Ultrasound molecular imaging (USMI) is an imaging approach that utilizes targeted microbubbles (MBs) to highlight biomarkers of disease. While differential targeted enhancement (DTE) is the current state-of-the-art for USMI, its reliance on destructive pulses hinders real-time clinical application. We have developed a neural network-based nondestructive USMI, validated in vivo using a transgenic mouse model of spontaneous breast cancer. To enhance training efficacy despite a limited animal number (N=14), we utilized several augmentation strategies including the use of several targeted MB types for each animal to generate independent image and texture patterns, alternative DTE approaches (sham and injection DTE), and random patch selection, overall resulting in a total of 15,350 patches to train the network. The resulting nondestructive USMI produces an image of the pixelwise MB classification score of the presence of targeted MBs. Our nondestructive USMI achieved a correlation coefficient of 0.954 with DTE, a continuous dice coefficient of 0.863 for a molecular signal coverage of the lesion over 20%, and a higher AUC than DTE (0.954 vs. 0.845) compared to the reference image developed from the contrast enhanced ultrasound (CEUS) image and manual lesion contour. Nondestructive imaging during continuous motion of the transducer under elevation sweeps yielded fewer artifacts and higher AUC than DTE (0.953 vs. 0.892), compared to the reference image. This demonstrates the potential of free-hand and real-time nondestructive imaging. Overall, nondestructive imaging showed comparable performance to DTE under stationary conditions and superior performance to DTE under transducer motion, indicating its clinical imaging potential.

In ultrasound imaging, propagation of an acoustic wavefront through heterogeneous media causes phase aberrations that degrade the coherence of the reflected wavefront, leading to reduced image resolution and contrast. Adaptive imaging techniques attempt to correct this phase aberration and restore coherence, leading to improved focusing of the image. We propose an autofocusing paradigm for aberration correction in ultrasound imaging by fitting an acoustic velocity field to pressure measurements, via optimization of the common midpoint phase error (CMPE), using a straight-ray wave propagation model for beamforming in diffusely scattering media. We show that CMPE induced by heterogeneous acoustic velocity is a robust measure of phase aberration that can be used for acoustic autofocusing. CMPE is optimized iteratively using a differentiable beamforming approach to simultaneously improve the image focus while estimating the acoustic velocity field of the interrogated medium. The approach relies solely on wavefield measurements using a straight-ray integral solution of the two-way time-of-flight without explicit numerical time-stepping models of wave propagation. We demonstrate method performance through in silico simulations, in vitro phantom measurements, and in vivo mammalian models, showing practical applications in distributed aberration quantification, correction, and velocity estimation for medical ultrasound autofocusing.

To produce high-resolution ultrasound images, synthetic aper-ture acquisitions are used, which contain a large amount of data that is difficult to beamform in real time with traditional methods like delay and sum (DAS). Although frequency do-main beamforming methods have been adapted to ultrasound, the existing adaptations require interpolation. The chirp scaling algorithm (CSA), which was originally developed for radar remote sensing applications, avoids interpolation by using the Fourier transform shift property, so modifying CSA for ultrasound could expedite computation while avoiding interpolation error, improving rapid image formation. This work adapts CSA theory for typical monostatic ultrasound acquisitions and tests the algorithm on point target and le-sion simulations to analyze image quality and computational speed. While CSA achieves similar image quality as related frequency domain beamformers (such as the range-Doppler algorithm - RDA), its runtime is lower than both DAS and RDA. With further adaptations, the algorithm could benefit real-time, high-resolution clinical imaging.

Frequency-domain beamforming has become increasingly popular for fast processing of large synthetic aperture data in medical ultrasound. Here, we modify the Range Doppler Algorithm (RDA) to focus ultrasound signals from multistatic acquisitions. RDA, which was first proposed for fast beamforming of monostatic data in radar remote sensing, is suitable for fast processing of large datasets because all operations are done in one dimension at a time, allowing for an efficient and intuitive implementation. We demonstrate through simulation that multistatic RDA achieves similar image quality as traditionally used, multistatic delay-and-sum (DAS), while increasing the reconstruction speed by approximately a factor of three. We also show that the RDA and DAS images from the multistatic acquisition show reduced sidelobe levels compared to their counterparts from the monostatic acquisition. Demonstrated version of the multistatic RDA might be applicable beyond ultrasound medical imaging, such as for processing of synthetic aperture radar (SAR) data from satellite constellations.

Diffuse reverberation is ultrasound image noise caused by multiple reflections of the transmitted pulse before returning to the transducer, which degrades image quality and impedes the estimation of displacement or flow in techniques such as elastography and Doppler imaging. Diffuse reverberation appears as spatially incoherent noise in the channel signals, where it also degrades the performance of adaptive beamforming methods, sound speed estimation, and methods that require measurements from channel signals. In this paper, we propose a custom 3D fully convolutional neural network (3DCNN) to reduce diffuse reverberation noise in the channel signals. The 3DCNN was trained with channel signals from simulations of random targets that include models of reverberation and thermal noise. It was then evaluated both on phantom and in-vivo experimental data. The 3DCNN showed improvements in image quality metrics such as generalized contrast to noise ratio (GCNR), lag one coherence (LOC) contrast-to-noise ratio (CNR) and contrast for anechoic regions in both phantom and in-vivo experiments. Visually, the contrast of anechoic regions was greatly improved. The CNR was improved in some cases, however the 3DCNN appears to strongly remove uncorrelated and low amplitude signal. In images of in-vivo carotid artery and thyroid, the 3DCNN was compared to short-lag spatial coherence (SLSC) imaging and spatial prediction filtering (FXPF) and demonstrated improved contrast, GCNR, and LOC, while FXPF only improved contrast and SLSC only improved CNR.

