Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications

Ultrasound information entropy is a flexible approach for analyzing ultrasound backscattering. Shannon entropy imaging based on probability distribution histograms (PDHs) has been implemented as a promising method for tissue characterization and diagnosis. However, the bin number affects the stability of entropy estimation. In this study, we introduced the k-nearest neighbor (KNN) algorithm to estimate entropy values and proposed ultrasound KNN entropy imaging. The proposed KNN estimator leveraged the Euclidean distance between data samples, rather than the histogram bins by conventional PDH estimators. We also proposed cumulative relative entropy (CRE) imaging to analyze time-series radiofrequency signals and applied it to monitor thermal lesions induced by microwave ablation (MWA). Computer simulation phantom experiments were conducted to validate and compare the performance of the proposed KNN entropy imaging, the conventional PDH entropy imaging, and Nakagami-m parametric imaging in detecting the variations of scatterer densities and visualizing inclusions. Clinical data of breast lesions were analyzed, and porcine liver MWA experiments ex vivo were conducted to validate the performance of KNN entropy imaging in classifying benign and malignant breast tumors and monitoring thermal lesions, respectively. Compared with PDH, the entropy estimation based on KNN was less affected by the tuning parameters. KNN entropy imaging was more sensitive to changes in scatterer densities and performed better visualizable capability than typical Shannon entropy (TSE) and Nakagami-m parametric imaging. Among different imaging methods, KNN-based Shannon entropy (KSE) imaging achieved the higher accuracy in classification of benign and malignant breast tumors and KNN-based CRE imaging had larger lesion-to-normal contrast when monitoring the ablated areas during MWA at different powers and treatment durations. Ultrasound KNN entropy imaging is a potential quantitative ultrasound approach for tissue characterization.

Ultrasound Molecular Imaging for Multiple Biomarkers by Serial Collapse of Targeting Microbubbles with Distinct Acoustic Pressures

Ultrasound molecular imaging (UMI) has shown promise for assessing the expression levels of biomarkers for the early detection of various diseases. However, it remains difficult to simultaneously image multiple biomarkers in a single systemic administration, which is important for the accurate diagnosis of diseases and for understanding the dynamic intermolecular mechanisms that drive their malignant progression. The authors develop an ultrasound molecular imaging method by serial collapse of targeting microbubbles with distinct acoustic pressures for the simultaneous detection of two biomarkers. To test this, α_v β₃ -targeting lipid microbubbles (L-MB_α ) and VEGFR2-targeting lipid-PLGA microbubbles (LP-MB_v ) are fabricated and simultaneously injected into tumor-bearing mice at 7 and 14 days, followed by the low-intensity acoustic collapse of L-MB_α and high-intensity acoustic collapse of LP-MB_v . The UMI signals of L-MB_α and LP-MB_v are obtained by subtracting the first post-burst signals from the first pre-burst signals, and subtracting the second post-burst signals from the first post-burst signals, respectively. Interestingly, the signal intensities from UMI agree with the immunohistochemical staining results for α_v β₃ and VEGFR2. Importantly, they find a better fit for the invasive behavior of MDA-MB-231 breast tumors by analyzing the ratio of α_v β₃ integrin to VEGFR2, but not the single α_v β₃ or VEGFR2 levels.

Recent advances in ultrasound-based diagnosis and therapy with micro- and nanometer-sized formulations

Ultrasound (US) is one of the most frequently used imaging methods in the clinic. The broad spectrum of its applications can be increased by the use of gas-filled microbubbles (MB) as ultrasound contrast agents (UCA). In recent years, also nanoscale UCA like nanobubbles (NB), echogenic liposomes (ELIP) and nanodroplets have been developed, which in contrast to MB, are able to extravasate from the vessels into the tissue. New disease-specific UCA have been designed for the assessment of tissue biomarkers and advanced US to a molecular imaging modality. For this purpose, specific binding moieties were coupled to the UCA surface. The vascular endothelial growth factor receptor-2 (VEGFR-2) and P-/E-selectin are prominent examples of molecular US targets to visualize tumor blood vessels and inflammatory diseases, respectively. Besides their application in contrast-enhanced imaging, MB can also be employed for drug delivery to tumors and across the blood-brain barrier (BBB). This review summarizes the development of micro- and nanoscaled UCA and highlights recent advances in diagnostic and therapeutic applications, which are ready for translation into the clinic.

