Spectral Intercept Research Articles

Can carbonation depth be measured in a nondestructive way? High-frequency quantitative ultrasound imaging for cement paste

Determining the carbonation depth is of paramount importance in assessing the durability of cementitious materials as the carbonation process alters their near-surface physicochemical properties. This paper introduces a novel quantitative ultrasound (QUS) technique capable of nondestructively identifying the carbonation depth, regardless of the tortuosity of the carbonation front. A 2.5 MHz phased array transducer was employed to construct beamformed radio frequency images and measure two microstructure-dependent parameters – spectral slope (SS) and spectral intercept (SI) – based on backscatter and attenuation coefficients. The change in obtained SS and SI were then visualized as QUS images, which displayed the geometry of the carbonation front, correlating with images constructed using a phenolphthalein solution (1.6 mm difference in depth). Mineral compositions and porosities were analyzed to account for microstructural changes in each layer. This study opens an opportunity for nondestructive detection of carbonation depth, overcoming the limitations in conventional methods or regression-based approaches.

Technical note: Noninvasive monitoring of normal tissue radiation damage using spectral quantitative ultrasound spectroscopy.

While radiation therapy (RT) is a critical component of breast cancer therapy and is known to decrease overall local recurrence rates, recent studies have shown that normal tissue radiation damage may increase recurrence risk. Fibrosis is a well-known consequence of RT, but the specific sequence of molecular and mechanical changes induced by RT remains poorly understood. To improve cancer therapy outcomes, there is a need to understand the role of the irradiated tissue microenvironment in tumor recurrence. This study seeks to evaluate the use of spectral quantitative ultrasound (spectral QUS) for real time determination of the normal tissue characteristic radiation response and to correlate these results to molecular features in irradiated tissues. Murine mammary fat pads (MFPs) were irradiated to 20Gy, and spectral QUS was used to analyze tissue physical properties pre-irradiation as well as at 1, 5, and 10 days post-irradiation. Tissues were processed for scanning electron microscopy imaging as well as histological and immunohistochemical staining to evaluate morphology and structure. Tissue morphological and structural changes were observed non-invasively following radiation using mid-band fit (MBF), spectral slope (SS), and spectral intercept (SI) measurements obtained from spectral QUS. Statistically significant shifts in MBF and SI indicate structural tissue changes in real time, which matched histological observations. Radiation damage was indicated by increased adipose tissue density and extracellular matrix (ECM) deposition. Our findings demonstrate the potential of using spectral QUS to noninvasively evaluate normal tissue changes resulting from radiation damage. This supports further pre-clinical studies to determine how the tissue microenvironment and physical properties change in response to therapy, which may be important for improving treatment strategies.

In vivo assessment of lymph nodes using quantitative ultrasound on a clinical scanner: A preliminary study

Quantitative ultrasound (QUS) methods evaluating the backscattered echo signal provide a means to infer the microstructural properties of soft tissues. High-frequency (≥20 MHz) QUS has been used to detect metastases in excised human lymph nodes (LNs), but limited evidence exists about the efficacy of conventional-frequency QUS for characterizing LNs in vivo using a standard clinical scanner. In this study, 11 LNs from 11 patients with known primary tumors were scanned in vivo using a GE Logiq E9 scanner with a 10-MHz linear array. Metastatic status was determined by fine needle aspiration. Echo frames were processed using QUS methods to compute five parameters from the backscattered echo power spectrum and four from histograms of the echo envelope values. The spectral slope was found to be significantly higher in metastatic nodes (p < 0.001), whereas benign nodes exhibited significantly lower values of spectral intercept (p < 0.001), midband fit (p = 0.004), scatterer diameter (ESD, p < 0.001), and acoustic concentration (EAC, p = 0.003). A linear discriminator operating on ESD and EAC correctly differentiated benign and metastatic nodes with sensitivity of 0.89, specificity of 0.83, and area under the ROC curve of 0.91. These preliminary results demonstrate the feasibility of characterizing LNs in vivo at conventional frequencies using a clinical scanner.

