Average Acoustic Concentration Research Articles

Noninvasive Evaluation of Breast Tumor Response to Combined Ultrasound-Stimulated Microbubbles and Hyperthermia Therapy Using Quantitative Ultrasound-Based Texture Analysis Method.

Quantitative ultrasound (QUS) is a noninvasive imaging technique that can be used for assessing response to anticancer treatment. In the present study, tumor cell death response to the ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) treatment was monitored in vivo using QUS. Human breast cancer cell lines (MDA-MB-231) were grown in mice and were treated with HT (10, 30, 50, and 60 minutes) alone, or in combination with USMB. Treatment effects were examined using QUS with a center frequency of 25 MHz (bandwidth range: 16 to 32 MHz). Backscattered radiofrequency (RF) data were acquired from tumors subjected to treatment. Ultrasound parameters such as average acoustic concentration (AAC) and average scatterer diameter (ASD), were estimated 24 hours prior and posttreatment. Additionally, texture features: contrast (CON), correlation (COR), energy (ENE), and homogeneity (HOM) were extracted from QUS parametric maps. All estimated parameters were compared with histopathological findings. The findings of our study demonstrated a significant increase in QUS parameters in both treatment conditions: HT alone (starting from 30 minutes of heat exposure) and combined treatment of HT plus USMB finally reaching a maximum at 50 minutes of heat exposure. Increase in AAC for 50 minutes HT alone and USMB +50 minutes was found to be 5.19 ± 0.417% and 5.91 ± 1.11%, respectively, compared to the control group with AAC value of 1.00 ± 0.44%. Furthermore, between the treatment groups, ΔASD-ENE values for USMB +30 minutes HT significantly reduced, depicting 0.00062 ± 0.00096% compared to 30 minutes HT only group, showing 0.0058 ± 0.0013%. Further, results obtained from the histological analysis indicated greater cell death and reduced nucleus size in both HT alone and HT combined with USMB. The texture-based QUS parameters indicated a correlation with microstructural changes obtained from histological data. This work demonstrated the use of QUS to detect HT treatment effects in breast cancer tumors in vivo.

QS2. High Frequency Spectral Ultrasound Imaging Detects Early Post Traumatic Muscle Fibrosis in Rodents

Purpose: Muscle fibrosis is a devastating condition that can occur following traumatic injury leading to loss of function and poor outcomes. Limited noninvasive modalities exist to diagnose and characterize the extent of muscle damage. Here we investigate spectral ultrasound imaging (SUSI), a quantitative imaging technique which detects tissue parameters based on intrinsic structure and composition, to identify early muscle fibrosis based on fibrotic changes established in a rat model. We hypothesize that SUSI will detect early traumatic muscle fibrosis in a clinically translational rat muscle injury model. Methods: Adult male Sprague Dawley rats were sedated, prepped, and underwent a soft tissue crush injury that generated 20lbs/in2 of pressure using a digital force gauge. A superficial 1.5 cm incisional wound was then made over the quadriceps and closed. Injured and contralateral uninjured hind limbs were collected and fixed for SUSI imaging. Imaging was performed with a VisualSonics VEVO770 high resolution small animal ultrasound imaging system, using a 30 MHz center frequency single element transducer. Injured muscles were imaged at the crush injury and the corresponding place on the contralateral limb. Analysis of radiofrequency data was done as previously described in literature and SUSI parameters were calculated using the average acoustic concentration (AAC) of the fibrotic region and the corresponding region in the contralateral muscle. AAC values were grouped by time point and tissue type for statistical comparison using unpaired t tests. Results: In our rodent model, SUSI identified muscle fibrosis as early as 7 days post injury. The average AAC of injured fibrotic versus uninjured muscle was significantly increased at post injury day 7 (36.33 vs 18.36 db/mm3, injured versus uninjured, p = 0.003) and day 28 (54.64 vs 28.29 db/mm3, injured versus uninjured, p<0.0001). Though not statistically significant at day 14 (38.69 vs 27.61 db/mm3, injured vs uninjured, p=0.14) the fibrotic muscle did trend toward a higher AAC value. These results indicate that at day 7 and 28, the SUSI measurement AAC can be used to distinguish injured fibrotic muscle from uninjured muscle. Conclusions: SUSI offers a portable, non-invasive, and safe diagnostic modality to detect muscle fibrosis, making it an ideal modality for post traumatic injuries. Here, we show SUSI distinguishes muscle fibrosis from healthy tissue at day 7 and 28 post injury in our model allowing for early treatment. Unlike qualitative grayscale imaging, SUSI determines unique parameters of the tissue based on a calibrated spectrum related to the inherent properties of the tissue, making it less user-dependent and more reproducible.

