Blinking Phase-Change Nanocapsules Enable Background-Free Ultrasound Imaging

Microbubbles are widely used as contrast agents to improve the diagnostic capability of conventional, highly speckled, low-contrast ultrasound imaging. However, while microbubbles can be used for molecular imaging, these agents are limited to the vascular space due to their large size (\u3e 1 μm). Smaller microbubbles are desired but their ultrasound visualization is limited due to lower echogenicity or higher resonant frequencies. Here we present nanometer scale, phase changing, blinking nanocapsules (BLInCs), which can be repeatedly optically triggered to provide transient contrast and enable background-free ultrasound imaging. In response to irradiation by near-infrared laser pulses, the BLInCs undergo cycles of rapid vaporization followed by recondensation into their native liquid state at body temperature. High frame rate ultrasound imaging measures the dynamic echogenicity changes associated with these controllable, periodic phase transitions. Using a newly developed image processing algorithm, the blinking particles are distinguished from tissue, providing a background-free image of the BLInCs while the underlying B-mode ultrasound image is used as an anatomical reference of the tissue. We demonstrate the function of BLInCs and the associated imaging technique in a tissue-mimicking phantom and in vivo for the identification of the sentinel lymph node. Our studies indicate that BLInCs may become a powerful tool to identify biological targets using a conventional ultrasound imaging system

Measuring Chemotherapy Response in Breast Cancer Using Optical and Ultrasound Spectroscopy

Purpose: This study comprises two subprojects. In subproject one, the study purpose was to evaluate response to neoadjuvant chemotherapy (NAC) using quantitative ultrasound (QUS) and diffuse optical spectroscopy imaging (DOS) in locally advanced breast cancer (LABC) during chemotherapy. In subproject two, DOS-based functional maps were analysed with texture-based image features to predict breast cancer response before the start of NAC. Patients and Measurements: The institution’s ethics review board approved this study. For subproject one, subjects (n=22) gave written consent before participating in the study. Participants underwent non-invasive, DOS and QUS imaging. Data were acquired at weeks 0 (i.e. baseline), 1, 4, 8 and before surgical removal of the tumour (mastectomy and/or lumpectomy); corresponding to chemotherapy schedules. QUS parameters including the midband fit (MBF), 0-MHz intercept (SI), and the spectral slope (SS) were determined from tumour ultrasound data using spectral analysis. In the same patients, DOS was used to measure parameters relating to tumour haemoglobin and tissue composition such as %Water and %Lipids. Discriminant analysis and receiver-operating characteristic (ROC) analyses were used to correlate the measured imaging parameters to Miller-Payne pathological response during treatment. Additionally, multivariate analysis was carried out for pairwise DOS and QUS parameter combinations to determine if an increase in the classification accuracy could be obtained using combination DOS and QUS parametric models. For subproject two, 15 additional patients we recruited after first giving their written informed consent. A pooled analysis was completed for all DOS baseline data (subproject 1 and subproject 2; n=37 patients). LABC patients planned for NAC had functional DOS maps and associated textural features generated. A grey-level co-occurrence matrix (texture) analysis was completed for parameters associated with haemoglobin, tissue composition, and optical properties (deoxy-haemoglobin [Hb], oxy-haemoglobin [HbO2], total haemoglobin [HbT]), %Lipids, %Water, and scattering power [SP], scattering amplitude [SA]) prior to treatment. Textural features included contrast (con), vi correlation (cor), energy (ene), and homogeneity (hom). Patients were classified as ‘responders’ or ‘non-responders’ using Miller-Payne pathological response criteria after treatment completion. In order to test if baseline univariate texture features could predict treatment response, a receiver operating characteristic (ROC) analysis was performed, and the optimal sensitivity, specificity and area under the curve (AUC) was calculated using Youden’s index (Q-point) from the ROC. Multivariate analysis was conducted to test 40 DOS-texture features and all possible bivariate combinations using a naïve Bayes model, and k-nearest neighbour (k-NN) model classifiers were included in the analysis. Using these machine-learning algorithms, the pretreatment DOS-texture parameters underwent dataset training, testing, and validation and ROC analysis were performed to find the maximum sensitivity and specificity of bivariate DOS-texture features. Results: For subproject one, individual DOS and QUS parameters, including the spectral intercept (SI), oxy-haemoglobin (HbO2), and total haemoglobin (HbT) were significant markers for response outcome after one week of treatment (p<0.01). Multivariate (pairwise) combinations increased the sensitivity, specificity and AUC at this time; the SI+HbO2 showed a sensitivity/specificity of 100%, and an AUC of 1.0 after one week of treatment. For subproject two, the results indicated that textural characteristics of pre-treatment DOS parametric maps can differentiate treatment response outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) = 86.5 and 89.0%, respectively and an accuracy of 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn = 78.0, a %Sp = 81.0% and an accuracy of 79.5% using the naïve Bayes model. Conclusion: DOS and QUS demonstrated potential as coincident markers for treatment response and may potentially facilitate response-guided therapies. Also, the results of this study demonstrated that DOS-texture analysis can be used to predict breast cancer response groups prior to starting NAC using baseline DOS measurements

