Microbubble Localization for Three-Dimensional Superresolution Ultrasound Imaging Using Curve Fitting and Deconvolution Methods

Advancements in Three-Dimensional Super-Resolution Ultrasound Imaging: A Narrative Review

The lack of sensibility of traditional ultrasound (US) imaging to the slow blood flow in small vessels resulted in the development of microbubble (MB) contrast agents. These MBs are given intravenously, and US imaging can detect them quite effectively. This noninvasive imaging method, known as contrast-enhanced US (CEUS), now makes it possible to accurately assess tissue perfusion and blood flow. Though CEUS offers several benefits, diffraction restricts the spatial resolution of all US imaging systems to length scales equal to roughly half the wavelength of the transmitted US beam. Based on individual MB detection and localization, the recently developed super-resolution US (SRUS) imaging method has shown unprecedentedly high spatial resolution exceeding the physical diffraction limit. It is now possible to visualize the microvasculature beyond the diffraction-limited resolution by localizing spatially isolated MBs across several frames. The highest resolution possible at clinical US frequencies can be on the order of several micrometers when tissue and probe motion are not present. Enhancing the functional study of tissue microvascular networks with structural data could lead to improved disease management. Through the localization and tracking of MBs, SRUS may reconstruct images of the microvasculature with resolution exceeding the diffraction limit in both 2-dimensional (2D) and 3-dimensional (3D) space. In contrast to the 2D approach, 3D SRUS imaging does not suffer from out-of-plane motion and can offer volumetric coverage with super-resolution in all three dimensions. Research has used two primary methods for 3D SRUS imaging including arrays that can electronically gather volumetric information or mechanically scanning the volume with a linear probe to produce a stack of 2D SRUS images. This manuscript aims to offer a comprehensive review of 3D SRUS imaging, clarifying methodologies, clinical applications, and notable challenges that could motivate future research and help facilitate clinical translation.

Computationally Efficient SVD Filtering for Ultrasound Flow Imaging and Real-Time Application to Ultrafast Doppler

Over the past decade, ultrasound microvasculature imaging has seen the rise of highly sensitive techniques, such as ultrafast power Doppler (UPD) and ultrasound localization microscopy (ULM). The cornerstone of these techniques is the acquisition of a large number of frames based on unfocused wave transmission, enabling the use of singular value decomposition (SVD) as a powerful clutter filter to separate microvessels from surrounding tissue. Unfortunately, SVD is computationally expensive, hampering its use in real-time UPD imaging and weighing down the ULM processing chain, with evident impact in a clinical context. To solve this problem, we propose a new approach to implement SVD filtering, based on simplified and elementary operations that can be optimally parallelized on GPU (GPU sSVD), unlike standard SVD algorithms that are mainly serial. First, we show that GPU sSVD filters UPD and ULM data with high computational efficiency compared to standard SVD implementations, and without losing image quality. Second, we demonstrate that the proposed method is suitable for real-time operation. GPU sSVD was embedded in a research scanner, along with the spatial similarity matrix (SSM), a well-known efficient approach to automate the selection of SVD blood components. High real-time throughput of GPU sSVD is demonstrated when using large packets of frames, with and without SSM. For example, more than 15000 frames/s were filtered with 512 packet size on a 128 × 64 samples beamforming grid. Finally, GPU sSVD was used to perform, for the first time, UPD imaging with real-time and adaptive SVD filtering on healthy volunteers.

Volumetric Ultrasound Localization Microscopy

Super-resolution ultrasound (SRUS) has evolved significantly with the advent of ultrasound localization microscopy (ULM). This technique enables subwavelength resolution imaging using microbubble contrast agents. Initially confined to 2-D imaging, ULM has progressed toward volumetric approaches, allowing for comprehensive 3-D visualization of microvascular networks. This review explores the technological advancements and challenges associated with volumetric ULM, focusing on key aspects such as transducer design, acquisition speed, data processing algorithms, or integration into clinical practice. We discuss the limitations of traditional 2-D ULM, including dependence on precise imaging plane selection and compromised resolution in microvasculature quantification. In contrast, volumetric ULM offers enhanced spatial resolution and allows motion correction in all directions, promising transformative insights into microvascular pathophysiology. By examining current research and future directions, this review highlights the potential of volumetric ULM to contribute significantly to diagnostic across various medical conditions, including cancers, arteriosclerosis, strokes, diabetes, and neurodegenerative diseases.

