not responding to NAC after the first dose of the drug, with AUC equal 0.83 ± $\pm$ 0.06 and 0.84 ± $\pm$ 0.07, respectively. After the third dose, the AUC increased to 0.90 ± $\pm$ 0.05 and 0.91 ± $\pm$ 0.04, respectively. CONCLUSIONS: The results indicate the potential usefulness of the proposed parameters in assessing the effectiveness of the NAC and early detection of nonresponding cases.

Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy.

The aim of the study was to improve monitoring the treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Ultrasound examinations were performed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was the standard of reference. Alteration in B-mode ultrasound (tumor echogenicity and volume) and the Kullback-Leibler divergence (kld), as a quantitative measure of amplitude difference, were used. Correlations of these parameters with RMC were assessed and Receiver Operating Characteristic curve (ROC) analysis was performed. Thirty-nine patients (mean age 57 y.) with 50 tumors were included. There was a significant correlation between RMC and changes in quantitative parameters (KLD) after the second, third and fourth course of NAC, and alteration in echogenicity after the third and fourth course. Multivariate analysis of the echogenicity and KLD after the third NAC course revealed a sensitivity of 91%, specificity of 92%, PPV = 77%, NPV = 97%, accuracy = 91%, and AUC of 0.92 for non-responding tumors (RMC ≥ 70%). In conclusion, monitoring the echogenicity and KLD parameters made it possible to accurately predict the treatment response from the second course of NAC.

Aortic root movement correlation with the function of the left ventricle.

Echocardiographic assessment of systolic and diastolic function of the heart is often limited by image quality. However, the aortic root is well visualized in most patients. We hypothesize that the aortic root motion may correlate with the systolic and diastolic function of the left ventricle of the heart. Data obtained from 101 healthy volunteers (mean age 46.6 ± 12.4) was used in the study. The data contained sequences of standard two-dimensional (2D) echocardiographic B-mode (brightness mode, classical ultrasound grayscale presentation) images corresponding to single cardiac cycles. They also included sets of standard echocardiographic Doppler parameters of the left ventricular systolic and diastolic function. For each B-mode image sequence, the aortic root was tracked with use of a correlation tracking algorithm and systolic and diastolic values of traveled distances and velocities were determined. The aortic root motion parameters were correlated with the standard Doppler parameters used for the assessment of LV function. The aortic root diastolic distance (ARDD) mean value was 1.66 ± 0.26 cm and showed significant, moderate correlation (r up to 0.59, p < 0.0001) with selected left ventricular diastolic Doppler parameters. The aortic root maximal diastolic velocity (ARDV) was 10.8 ± 2.4 cm/s and also correlated (r up to 0.51, p < 0.0001) with some left ventricular diastolic Doppler parameters. The aortic root systolic distance (ARSD) was 1.63 ± 0.19 cm and showed no significant moderate correlation (all r values < 0.40). The aortic root maximal systolic velocity (ARSV) was 9.2 ± 1.6 cm/s and correlated in moderate range only with peak systolic velocity of medial mitral annulus (r = 0.44, p < 0.0001). Based on these results, we conclude, that in healthy subjects, aortic root motion parameters correlate significantly with established measurements of left ventricular function. Aortic root motion parameters can be especially useful in patients with low ultrasound image quality precluding usage of typical LV function parameters.

Early Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Sonography Using Siamese Convolutional Neural Networks.

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

Estimating the ultrasound attenuation coefficient using complementary Golay codes.

Accurate evaluation of ultrasonic wave attenuation is important in many medical applications of ultrasound. The aim of this work is to present a thorough analysis of the effectiveness of using complementary Golay coded sequences (CGCS) during the evaluation of ultrasound attenuation in tissue-like materials, especially at greater depths or at high attenuation. In order to compare the results of the attenuation measurement with the use of CGCS transmission and a short two sine cycles pulse, ultrasound backscattered from medium with predefined attenuation of 0.3, 0.7 and 2 dB/[MHz × cm] were simulated. Also for the same transmission signals, measurements of ultrasound echoes scattered in the tissue phantom with an attenuation of 0.5 dB/[MHz × cm] were performed. In the case of numerically simulated data, for the CGCS excitation, the maximum depth for which the attenuation was correctly determined increased from 55 mm to 80 mm for the 0.7 dB/[MHz × cm] phantom and from 20 mm to 50 mm for the 2 dB/[MHz × cm] phantom compared to excitation of the transducer with a short two sine cycles pulse. When the measurement data obtained using the tissue phantom was used to estimate the attenuation coefficient, the relative error was determined to be 6% and 16% for the depths of 10 mm and 40 mm for the short two sine cycles pulse excitation, respectively. Corresponding values for CGCS excitation and considered depths were 2% and 4%. The use of CGSC sequence during attenuation measurements increases measurement accuracy and can improve medical diagnostic techniques.

