Ultrasound localization microscopy (ULM) enables super-resolution ultrasound (SRUS) imaging of microvasculature, while ultrasound molecular imaging (USMI) characterizes molecular signatures using microbubbles (MBs) targeted to specific biomarkers. Although the co-localization of SRUS and USMI has been demonstrated previously, USMI resolution is limited by ultrasound diffraction-based effects and does not match the super-resolved microvasculature. This study introduces the Incremental Burst Sequence (IBS) method to induce the population of polydisperse targeted MBs to burst progressively, achieving MBs spatial separation and enabling high-resolution USMI (HR-USMI) localization. IBS method employs interleaved imaging and bursting pulses, with transmit voltages of bursting pulses incrementally increased to produce a gradual rise in the acoustic pressure. IBS is first validated optically in vitro using a cellulose tubing phantom, and MB remaining count during IBS is measured. Thereafter, in vivo validation is performed in a murine tumor model, and the intra-tumoral targeted MB signal intensity is measured during IBS. Furthermore, high frame-rate data for SRUS and IBS data for HR-USMI are acquired from a single bolus injection of MBs to generate composite images with high-resolution molecular signatures superimposed on the tumor microvasculature. Both in vitro and in vivo results validate the technical feasibility of the proposed IBS method. In addition, we demonstrate that higher bursting pulse repetitions lead to a faster disruption of the MB population during IBS. Finally, HR-USMI signals localized within a 50 μm × 50 μm grid are aligned with microvessels resolved better than 100 μm, presenting a combination of molecular signatures and anatomical structures at fine resolution.

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.

Correlating data acquired using different ultrasound imaging techniques provides the opportunity to discover complementary diagnostic information. The aim of this study was to develop a combined imaging technique that correlated ultrasound detected molecular markers to super-resolved microvessels to enable pairing of the diagnostic utility of both ultrasound molecular imaging (USMI) and ultrasound localization microscopy (ULM) over common tissue. The feasibility of the proposed method was validated in a murine model to image both tumor tissue and control (tumor free) tissue. The microvessel density (VI) and molecular signature index (MSI) were both quantified. Furthermore, a new metric, ratio of endothelial molecular signature area to vessel area (MVR) specifically obtained by the proposed correlation method, was calculated. Statistical differences in MVR values were found between the control leg tissue and tumor tissue (p < 0.005), which validated the feasibility of the proposed method in evaluating evolving diseased vessels.

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.

Ultrasound molecular imaging is a powerful diagnostic ultrasound imaging technique which has potential to enhance diagnosis of cancer and other vascular diseases. However, USMI is hampered by the presence of nonlinear artifacts from static tissue signals as well as the lack of non-destructive techniques to quantify adherent microbubble signals. In this study, singular value thresholding (SVT) and normalized singular spectrum area (NSSA) are used simultaneously to enable a filtering technique which automatically suppresses static tissue signals and differentiates between adherent and non-adherent MB signals.

Ultrasound molecular imaging is a diagnostic technique wherein molecularly targeted microbubble contrast agents are imaged to reveal disease markers on the blood vessel endothelium. Currently, microbubble adhesion to affected tissue can be quantified using differential targeted enhancement (dTE), which measures the late enhancement of adherent microbubbles through administration of destructive ultrasound pressures. In this study, we investigated a statistical parameter called the normalized singular spectrum area (NSSA) as a means to detect microbubble adhesion without microbubble destruction. We compared the signal differentiation capability of NSSA with matched dTE measurements in a mouse hindlimb tumor model. Results indicated that NSSA-based signal classification performance matches dTE when differentiating adherent microbubble from non-adherent microbubble signals (receiver operating characteristic area under the curve = 0.95), and improves classification performance when differentiating microbubble from tissue signals (p < 0.005). NSSA-based signal classification eliminates the need for destruction of contrast, and may offer better sensitivity, specificity and the opportunity for real-time microbubble detection and classification.