In vivo assessment of pulmonary edema in rats using the backscatter coefficient and envelope statistics

Ultrasound could be used as a convenient, non-ionizing method for monitoring pulmonary edema. In this study, the ability of backscatter coefficient (BSC) and envelope statistical quantitative ultrasound (QUS) parameters to evaluate edema in rat lungs was investigated. Ultrasound data were acquired from six healthy rats and six with pulmonary edema using a Verasonics Vantage ultrasound scanner. After ultrasound data acquisition, a piece of the lung was excised and immediately weighed (wet weight), before being dried in an oven and weighed again (dry weight). The wet and dry weight were used to compute the wet-to-dry (W/D) weight ratio, used as the gold standard for pulmonary edema severity. BSC and envelope statistical parameters were computed in overlapping time gates over the lung ultrasound data. The average and standard deviation of each parameter, along with the slope and intercept of a linear function fit to each parameter over acoustic propagation time, were obtained. Each QUS parameter was correlated with the W/D ratio. The strongest correlation was achieved by the standard deviation of spectral intercept and by the average homodyned K structure parameter, both with a 0.78 correlation coefficient magnitude. Therefore, BSC and envelope statistical parameters have the potential to improve monitoring of pulmonary edema.

Diagnostic Performance of 9 Quantitative Ultrasound Parameters for Detection and Classification of Hepatic Steatosis in Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. Quantitative ultrasound (QUS) parameters based on radiofrequency raw data show promise in quantifying liver fat. The aim of this study was to evaluate the diagnostic performance of 9 QUS parameters compared with magnetic resonance imaging (MRI)-estimated proton density fat fraction (PDFF) in detecting and staging hepatic steatosis in patients with or suspected of NAFLD. In this Health Insurance Portability and Accountability Act-compliant institutional review board-approved prospective study, 31 participants with or suspected of NAFLD, without other underlying chronic liver diseases (13 men, 18 women; average age, 52 years [range, 26-90 years]), were examined. The following parameters were obtained: acoustic attenuation coefficient (AC); hepatorenal index (HRI); Nakagami parameter; shear wave elastography measures such as shear wave elasticity, viscosity, and dispersion; and spectroscopy-derived parameters including spectral intercept (SI), spectral slope (SS), and midband fit (MBF). The diagnostic ability (area under the receiver operating characteristic curves and accuracy) of QUS parameters was assessed against different MRI-PDFF cutoffs (the reference standard): 6.4%, 17.4%, and 22.1%. Linearity with MRI-PDFF was evaluated with Spearman correlation coefficients (p). The AC, SI, Nakagami, SS, HRI, and MBF strongly correlated with MRI-PDFF (P = 0.89, 0.89, 0.88, -0.87, 0.81, and 0.71, respectively [P < 0.01]), with highest area under the receiver operating characteristic curves (ranging from 0.85 to 1) for identifying hepatic steatosis using 6.4%, 17.4%, and 22.1% MRI-PDFF cutoffs. In contrast, shear wave elasticity, shear wave viscosity, and shear wave dispersion did not strongly correlate to MRI-PDFF (P = 0.45, 0.38, and 0.07, respectively) and had poor diagnostic performance. The AC, Nakagami, SI, SS, MBF, and HRI best correlate with MRI-PDFF and show high diagnostic performance for detecting and classifying hepatic steatosis in our study population. Quantitative ultrasound is an accurate alternative to MRI-based techniques for evaluating hepatic steatosis in patients with or at risk of NAFLD. Our preliminary results show that specific quantitative ultrasound parameters accurately detect different degrees of hepatic steatosis in NAFLD.

