Midband Fit Research Articles

Spectral Ultrasound Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastasis in Breast Cancer.

To explore the clinical value of the nomogram based on spectral Doppler ultrasound combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer. We prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively. Univariate analysis showed that lymph node metastasis was related with tumor size, Ki-67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki-67, axillary ultrasound, quantitative parameter (the mean of the mid-band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki-67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C-index of 0.83 (95% confidence interval 0.73-0.93). Axillary ultrasound combined with Ki-67 and spectral ultrasound parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.

Radioenhancement with the Combination of Docetaxel and Ultrasound Microbubbles: In Vivo Prostate Cancer.

Using an in vitro prostate cancer model, we previously demonstrated the significant enhancement of radiotherapy (XRT) with the combined treatment of docetaxel (Taxotere; TXT) and ultrasound-microbubbles (USMB). Here, we extend these findings to an in vivo cancer model. Severe combined immune-deficient male mice were xenografted with the PC-3 prostate cancer cell line in the hind leg and treated with USMB, TXT, radiotherapy (XRT), and their combinations. The tumors were imaged with ultrasound pre-treatment and 24 h post-treatment, following which they were extracted for the histological analysis of the tumor-cell death (DN; H&E) and apoptosis (DA; TUNEL). The tumors' growths were assessed for up to ~6 weeks and analysed using the exponential Malthusian tumor-growth model. The tumors' doubling time (VT) was characterized as growth (positive) or shrinkage (negative). The cellular death and apoptosis increased ~5-fold with the TXT + USMB + XRT (Dn = 83% and Da = 71%) compared to the XRT alone (Dn = 16% and Da = 14%), and by ~2-3-fold with the TXT + XRT (Dn = 50% and Da = 38%) and USMB + XRT (Dn = 45% and Da = 27%) compared to the XRT. The USMB enhanced the cellular bioeffects of the TXT by ~2-5-fold with the TXT + USMB (Dn = 42% and Da = 50%), compared with the TXT alone (Dn = 19% and Da = 9%). The USMB alone caused cell death (Dn = 17% and Da = 10%) compared to the untreated control (Dn = 0.4% and Da = 0%). The histological cellular bioeffects were correlated with the changes in the ultrasound RF mid-band-fit data, which were associated with the cellular morphology. The linear regression analysis displayed a positive linear correlation between the mid-band fit and the overall cell death (R2 = 0.9164), as well as a positive linear correlation between the mid-band fit and the apoptosis (R2 = 0.8530). These results demonstrate a correlation between the histological and spectral measurements of the tissue microstructure and that cellular morphological changes can be detected by ultrasound scattering analysis. In addition, the tumor volumes from the triple-combination treatment were significantly smaller than those from the control, XRT, USMB + XRT, and TXT + XRT, from day 2 onward. The TXT + USMB + XRT-treated tumors shrank from day 2 and at each subsequent time-point measured (VT ~-6 days). The growth of the XRT-treated tumors was inhibited during the first 16 days, following which the tumors grew (VT ~9 days). The TXT + XRT and USMB + XRT groups displayed an initial decrease in tumor size (day 1-14; TXT + XRT VT ~-12 days; USMB + XRT VT ~-33 days), followed by a growth phase (day 15-37; TXT + XRT VT ~11 days; USMB + XRT VT ~22 days). The triple-combination therapy induced tumor shrinkage to a greater extent than any of the other treatments. This study demonstrates the in vivo radioenhancement potential of chemotherapy combined with therapeutic ultrasound-microbubble treatment in inducing cell death and apoptosis, as well as long-term tumor shrinkage.

Effect of Clutter Filter in High-Frame-Rate Ultrasonic Backscatter Coefficient Analysis.