Photoacoustic (PA) imaging can revolutionize medical ultrasound by augmenting it with molecular information. However, clinical translation of PA imaging remains a challenge due to the limited viewing angles and imaging depth. Described here is a new robust algorithm called Superiorized Photo-Acoustic Non-NEgative Reconstruction (SPANNER), designed to reconstruct PA images in real-time and to address the artifacts associated with limited viewing angles and imaging depth. The method utilizes precise forward modeling of the PA propagation and reception of signals while accounting for the effects of acoustic absorption, element size, shape, and sensitivity, as well as the transducer's impulse response and directivity pattern. A fast superiorized conjugate gradient algorithm is used for inversion. SPANNER is compared to three reconstruction algorithms: delay-and-sum (DAS), universal back-projection (UBP), and model-based reconstruction (MBR). All four algorithms are applied to both simulations and experimental data acquired from tissue-mimicking phantoms, ex vivo tissue samples, and in vivo imaging of the prostates in patients. Simulations and phantom experiments highlight the ability of SPANNER to improve contrast to background ratio by up to 20 dB compared to all other algorithms, as well as a 3-fold increase in axial resolution compared to DAS and UBP. Applying SPANNER on contrast-enhanced PA images acquired from prostate cancer patients yielded a statistically significant difference before and after contrast agent administration, while the other three image reconstruction methods did not, thus highlighting SPANNER's performance in differentiating intrinsic from extrinsic PA signals and its ability to quantify PA signals from the contrast agent more accurately.

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.

Ultrasound molecular imaging (USMI) is accomplished by detecting microbubble (MB) contrast agents that have bound to specific biomarkers, and can be used for a variety of imaging applications, such as the early detection of cancer. USMI has been widely utilized in preclinical imaging in mice; however, USMI in humans can be challenging because of the low concentration of bound MBs and the signal degradation caused by the presence of heterogenous soft tissue between the transducer and the lesion. Short-lag spatial coherence (SLSC) beamforming has been proposed as a robust technique that is less affected by poor signal quality than standard delay-and-sum (DAS) beamforming. In this paper, USMI performance was assessed using contrast-enhanced ultrasound imaging combined with DAS (conventional CEUS) and with SLSC (SLSC-CEUS). Each method was characterized by flow channel phantom experiments. In a USMI-mimicking phantom, SLSC-CEUS was found to be more robust to high levels of additive thermal noise than DAS, with a 6dB SNR improvement when the thermal noise level was +6dB or higher. However, SLSC-CEUS was also found to be insensitive to increases in MB concentration, making it a poor choice for perfusion imaging. USMI performance was also measured in vivo using VEGFR2-targeted MBs in mice with subcutaneous human hepatocellular carcinoma tumors, with clinical imaging conditions mimicked using a porcine tissue layer between the tumor and the transducer. SLSC-CEUS improved the SNR in each of ten tumors by an average of 41%, corresponding to 3.0dB SNR. These results indicate that the SLSC beamformer is well-suited for USMI applications because of its high sensitivity and robust properties under challenging imaging conditions.

Ultrasound imaging has indications across many areas of medicine, but the need for training and the variability in skill and acquired image quality among 2-D ultrasound users have limited its wider adoption and utilization. Low-cost volumetric ultrasound with a known frame of reference has the potential to lower these operator-dependent barriers and enhance the clinical utility of ultrasound imaging. In this paper, we improve upon our previous research-scanner-based prototype to implement a versatile volumetric imaging platform for existing clinical 2-D ultrasound systems. We present improved data acquisition and image reconstruction schemes to increase quality, streamline workflow, and provide real-time visual feedback. We present initial results using the platform on a Vimedix simulator, as well as on phantom and in vivo targets using a variety of clinical ultrasound systems and probes.

Measurements of plaque stiffness may provide important prognostic and diagnostic information to help clinicians distinguish vulnerable plaques containing soft lipid pools from more stable, stiffer plaques. In this preliminary study, we compare in vivo ultrasonic Acoustic Radiation Force Impulse (ARFI) imaging derived measures of carotid plaque stiffness with composition determined by spatially registered Magnetic Resonance Imaging (MRI) in five human subjects with stenosis > 50%. Ultrasound imaging was implemented on a commercial diagnostic scanner with custom pulse sequences to collect spatially registered 2D longitudinal B-mode and ARFI images. A standardized, multi-contrast weighted MRI sequence was used to obtain 3D Time of Flight (TOF), T1 weighted (T1W), T2 weighted (T2W), and Proton Density Weighted (PDW) transverse image stacks of volumetric data. The MRI data was segmented to identify lipid, calcium, and normal loose matrix components using commercially available software. 3D MRI segmented plaque models were rendered and spatially registered with 2D B-mode images to create fused ultrasound and MRI volumetric images for each subject. ARFI imaging displacements in regions of interest (ROIs) derived from MRI segmented contours of varying composition were compared. Regions of calcium and normal loose matrix components identified by MRI presented as homogeneously stiff regions of similarly low (typically ≈ 1 μm) displacement in ARFI imaging. MRI identified lipid pools > 2 mm(2), found in three out of five subjects, presented as softer regions of increased displacement that were on average 1.8 times greater than the displacements in adjacent regions of loose matrix components in spatially registered ARFI images. This work provides early evidence supporting the use of ARFI imaging to noninvasively identify lipid regions in carotid artery plaques in vivo that are believed to increase the propensity of a plaque to rupture. Additionally, the results provide early training data for future studies and aid in the interpretation and possible clinical utility of ARFI imaging for identifying the elusive vulnerable plaque.