A set of composite, non-redundant EEG measures of NREM sleep based on the power law scaling of the Fourier spectrum

Features of sleep were shown to reflect aging, typical sex differences and cognitive abilities of humans. However, these measures are characterized by redundancy and arbitrariness. Our present approach relies on the assumptions that the spontaneous human brain activity as reflected by the scalp-derived electroencephalogram (EEG) during non-rapid eye movement (NREM) sleep is characterized by arrhythmic, scale-free properties and is based on the power law scaling of the Fourier spectra with the additional consideration of the rhythmic, oscillatory waves at specific frequencies, including sleep spindles. Measures derived are the spectral intercept and slope, as well as the maximal spectral peak amplitude and frequency in the sleep spindle range, effectively reducing 191 spectral measures to 4, which were efficient in characterizing known age-effects, sex-differences and cognitive correlates of sleep EEG. Future clinical and basic studies are supposed to be significantly empowered by the efficient data reduction provided by our approach.

A priori prediction of response in multicentre locally advanced breast cancer (LABC) patients using quantitative ultrasound and derivative texture methods.

Purpose: We develop a multi-centric response predictive model using QUS spectral parametric imaging and novel texture-derivate methods for determining tumour responses to neoadjuvant chemotherapy (NAC) prior to therapy initiation.Materials and Methods: QUS Spectroscopy provided parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average-scatterer-diameter (ASD), and average-acoustic-concentration (AAC) in 78 patients with locally advanced breast cancer (LABC) undergoing NAC. Ultrasound radiofrequency data were collected from Sunnybrook Health Sciences Center (SHSC), University of Texas MD Anderson Cancer Center (MD-ACC), and St. Michaels Hospital (SMH) using two different systems. Texture analysis was used to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS, texture- and texture-derivate parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis for developing a response predictive model to classify responders versus non-responders. Model performance was assessed using leave-one-out cross-validation. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest-neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.Results: A combination of tumour core and margin classification resulted in a peak response prediction performance of 88% sensitivity, 78% specificity, 84% accuracy, 0.86 AUC, 84% PPV, and 83% NPV, achieved using the SVM-RBF classification algorithm. Other parameters and classifiers performed less well running from 66% to 80% accuracy.Conclusions: A QUS-based framework and novel texture-derivative method enabled accurate prediction of responses to NAC. Multi-centric response predictive model provides indications of the robustness of the approach to variations due to different ultrasound systems and acquisition parameters.

Matching pursuit-based sparse spectral analysis: Estimating frequency-dependent anomalies from nonstationary seismic data

Spectral decomposition has been widely used to detect frequency-dependent anomalies associated with hydrocarbons. By ignoring the time-variant feature of the frequency content of individual reflected wavelets, we have adopted a sparse time-frequency spectrum and developed a matching pursuit-based sparse spectral analysis (MP-SSA) method to estimate the sparse time-frequency representation of the seismic data. Further, we evaluate a generalized nonstationary convolution model concerning propagation attenuation and frequency-dependent reflectivity, and we mathematically evaluate the sparse time-frequency spectrum of the nonstationary seismic data as being equal to the product of the Fourier spectrum of the source wavelet, frequency-dependent reflection coefficient, and the cumulative attenuation during seismic wave propagation. Therefore, the reflectivity spectrum, which is a combination of the frequency-dependent reflectivity and the propagation attenuation, can be determined by dividing the sparse time-frequency spectrum of the seismic data by the Fourier spectrum of the source wavelet. Application of the matching pursuit-based decomposition methods to synthetic nonstationary convolutional data illustrates that the adopted MP-SSA spectrum shows a higher time resolution than the matching pursuit-based Wigner-Ville distribution and the matching pursuit-based instantaneous spectral analysis spectra. Notably, the MP-SSA method can avoid spectral smearing, which may introduce distortions to the frequency-dependent anomaly estimation. Application of the amplitude versus frequency analysis based on MP-SSA to field data illustrates the potential of using the sparse reflectivity spectral intercept and gradient to detect the hydrocarbon reservoirs.

Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods.