High-frame-rate imaging with a clutter filter can clearly visualize blood flow signals and provide more efficient discrimination with tissue signals. In vitro studies using clutter-less phantom and high-frequency ultrasound suggested a possibility of evaluating the red blood cell (RBC) aggregation by analyzing the frequency dependence of the backscatter coefficient (BSC). However, in in vivo applications, clutter filtering is required to visualize echoes from the RBC. This study initially evaluated the effect of the clutter filter for ultrasonic BSC analysis for in vitro and preliminary in vivo data to characterize hemorheology. Coherently compounded plane wave imaging at a frame rate of 2 kHz was carried out in high-frame-rate imaging. Two samples of RBCs suspended by saline and autologous plasma for in vitro data were circulated in two types of flow phantoms without or with clutter signals. The singular value decomposition was applied to suppress the clutter signal in the flow phantom. The BSC was calculated using the reference phantom method, and it was parametrized by spectral slope and mid-band fit (MBF) between 4-12 MHz. The velocity distribution was estimated by the block matching method, and the shear rate was estimated by the least squares approximation of the slope near the wall. Consequently, the spectral slope of the saline sample was always around four (Rayleigh scattering), independently of the shear rate, because the RBCs did not aggregate in the solution. Conversely, the spectral slope of the plasma sample was lower than four at low shear rates but approached four by increasing the shear rate, because the aggregations were presumably dissolved by the high shear rate. Moreover, the MBF of the plasma sample decreased from -36 to -49 dB in both flow phantoms with increasing shear rates, from approximately 10 to 100 s-1. The variation in the spectral slope and MBF in the saline sample was comparable to the results of in vivo cases in healthy human jugular veins when the tissue and blood flow signals could be separated.

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.

Quantitative Ultrasound for Evaluation of Tumour Response to Ultrasound-Microbubbles and Hyperthermia.

Objectives: Prior study has demonstrated the implementation of quantitative ultrasound (QUS) for determining the therapy response in breast tumour patients. Several QUS parameters quantified from the tumour region showed a significant correlation with the patient's clinical and pathological response. In this study, we aim to identify if there exists such a link between QUS parameters and changes in tumour morphology due to combined ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) using the breast xenograft model (MDA-MB-231). Method: Tumours grown in the hind leg of severe combined immuno-deficient mice were treated with permutations of USMB and HT. Ultrasound radiofrequency data were collected using a 25 MHz array transducer, from breast tumour-bearing mice prior and post-24-hour treatment. Result: Our result demonstrated an increase in the QUS parameters the mid-band fit and spectral 0-MHz intercept with an increase in HT duration combined with USMB which was found to be reflective of tissue structural changes and cell death detected using haematoxylin and eosin and terminal deoxynucleotidyl transferase dUTP nick end labelling stain. A significant decrease in QUS spectral parameters was observed at an HT duration of 60 minutes, which is possibly due to loss of nuclei by the majority of cells as confirmed using histology analysis. Morphological alterations within the tumour might have contributed to the decrease in backscatter parameters. Conclusion: The work here uses the QUS technique to assess the efficacy of cancer therapy and demonstrates that the changes in ultrasound backscatters mirrored changes in tissue morphology.

Multiplexed Ultrasound Imaging Using Spectral Analysis on Gas Vesicles.

Current advances in ultrasound imaging techniques combined with the next generation contrast agents such as gas vesicles (GV) revolutionize the visualization of biological tissues with spatiotemporal precision. In optics, fluorescent proteins enable understanding of molecular and cellular functions in biological systems due to their multiplexed imaging capability. Here, a panel of GVs is investigated using mid-band fit (MBF) spectral imaging to realize multiplexed ultrasound imaging to uniquely visualize locations of different types of stationary GVs. The MBF spectral imaging technique demonstrates that stationary clustered GVs are efficiently localized and distinguished from unclustered GVs in agarose gel phantom and 3D vessel structures are visualized in ex vivo mouse liver specimens. Mouse macrophages serve as carriers of clustered and unclustered GVs and multiplexing beacons to report cells' spatial locations by emitting distinct spectral signals. 2D MBF spectral images are reconstructed, and pixels in these images are classified depending on MBF values by comparing predetermined filters that predict the existence of cells with clustered and unclustered GVs. This pseudo-coloring scheme clearly distinguishes the locations of two classes of cells like pseudo-color images in fluorescence microscopy.

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 &amp;lt; 0.001), whereas benign nodes exhibited significantly lower values of spectral intercept (p &amp;lt; 0.001), midband fit (p = 0.004), scatterer diameter (ESD, p &amp;lt; 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.