Progress Report No. 21

Progress report of the Biomedical Computer Laboratory, covering period 1 July 1984 to 30 June 1985

High-Speed Photoacoustic Microscopy In Vivo

The overarching goal of this research is to develop a novel photoacoustic microscopy: PAM) technology capable of high-speed, high-resolution 3D imaging in vivo. PAM combines the advantages of optical absorption contrast and ultrasonic resolution for deep imaging beyond the quasi-ballistic regime. Its high sensitivity to optical absorption enables the imaging of important physiological parameters, such as hemoglobin concentration and oxygen saturation, which closely correlate with angiogenesis and hypermetabolism--two hallmarks of cancer. To translate PAM to the clinic, both high imaging speed and high spatial resolution are desired. With high spatial resolution, PAM can detect small structural and functional changes early; whereas, high-speed image acquisition helps reduce motion artifacts, patient discomfort, cost, and potentially the risks associated with minimally invasive procedures such as endoscopy and intravascular imaging. To achieve high imaging speed, we have constructed a PAM system using a linear ultrasound array and a kHz-repetition-rate tunable laser. The system has achieved a 249-Hz B-scan rate and a 0.5-Hz 3D imaging rate: over ~6 mm × 10 mm × 3 mm), over 200 times faster than existing mechanical scanning PAM using a single ultrasonic transducer. In addition, high-speed optical-resolution photoacoustic microscopy: OR-PAM) technology has been developed, in which the spatial resolution in one or two dimension(s) is defined by the diffraction-limited optical focus. Using section illumination, the elevational resolution of the system has been improved from ~300 micron to ~28 micron, resulting in a significant improvement in the 3D image quality. Furthermore, multiple optical foci with a microlens array have been used to provide finer than 10-micron lateral resolution--enabling the system to image capillary-level microvessels in vivo--while offering a speed potentially 20 times faster than previously existing single-focus OR-PAM. Finally, potential biomedical applications of the developed technology have been demonstrated through in vivo imaging of murine sentinel lymph nodes, microcirculation dynamics, and human pulsatile dynamics. In the future, this high-speed PAM technology may be adapted for clinical imaging of diabetes-induced vascular complications or tumor angiogenesis, or miniaturized for gastrointestinal or intravascular applications