In vivo assessment of prostate cancer response using quantitative ultrasound characterization of ultrasonic scattering properties

BackgroundThe study here investigated quantitative ultrasound (QUS) parameters to assess tumour response to ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) treatment in vivo. Mice bearing prostate cancer xenografts were exposed to various treatment conditions including 1% (v/v) Definity microbubbles stimulated at ultrasound pressures 246 kPa and 570 kPa and HT duration of 0, 10, 40, and 50 min. Ultrasound radiofrequency (RF) data were collected using an ultrasound transducer with a central frequency of 25 MHz. QUS parameters based on form factor models were used as potential biomarkers of cell death in prostate cancer xenografts.ResultsThe average acoustic concentration (AAC) parameter from spherical gaussian and the fluid-filled spherical models were the most efficient imaging biomarker of cell death. Statistical significant increases of AAC were found in the combined treatment groups: 246 kPa + 40 min, 246 kPa + 50 min, and 570 kPa + 50 min, in comparison with control tumours (0 kPa + 0 min). Changes in AAC correlates strongly (r2 = 0.62) with cell death fraction quantified from the histopathological analysis.ConclusionScattering property estimates from spherical gaussian and fluid-filled spherical models are useful imaging biomarkers for assessing tumour response to treatment. Our observation of changes in AAC from high ultrasound frequencies was consistent with previous findings where parameters related to the backscatter intensity (AAC) increased with cell death.

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.

High-frequency spectral ultrasound imaging (SUSI) visualizes early post-traumatic heterotopic ossification (HO) in a mouse model.

Early treatment of heterotopic ossification (HO) is currently limited by delayed diagnosis due to limited visualization at early time points. In this study, we validate the use of spectral ultrasound imaging (SUSI) in an animal model to detect HO as early as one week after burn tenotomy. Concurrent SUSI, micro CT, and histology at 1, 2, 4, and 9weeks post-injury were used to follow the progression of HO after an Achilles tenotomy and 30% total body surface area burn (n=3-5 limbs per time point). To compare the use of SUSI in different types of injury models, mice (n=5 per group) underwent either burn/tenotomy or skin incision injury and were imaged using a 55MHz probe on VisualSonics VEVO 770 system at one week post injury to evaluate the ability of SUSI to distinguish between edema and HO. Average acoustic concentration (AAC) and average scatterer diameter (ASD) were calculated for each ultrasound image frame. Micro CT was used to calculate the total volume of HO. Histology was used to confirm bone formation. Using SUSI, HO was visualized as early as 1week after injury. HO was visualized earliest by 4weeks after injury by micro CT. The average acoustic concentration of HO was 33% more than that of the control limb (n=5). Spectroscopic foci of HO present at 1week that persisted throughout all time points correlated with the HO present at 9weeks on micro CT imaging. SUSI visualizes HO as early as one week after injury in an animal model. SUSI represents a new imaging modality with promise for early diagnosis of HO.

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.

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...

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.