Entropy vs. Energy Waveform Processing: A Comparison Based on the Heat Equation

Virtually all modern imaging devices collect electromagnetic or acoustic waves and use the energy carried by these waves to determine pixel values to create what is basically an “energy” picture. However, waves also carry “information”, as quantified by some form of entropy, and this may also be used to produce an “information” image. Numerous published studies have demonstrated the advantages of entropy, or “information imaging”, over conventional methods. The most sensitive information measure appears to be the joint entropy of the collected wave and a reference signal. The sensitivity of repeated experimental observations of a slowly-changing quantity may be defined as the mean variation (i.e., observed change) divided by mean variance (i.e., noise). Wiener integration permits computation of the required mean values and variances as solutions to the heat equation, permitting estimation of their relative magnitudes. There always exists a reference, such that joint entropy has larger variation and smaller variance than the corresponding quantities for signal energy, matching observations of several studies. Moreover, a general prescription for finding an “optimal” reference for the joint entropy emerges, which also has been validated in several studies.

Improving the detection sensitivity of chromatography by stochastic resonance

This review aims to provide readers with an overview of various methodologies and approaches used to improve sensitivity through stochastic resonance (SR) methods, with special emphasis on applications to improve the detectability of analytes in chromatographic signals.

Joint entropy of continuously differentiable ultrasonic waveforms

This study is based on an extension of the concept of joint entropy of two random variables to continuous functions, such as backscattered ultrasound. For two continuous random variables, X and Y, the joint probability density p(x,y) is ordinarily a continuous function of x and y that takes on values in a two dimensional region of the real plane. However, in the case where X=f(t) and Y=g(t) are both continuously differentiable functions, X and Y are concentrated exclusively on a curve, γ(t)=(f(t),g(t)), in the x,y plane. This concentration can only be represented using a mathematically "singular" object such as a (Schwartz) distribution. Its use for imaging requires a coarse-graining operation, which is described in this study. Subsequently, removal of the coarse-graining parameter is accomplished using the ergodic theorem. The resulting expression for joint entropy is applied to several data sets, showing the utility of the concept for both materials characterization and detection of targeted liquid nanoparticle ultrasonic contrast agents. In all cases, the sensitivity of these techniques matches or exceeds, sometimes by a factor of two, that demonstrated in previous studies that employed signal energy or alternate entropic quantities.

Programmable nanoparticle functionalization for in vivo targeting

The emerging demand for programmable functionalization of existing base nanocarriers necessitates development of an efficient approach for cargo loading that avoids nanoparticle redesign for each individual application. Herein, we demonstrate in vivo a postformulation strategy for lipidic nanocarrier functionalization with the use of a linker peptide, which rapidly and stably integrates cargos into lipidic membranes of nanocarriers after simple mixing through a self-assembling process. We exemplified this strategy by generating a VCAM-1-targeted perfluorocarbon nanoparticle for in vivo targeting in atherosclerosis (ApoE-deficient) and breast cancer (STAT-1-deficient) models. In the atherosclerotic model, a 4.1-fold augmentation in binding to affected aortas was observed for targeted vs. nontargeted nanoparticles (P<0.0298). Likewise, in the breast cancer model, a 4.9-fold increase in the nanoparticle signal from tumor vasculature was observed for targeted vs. nontargeted nanoparticles (P<0.0216). In each case, the nanoparticle was registered with fluorine ((19)F) magnetic resonance spectroscopy of the nanoparticle perfluorocarbon core, yielding a quantitative estimate of the number of tissue-bound nanoparticles. Because other common nanocarriers with lipid coatings (e.g., liposomes, micelles, etc.) can employ this strategy, this peptide linker postformulation approach is applicable to more than half of the available nanosystems currently in clinical trials or clinical uses.