Validation of Normalized Singular Spectrum Area as a Classifier for Molecularly Targeted Microbubble Adherence

In Vivo Validation of Modulated Acoustic Radiation Force-Based Imaging in Murine Model of Abdominal Aortic Aneurysm Using VEGFR-2-Targeted Microbubbles

OBJECTIVES

The objective of this study is to validate the modulated acoustic radiation force (mARF)-based imaging method in the detection of abdominal aortic aneurysm (AAA) in murine models using vascular endothelial growth factor receptor 2 (VEGFR-2)-targeted microbubbles (MBs).

MATERIALS AND METHODS

The mouse AAA model was prepared using the subcutaneous angiotensin II (Ang II) infusion combined with the β-aminopropionitrile monofumarate solution dissolved in drinking water. The ultrasound imaging session was performed at 7 days, 14 days, 21 days, and 28 days after the osmotic pump implantation. For each imaging session, 10 C57BL/6 mice were implanted with Ang II-filled osmotic pumps, and 5 C57BL/6 mice received saline infusion only as the control group. Biotinylated lipid MBs conjugated to either anti-mouse VEGFR-2 antibody (targeted MBs) or isotype control antibody (control MBs) were prepared before each imaging session and were injected into mice via tail vein catheter. Two separate transducers were colocalized to image the AAA and apply ARF to translate MBs simultaneously. After each imaging session, tissue was harvested and the aortas were used for VEGFR-2 immunostaining analysis. From the collected ultrasound image data, the signal magnitude response of the adherent targeted MBs was analyzed, and a parameter, residual-to-saturation ratio ( Rres - sat ), was defined to measure the enhancement in the adherent targeted MBs signal after the cessation of ARF compared with the initial signal intensity. Statistical analysis was performed with the Welch t test and analysis of variance test.

RESULTS

The Rres - sat of abdominal aortic segments from Ang II-challenged mice was significantly higher compared with that in the saline-infused control group ( P < 0.001) at all 4 time points after osmotic pump implantation (1 week to 4 weeks). In control mice, the Rres - sat values were 2.13%, 1.85%, 3.26%, and 4.85% at 1, 2, 3, and 4 weeks postimplantation, respectively. In stark contrast, the Rres - sat values for the mice with Ang II-induced AAA lesions were 9.20%, 20.6%, 22.7%, and 31.8%, respectively. It is worth noting that there was a significant difference between the Rres - sat for Ang II-infused mice at all 4 time points ( P < 0.005), a finding not present in the saline-infused mice. Immunostaining results revealed the VEGFR-2 expression was increased in the abdominal aortic segments of Ang II-infused mice compared with the control group.

CONCLUSIONS

The mARF-based imaging technique was validated in vivo using a murine model of AAA and VEGFR-2-targeted MBs. Results in this study indicated that the mARF-based imaging technique has the ability to detect and assess AAA growth at early stages based on the signal intensity of adherent targeted MBs, which is correlated with the expression level of the desired molecular biomarker. The results may suggest, in very long term, a pathway toward eventual clinical implementation for an ultrasound molecular imaging-based approach to AAA risk assessment in asymptomatic patients.

Acoustic Molecular Imaging Beyond the Diffraction Limit In Vivo

Ultrasound molecular imaging (USMI) is a technique used to noninvasively estimate the distribution of molecular markers in vivo by imaging microbubble contrast agents (MCAs) that have been modified to target receptors of interest on the vascular endothelium. USMI is especially relevant for preclinical and clinical cancer research and has been used to predict tumor malignancy and response to treatment. In the last decade, methods that improve the resolution of contrast-enhanced ultrasound by an order of magnitude and allow researchers to noninvasively image individual capillaries have emerged. However, these approaches do not translate directly to molecular imaging. In this work, we demonstrate super-resolution visualization of biomarker expression in vivo using superharmonic ultrasound imaging (SpHI) with dual-frequency transducers, targeted contrast agents, and localization microscopy processing. We validate and optimize the proposed method in vitro using concurrent optical and ultrasound microscopy and a microvessel phantom. With the same technique, we perform a proof-of-concept experiment in vivo in a rat fibrosarcoma model and create maps of biomarker expression co-registered with images of microvasculature. From these images, we measure a resolution of 23 μm, a nearly fivefold improvement in resolution compared to previous diffraction-limited molecular imaging studies.