Quantification of Ultrasonic Scattering Properties of In Vivo Tumor Cell Death in Mouse Models of Breast Cancer

INTRODUCTION: Quantitative ultrasound parameters based on form factor models were investigated as potential biomarkers of cell death in breast tumor (MDA-231) xenografts treated with chemotherapy. METHODS: Ultrasound backscatter radiofrequency data were acquired from MDA-231 breast cancer tumor–bearing mice (n = 20) before and after the administration of chemotherapy drugs at two ultrasound frequencies: 7 MHz and 20 MHz. Radiofrequency spectral analysis involved estimating the backscatter coefficient from regions of interest in the center of the tumor, to which form factor models were fitted, resulting in estimates of average scatterer diameter and average acoustic concentration (AAC). RESULTS: The ∆AAC parameter extracted from the spherical Gaussian model was found to be the most effective cell death biomarker (at the lower frequency range, r2 = 0.40). At both frequencies, AAC in the treated tumors increased significantly (P = .026 and .035 at low and high frequencies, respectively) 24 hours after treatment compared with control tumors. Furthermore, stepwise multiple linear regression analysis of the low-frequency data revealed that a multiparameter quantitative ultrasound model was strongly correlated to cell death determined histologically posttreatment (r2 = 0.74). CONCLUSION: The Gaussian form factor model–based scattering parameters can potentially be used to track the extent of cell death at clinically relevant frequencies (7 MHz). The 20-MHz results agreed with previous findings in which parameters related to the backscatter intensity (i.e., AAC) increased with cell death. The findings suggested that, in addition to the backscatter coefficient parameter ∆AAC, biological features including tumor heterogeneity and initial tumor volume were important factors in the prediction of cell death response.

Noninvasive Characterization of Locally Advanced Breast Cancer Using Textural Analysis of Quantitative Ultrasound Parametric Images

PURPOSE: The identification of tumor pathologic characteristics is an important part of breast cancer diagnosis, prognosis, and treatment planning but currently requires biopsy as its standard. Here, we investigated a noninvasive quantitative ultrasound method for the characterization of breast tumors in terms of their histologic grade, which can be used with clinical diagnostic ultrasound data. METHODS: Tumors of 57 locally advanced breast cancer patients were analyzed as part of this study. Seven quantitative ultrasound parameters were determined from each tumor region from the radiofrequency data, including mid-band fit, spectral slope, 0-MHz intercept, scatterer spacing, attenuation coefficient estimate, average scatterer diameter, and average acoustic concentration. Parametric maps were generated corresponding to the region of interest, from which four textural features, including contrast, energy, homogeneity, and correlation, were determined as further tumor characterization parameters. Data were examined on the basis of tumor subtypes based on histologic grade (grade I versus grade II to III). RESULTS: Linear discriminant analysis of the means of the parametric maps resulted in classification accuracy of 79%. On the other hand, the linear combination of the texture features of the parametric maps resulted in classification accuracy of 82%. Finally, when both the means and textures of the parametric maps were combined, the best classification accuracy was obtained (86%). CONCLUSIONS: Textural characteristics of quantitative ultrasound spectral parametric maps provided discriminant information about different types of breast tumors. The use of texture features significantly improved the results of ultrasonic tumor characterization compared to conventional mean values. Thus, this study suggests that texture-based quantitative ultrasound analysis of in vivo breast tumors can provide complementary diagnostic information about tumor histologic characteristics.

Non-invasive evaluation of breast cancer response to chemotherapy using quantitative ultrasonic backscatter parameters.