Ultrafast 3-D Transcutaneous Super Resolution Ultrasound Using Row-Column Array Specific Coherence-Based Beamforming and Rolling Acoustic Sub-aperture Processing: In Vitro, in Rabbit and in Human Study

OBJECTIVE

This study aimed to realise 3-D super-resolution ultrasound imaging transcutaneously with a row-column array which has far fewer independent electronic channels and a wider field of view than typical fully addressed 2-D matrix arrays. The in vivo image quality of the row-column array is generally poor, particularly when imaging non-invasively. This study aimed to develop a suite of image formation and post-processing methods to improve image quality and demonstrate the feasibility of ultrasound localisation microscopy using a row-column array, transcutaneously on a rabbit model and in a human.

METHODS

To achieve this, a processing pipeline was developed which included a new type of rolling window image reconstruction, which integrated a row-column array specific coherence-based beamforming technique with acoustic sub-aperture processing. This and other processing steps reduced the 'secondary' lobe artefacts, and noise and increased the effective frame rate, thereby enabling ultrasound localisation images to be produced.

RESULTS

Using an in vitro cross tube, it was found that the procedure reduced the percentage of 'false' locations from ∼26% to ∼15% compared to orthogonal plane wave compounding. Additionally, it was found that the noise could be reduced by ∼7 dB and the effective frame rate was increased to over 4000 fps. In vivo, ultrasound localisation microscopy was used to produce images non-invasively of a rabbit kidney and a human thyroid.

CONCLUSION

It has been demonstrated that the proposed methods using a row-column array can produce large field of view super-resolution microvascular images in vivo and in a human non-invasively.

Improving sub-pixel accuracy in ultrasound localization microscopy using supervised and self-supervised deep learning

With a spatial resolution of tens of microns, ultrasound localization microscopy (ULM) reconstructs microvascular structures and measures intravascular flows by tracking microbubbles (1-5 μm) in contrast enhanced ultrasound (CEUS) images. Since the size of CEUS bubble traces, e.g. 0.5-1 mm for ultrasound with a wavelength λ = 280 μm, is typically two orders of magnitude larger than the bubble diameter, accurately localizing microbubbles in noisy CEUS data is vital to the fidelity of the ULM results. In this paper, we introduce a residual learning based supervised super-resolution blind deconvolution network (SupBD-net), and a new loss function for a self-supervised blind deconvolution network (SelfBD-net), for detecting bubble centers at a spatial resolution finer than λ/10. Our ultimate purpose is to improve the ability to distinguish closely located microvessels and the accuracy of the velocity profile measurements in macrovessels. Using realistic synthetic data, the performance of these methods is calibrated and compared against several recently introduced deep learning and blind deconvolution techniques. For bubble detection, errors in bubble center location increase with the trace size, noise level, and bubble concentration. For all cases, SupBD-net yields the least error, keeping it below 0.1 λ. For unknown bubble trace morphology, where all the supervised learning methods fail, SelfBD-net can still maintain an error of less than 0.15 λ. SupBD-net also outperforms the other methods in separating closely located bubbles and parallel microvessels. In macrovessels, SupBD-net maintains the least errors in the vessel radius and velocity profile after introducing a procedure that corrects for terminated tracks caused by overlapping traces. Application of these methods is demonstrated by mapping the cerebral microvasculature of a neonatal pig, where neighboring microvessels separated by 0.15 λ can be readily distinguished by SupBD-net and SelfBD-net, but not by the other techniques. Hence, the newly proposed residual learning based methods improve the spatial resolution and accuracy of ULM in micro- and macro-vessels.