Reaction kinetics of sonochemical oxidation of potassium hexacyanoferrate(II) in aqueous solutions.

We studied sonochemical reactions resulting from ultrasonic treatment of potassium hexacyanoferrate(II) in aqueous solutions using a custom-built apparatus working at 536 kHz. We concluded that primary reactions are completely dominated by oxidation of Fe(II) to Fe(III) and did not find any evidences for degradation of cyanide. At the highest concentration used in the present study (0.1 M) we detected formation of pentacyanoaquaferrate(II) complex, which is most probably formed in reactions between hexacyanoferrate(III) anions and hydrogen atoms or hydrated electrons formed in sonochemical processes. We also determined that hydroxyl radicals formation rate in our system, (8.7 ± 1.5)∙10-8 M∙s-1, is relatively high compared to other reported experiments. We attribute this to focusing of the ultrasonic wave in the sample vessel. Finally, we suggest that oxidation rate of hexacyanoferrate(II) anions can be a convenient benchmark of efficiency of sonochemical reactors.

Ultrasound assessment of the conversion of sound energy into heat in tissue phantoms enriched with magnetic micro- and nanoparticles.

PURPOSE: Nowadays, the improvement of ultrasonic hyperthermia therapy is often achieved by adding hard particles to the sonicated medium in order to increase the heating efficiency. The explanation of the phenomenon of ultrasonic heating still requires testing on tissue mimicking materials (TMMs), enriched with particles of different sizes and physical properties. Our goal was to determine, by comparing their quantitative acoustic properties, which TMMs, with magnetic micro- or nanoparticles, convert more ultrasonic energy into heat or which of the particles embedded in the agar gel act as more effective thermal sonosensitizers. METHODS: We manufactured a pure agar gel and an agar gel with the addition of magnetic micro- or nanoparticles in two proportions of 8 and 16 mg/ml. Ultrasound quantitative techniques, the broadband reflection substitution technique and backscattered spectrum analysis were used to characterize the samples by speed of sound (SOS), frequency-dependent attenuation, and backscattering coefficients. The integrated backscattering coefficients were also calculated. The quantitative parameters, scattering, and attenuation coefficients of ultrasound in phantoms with micro- and nanoparticles were estimated. Based on the attenuation and scattering of ultrasound in the samples, the ultrasonic energy absorption, which determines the heating efficiency, was evaluated. Additionally, the temperature increase during sonication of the phantoms by an ultrasonic beam was directly measured using thermocouples. RESULTS: The density of the materials with nanoparticles was higher than for the materials with microparticles with the same fractions of particles. The SOS for all materials ranged from 1489 to 1499 m/s. The attenuation in the whole frequency range (3-8 MHz) was higher for the materials with nanoparticles than for the materials with microparticles. For the materials with the lower content (8 mg/ml) of particles, the attenuation coefficient was 0.2 dB/(MHz cm). For the 16 mg/ml concentration of nanoparticles and microparticles, the attenuation coefficients were 0.66 and 0.45 dB/(MHz cm), respectively. The value of backscattering coefficient in the whole frequency range was greater for the materials with microparticles than for the materials with nanoparticles. The values of the integrated backscattering coefficient were 0.05 and 0.08 1/m for the materials with nanoparticles and 0.46 and 0.82 1/m for the materials with microparticles and concentrations of 8 and 16 mg/ml, respectively. The rates of temperature increase in the first 3 s due to ultrasonic heating were higher for the materials with nanoparticles than for the materials with microparticles. CONCLUSIONS: Based on acoustical measurements, we confirmed that all materials can be used as tissue phantoms in the study of ultrasonic hyperthermia, as their properties were in the range of soft tissue properties. We found that the nanoparticle-doped materials had greater attenuation and smaller scattering of ultrasound than the materials with microparticles, so absorption in these materials is greater. Thus, the TMMs with nanoparticles convert more acoustic energy into heat and we conclude that magnetic nanoparticles are more effective thermal sonosensitizers than microparticles. This conclusion is confirmed by direct measurement of the temperature increase in the samples subjected to sonification.