Theranostic Microbubbles with Homogeneous Ligand Distribution for Higher Binding Efficacy

Phospholipid-coated targeted microbubbles are used for ultrasound molecular imaging and locally enhanced drug delivery, with the binding efficacy being an important trait. The use of organic solvent in microbubble production makes the difference between a heterogeneous or homogeneous ligand distribution. This study demonstrates the effect of ligand distribution on the binding efficacy of phospholipid-coated ανβ3-targeted microbubbles in vitro using a monolayer of human umbilical-vein endothelial cells and in vivo using chicken embryos. Microbubbles with a homogeneous ligand distribution had a higher binding efficacy than those with a heterogeneous ligand distribution both in vitro and in vivo. In vitro, 1.55× more microbubbles with a homogeneous ligand distribution bound under static conditions, while this was 1.49× more under flow with 1.25 dyn/cm2, 1.56× more under flow with 2.22 dyn/cm2, and 1.25× more in vivo. The in vitro dissociation rate of bound microbubbles with homogeneous ligand distribution was lower at low shear stresses (1–5 dyn/cm2). The internalized depth of bound microbubbles was influenced by microbubble size, not by ligand distribution. In conclusion, for optimal binding the use of organic solvent in targeted microbubble production is preferable over directly dispersing phospholipids in aqueous medium.

Dynamic Filtering of Adherent and Non-adherent Microbubble Signals Using Singular Value Thresholding and Normalized Singular Spectrum Area Techniques

Ultrasound molecular imaging techniques rely on the separation and identification of three types of signals: static tissue, adherent microbubbles and non-adherent microbubbles. In this study, the image filtering techniques of singular value thresholding (SVT) and normalized singular spectrum area (NSSA) were combined to isolate and identify vascular endothelial growth factor receptor 2-targeted microbubbles in a mouse hindlimb tumor model (n = 24). By use of a Verasonics Vantage 256 imaging system with an L12-5 transducer, a custom-programmed pulse inversion sequence employing synthetic aperture virtual source element imaging was used to collect contrast images of mouse tumors perfused with microbubbles. SVT was used to suppress static tissue signals by 9.6 dB while retaining adherent and non-adherent microbubble signals. NSSA was used to classify microbubble signals as adherent or non-adherent with high accuracy (receiver operating characteristic area under the curve [ROC AUC] = 0.97), matching the classification performance of differential targeted enhancement. The combined SVT + NSSA filtering method also outperformed differential targeted enhancement in differentiating MB signals from all other signals (ROC AUC = 0.89) without necessitating destruction of the contrast agent. The results from this study indicate that SVT and NSSA can be used to automatically segment and classify contrast signals. This filtering method with potential real-time capability could be used in future diagnostic settings to improve workflow and speed the clinical uptake of ultrasound molecular imaging techniques.

Phospholipid-coated targeted microbubbles for ultrasound molecular imaging and therapy

Phospholipid-coated microbubbles are ultrasound contrast agents that, when functionalized, adhere to specific biomarkers on cells. In this concise review, we highlight recent developments in strategies for targeting the microbubbles and their use for ultrasound molecular imaging (UMI) and therapy. Recently developed novel targeting strategies include magnetic functionalization, triple targeting, and the use of several new ligands. UMI is a powerful technique for studying disease progression, diagnostic imaging, and monitoring of therapeutic responses. Targeted microbubbles (tMBs) have been used for the treatment of cardiovascular diseases and cancer, with therapeutics either coadministered or loaded onto the tMBs. Regardless of which disease was treated, the use of tMBs always resulted in a better therapeutic outcome than non-tMBs when compared in vitro or in vivo.