Tumor response to neoadjuvant chemotherapy in patients (n=30) with locally advanced breast cancer (LABC) was examined using quantitative ultrasound. Three ultrasound backscatter parameters, the integrated backscatter coefficient (IBC), average scatterer diameter (ASD), and average acoustic concentration (AAC), were estimated from tumors prior to treatment and at four times during neoadjuvant chemotherapy treatment (weeks 0, 1, 4, 8, and prior to surgery) and compared to ultimate clinical and pathological tumor responses. Results demonstrated that among all parameters, AAC was the best indicator of tumor response early after starting treatment. The AAC parameter increased substantially in treatment-responding patients as early as one week after treatment initiation, further increased at week 4, and attained a maximum at week 8. In contrast, the backscatter parameters from non-responders did not show any changes after treatment initiation. The two patient populations exhibited a statistically significant difference in changes of AAC (p<0.001) and ASD (p=0.023) over all treatment times examined. The best prediction of treatment response was achieved with the combination of AAC and ASD at week 4 (82% sensitivity, 100% specificity, and 86% accuracy) of 12-18 weeks of treatment. The survival of patients with responsive ultrasound parameters was higher than patients with non-responsive ultrasound parameters (35 ± 11 versus 27 ± 11 months, respectively, p=0.043). This study demonstrates that ultrasound parameters derived from the ultrasound backscattered power spectrum can potentially serve as non-invasive early measures of clinical tumor response to chemotherapy treatments.

Evaluation of tumor cell death response in locally advanced breast cancer patients to chemotherapy treatment by scattering property estimates using ultrasound backscatter

Breast cancer tumor response to chemotherapy in twenty nine patients was examined using quantitative ultrasound techniques. Backscatter parameters, such as the average scatterer diameter (ASD) and average acoustic concentration (AAC), were estimated from regions-of-interest in tumors prior to treatment onset and at four times during neoadjuvant chemotherapy treatment (weeks 1, 4, 8 and prior to surgery). Gaussian and Anderson form factor models were used over an analysis bandwidth of 4.5 to 9 MHz to obtain ASD and AAC estimates. The Gaussian model did not fit with the measured data as well as Anderson model. The AAC estimates yielded significant differences with therapy times in clinically treatment responded patients. Data indicated increases of 6.6 dB/cm in acoustic concentration obtained maximum at week 8 in treatment responding tumors. Non-responding tumors didn't show any significant difference in the parameters during treatment. This study demonstrates that the scattering parameters have the potential to being used in quantifying the changes in tumors during treatment noninvasively and distinguishing treatment responders and non-responders early after of treatment initiation.

Estimation of average acoustic concentration of Chinese hamster ovary cells using ultrasonic backscatter.

Average acoustic concentration (AAC) is one important parameter of quantitative ultrasonic assessment of soft tissues. The objective of this study is to examine the AAC of Chinese hamster ovary (CHO) cells at varied cell densities using ultrasonic backscatter. The CHO cells were evenly distributed in 2% agar and the densities ranged from 500,000 to 54,000,000 cells/mL. Measurements of backscattered ultrasound were obtained over the frequency range of 17 to 80 MHz. Estimates of Average Scatterer Diameters and AAC were made by comparing the backscattered power spectra with three scattering models: Fluid‐filled Sphere model (FFSM), Spherical Gaussian model (SGM), and Concentric Sphere Model (CSM). Over the frequency range of 28 to 50 MHz, the estimated ASDs were 20.0±0 μm with FFSM, 22.2±6.0 μm with SGM, and 20.1±6.1 μm with CSM. In the cell density range of 500,000 to 10,000,000 cells/mL, the estimated AACs increased almost linearly from 10 to 34 dB/mm3 using FFSM, from 16 to 39 dB/mm3 using SGM, and from 8...

Improved cancer detection and classification using multi‐parameter quantitative ultrasound

Quantitative ultrasound (QUS) has been used successfully to differentiate benign from malignant solid tumors in animal models of breast cancer. In these studies, QUS made use of estimates of average scatterer diameter (ASD) and average acoustic concentration (AAC) from the ultrasonic backscatter. However, initial attempts to classify different kinds of malignant tumors based on ASD and AAC estimates were not successful. New models for ultrasonic backscatter were created by considering the cytoskeletal structure of cells. In addition, the homodyned K distribution was used to model the amplitude of the envelope from regions‐of‐interest in the malignant tumors. The homodyned K distribution yielded two parameters: the S parameter, which quantified the randomness of scatterer spacings, and the β parameter, which quantified the amount of clustering of scatterers in the interrogated tissue. Statistically significant differences (P < 0.05) were observed between average S and β parameters from the malignant tumors...