Z-section and 3D rendering imaging using 130 and 50 MHz ultrasound and bacteria generated ultrasound contrast agents

We present an ultrasound z-sectioning imaging approach to visualize small vasculature. The major difference between confocal and conventional microscopy is the sectioning capability to generate three-dimensional images by stacking multiple sections. To visualize small vessels, we used 130 and 50 MHz ultrasonic transducers and gas vesicles (GV) isolated from bacteria as contrast agents. We developed 130 and 50 MHz ultrasonic transducers using lithium niobate. The focus and aperture of 130 MHz transducer are 1.5 and 1 mm and 50 MHz transducer has 3 mm aperture and 4 mm focus. These transducers were attached to ultrasound biomicroscopy for scanning phantoms and mouse liver specimen ex vivo with 1 micrometer steps. Saved radiofrequency data was post-process to reconstruct B-mode images and mid-band fit (MBF) maps. Four types of GVs were isolated in house. GV sizes were ranging from 200 to 600 nm. Three GV species present unclustered morphology but GVs isolated from one bacteria species present clustered morphology. Distinct MBF maps were created from clustered and unclustered GVs which were suitable for multiplexed imaging. The results demonstrated that MBF and GV based ultrasound imaging approach can image thin slices of tissue specimen with multiplexed imaging capability.

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 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.

Quantitative characterization of the colorectal cancer in a rabbit model using high-frequency endoscopic ultrasound

PurposeColonoscopy accompanied with biopsy works as the routine endoscopic strategy for the diagnosis of colorectal cancer (CRC) in clinic; however, the colonoscopy is limited to the tissue surface. During the last decades, enabling technologies are emerging to complement with the colonoscopy for better administration of CRC. The conventional low-frequency (<12 MHz) endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA) has been widely used to assess the lesion penetration. With the high-frequency ultrasound transducer (>20 MHz), EUS allows more precise visualization of the colorectal abnormalities. In order to achieve the accurate detection or in situ characterization of the colorectal lesions, the EUS diagnosis needs more patho-physiological related information in the micro-structural or molecular level. Quantitative ultrasound (QUS) technique, which could extract the micro-structural information from the ultrasound radio-frequency (RF) signal, is promising for the non-invasive tissue characterization. To date, the knowledge of the high-frequency endoscopic QUS for the CRC characterization has not been fully determined. MethodsIn this work, to our best knowledge, it is the first application of the QUS technique based on a customized high-frequency EUS system (30.5 MHz center frequency) to characterize the colorectal malignancies in a VX2 rabbit CRC model. To eliminate the response from the ultrasound electronic system and transducer, the ultrasound signals from colon tissue were calibrated. And, the resulting quasi-liner ultrasound spectra were fit by the linear regression test. As a result, three spectral parameters, including the slope (k), intercept (I) and Midband Fit (M), were obtained from the best-fit line. The three spectral parameters were compared between the malignant tissue regions and adjacent normal tissue regions of the colon tissue specimen ex vivo. The independent t-test was conducted between the three parameters from the normal and malignant group. The statistical method of Fisher Linear Discriminant (FLD) was used to explore the linear combinations of the three parameters, so as to provide more tissue micro-structural features than the single parameter alone. The three FLD values were derived from three different combinations among k, I and M. The threshold was selected from the statistical analysis to optimize the differentiation criteria between the malignant and the normal tissues. The color-coded images were used to display the local FLD values and combined with the EUS B-mode image. Results and ConclusionsThe mean Midband Fit (M) and intercept (I) showed significant differences between the malignant and normal tissue regions. The statistical analysis showed that there were significant differences in all the mean FLD values of the spectral parameter combinations (kI, kM and IM) (t test, P < 0.05). And, the combined image result from the B-mode image and color-coded image could visually correlate with the histology result. In conclusion, the high-frequency endoscopic QUS technique was potential to be used as a complementary method to distinguish the colorectal malignancies by leveraging its morphological and micro-structural ultrasound information.