BACKGROUND:This study evaluated the efficacy of spinal anesthesia administration by resident physicians when using an ultrasound system with automated neuraxial landmark detection capabilities.METHODS:150 patients were enrolled in this trial. Anesthesiology residents placed spinals in subjects undergoing scheduled cesarean delivery using one of three techniques to identify neuraxial landmarks: palpation, ultrasound, or combined palpation and ultrasound. Ultrasound was performed using a handheld system that automatically identified neuraxial landmarks (e.g. midline, intervertebral spaces). All residents watched a 10-minute video and received 20 minutes of hands-on training prior to participating in the study. First insertion success rate was the primary end point.RESULTS:Among all patients, use of ultrasound resulted in a 11% greater first-insertion success rate (RR: 1.11 [0.85-1.47], p=0.431), a 15% reduction in needle insertions (RR: 0.85, p=0.052), and a 26% decrease in needle passes (RR: 0.74, p=0.070). In obese patients of BMI ≥ 30 kg/m2, use of ultrasound resulted in 26% greater first-insertion success rates (RR: 1.26, p=0.187), a 21% decrease in needle insertions (RR: 0.79, p=0.025), a 38% decrease in needle passes (RR: 0.62, p=0.030), and a 75% decrease in patients reporting neutral or low patient satisfaction with anesthesia administration (RR: 0.25, p=0.004).DISCUSSION:Resident anesthesiologists competently utilized the ultrasound system after receiving minimal training. Technical endpoints and patient satisfaction trended towards improvement when ultrasound was used prior to spinal placement, with stronger trends observed in obese patients. Additional study is required to fully characterize the impact of the ultrasound system on clinical efficacy.

Molecularly targeted microbubbles (MBs), comprising a gas core and a functionalized shell, 1-5 μm in diameter, enable visualization of disease marker concentration using a method termed ultrasound molecular imaging (USMI). The current state-of-the-art method for USMI based quantification of molecular markers is differential targeted enhancement (dTE), which employs destructive pulses of ultrasound to quantify the amount of targeted MB adherence. In this study, we sought to quantify the signal from adherent MBs non-destructively using a statistical parameter termed normalized singular spectrum area (NSSA) in a murine tumor model. The sensitivity and specificity of NSSA-based signal classification was compared to matched dTE measurements in a mouse hindlimb tumor.

Previous experiments from our laboratory suggested that normalized singular spectrum area (NSSA), a statistical property of data, can be used to differentiate between fast-moving and slow-moving signal. We showed qualitative evidence that signals from free-flowing microbubbles (MBs) exhibit higher NSSA values, static tissue signals exhibit lower NSSA values, and adherent MB signals exhibit intermediate NSSA values. In this study, we seek to validate the correlation between MB adherence and NSSA value. To achieve this, we combined NSSA measurements with differential targeted enhancement imaging, which is the current "gold standard" for preclinical measurements of MB adherence.

The use of X-ray imaging to detect distal forearm fractures has been the standard of care for over 100 years. However, with changes in emergency room workflows, there exists the need to rapidly differentiate between fractures and sprains at the bedside so as to best utilize emergency room resources and quickly triage patients between different treatment options (e.g. acquisition and interpretation of X-rays leading to casting versus splinting and rapid discharge). To this end, we present a 3D ultrasound imaging method intended to be performed at the bedside that aims to produce 'CT-like' 3D reconstructions of boney wrist anatomy. Cadaver forearms were imaged before and after fracture and results were compared to conventional 2D X-ray images.