PurposeAccurate and timely diagnosis of breast cancer is extremely important because of its high incidence and high morbidity. Early diagnosis of breast cancer through screening can improve overall prognosis. Currently, biopsy remains as the gold standard for tumor pathological confirmation. Development of diagnostic imaging techniques for rapid and accurate characterization of breast lesions is required. We aim to evaluate the usefulness of texture-derivate features of QUS spectral parametric images for non-invasive characterization of breast lesions.MethodsQUS Spectroscopy was used to determine parametric images of mid-band fit (MBF), spectral slope (SS), spectral intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) in 204 patients with suspicious breast lesions. Subsequently, texture analysis techniques were used to generate texture maps from parametric images to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS parameters, texture-parameters and texture-derivate parameters were determined from both tumor core and a 5-mm tumor margin and were used in comparison to histopathological analysis in order to develop a diagnostic model for classifying breast lesions as either benign or malignant. Both leave-one-out and hold-out cross-validations were used to evaluate the performance of the diagnostic model. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.ResultsCore and margin information using the SVM-RBF attained the best classification performance of 90% sensitivity, 92% specificity, 91% accuracy, and 0.93 AUC utilizing QUS parameters and their texture derivatives, evaluated using leave-one-out cross-validation. Implementation of hold-out cross-validation using combination of both core and margin information and SVM-RBF achieved average accuracy and AUC of 88% and 0.92, respectively.ConclusionsQUS-based framework and derivative texture methods enable accurate classification of breast lesions. Evaluation of the proposed technique on a large cohort using hold-out cross-validation demonstrates its robustness and its generalization.

Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer.

Aim:We aimed to identify quantitative ultrasound (QUS)-radiomic markers to predict radiotherapy response in metastatic lymph nodes of head and neck cancer.Materials & methods:Node-positive head and neck cancer patients underwent pretreatment QUS imaging of their metastatic lymph nodes. Imaging features were extracted using the QUS spectral form, and second-order texture parameters. Machine-learning classifiers were used for predictive modeling, which included a logistic regression, naive Bayes, and k-nearest neighbor classifiers.Results:There was a statistically significant difference in the pretreatment QUS-radiomic parameters between radiological complete responders versus partial responders (p < 0.05). The univariable model that demonstrated the greatest classification accuracy included: spectral intercept (SI)-contrast (area under the curve = 0.741). Multivariable models were also computed and showed that the SI-contrast + SI-homogeneity demonstrated an area under the curve = 0.870. The three-feature model demonstrated that the spectral slope-correlation + SI-contrast + SI-homogeneity-predicted response with accuracy of 87.5%.Conclusion:Multivariable QUS-radiomic features of metastatic lymph nodes can predict treatment response a priori.

Quantitative Ultrasound Spectroscopy for Differentiation of Hepatocellular Carcinoma from At-Risk and Normal Liver Parenchyma.

Quantitative ultrasound approaches can capture tissue morphologic properties to augment clinical diagnostics. This study aims to clinically assess whether quantitative ultrasound spectroscopy (QUS) parameters measured in hepatocellular carcinoma (HCC) tissues can be differentiated from those measured in at-risk or healthy liver parenchyma. This prospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study was approved by the Institutional Review Board. Fifteen patients with HCC, 15 non-HCC patients with chronic liver disease, and 15 healthy volunteers were included (31.1% women; 68.9% men). Ultrasound radiofrequency data were acquired in each patient in both liver lobes at two focal depths (3/9 cm). Region of interests (ROIs) were drawn on HCC and liver parenchyma. The average normalized power spectrum for each ROI was extracted, and a linear regression was fit within the -6 dB bandwidth, from which the midband fit (MBF), spectral intercept (SI), and spectral slope (SS) were extracted. Differences in QUS parameters between the ROIs were tested by a mixed-effects regression. There was a significant intraindividual difference in MBF, SS, and SI between HCC and adjacent liver parenchyma (P < 0.001), and a significant interindividual difference between HCC and at-risk and healthy non-HCC parenchyma (P < 0.001). In patients with HCC, cirrhosis (n = 13) did not significantly change any of the three parameters (P > 0.8) in differentiating HCC from non-HCC parenchyma. MBF (P = 0.12), SI (P = 0.33), and SS (P = 0.57) were not significantly different in non-HCC tissue among the groups. The QUS parameters are significantly different in HCC versus non-HCC liver parenchyma, independent of underlying cirrhosis. This could be leveraged for improved HCC detection with ultrasound in the future.