Broad Elevation Projection Super-Resolution Ultrasound (BEP-SRUS) Imaging With a 1-D Unfocused Linear Array

Super-resolution ultrasound (SRUS) through localizing spatially isolated microbubbles (MBs) has been demonstrated to overcome the wave diffraction limit and reveal the microvascular structure and flow information at the microscopic scale. However, 3-D SRUS imaging remains a challenge due to the fabrication and computational complexity of 2-D matrix array probes. Inspired by X-ray radiography which can present information within a volume in a single projection image with much simpler hardware than X-ray computerized tomography (CT), this study investigates the feasibility of broad elevation projection super-resolution (BEP-SR) ultrasound using a 1-D unfocused linear array. Both simulation and in vitro experiments were conducted on 3-D microvessel phantoms. In vivo demonstration was done on the Rabbit kidney. Data from a 1-D linear array with and without an elevational focus were synthesized by summing up row signals acquired from a 2-D matrix array with and without delays. A full 3-D reconstruction was also generated as the reference, using the same data of the 2-D matrix array but without summing row signals. Results show that using an unfocused 1-D array probe, BEP-SR can capture significantly more information within a volume in both vascular structure and flow velocity than the conventional 1-D elevational-focused probe. Compared with the 2-D projection image of the full 3-D SRUS results using the 2-D array probe with the same aperture size, the 2-D projection SRUS image of BEP-SR has similar volume coverage, using 32 folds fewer independent elements. This study demonstrates BEP-SR's ability of high-resolution imaging of microvascular structures and flow velocity within a 3-D volume at significantly reduced costs. The proposed BEP method could significantly benefit the clinical translation of the SRUS imaging technique by making it more affordable and repeatable.

Three-dimensional super resolution ultrasound imaging with a multi-frequency hemispherical phased array

BACKGROUND

High resolution imaging of the microvasculature plays an important role in both diagnostic and therapeutic applications in the brain. However, ultrasound pulse-echo sonography imaging the brain vasculatures has been limited to narrow acoustic windows and low frequencies due to the distortion of the skull bone, which sacrifices axial resolution since it is pulse length dependent.

PURPOSE

To overcome the detect limit, a large aperture 256-module sparse hemispherical transmit/receive array was used to visualize the acoustic emissions of ultrasound-vaporized lipid-coated decafluorobutane nanodroplets flowing through tube phantoms and within rabbit cerebral vasculature in vivo via passive acoustic mapping and super resolution techniques.

METHODS

Nanodroplets were vaporized with 55 kHz burst-mode ultrasound (burst length = 145 μs, burst repetition frequency = 9-45 Hz, peak negative acoustic pressure = 0.10-0.22 MPa), which propagates through overlying tissues well without suffering from severe distortions. The resulting emissions were received at a higher frequency (612 or 1224 kHz subarray) to improve the resulting spatial resolution during passive beamforming. Normal resolution three-dimensional images were formed using a delay, sum, and integrate beamforming algorithm, and super-resolved images were extracted via Gaussian fitting of the estimated point-spread-function to the normal resolution data.

RESULTS

With super resolution techniques, the mean lateral (axial) full-width-at-half-maximum image intensity was 16 ± 3 (32 ± 6) μm, and 7 ± 1 (15 ± 2) μm corresponding to ∼1/67 of the normal resolution at 612 and 1224 kHz, respectively. The mean positional uncertainties were ∼1/350 (lateral) and ∼1/180 (axial) of the receive wavelength in water. In addition, a temporal correlation between nanodroplet vaporization and the transmit waveform shape was observed, which may provide the opportunity to enhance the signal-to-noise ratio in future studies.

CONCLUSIONS

Here, we demonstrate the feasibility of vaporizing nanodroplets via low frequency ultrasound and simultaneously performing spatial mapping via passive beamforming at higher frequencies to improve the resulting spatial resolution of super resolution imaging techniques. This method may enable complete four-dimensional vascular mapping in organs where a hemispherical array could be positioned to surround the target, such as the brain, breast, or testicles.

Real-Time Passive Acoustic Mapping With Enhanced Spatial Resolution in Neuronavigation-Guided Focused Ultrasound for Blood-Brain Barrier Opening

OBJECTIVE

Passive acoustic mapping (PAM) provides the spatial information of acoustic energy emitted from microbubbles during focused ultrasound (FUS), which can be used for safety and efficacy monitoring of blood-brain barrier (BBB) opening. In our previous work with a neuronavigation-guided FUS system, only part of the cavitation signal could be monitored in real time due to the computational burden although full-burst analysis is required to detect transient and stochastic cavitation activity. In addition, the spatial resolution of PAM can be limited for a small-aperture receiving array transducer. For full-burst real-time PAM with enhanced resolution, we developed a parallel processing scheme for coherence-factor-based PAM (CF-PAM) and implemented it onto the neuronavigation-guided FUS system using a co-axial phased-array imaging transducer.