Objectives: The aimof this studywas to demonstrate a newclinically translatable ultrasound molecular imaging approach, modulated acoustic radiation forcebased imaging, which is capable of rapid and reliable detection of inflammation as validated in mouse abdominal aorta.Materials and Methods: Animal studies were approved by the Institutional Animal Care and Use Committee at the University of Virginia. C57BL/6 mice stimulated with tumor necrosis factor a, or fed with a high-fat diet, were used as inflammation (MInflammation) and diet-induced obesity (DIO) (MDIO) models, respectively. C57BL/6 mice, not exposed to tumor necrosis factor a or DIO, were used as controls (MNormal). P-selectin-targeted (MBP-selectin), vascular cell adhesion molecule (VCAM)-1-targeted (MBVCAM-1), and isotype control (MBControl) microbubbles were synthesized by conjugating anti-P-selectin, anti-VCAM-1, and isotype control antibodies to microbubbles, respectively. The abdominal aortas were imaged for 180 seconds during a constant infusion of microbubbles. A parameter, residual-to-saturation ratio (RSR), was used to assess P-selectin and VCAM-1. Statistical analysis was performed with the Student t test.Results: For the inflammation model, RSR of the MInflammation + MBP-selectin group was significantly higher (40.9%, P < 0.0005) than other groups. For the DIO model, RSR of the MDIO + MBVCAM-1 group was significantly higher (60.0%, P < 0.0005) than other groups. Immunohistochemistry staining of the abdominal aorta confirmed the expression of P-selectin and VCAM-1.Conclusions: A statistically significant assessment of P-selectin and VCAM-1 in mouse abdominal aorta was achieved. This technique yields progress toward rapid targeted molecular imaging in large blood vessels and thus has the potential for early diagnosis, treatment selection, and risk stratification of atherosclerosis.

Microbubbles (MBs) are capable of binding specifically to molecular markers on the vascular endothelium to enable sensitive detection of early stage disease. Traditional microbubble imaging techniques rely on the nonlinearity of microbubble signal to differentiate it from surrounding tissue. However, the presence of harmonic energy among echogenic tissue interfaces can limit the effectiveness of these methods in enhancing microbubble contrast. In many cases, it is challenging to achieve any quantitative separation between static tissue signal and bound MB signal (Fig. 1B). In this study, we use normalized singular spectrum area (NSSA) to differentiate between static tissue signal, bound MB signal, and free MB signal in a mouse hindlimb tumor.

Objectives The aim of this study was to evaluate the imaging performance of a handheld ultrasound system and the accuracy of an automated lumbar spine computer-aided detection (CAD) algorithm in the spines of human subjects. Materials and Methods This study was approved by the institutional review board of the University of Virginia. The authors designed a handheld ultrasound system with enhanced bone image quality and fully automated CAD of lumbar spine anatomy. The imaging performance was evaluated by imaging the lumbar spines of 68 volunteers with body mass index between 18.5 and 48 kg/m2. The accuracy, sensitivity, and specificity of the lumbar spine CAD algorithm were assessed by comparing the algorithm's results to ground-truth segmentations of neuraxial anatomy provided by radiologists. Results The lumbar spine CAD algorithm detected the epidural space with a sensitivity of 94.2% (95% confidence interval [CI], 85.1%–98.1%) and a specificity of 85.5% (95% CI, 81.7%–88.6%) and measured its depth with an error of approximately ±0.5 cm compared with measurements obtained manually from the 2-dimensional ultrasound images. The spine midline was detected with a sensitivity of 93.9% (95% CI, 85.8%–97.7%) and specificity of 91.3% (95% CI, 83.6%–96.9%), and its lateral position within the ultrasound image was measured with an error of approximately ±0.3 cm. The bone enhancement imaging mode produced images with 5.1- to 10-fold enhanced bone contrast when compared with a comparable handheld ultrasound imaging system. Conclusions The results of this study demonstrate the feasibility of CAD for assisting with real-time interpretation of ultrasound images of the lumbar spine at the bedside.

Targeted ultrasound contrast agents, comprising shell stabilized gas filled microbubbles (MBs), can be used to detect molecular markers of disease present on the vascular endothelium. Current MB imaging techniques exploit the nonlinear echo response of microbubbles to provide signal contrast with respect to adjacent tissue signal. However, these methods are hampered by false positive artifacts arising from nonlinear signals from strongly reflecting tissue interfaces. In this study, we demonstrate an image processing method that utilizes normalized singular spectrum area (NSSA) to distinguish adherent and non-adherent MB signals from static tissue signals with high specificity.

