Development of a Fluid Dynamic Model for Quantitative Contrast-Enhanced Ultrasound Imaging

Deep Learning With Physics-Embedded Neural Network for Full Waveform Ultrasonic Brain Imaging

The convenience, safety, and affordability of ultrasound imaging make it a vital non-invasive diagnostic technique for examining soft tissues. However, significant differences in acoustic impedance between the skull and soft tissues hinder the successful application of traditional ultrasound for brain imaging. In this study, we propose a physics-embedded neural network with deep learning based full waveform inversion (PEN-FWI), which can achieve reliable quantitative imaging of brain tissues. The network consists of two fundamental components: forward convolutional neural network (FCNN) and inversion sub-neural network (ISNN). The FCNN explores the nonlinear mapping relationship between the brain model and the wavefield, replacing the tedious wavefield calculation process based on the finite difference method. The ISNN implements the mapping from the wavefield to the model. PEN-FWI includes three iterative steps, each embedding the F CNN into the ISNN, ultimately achieving tomography from wavefield to brain models. Simulation and laboratory tests indicate that PEN-FWI can produce high-quality imaging of the skull and soft tissues, even starting from a homogeneous water model. PEN-FWI can achieve excellent imaging of clot models with constant uniform distribution of velocity, randomly Gaussian distribution of velocity, and irregularly shaped randomly distributed velocity. Robust differentiation can also be achieved for brain slices of various tissues and skulls, resulting in high-quality imaging. The imaging time for a horizontal cross-sectional imag e of the brain is only 1.13 seconds. This algorithm can effectively promote ultrasound-based brain tomography and provide feasible solutions in other fields.

Reformulating Optical Flow to Solve Image-Based Inverse Problems and Quantify Uncertainty

From meteorology to medical imaging and cell mechanics, many scientific domains use inverse problems (IPs) to extract physical measurements from image movement. To this end, motion estimation methods such as optical flow (OF) pre-process images into motion data to feed the IP, which then inverts for the measurements through a physical model. However, this combined OFIP pipeline exacerbates the ill-posedness inherent to each technique, propagating errors and preventing uncertainty quantification. We introduce a Bayesian PDE-constrained framework that transforms visual information directly into physical measurements in the context of probability distributions. The posterior mean is a constrained IP that tracks brightness while satisfying the physical model, thereby translating the aperture problem from the motion to the underlying physics; whereas the posterior covariance derives measurement error out of image noise. As we illustrate with traction force microscopy, our approach offers several advantages: more accurate reconstructions; unprecedented flexibility in experiment design (e.g., arbitrary boundary conditions); and the exclusivity of measurement error, central to empirical science, yet still unavailable under the OFIP strategy.

Voluntary Wheel Running Does Not Enhance Radiotherapy Efficiency in a Preclinical Model of Prostate Cancer: The Importance of Physical Activity Modalities?

Physical activity is increasingly recognized as a strategy able to improve cancer patient outcome, and its potential to enhance treatment response is promising, despite being unclear. In our study we used a preclinical model of prostate cancer to investigate whether voluntary wheel running (VWR) could improve tumor perfusion and enhance radiotherapy (RT) efficiency. Nude athymic mice were injected with PC-3 cancer cells and either remained inactive or were housed with running wheels. Apparent microbubble transport was enhanced with VWR, which we hypothesized could improve the RT response. When repeating the experiments and adding RT, however, we observed that VWR did not influence RT efficiency. These findings contrasted with previous results and prompted us to evaluate if the lack of effects observed on tumor growth could be attributable to the physical activity modality used. Using PC-3 and PPC-1 xenografts, we randomized mice to either inactive controls, VWR, or treadmill running (TR). In both models, TR (but not VWR) slowed down tumor growth, suggesting that the anti-cancer effects of physical activity are dependent on its modalities. Providing a better understanding of which activity type should be recommended to cancer patients thus appears essential to improve treatment outcomes.

Deep Unfolded Robust PCA With Application to Clutter Suppression in Ultrasound

Contrast enhanced ultrasound is a radiation-free imaging modality which uses encapsulated gas microbubbles for improved visualization of the vascular bed deep within the tissue. It has recently been used to enable imaging with unprecedented subwavelength spatial resolution by relying on super-resolution techniques. A typical preprocessing step in super-resolution ultrasound is to separate the microbubble signal from the cluttering tissue signal. This step has a crucial impact on the final image quality. Here, we propose a new approach to clutter removal based on robust principle component analysis (PCA) and deep learning. We begin by modeling the acquired contrast enhanced ultrasound signal as a combination of low rank and sparse components. This model is used in robust PCA and was previously suggested in the context of ultrasound Doppler processing and dynamic magnetic resonance imaging. We then illustrate that an iterative algorithm based on this model exhibits improved separation of microbubble signal from the tissue signal over commonly practiced methods. Next, we apply the concept of deep unfolding to suggest a deep network architecture tailored to our clutter filtering problem which exhibits improved convergence speed and accuracy with respect to its iterative counterpart. We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and with the fast iterative shrinkage algorithm. We show that our architecture exhibits better image quality and contrast.

Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning

Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell. With a rheology comparable to that of red blood cells, UCAs provide an intravascular indicator for functional imaging of the (micro)vasculature by quantitative DCE-US. Several models of the UCA intravascular kinetics have been proposed to provide functional quantitative maps, aiding diagnosis of different pathological conditions. This article is a comprehensive review of the available methods for quantitative DCE-US imaging based on temporal, spatial and spatiotemporal analysis of the UCA kinetics. The recent introduction of novel UCAs that are targeted to specific vascular receptors has advanced DCE-US to a molecular imaging modality. In parallel, new kinetic models of increased complexity have been developed. The extraction of multiple quantitative maps, reflecting complementary variables of the underlying physiological processes, requires an integrative approach to their interpretation. A probabilistic framework based on emerging machine-learning methods represents nowadays the ultimate approach, improving the diagnostic accuracy of DCE-US imaging by optimal combination of the extracted complementary information. The current value and future perspective of all these advances are critically discussed.