Monitoring the response to neoadjuvant chemotherapy in patients with breast cancer using ultrasound scattering coefficient: A preliminary report.

Objective: Neoadjuvant chemotherapy was initially used in locally advanced breast cancer, and currently it is recommended for patients with Stage 3 and with early-stage disease with human epidermal growth factor receptors positive or triple-negative breast cancer. Ultrasound imaging in combination with a quantitative ultrasound method is a novel diagnostic approach. Aim of study: The aim of this study was to analyze the variability of the integrated backscatter coefficient, and to evaluate their use to predict the effectiveness of treatment and compare to ultrasound examination results. Material and method: Ten patients (mean age 52.9) with 13 breast tumors (mean dimension 41 mm) were selected for neoadjuvant chemotherapy. Ultrasound was performed before the treatment and one week after each course of neoadjuvant chemotherapy. The dimensions were assessed adopting the RECIST criteria. Tissue responses were classified as pathological response into the following categories: not responded to the treatment (G1, cell reduction by ≤9%) and responded to the treatment partially: G2, G3, G4, cell reduction by 10-29% (G2), 30-90% (G3), >90% (G4), respectively, and completely. Results: In B-mode examination partial response was observed in 9/13 cases (completely, G1, G3, G4), and stable disease was demonstrated in 3/13 cases (completely, G1, G4). Complete response was found in 1/13 cases. As for backscatter coefficient, 10/13 tumors (completely, and G2, G3, and G4) were characterized by an increased mean value of 153%. Three tumors 3/13 (G1) displayed a decreased mean value of 31%. Conclusion: The variability of backscatter coefficient, could be associated with alterations in the structure of the tumor tissue during neoadjuvant chemotherapy. There were unequivocal differences between responded and non-responded patients. The backscatter coefficient analysis correlated better with the results of histopathological verification than with the B-mode RECIST criteria.Objective: Neoadjuvant chemotherapy was initially used in locally advanced breast cancer, and currently it is recommended for patients with Stage 3 and with early-stage disease with human epidermal growth factor receptors positive or triple-negative breast cancer. Ultrasound imaging in combination with a quantitative ultrasound method is a novel diagnostic approach. Aim of study: The aim of this study was to analyze the variability of the integrated backscatter coefficient, and to evaluate their use to predict the effectiveness of treatment and compare to ultrasound examination results. Material and method: Ten patients (mean age 52.9) with 13 breast tumors (mean dimension 41 mm) were selected for neoadjuvant chemotherapy. Ultrasound was performed before the treatment and one week after each course of neoadjuvant chemotherapy. The dimensions were assessed adopting the RECIST criteria. Tissue responses were classified as pathological response into the following categories: not responded to the treatment (G1, cell reduction by ≤9%) and responded to the treatment partially: G2, G3, G4, cell reduction by 10­29% (G2), 30­90% (G3), >90% (G4), respectively, and completely. Results: In B-mode examination partial response was observed in 9/13 cases (completely, G1, G3, G4), and stable disease was demonstrated in 3/13 cases (completely, G1, G4). Complete response was found in 1/13 cases. As for backscatter coefficient, 10/13 tumors (completely, and G2, G3, and G4) were characterized by an increased mean value of 153%. Three tumors 3/13 (G1) displayed a decreased mean value of 31%. Conclusion: The variability of backscatter coefficient, could be associated with alterations in the structure of the tumor tissue during neoadjuvant chemotherapy. There were unequivocal differences between responded and non-responded patients. The backscatter coefficient analysis correlated better with the results of histopathological verification than with the B-mode RECIST criteria.

Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue.