Examination of cancer in mouse models using high-frequency quantitative ultrasound

Two mouse models of mammary cancer (a carcinoma and sarcoma) were examined using quantitative ultrasound (QUS). Scatterer property estimates, i.e., the average scatterer diameter (ASD) and average acoustic concentration (AAC), were estimated from regions-of-interest (ROIs) inside the tumors. Initially, the spherical Gaussian model was used over an analysis bandwidth of 10 to 25 MHz to obtain ASD and AAC estimates. ASD estimates were 31.7 ± 9.36 μm and 31.0 ± 7.20 μm for the carcinomas and sarcomas, respectively. AAC estimates were 6.77 ± 8.75 dB[mm –3] and 9.87 ± 9.24 dB[mm –3], respectively. The initial ASD and AAC estimates did not yield statistically significant differences between the two kinds of tumors ( p = 0.83, 0.86 for the ASD and AAC estimates, respectively). However, optical photomicrographs revealed distinct morphologic differences between the tumors. F-tests on the average power spectra from the tumors revealed statistically significant differences between the spectra over the range of 16 to 25 MHz. ASD and AAC estimates using the spherical Gaussian model were then obtained over the new analysis bandwidth of 16 to 25 MHz. The new ASD estimates were 42.1 ± 4.01 μm and 32.1 ± 3.81 μm for the carcinomas and sarcomas, respectively. The new AAC estimates were 16.4 ± 17.1 dB[mm –3] and 36.4 ± 11.9 dB[mm –3], respectively. Statistically significant differences were observed for both the ASD and AAC estimates when using the new analysis bandwidth. Structural differences between the tumors were revealed by both QUS and optical photomicrographs. (E-mail: oelze@uiuc.edu)

Characterization and differentiation of three solid tumors using quantitative ultrasound

Three kinds of solid tumors were acquired and scanned in vivo ultrasonically. The first tumor series (fibroadenoma) was acquired from tumors that had spontaneously developed in rats. The second tumor series was acquired by culturing a carcinoma cell line (4T1-MMT) in culture media and injecting the cells into Balb/c mice. The third tumor was acquired by transplanting a soft-tissue sarcoma cell line (EHS) into C57BL mice. The tumors were allowed to grow to 1 cm in size and then scanned ultrasonically. The scatterer properties of average scatterer diameter and acoustic concentration were estimated using a Gaussian form factor from the backscattered ultrasound measured from the tumors. Parametric images of the tumors were constructed utilizing estimated scatterer properties for regions of interest inside the tumors. The parametric images showed distinct differences between the various tumor types. Quantitatively, the tumors could be distinguished through feature analysis plots of average scatterer size versus acoustic concentration. Comparison with photomicrographs of the tumors showed structures similar in size to the ultrasound estimates. [Work supported by NIH Grant F32 CA96419 to MLO and by the University of Illinois Research Board.]

Characterization and differentiation of two mammary tumors using parametric imaging with ultrasound

Two kinds of solid tumors were acquired and scanned in vivo ultrasonically. The first tumor series (fibroadenoma) was acquired from tumors that developed spontaneously in rats. The second tumor series was acquired by culturing a carcinoma cell line (4T1-MMT) and injecting the cells into Balb/c mice. The scatterer properties (average scatterer diameter and acoustic concentration) were estimated using a Gaussian form factor from the backscattered ultrasound measured from both kinds of tumors. Parametric images of tumors were constructed utilizing estimated scatterer properties for regions of interest inside the tumors and surrounding normal tissues. The average scatterer diameter and acoustic concentration for the fibroadenomas were estimated at 107±14 micrometers and 15.2±5 dB (mm−3), respectively. The average scatterer diameter and acoustic concentration for the carcinomas was estimated at 30±4.6 micrometers and 10.3±6.9 dB (mm−3), respectively. A comparison with light microscopic evaluations of the fibroadenomas showed cellular structures around 100 micrometers in size, and carcinomas showed cell nuclei with an average size of 12.5 micrometers in diameter (the total cellular size ranging from 50% to 200% larger than the nucleus size). [Work supported by NIH F32 CA96419 to MLO and by the University of Illinois Research Board.]