Palpation-based techniques are the most common approaches for facilitating lumbar spine bedside procedures such as lumbar puncture and spinal and/or epidural placement. However, the efficacy of these approaches is diminished in patients whose surface landmarks are difficult to palpate due to obesity or spinal deformities. In this work, we present the imaging results of a handheld ultrasound system that was developed to image the lumbar spine and facilitate lumbar punctures in patients whose landmarks are not easily palpable. The system features two novel technologies that permit improved imaging of the lumbar spine: 1) a bone-specific ultrasound imaging mode that enhances the contrast of bone surfaces relative to nearby soft-tissue structures and 2) a real-time computer-aided detection (CAD) algorithm that automatically identifies the spine midline, spinous processes, interlaminar spaces, and the depths to key landmarks. The imaging performance of these algorithms was assessed in 80 volunteers (BMI between 18.5 and 48 kg/m 2 ) and was benchmarked against image interpretations provided by three radiologists. The CAD algorithm detected the interlaminar space with a sensitivity of 94.2% and a specificity of 85.5% and the spine midline with a sensitivity of 93.9% and a specificity of 91.3%. Overall, the results of this study demonstrate the feasibility of using a lumbar spine CAD algorithm and bone enhancement imaging technology to assist with image interpretation of ultrasound images of the lumbar spine.

ObjectivesThe objective of this study was to evaluate the minimum microbubble dose for ultrasound molecular imaging to achieve statistically significant detection of angiogenesis in a mouse model. Materials and MethodsThe preburst minus postburst method was implemented on a Verasonics ultrasound research scanner using a multiframe compounding pulse inversion imaging sequence. Biotinylated lipid (distearoyl phosphatidylcholine–based) microbubbles that were conjugated with antivascular endothelial growth factor 2 (VEGFR2) antibody (MBVEGFR2) or isotype control antibody (MBControl) were injected into mice carrying adenocarcinoma xenografts. Different injection doses ranging from 5 × 104 to 1 × 107 microbubbles per mouse were evaluated to determine the minimum diagnostically effective dose. ResultsThe proposed imaging sequence was able to achieve statistically significant detection (P < 0.05, n = 5) of VEGFR2 in tumors with a minimum MBVEGFR2 injection dose of only 5 × 104 microbubbles per mouse (distearoyl phosphatidylcholine at 0.053 ng/g mouse body mass). Nonspecific adhesion of MBControl at the same injection dose was negligible. In addition, the targeted contrast ultrasound signal of MBVEGFR2 decreased with lower microbubble doses, whereas nonspecific adhesion of MBControl increased with higher microbubble doses. ConclusionsThe dose of 5 × 104 microbubbles per animal is now the lowest injection dose on record for ultrasound molecular imaging to achieve statistically significant detection of molecular targets in vivo. Findings in this study provide us with further guidance for future developments of clinically translatable ultrasound molecular imaging applications using a lower dose of microbubbles.

Targeted microbubbles are currently used as ultrasound contrast agents in pre-clinical studies to visualize disease markers present on the blood vessel endothelium. Rapid clinical translation of targeted microbubble imaging requires a minimization of the diagnostically effective microbubble dose. In this study, we demonstrate an imaging method that improves signal-to-noise ratio (SNR) for microbubble detection and allows for accurate prediction of VEGFR2 targeting in a murine tumor model at 2.5% of the previously established minimum microbubble dose.

Objectives: The use of ultrasound imaging for cancer diagnosis and screening can be enhanced with the use of molecularly targeted microbubbles. Nonlinear imaging strategies such as pulse inversion (PI) and "contrast pulse sequences" (CPS) can be used to differentiate microbubble signal, but often fail to suppress highly echogenic tissue interfaces. This failure results in false-positive detection and potential misdiagnosis. In this study, a novel acoustic radiation force (ARF)-based approach was developed for superior microbubble signal detection. The feasibility of this technique, termed ARF decorrelation-weighted PI (ADW-PI), was demonstrated in vivo using a subcutaneous mouse tumor model.Materials and Methods: Tumors were implanted in the hindlimb of C57BL/6 mice by subcutaneous injection of MC38 cells. Lipid-shelled microbubbles were conjugated to anti-VEGFR2 antibody and administered via bolus injection. An image sequence using ARF pulses to generate microbubble motion was combined with PI imaging on a Verasonics Vantage programmable scanner. ADW-PI images were generated by combining PI images with interframe signal decorrelation data. For comparison, CPS images of the same mouse tumor were acquired using a Siemens Sequoia clinical scanner.Results: Microbubble-bound regions in the tumor interior exhibited significantly higher signal decorrelation than static tissue (n = 9, P < 0.001). The application of ARF significantly increased microbubble signal decorrelation (n = 9, P < 0.01). Using these decorrelation measurements, ADW-PI imaging demonstrated significantly improved microbubble contrast-to-tissue ratio when compared with corresponding CPS or PI images (n = 9, P < 0.001). Contrast-to-tissue ratio improved with ADW-PI by approximately 3 dB compared with PI images and 2 dB compared with CPS images.Conclusions: Acoustic radiation force can be used to generate adherent microbubble signal decorrelation without microbubble bursting. When combined with PI, measurements of the resulting microbubble signal decorrelation can be used to reconstruct images that exhibit superior suppression of highly echogenic tissue interfaces when compared with PI or CPS alone.