The presented studies evaluate for the first time the efficiency of tumour classification based on the quantitative analysis of ultrasound data originating from the tissue surrounding the tumour. 116 patients took part in the study after qualifying for biopsy due to suspicious breast changes. The RF signals collected from the tumour and tumour-surroundings were processed to determine quantitative measures consisting of Nakagami distribution shape parameter, entropy, and texture parameters. The utility of parameters for the classification of benign and malignant lesions was assessed in relation to the results of histopathology. The best multi-parametric classifier reached an AUC of 0.92 and of 0.83 for outer and intra-tumour data, respectively. A classifier composed of two types of parameters, parameters based on signals scattered in the tumour and in the surrounding tissue, allowed the classification of breast changes with sensitivity of 93%, specificity of 88%, and AUC of 0.94. Among the 4095 multi-parameter classifiers tested, only in eight cases the result of classification based on data from the surrounding tumour tissue was worse than when using tumour data. The presented results indicate the high usefulness of QUS analysis of echoes from the tissue surrounding the tumour in the classification of breast lesions.

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

BACKGROUND: Neoadjuvant 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. METHODS: The 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. RESULTS: Statistically 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. CONCLUSIONS: Quantitative 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.

Ultrasound echogenicity reveals the response of breast cancer to chemotherapy.

PURPOSE: To evaluate the ultrasound (US) response in patients with breast cancer (BC) during neoadjuvant chemotherapy (NAC). METHODS: Prospective US analysis was performed on 19 malignant tumors prior to NAC treatment and 7 days after each first four courses of NAC in 13 patients (median age = 57 years). Echogenicity, size, vascularity, and sonoelastography were measured and compared with posttreatment scores of residual cancers burden. RESULTS: Changes in the echogenicity of tumors after 3 courses of NAC had the most statistically strong correlation with the percentage of residual malignant cells used in histopathology to assess the response to treatment (odds ratio = 60, p < 0.05). Changes in lesion size and elasticity were also significant (p < 0.05). CONCLUSIONS: There is a statistically significant relationship between breast tumors' echogenicity in US, neoplasm size, and stiffness and the response to NAC. In particular, our results show that the change in tumor echogenicity could predict a pathological response with satisfactory accuracy and may be considered in NAC monitoring.

Improving broadband ultrasound attenuation assessment in cancellous bone by mitigating the influence of cortical bone: Phantom and in-vitro study.

The purpose of this work was to present a new approach that allows the influence of cortical bone on noninvasive measurement of broadband ultrasound attenuation (BUA) to be corrected. The method, implemented here at 1 MHz makes use of backscattered signal and once refined and clinically confirmed, it would offer an alternative to ionizing radiation based methods, such as DEXA (Dual-energy X-ray absorptiometry), quantitative computed tomography (QCT), radiographic absorptiometry (RA) or single X-ray absorptiometry (SXA), which are clinically approved for assessment of progress of osteoporosis. In addition, as the method employs reflected waves, it might substantially enhance the applicability of BUA - from being suitable to peripheral bones only it would extend this applicability to include such embedded bones as hip and femoral neck. The proposed approach allows the cortical layer parameters used for correction and the corrected value and parameter of the cancellous bone (BUA) to be determined simultaneously from the single (pulse-echo) bone backscattered wave; to the best of the authors' knowledge such approach was not previously reported. The validity of the method was tested using acoustic data obtained from a custom-designed bone-mimicking phantom and a calf femur. The relative error of the attenuation coefficient assessment was determined to be 3.9% and 4.7% for the bone phantom and calf bone specimens, respectively. When the cortical shell influence was not taken into account the corresponding errors were considerably higher 8.3% (artificial bone) and 9.2% (calf femur). As indicated above, once clinically proven, the use of this BUA measurement technique in reflection mode would augment diagnostic power of the attending physician by permitting to include bones, which are not accessible for transmission mode evaluation, e.g. hip, spine, humerus and femoral neck.

Tissue Attenuation Estimation by Mean Frequency Downshift and Bandwidth Limitation.