Po-topic V-01: Parametric imaging of spontaneous mammary tumors in rats for the purposes of tissue microstructure characterization

The characterization of tissue microstructure using ultrasound backscatter can assist in the detection and classification of diseased tissues. Parametric images can be formed from tissue characterizations that show structure information below that resolved for conventional ultrasound. Eight retired breeders were acquired that had developed spontaneous mammary tumors. 2D B-mode images of the rat tumors were constructed from backscattered echoes. After scanning, tumors were dissected free from each rat, trimmed in the plane of ultrasound exposure, fixed in 10% neutral-buffered formalin, embedded in paraffin, sectioned at 5 μm, and stained with hematoxylin and eosin. Tumors were diagnosed microscopically as mammary gland fibroadenomas. Regions of interest (ROIs) were selected from the ultrasound B-mode images of the tumors and surrounding tissues. Average scatterer properties (effective scatterer size and acoustic concentration) were estimated from the backscattered RF signal used to make the B-mode images. Scatterer estimates were made by using least squares to fit a line to the measured form factor that was calculated from the backscattered power spectrum. The effect of noise in the signal from increasing attenuation with depth was to reduce the ability to make estimates of scatterer properties at greater depths. A weighting scheme was used to reduce the effects of noise and increase the ability to make accurate estimates to greater depths. Comparison of scatterer estimates between normal tissues (intercostal tissues) and tissues inside the tumors were made. On average, the estimated effective scatterer diameters inside the tumors were 30% larger at 107 μm than estimates of effective scatterer diameters outside the tumors averaging 82 μm. The average acoustic concentration estimated inside the tumor was 3.16 × 10−2/mm−3 as opposed to 0.746 mm−3 for outside the tumor. In all but one of the rats, there was a statistically significant difference (P < .05) between estimates of scatterer properties made inside the tumors and in surrounding healthy tissues. Feature analysis plots clearly showed distinction between the diseased tissues and the normal tissues. Enhanced B-mode images were constructed by superimposing colored pixels corresponding to the estimated average scatterer properties in different ROIs on the gray-scale B-mode images. The enhanced B-mode images highlighted differences between tissues inside and outside the tumors.

Characterization of tissue microstructure using ultrasonic backscatter: theory and technique for optimization using a Gaussian form factor.

Characterization of tissue microstructure through ultrasonic backscatter is hypothesized to aid in detection and classification of diseased tissues. Radio frequency signals backscattered from tissues can be modeled according to the assumed shape, size, and distribution of scatterers in tissues. Power spectra of rf backscattered signals describe the frequency dependence of scatterers. Experimental measurements of ultrasonic backscatter from spontaneous mammary tumors in rats are obtained over the frequency range of 4 to 12 MHz. The power spectra measured from rat tumors are compared to theoretical power spectra derived from a 3D spatial autocorrelation function assuming a Gaussian distribution. Independent values of average scatterer diameter and acoustic concentration are obtained by approximating the measured power spectrum with a best-fit line. Enhanced B-mode images are made of the rat tumors and surrounding tissues with superimposed regions of interest quantified by estimated average scatterer sizes and acoustic concentrations. Scattering properties estimated inside the tumors and in surrounding tissues are shown to be distinct. Overall, estimates showed a 44.8% increase of average scatterer diameter inside the tumor as compared to tissues outside the tumor. With the exception of one rat, all estimates of the scatterers' average acoustic concentration inside the tumor were less than outside the tumors.