Probing the Cellular Size Distribution in Cell Samples Undergoing Cell Death

A polydisperse scattering model adapted for concentrated medium, namely the polydisperse structure factor model, was examined to explain the backscatter coefficients (BSCs) measured from packed cell samples undergoing cell death. Cell samples were scanned using high-frequency ultrasound in the 10–42 MHz bandwidth. A parameter estimation procedure was proposed to estimate the volume fraction and the relative impedance contrast that could explain the changes in BSC pattern by considering the actual change in cellular size distribution. Quantitative ultrasound parameters were estimated and related to the percentage of dead cells determined by flow cytometry. The standard deviation of scatterer size distribution extracted from the polydisperse structure factor model and the spectral intercept were found to be strongly correlated to the percentage of dead cells (r2 = 0.79 and r2 = 0.72, respectively). This study contributes to the understanding of ultrasonic scattering from cells undergoing cell death toward the monitoring of cancer therapy.

Physical barrier type abuse-deterrent formulations: monitoring sintering-induced microstructural changes in polyethylene oxide placebo tablets by near infrared spectroscopy (NIRS)

Objective: The monitoring and evaluation of sintering-induced tablet strength of a polyethylene oxide (PEO) based placebo tables was accomplished using infrared spectroscopy (NIRS).Significance: Evaluation of high molecular weight PEO-based tablet matrices for abuse deterrent formulation applications is an analytical challenge. NIRS is one tool that can provide physical and chemical evaluation of this polymer and tablet system. In addition, the use of NIRS as a process analytical tool (PAT) to monitor oven sintering of pharmaceutical tablets has not been recorded in the literature. The multiplicative scattering correction (MSC) algorithm was also successfully applied as a new and fast way to calculate NIRS spectral slopes and intercepts to build models against tablet tensile strength with respect to sinter time.Methods: Both spectral slope regression (SSR) and spectral intercept regression (SIR) models were compared to commonly used partial least squares analysis (PLS) to evaluate placebo PEO based pharmaceutical tablets comprised of PEO at 70, 50, 30% w/w that were compressed at two solid fraction (SF) levels.Results: All three regression techniques, PLS, SSR, SIR, were evaluated for robustness and reliability and physical relevancy to the system studied. The methods were ranked in utility with SSR being the best method followed by SIR then PLS.Conclusions: The MSC algorithm was presented to quickly calculate spectral slopes and intercepts for use in SSR and SIR analysis. SSR models were successfully applied and assessed as the optimal modeling technique to monitor sintering of PEO-based matrix tablets.

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic.

Ultrasound spectral analysis-based assessment of macular areas in patients with age related macular degeneration

Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss in people aged 50 and older in the developed world. Early detection and treatment of AMD play the key role in helping patients at high risk for developing exudative to maintain their vision or slow down the progression of the disease. In this paper, the new technique for AMD evaluation using ultrasound spectral analysis is presented. Our aim was to compare B scan ultrasound signals backscattered from healthy tissues of macular area with signals from nonexudative and exudative AMD areas. In case-control study of 64 patients (144 tissues) we compared 82 cases of healthy tissue of macular area (I group) with 35 cases of nonexudative AMD (II group) and 30 cases of exudative AMD (III group) using B scan ultrasound. Estimated results showed that B scan amplitude and spectral intercept are informative to distinguish healthy tissues of macular area from AMD and spectral slope spectral, momentary bandwidth allow us to distinguish forms of AMD (nonexudative and exudative).We suggest that ultrasound spectral analysis is noninvasive, widely available, easy-to-use and less expensive method which can be used as an additional diagnostic tool in AMD detecting and differentiating when there are no possibilities to use more specific examination.