Introduction: Ultrasound molecular imaging has demonstrated the ability to detect the molecular signature of early vascular inflammation. Consequently, it may enable more timely detection of atherosclerosis. Unfortunately, existing methods face substantial challenges in large blood vessels. Recently, a new modulated acoustic radiation force (ARF)-based imaging method has demonstrated rapid and quantitative measurements of biomarkers in large vessels. Hypothesis: We hypothesized that a clinically translatable method could be tested pre-clinically for non-invasive rapid detection of vascular cell adhesion molecule (VCAM)-1 in the mouse abdominal aorta. Methods: C57BL/6 mice fed with a high-fat diet were used as a diet-induced obesity model (M DIO ). Normal mice were used as controls (M Normal ). Anti-VCAM-1 and isotype control antibodies were conjugated to microbubbles to form the VCAM-1 targeted (MB VCAM-1 ) and control (MB Control ) microbubbles, respectively. Constant infusions of microbubbles were performed, during imaging, for 180 s ( A ). A parameter, residual-to-saturation ratio (RSR), was extracted and used to assess VCAM-1 expression. Four groups of mice were studied (5 mice per group): M Normal + MB Control , M DIO + MB Control , M Normal + MB VCAM-1 , and M DIO + MB VCAM-1 . Results: RSR of the M DIO + MB VCAM-1 group was 56.0%, significantly higher ( p < 0.0005) than that of any other group (-94.9% for M Normal + MB Control , -13.9% for M DIO + MB Control , and -37.4% for M Normal + MB VCAM-1 ) ( B ). Immunohistochemistry confirmed M Normal exhibited minimal VCAM-1 staining while M DIO exhibited VCAM-1 staining localized to the endothelium ( C ). Conclusions: In conclusion, a statistically significant assessment of VCAM-1 in mouse abdominal aorta was achieved using the modulated ARF-based method. This technique yields progress towards rapid targeted molecular imaging in large blood vessels, and thus has long-term potential for early diagnosis of atherosclerosis.

Ultrasound-based molecular imaging has been implemented in pre-clinical studies of cancer and cardiovascular diseases. Unfortunately, existing methods face substantial challenges in large blood vessel environments. We hypothesized that a clinically translatable method, the modulated Acoustic Radiation Force (ARF)-based imaging, is capable of rapid detection of inflammation in the abdominal aorta of a murine model. Mice stimulated with tumor necrosis factor (TNF)-α were used as an inflammation model (M Inflammation ). Age-matched normal mice were used as controls (M Normal ). P-selectin-targeted (MB P-selectin ), and isotype control (MB Control ) microbubbles were synthesized by conjugating anti-P-selectin, and isotype control antibodies to the shell of microbubbles, respectively. The abdominal aorta of mice were imaged for 180 s during a constant infusion of microbubbles. The parameter produced from the new imaging sequence, residual-to-saturation ratio (RSR), was used to assess P-selectin expression. For the inflammation model, RSR of the M Inflammation + MB P-selectin group was 40.9%, significantly higher (p < 0.0005) than other groups. Feasibility was demonstrated to achieve rapid and statistically significant assessment of P-selectin in a mouse abdominal aorta for the first time. The proposed technique closes the gap toward rapid targeted molecular imaging in large blood vessels, and thus has the potential for early diagnosis and treatment selection of atherosclerosis via ultrasound molecular imaging.