Comparison between A-mode and B-mode ultrasound in local hyperthermia monitoring

Hyperthermia therapy is one of the therapy methods used for cancer treatment. It has shown to be an effective way of treating the cancerous tissue when compared to surgery, chemotherapy and radiation. However, real time monitoring method is capable in delivering a consistent heat and preventing any damages to the nearby tissue. Ultrasound is among the widely used technique in clinical setting. A-Mode ultrasound involves one-dimensional (1D) signal processing which enables a quantitative measurement on different types of breast tissues to be conducted faster as it has relatively simple signal processing requirement. On the other hand, B-Mode ultrasound offers good spatial resolution for thermal monitoring. Therefore, the aim of this study is to investigate and to compare the most optimum A-Mode and B-Mode ultrasound parameters to monitor hyperthermia in normal and pathological breast tissue. A series of experiment was conducted on 40 female Sprague Dawley rats. The pathological and normal rats were dissected and exposed to hyperthermia at variation temperature of 37oC (body temperature) and 40oC, 45oC, 50oC and 55oC for hyperthermia temperatures. A-Mode and B-Mode of 7.5 Mhz and 6Mhz was used simultaneously during the experiment for collecting acoustic information and scanning purposes before and after the hyperthermia exposure. Result obtained shows that, for normal tissue condition of both A-Mode and B-Mode, the attenuation calculation to mean of pixel intensity found to be (3.59±0.04)dB and 187.68 at temperature value of 50 oC. Meanwhile, in pathological tissue condition, the attenuation value with respect to pixel intensity was obtained by (3.36±0.26)dB at temperature value of 45oC and 199.26 was achieved at temperature value of 40oC. For backscatter coefficient to variance analysis, the result found that, in both A-Mode and B-Mode normal tissue condition, at temperature value of 40oC, (1.81±0.25) of backscatter coefficient was obtained while at 45oC, the variance value of 3298.94 was achieved. In pathological tissue, the temperature value of 40oC and 55oC was the most pronounce temperature dependent of (1.45±0.28) for backscatter coefficient with respect to 3275.35 of variance analysis. The result obtained from artificial neural network have shown that, 91.67% to 87.5% of testing to validation percentage accuracy of A-Mode was achieved, while in B-Mode, 88.89% and 81.25% of testing and validation data was obtained. Therefore, it is shown that, the use of A-Mode with comparison to B-Mode ultrasound can be used as another potential approach since its attenuation to pixel intensity and backscatter coefficient with respect to variance of A-Mode and B-Mode is very sensitive to the tissue structure in monitoring hyperthermia therapy with respect to the changes of temperature

Spectral Ultrasound Characterization of Tissues and Tissue Engineered Constructs.

Even though ultrasound imaging is widely used in clinical diagnosis and image-guided interventions, the field is far behind other areas of clinical quantitative image analysis, such as MRI, CT and X-ray mammography. In this thesis, non-destructive and non-invasive ultrasound characterization techniques were developed to study the tissue micro-structural details using high frequency spectral ultrasound imaging (SUSI). The techniques were explored in in-vitro conditions of acellular and cellular tissue engineered constructs and then on ex-vivo tissues for their characterization. SUSI was used to assess the amount of hydroxyl-apatite (HA) mineral, differentiate HA mineral types and study their distribution in acellular tissue engineered constructs. The process of mineral deposition from surrounding mineralizing media onto simple collagen constructs was also studied and characterized with SUSI. 3D morphological changes of the constructs with MC3t3 cells was monitored and characterized for the developmental changes such as net cell proliferation/apoptosis and cell differentiation process through mineral production by the early osteoblastic MC3t3-cell constructs in-situ. A novel method was introduced using SUSI to estimate the amount of mineral secreted by the differentiated osteoblast cells in a non-destructive method. Then, SUSI was investigated in ex-vivo cardiac tissues to monitor and characterize the cellular changes during high-intensity focused ultrasound ablation with high-frame-rate and high-resolution ultrasound imaging. The mechanistic hypotheses behind the improvement in lesion detection were investigated and best identification methods to assess lesion formation and transient gas body activities were proposed to provide a method for visualizing spatiotemporal evolution of lesion and gas–body activity and for predicting macroscopic cavity formation upon its implementation as a real-time monitoring technique with feedback control system for HIFU treatment of atrial fibrillation to improve the ablation process. Even though the results from the developed techniques show great promise in in-vitro and ex-vivo settings, additional work needs to be carried out to demonstrate the applicability of the techniques in in-vivo.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99788/1/msreddy_1.pd