Effect of chromatin structure on quantitative ultrasound parameters.

High-frequency ultrasound (~20 MHz) techniques were investigated using in vitro and ex vivo models to determine whether alterations in chromatin structure are responsible for ultrasound backscatter changes in biological samples. Acute myeloid leukemia (AML) cells and their isolated nuclei were exposed to various chromatin altering treatments. These included 10 different ionic environments, DNA cleaving and unfolding agents, as well as DNA condensing agents. Raw radiofrequency (RF) data was used to generate quantitative ultrasound parameters from spectral and form factor analyses. Chromatin structure was evaluated using electron microscopy. Results indicated that trends in quantitative ultrasound parameters mirrored trends in biophysical chromatin structure parameters. In general, higher ordered states of chromatin compaction resulted in increases to ultrasound paramaters of midband fit, spectral intercept, and estimated scatterer concentration, while samples with decondensed forms of chromatin followed an opposite trend. Experiments with isolated nuclei demonstrated that chromatin changes alone were sufficient to account for these observations. Experiments with ex vivo samples indicated similar effects of chromatin structure changes. The results obtained in this research provide a mechanistic explanation for ultrasound investigations studying scattering from cells and tissues undergoing biological processes affecting chromatin.

Quantitative ultrasound for tumour characterization and a priorichemotherapy response prediction

Previous studies have demonstrated that quantitative ultrasound (QUS) is an effective tool for monitoring breast cancer patients undergoing neoadjuvant chemotherapy. Here, for the first time, we demonstrate the clinical utility of pre-treatment QUS texture features in predicting the response of breast cancer patients to NAC. Using a 6 MHz center frequency clinical ultrasound imaging system, radiofrequency breast ultrasound data were acquired from 92 locally advanced breast cancer patients prior to their NAC treatment. QUS spectral parameters including mid-band fit, spectral slope, and spectral intercept, and average acoustic concentration and average scatterer diameter were computed from regions of interest in the tumor core and its margin. Subsequently, gray-level co-occurrence matrix textural features were extracted from the parametric images as potential predictive indicators. Results were compared with the clinico-pathological response of each patient determined at their treatment end. A composite QUS...

TU‐H‐CAMPUS‐IeP3‐04: Evaluation of Changes in Quantitative Ultrasound Parameters During Prostate Radiotherapy

Purpose:Clarity Autoscan ultrasound monitoring system allows acquisition of raw radiofrequency (RF) ultrasound data prior and during radiotherapy. This enables the computation of 3D Quantitative Ultrasound (QUS) tissue parametric maps from. We aim to evaluate whether QUS parameters undergo changes with radiotherapy and thus potentially be used as early predictors and/or markers of treatment response in prostate cancer patients.Methods:In‐vivo evaluation was performed under IRB protocol to allow data collection in prostate patients treated with VMAT whereby prostate was imaged through the acoustic window of the perineum. QUS spectroscopy analysis was carried out by computing a tissue power spectrum normalized to the power spectrum obtained from a quartz to remove system transfer function effects. A ROI was selected within the 3D image volume of the prostate. Because longitudinal registration was optimal, the same features could be used to select ROIs at roughly the same location in images acquired on different days. Parametric maps were generated within the rectangular ROIs with window sizes that were approximately 8 times the wavelength of the ultrasound. The mid‐band fit (MBF), spectral slope (SS) and spectral intercept (SI) QUS parameters were computed for each window within the ROI and displayed as parametric maps. Quantitative parameters were obtained by averaging each of the spectral parameters over the whole ROI.Results:Data was acquired for over 21 treatment fractions. Preliminary results show changes in the parametric maps. MBF values decreased from −33.9 dB to −38.7 dB from pre‐treatment to the last day of treatment. The spectral slope increased from −1.1 a.u. to −0.5 a.u., and spectral intercept decreased from −28.2 dB to −36.3 dB over the 21 treatment regimen.Conclusion:QUS parametric maps change over the course of treatment which warrants further investigation in their potential use for treatment planning and predicting treatment outcomes.Research was supported by Elekta.

Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach.

PurposeThis study demonstrated the ability of quantitative ultrasound (QUS) parameters in providing an early prediction of tumor response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC).MethodsUsing a 6-MHz array transducer, ultrasound radiofrequency (RF) data were collected from 58 LABC patients prior to NAC treatment and at weeks 1, 4, and 8 of their treatment, and prior to surgery. QUS parameters including midband fit (MBF), spectral slope (SS), spectral intercept (SI), spacing among scatterers (SAS), attenuation coefficient estimate (ACE), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined from the tumor region of interest. Ultrasound data were compared with the ultimate clinical and pathological response of the patient's tumor to treatment and patient recurrence-free survival.ResultsMulti-parameter discriminant analysis using the κ-nearest-neighbor classifier demonstrated that the best response classification could be achieved using the combination of MBF, SS, and SAS, with an accuracy of 60 ± 10% at week 1, 77 ± 8% at week 4 and 75 ± 6% at week 8. Furthermore, when the QUS measurements at each time (week) were combined with pre-treatment (week 0) QUS values, the classification accuracies improved (70 ± 9% at week 1, 80 ± 5% at week 4, and 81 ± 6% at week 8). Finally, the multi-parameter QUS model demonstrated a significant difference in survival rates of responding and non-responding patients at weeks 1 and 4 (p=0.035, and 0.027, respectively).ConclusionThis study demonstrated for the first time, using new parameters tested on relatively large patient cohort and leave-one-out classifier evaluation, that a hybrid QUS biomarker including MBF, SS, and SAS could, with relatively high sensitivity and specificity, detect the response of LABC tumors to NAC as early as after 4 weeks of therapy. The findings of this study also suggested that incorporating pre-treatment QUS parameters of a tumor improved the classification results. This work demonstrated the potential of QUS and machine learning methods for the early assessment of breast tumor response to NAC and providing personalized medicine with regards to the treatment planning of refractory patients.

Quantitative ultrasound characterization of tumor cell death: ultrasound-stimulated microbubbles for radiation enhancement.

The aim of this study was to assess the efficacy of quantitative ultrasound imaging in characterizing cancer cell death caused by enhanced radiation treatments. This investigation focused on developing this ultrasound modality as an imaging-based non-invasive method that can be used to monitor therapeutic ultrasound and radiation effects. High-frequency (25 MHz) ultrasound was used to image tumor responses caused by ultrasound-stimulated microbubbles in combination with radiation. Human prostate xenografts grown in severe combined immunodeficiency (SCID) mice were treated using 8, 80, or 1000 µL/kg of microbubbles stimulated with ultrasound at 250, 570, or 750 kPa, and exposed to 0, 2, or 8 Gy of radiation. Tumors were imaged prior to treatment and 24 hours after treatment. Spectral analysis of images acquired from treated tumors revealed overall increases in ultrasound backscatter intensity and the spectral intercept parameter. The increase in backscatter intensity compared to the control ranged from 1.9±1.6 dB for the clinical imaging dose of microbubbles (8 µL/kg, 250 kPa, 2 Gy) to 7.0±4.1 dB for the most extreme treatment condition (1000 µL/kg, 750 kPa, 8 Gy). In parallel, in situ end-labelling (ISEL) staining, ceramide, and cyclophilin A staining demonstrated increases in cell death due to DNA fragmentation, ceramide-mediated apoptosis, and release of cyclophilin A as a result of cell membrane permeabilization, respectively. Quantitative ultrasound results indicated changes that paralleled increases in cell death observed from histology analyses supporting its use for non-invasive monitoring of cancer treatment outcomes.