Liver contrast-enhanced sonography: computer-assisted differentiation between focal nodular hyperplasia and inflammatory hepatocellular adenoma by reference to microbubble transport patterns

OBJECTIVE

A new computer tool is proposed to distinguish between focal nodular hyperplasia (FNH) and an inflammatory hepatocellular adenoma (I-HCA) using contrast-enhanced ultrasound (CEUS). The new method was compared with the usual qualitative analysis.

METHODS

The proposed tool embeds an "optical flow" algorithm, designed to mimic the human visual perception of object transport in image series, to quantitatively analyse apparent microbubble transport parameters visible on CEUS. Qualitative (visual) and quantitative (computer-assisted) CEUS data were compared in a cohort of adult patients with either FNH or I-HCA based on pathological and radiological results. For quantitative analysis, several computer-assisted classification models were tested and subjected to cross-validation. The accuracies, area under the receiver-operating characteristic curve (AUROC), sensitivity and specificity, positive predictive values (PPVs), negative predictive values (NPVs), false predictive rate (FPRs) and false negative rate (FNRs) were recorded.

RESULTS

Forty-six patients with FNH (n = 29) or I-HCA (n = 17) with 47 tumours (one patient with 2 I-HCA) were analysed. The qualitative diagnostic parameters were accuracy = 93.6%, AUROC = 0.94, sensitivity = 94.4%, specificity = 93.1%, PPV = 89.5%, NPV = 96.4%, FPR = 6.9% and FNR = 5.6%. The quantitative diagnostic parameters were accuracy = 95.9%, AUROC = 0.97, sensitivity = 93.4%, specificity = 97.6%, PPV = 95.3%, NPV = 96.7%, FPR = 2.4% and FNR = 6.6%.

CONCLUSIONS

Microbubble transport patterns evident on CEUS are valuable diagnostic indicators. Machine-learning algorithms analysing such data facilitate the diagnosis of FNH and I-HCA tumours.

KEY POINTS

• Distinguishing between focal nodular hyperplasia and an inflammatory hepatocellular adenoma using dynamic contrast-enhanced ultrasound is sometimes difficult. • Microbubble transport patterns evident on contrast-enhanced sonography are valuable diagnostic indicators. • Machine-learning algorithms analysing microbubble transport patterns facilitate the diagnosis of FNH and I-HCA.

Exploiting Flow Dynamics for Superresolution in Contrast-Enhanced Ultrasound

Ultrasound (US) localization microscopy offers new radiation-free diagnostic tools for vascular imaging deep within the tissue. Sequential localization of echoes returned from inert microbubbles (MBs) with low concentration within the bloodstream reveals the vasculature with capillary resolution. Despite its high spatial resolution, low MB concentrations dictate the acquisition of tens of thousands of images, over the course of several seconds to tens of seconds, to produce a single superresolved image. Such long acquisition times and stringent constraints on MB concentration are undesirable in many clinical scenarios. To address these restrictions, sparsity-based approaches have recently been developed. These methods reduce the total acquisition time dramatically, while maintaining good spatial resolution in settings with considerable MB overlap. Here, we further improve the spatial resolution and visual vascular reconstruction quality of sparsity-based superresolution US imaging from low-frame rate acquisitions, by exploiting the inherent flow of MBs and utilize their motion kinematics. We also provide quantitative measurements of MB velocities and show that our approach achieves higher MB recall rate than the state-of-the-art techniques, while increasing contrast agents concentration. Our method relies on simultaneous tracking and sparsity-based detection of individual MBs in a frame-by-frame manner, and as such, may be suitable for real-time implementation. The effectiveness of the proposed approach is demonstrated on both simulations and an in vivo contrast-enhanced human prostate scan, acquired with a clinically approved scanner operating at a 10-Hz frame rate.

Longitudinal characterization of local perfusion of the rat placenta using contrast-enhanced ultrasound imaging

The placenta performs many physiological functions critical for development. Insufficient placental perfusion, due to improper vascular remodelling, has been linked to many pregnancy-related diseases. To study longitudinal in vivo placental perfusion, we have implemented a pixel-wise time-intensity curve (TIC) analysis of contrast-enhanced ultrasound (CEUS) images. CEUS images were acquired of pregnant Sprague Dawley rats after bolus injections of gas-filled microbubble contrast agents. Conventionally, perfusion can be quantified using a TIC of contrast enhancement in an averaged region of interest. However, the placenta has a complex structure and flow profile, which is insufficiently described using the conventional technique. In this work, we apply curve fitting in each pixel of the CEUS image series in order to quantify haemodynamic parameters in the placenta and surrounding tissue. The methods quantified an increase in mean placental blood volume and relative blood flow from gestational day (GD) 14 to GD18, while the mean transit time of the microbubbles decreased, demonstrating an overall rise in placental perfusion during gestation. The variance of all three parameters increased during gestation, showing that regional differences in perfusion are observable using the pixel-wise TIC approach. Additionally, the high-resolution parametric images show distinct regions of high blood flow developing during late gestation. The developed methods could be applied to assess placental vascular remodelling during the treatment of the pathologies of pregnancy.