Attenuation of ultrasound in tissue can be estimated from the propagating pulse center frequency downshift. This method assumes that the envelope of the emitted pulse can be approximated by a Gaussian function and that the attenuation linearly depends on frequency. The resulting downshift of the mean frequency depends not only on attenuation but also on pulse bandwidth and propagation distance. This kind of approach is valid for narrowband pulses and shallow penetration depth. However, for short pulses and deep penetration, the frequency downshift is rather large and the received spectra are modified by the limited bandwidth of the receiving system. In this paper, the modified formula modeling the mean frequency of backscattered echoes is presented. The equation takes into account the limitation of the bandwidth due to bandpass filtration of the received echoes. This approach was applied to simulate the variation of the mean frequency of the pulse propagating for both weakly and strongly attenuating media and for narrowband and wideband pulses. The behavior of both the standard and modified estimates of attenuation has been validated using RF data from a tissue-mimicking phantom. The ultrasound attenuation of the phantom, determined with a corrected equation, was close to its true value, while the result obtained using the original formula was lower by as much as 50% at a depth of 8 cm.

Simultaneous estimation of cortical bone thickness and acoustic wave velocity using a multivariable optimization approach: Bone phantom and in-vitro study.

The paper presents a method that allows the thickness of a compact bone layer and longitudinal wave velocity in the bone to be determined simultaneously with the use of reflected waves, with particular emphasis on the case of layers when the propagation time through the layer is shorter than the time duration of the interrogating pulse. The proposed method estimates simultaneously the thickness of the cortical bone layer and acoustic wave velocity by fitting the temporal spectrum of the simulated reflected wave to the spectrum of the reflected wave measured experimentally. For the purpose of echo-simulations the model of "soft tissue - compact bone layer - cancellous bone" was developed. Next, the cost function was defined as the least square error between the measured and simulated temporal spectra. Minimization of the cost function allowed us to determine the values of the parameters of the cortical bone layer which best fitted the measurements. To solve the optimization problem a simulated annealing algorithm was used. The method was tested using acoustic data obtained at the frequency of 0.6 MHz and 1 MHz respectively for a custom designed bone mimicking phantom and a calf femur. For the cortical shell of the calf femur whose thickness varies from 2.1 mm to 2.4 mm and velocity of 2910 m/s, the relative errors of the thickness estimation ranged from 0.4% to 5.5%. The corresponding error of the acoustic wave velocity estimation in the layer was 3.1%. In the case of artificial bone the thickness of the cortical layer was equal to 1.05 and 1.2 mm and acoustic wave velocity was 2900 m/s. These parameters were determined with the errors ranging from 1.9% to 10.8% and from 3.9% to 4.5% respectively.

Determining temperature distribution in tissue in the focal plane of the high (>100 W/cm(2)) intensity focused ultrasound beam using phase shift of ultrasound echoes.

In therapeutic applications of High Intensity Focused Ultrasound (HIFU) the guidance of the HIFU beam and especially its focal plane is of crucial importance. This guidance is needed to appropriately target the focal plane and hence the whole focal volume inside the tumor tissue prior to thermo-ablative treatment and beginning of tissue necrosis. This is currently done using Magnetic Resonance Imaging that is relatively expensive. In this study an ultrasound method, which calculates the variations of speed of sound in the locally heated tissue volume by analyzing the phase shifts of echo-signals received by an ultrasound scanner from this very volume is presented. To improve spatial resolution of B-mode imaging and minimize the uncertainty of temperature estimation the acoustic signals were transmitted and received by 8 MHz linear phased array employing Synthetic Transmit Aperture (STA) technique. Initially, the validity of the algorithm developed was verified experimentally in a tissue-mimicking phantom heated from 20.6 to 48.6 °C. Subsequently, the method was tested using a pork loin sample heated locally by a 2 MHz pulsed HIFU beam with focal intensity ISATA of 129 W/cm(2). The temperature calibration of 2D maps of changes in the sound velocity induced by heating was performed by comparison of the algorithm-determined changes in the sound velocity with the temperatures measured by thermocouples located in the heated tissue volume. The method developed enabled ultrasound temperature imaging of the heated tissue volume from the very inception of heating with the contrast-to-noise ratio of 3.5-12 dB in the temperature range 21-56 °C. Concurrently performed, conventional B-mode imaging revealed CNR close to zero dB until the temperature reached 50 °C causing necrosis. The data presented suggest that the proposed method could offer an alternative to MRI-guided temperature imaging for prediction of the location and extent of the thermal lesion prior to applying the final HIFU treatment.