Multiparametric Ultrasound for Targeting Prostate Cancer: Combining ARFI, SWEI, QUS and B-Mode

Diagnosing prostate cancer through standard transrectal ultrasound (TRUS)-guided biopsy is challenging because of the sensitivity and specificity limitations of B-mode imaging. We used a linear support vector machine (SVM) to combine standard TRUS imaging data with acoustic radiation force impulse (ARFI) imaging data, shear wave elasticity imaging (SWEI) data and quantitative ultrasound (QUS) midband fit data to enhance lesion contrast into a synthesized multiparametric ultrasound volume. This SVM was trained and validated using a subset of 20 patients and tested on a second subset of 10 patients. Multiparametric US led to a statistically significant improvements in contrast, contrast-to-noise ratio (CNR) and generalized CNR (gCNR) when compared with standard TRUS B-mode and SWEI; in contrast and CNR when compared with MF; and in CNR when compared with ARFI. ARFI, MF and SWEI also outperformed TRUS B-mode in contrast, with MF outperforming B-mode in CNR and gCNR as well. ARFI, although only yielding statistically significant differences in contrast compared with TRUS B-mode, captured critical qualitative features for lesion identification. Multiparametric US enhanced lesion visibility metrics and is a promising technique for targeted TRUS-guided prostate biopsy in the future.

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.

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.

Photoacoustic simulations of microvascular bleeding: spectral analysis and its application for monitoring vascular-targeted treatments.

.Solid tumors are typically supplied nutrients by a network of irregular blood vessels. By targeting these vascular networks, it might be possible to hinder cancer growth and metastasis. Vascular disrupting agents induce intertumoral hemorrhaging, making photoacoustic (PA) imaging well positioned to detect bleeding due to its sensitivity to hemoglobin and its various states. We introduce a fractal-based numerical model of intertumoral hemorrhaging to simulate the PA signals from disrupted tumor blood vessels. The fractal model uses bifurcated cylinders to represent vascular trees. To mimic bleeding from blood vessels, hemoglobin diffusion from microvessels was simulated. In the simulations, the PA signals were detected by a linear array transducer (30 MHz center frequency) of four different vascular trees. The power spectrum of each beamformed PA signal was computed and fitted to a straight line within the bandwidth of the receiving transducer. The spectral slope and midband fit (MBF) based on the fit decreased by and 2.12 dB, respectively, 1 h post bleeding, while the -intercept increased by 1.21 dB. The results suggest that spectral PA analysis can be used to measure changes in the concentration and spatial distribution of hemoglobin in tissue without the need to resolve individual vessels. The simulations support the feasibility of using PA imaging and spectral analysis in cancer treatment monitoring by detecting microvessel disruption.

In-vivo-quantitative-ultrasound assessment of thyroid nodules

Thyroid nodules suspicious for malignancy during clinical-ultrasound (US) evaluation undergo fine-needle-aspiration biopsy (FNAB) followed by cytological evaluation and, in indeterminate cases, an additional surgical intervention or molecular testing is recommended. Quantitative ultrasound (QUS) was investigated as a non-invasive approach to detect thyroid cancer and potentially reduce the currently large number of unnecessary FNABs. RF data were acquired from 225 nodules of 208 patients using a GE Logiq E9 US equipped with a 10-MHz linear-array probe. Five QUS estimates were computed throughout each nodule. Specifically, effective scatter diameter, effective scatter concentration, midband fit, and intercept were obtained using a reference-phantom method. The Nakagami shape parameter was computed using a maximum-likelihood estimator. Mean and standard deviations (SD) of QUS estimates within nodules were used for classification. QUS-based classification performance was quantified using area-under-the-curve (AUC) values obtained from receiver-operating- characteristic analyses. An AUC value of 0.77 was obtained using the SD of intercept alone; the value increased to 0.86 when five QUS estimates were linearly combined. Our in vivo results demonstrate the potential of QUS methods to detect cancerous nodules. Ultimately, the clinical protocol could be altered to prevent FNABs of nodules highly unlikely to be malignant based on QUS analysis.