A Targeted Molecular Localization Imaging Method Applied to Tumor Microvasculature

OBJECTIVES

Ultrasound contrast agents, consisting of gas-filled microbubbles (MBs), have been imaged using several techniques that include ultrasound localization microscopy and targeted molecular imaging. Each of these techniques aims to provide indicators of the disease state but has traditionally been performed independently without co-localization of molecular markers and super-resolved vessels. In this article, we present a new imaging technology: a targeted molecular localization (TML) approach, which uses a single imaging sequence and reconstruction approach to co-localize super-resolved vasculature with molecular imaging signature to provide simultaneous anatomic and biological information for potential multiscale disease evaluation.

MATERIALS AND METHODS

The feasibility of the proposed TML technique was validated in a murine hindlimb tumor model. Targeted molecular localization imaging was performed on 3 groups, which included control tissue (leg), tumor tissue, and tumor tissue after sunitinib an-tivascular treatment. Quantitative measures for vascular index (VI) and molecular index (MITML) were calculated from the microvasculature and TML images, respectively. In addition to these conventional metrics, a new metric unique to the TML technique, reporting the ratio of targeted molecular index to vessel surface, was assessed.

RESULTS

The quantitative resolution results of the TML approach showed resolved resolution of the microvasculature down to 28.8 μm. Vascular index increased in tumors with and without sunitinib compared with the control leg, but the trend was not statistically significant. A decrease in MITML was observed for the tumor after treatment (P < 0.0005) and for the control leg (P < 0.005) compared with the tumor before treatment. Statistical differences in the ratio of molecular index to vessel surface were found between all groups: the control leg and tumor (P < 0.05), the control leg and tumor after sunitinib treatment (P < 0.05), and between tumors with and without sunitinib treatment (P < 0.001).

CONCLUSIONS

These findings validated the technical feasibility of the TML method and pre-clinical feasibility for differentiating between the normal and diseased tissue states.

Nondestructive Detection of Targeted Microbubbles Using Dual-Mode Data and Deep Learning for Real-Time Ultrasound Molecular Imaging

Ultrasound molecular imaging (UMI) is enabled by targeted microbubbles (MBs), which are highly reflective ultrasound contrast agents that bind to specific biomarkers. Distinguishing between adherent MBs and background signals can be challenging in vivo. The preferred preclinical technique is differential targeted enhancement (DTE), wherein a strong acoustic pulse is used to destroy MBs to verify their locations. However, DTE intrinsically cannot be used for real-time imaging and may cause undesirable bioeffects. In this work, we propose a simple 4-layer convolutional neural network to nondestructively detect adherent MB signatures. We investigated several types of input data to the network: "anatomy-mode" (fundamental frequency), "contrast-mode" (pulse-inversion harmonic frequency), or both, i.e., "dual-mode", using IQ channel signals, the channel sum, or the channel sum magnitude. Training and evaluation were performed on in vivo mouse tumor data and microvessel phantoms. The dual-mode channel signals yielded optimal performance, achieving a soft Dice coefficient of 0.45 and AUC of 0.91 in two test images. In a volumetric acquisition, the network best detected a breast cancer tumor, resulting in a generalized contrast-to-noise ratio (GCNR) of 0.93 and Kolmogorov-Smirnov statistic (KSS) of 0.86, outperforming both regular contrast mode imaging (GCNR = 0.76, KSS = 0.53) and DTE imaging (GCNR = 0.81, KSS = 0.62). Further development of the methodology is necessary to distinguish free from adherent MBs. These results demonstrate that neural networks can be trained to detect targeted MBs with DTE-like quality using nondestructive dual-mode data, and can be used to facilitate the safe and real-time translation of UMI to clinical applications.