Multiparametric monitoring of chemotherapy treatment response in locally advanced breast cancer using quantitative ultrasound and diffuse optical spectroscopy

Purpose: This study evaluated pathological response to neoadjuvant chemotherapy using quantitative ultrasound (QUS) and diffuse optical spectroscopy imaging (DOSI) biomarkers in locally advanced breast cancer (LABC). Materials and Methods: The institution’s ethics review board approved this study. Subjects (n = 22) gave written informed consent prior to participating. US and DOSI data were acquired, relative to the start of neoadjuvant chemotherapy,at weeks 0, 1, 4, 8 and preoperatively. QUS parameters including the mid-band fit (MBF), 0-MHz intercept (SI), and the spectral slope (SS) were determined from tumor ultrasound data using spectral analysis. In the same patients, DOSI was used to measure parameters relating to tumor hemoglobin and composition. Discriminant analysis and receiver-operating characteristic (ROC) analysis was used to classify clinical and pathological response during treatment and to estimate the area under the curve (AUC). Additionally, multivariate analysis was carried out for pairwise QUS/DOSI parameter combinations using a logistic regression model. Results: Individual QUS and DOSI parameters, including the (SI), oxy-haemoglobin (HbO2), and total hemoglobin (HbT) were significant markers for response after one week of treatment (p < 0.01). Multivariate (pairwise) combinations increased the sensitivity, specificity and AUC at this time; the SI + HbO2 showed a sensitivity/ specificity of 100%, and an AUC of 1.0. Conclusions: QUS and DOSI demonstrated potential as coincident markers for treatment response and may potentially facilitate response-guided therapies. Multivariate QUS and DOSI parameters increased the sensitivity and specificity of classifying LABC patients as early as one week after treatment

Machine Learning Strategies to Analyze Quantitative Ultrasound Multi-Parametric Images for Prediction of Therapy Response in Breast Cancer Patients

In this thesis project, two novel machine learning strategies were investigated to predict tumor response to neoadjuvant chemotherapy (NAC) at pre-treatment using quantitative ultrasound (QUS) multi-parametric images. The ultrasound data for analytical development and evaluation of the methodologies investigated in this project were acquired from 181 patients diagnosed with locally advanced breast cancer (LABC) and planned for NAC followed by surgery. The QUS multi-parametric images were generated using spectral analyses on the raw ultrasound radiofrequency (RF) data acquired before starting the NAC. In the first machine learning approach investigated in this project, distinct intra-tumor regions were identified within the parametric maps using a hidden Markov random field (HMRF) and its expectation-maximization (EM) algorithm. Several hand-crafted features characterizing the tumor, intra-tumor regions, and the tumor margin were extracted from different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker for response prediction. Evaluation results on an independent test set indicated that the developed biomarker using the characteristics of intra-tumor regions and tumor margin in conjunction with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patients at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In the second machine learning approach investigated in this project, two deep convolutional neural network (DCNN) architectures including the residual network (ResNet) and residual attention network (RAN) were explored for extracting optimal feature maps from the parametric images, with a fully connected network for response prediction. Results demonstrated that the developed model with the RAN architecture to extract feature maps from the expanded parametric images of the tumor core and margin had a superior performance with an accuracy of 0.88 and an AUC of 0.86 on the independent test set. Also, survival analysis demonstrated a statistically significant difference between survival curves of the two response cohorts identified at pre-treatment based on both the conventional machine learning method and the deep learning model. Obtained results in this study demonstrated a great promise of QUS multi-parametric imaging integrated with both unsupervised learning methods in identifying distinct breast cancer intra-tumor regions and traditional classification techniques, as well as deep convolutional neural networks in predicting tumor response to NAC prior to start of treatment