Quantitative multiparametric ultrasound for prostate cancer targeted biopsy

Prostate cancer (PCa) is conventionally diagnosed using transrectal ultrasound (TRUS) guided biopsy. B-mode ultrasound is used to direct the biopsy needle to regions of the prostate gland, but is not used to target cancer-suspicious regions due to its low specificity for PCa. We are developing multiparametric quantitative ultrasound imaging approaches to facilitate targeting ultrasound-guided biopsies toward regions suspicious for PCa. To identify PCa during TRUS imaging, we combine information from four ultrasonic techniques: acoustic radiation force impulse (ARFI) imaging, shear wave elasticity imaging (SWEI), quantitative ultrasound (QUS) and B-mode ultrasound. 3-D co-registered in vivo ultrasound data, and MRI T2-weighted and ADC data volumes have been acquired from 30 patients prior to radical prostatectomy, obtaining estimates of ARFI displacement, shear wave speed, QUS midband fit, B-mode brightness, T2 intensity, and ADC values. In each data volume, healthy and cancerous regions were manually segmented with guidance from whole mount histology; the intersection of the cancerous segmentations from all modalities was labeled as the ground truth. We will present a range of classification approaches, including LDA and SVM, to combine information from each modality in order to develop automated tools for targeted biopsy.Prostate cancer (PCa) is conventionally diagnosed using transrectal ultrasound (TRUS) guided biopsy. B-mode ultrasound is used to direct the biopsy needle to regions of the prostate gland, but is not used to target cancer-suspicious regions due to its low specificity for PCa. We are developing multiparametric quantitative ultrasound imaging approaches to facilitate targeting ultrasound-guided biopsies toward regions suspicious for PCa. To identify PCa during TRUS imaging, we combine information from four ultrasonic techniques: acoustic radiation force impulse (ARFI) imaging, shear wave elasticity imaging (SWEI), quantitative ultrasound (QUS) and B-mode ultrasound. 3-D co-registered in vivo ultrasound data, and MRI T2-weighted and ADC data volumes have been acquired from 30 patients prior to radical prostatectomy, obtaining estimates of ARFI displacement, shear wave speed, QUS midband fit, B-mode brightness, T2 intensity, and ADC values. In each data volume, healthy and cancerous regions were manually segm...

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

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

Characterizing Fatty Liver in vivo in Rabbits, Using Quantitative Ultrasound

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease and can often lead to fibrosis, cirrhosis, cancer and complete liver failure. Liver biopsy is the current standard of care to quantify hepatic steatosis, but it comes with increased patient risk and only samples a small portion of the liver. Imaging approaches to assess NAFLD include proton density fat fraction estimated via magnetic resonance imaging (MRI) and shear wave elastography. However, MRI is expensive and shear wave elastography is not proven to be sensitive to fat content of the liver (Kramer et al. 2016). On the other hand, ultrasonic attenuation and the backscatter coefficient (BSC) have been observed to be sensitive to levels of fat in the liver (Lin et al. 2015; Paige et al. 2017). In this study, we assessed the use of attenuation and the BSC to quantify hepatic steatosis in vivo in a rabbit model of fatty liver. Rabbits were maintained on a high-fat diet for 0, 1, 2, 3 or 6 wk, with 3 rabbits per diet group (total N = 15). An array transducer (L9-4) with a center frequency of 4.5 MHz connected to a SonixOne scanner was used to gather radio frequency (RF) backscattered data in vivo from rabbits. The RF signals were used to estimate an average attenuation and BSC for each rabbit. Two approaches were used to parameterize the BSC (i.e., the effective scatterer diameter and effective acoustic concentration using a spherical Gaussian model and a model-free approach using a principal component analysis [PCA]). The 2 major components of the PCA from the BSCs, which captured 96% of the variance of the transformed data, were used to generate input features to a support vector machine for classification. Rabbits were separated into two liver fat-level classes, such that approximately half of the rabbits were in the low-lipid class (≤9% lipid liver level) and half of the rabbits in the high-lipid class (>9% lipid liver level). The slope and the midband fit of the attenuation coefficient provided statistically significant differences (p value = 0.00014 and p value = 0.007, using a two-sample t test) between low and high-lipid fat classes. The proposed model-free and model-based parameterization of the BSC and attenuation coefficient parameters yielded classification accuracies of 84.11 %, 82.93 % and 78.91 % for differentiating low-lipid versus high-lipid classes, respectively. The results suggest that attenuation and BSC analysis can differentiate low-fat versus high-fat livers in a rabbit model of fatty liver disease.

Radiofrequency spectral analysis of EBUS for peripheral pulmonary lesions.

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