METHODS

Simulation and in-vitro human skull studies were conducted for the performance evaluation of the proposed method in terms of spatial resolution and processing speed. We also carried out real-time cavitation mapping during BBB opening in non-human primates (NHPs).

RESULTS

CF-PAM with the proposed processing scheme provided better resolution than that of traditional time-exposure-acoustics PAM with a higher processing speed than that of eigenspace-based robust Capon beamformer, which facilitated the full-burst PAM with the integration time of 10 ms at a rate of 2 Hz. In vivo feasibility of PAM with the co-axial imaging transducer was also demonstrated in two NHPs, showing the advantages of using real-time B-mode and full-burst PAM for accurate targeting and safe treatment monitoring.

SIGNIFICANCE

This full-burst PAM with enhanced resolution will facilitate the clinical translation of online cavitation monitoring for safe and efficient BBB opening.

Fast 3D Super-Resolution Ultrasound With Adaptive Weight-Based Beamforming

OBJECTIVE

Super-resolution ultrasound (SRUS) imaging through localising and tracking sparse microbubbles has been shown to reveal microvascular structure and flow beyond the wave diffraction limit. Most SRUS studies use standard delay and sum (DAS) beamforming, where high side lobes and broad main lobes make isolation and localisation of densely distributed bubbles challenging, particularly in 3D due to the typically small aperture of matrix array probes.

METHOD

This study aimed to improve 3D SRUS by implementing a new fast 3D coherence beamformer based on channel signal variance. Two additional fast coherence beamformers, that have been implemented in 2D were implemented in 3D for the first time as comparison: a nonlinear beamformer with p-th root compression and a coherence factor beamformer. The 3D coherence beamformers, together with DAS, were compared in computer simulation, on a microflow phantom and in vivo.

RESULTS

Simulation results demonstrated that all three adaptive weight-based beamformers can narrow the main lobe, suppress the side lobes, while maintaining the weaker scatter signals. Improved 3D SRUS images of microflow phantom and a rabbit kidney within a 3-second acquisition were obtained using the adaptive weight-based beamformers, when compared with DAS.

CONCLUSION

The adaptive weight-based 3D beamformers can improve the SRUS and the proposed variance-based beamformer performs best in simulations and experiments.

SIGNIFICANCE

Fast 3D SRUS would significantly enhance the potential utility of this emerging imaging modality in a broad range of biomedical applications.

Detecting Early Ocular Choroidal Melanoma Using Ultrasound Localization Microscopy

Ocular choroidal melanoma (OCM) is the most common ocular primary malignant tumor in adults, and there is an increasing emphasis on its early detection and treatment worldwide. The main obstacle in early detection of OCM is its overlapping clinical features with benign choroidal nevus. Thus, we propose ultrasound localization microscopy (ULM) based on the image deconvolution algorithm to assist the diagnosis of small OCM in early stages. Furthermore, we develop ultrasound (US) plane wave imaging based on three-frame difference algorithm to guide the placement of the probe on the field of view. A high-frequency Verasonics Vantage system and an L22-14v linear array transducer were used to perform experiments on both custom-made modules in vitro and a SD rat with ocular choroidal melanoma in vivo. The results demonstrate that our proposed deconvolution method implement more robust microbubble (MB) localization, reconstruction of microvasculature network in a finer grid and more precise flow velocity estimation. The excellent performance of US plane wave imaging was successfully validated on the flow phantom and in an in vivo OCM model. In the future, the super-resolution ULM, a critical complementary imaging modality, can provide doctors with conclusive suggestions for early diagnosis of OCM, which is significant for the treatment and prognosis of patients.

An optical and acoustic investigation of microbubble cavitation in small channels under therapeutic ultrasound conditions

Therapeutic focused ultrasound in combination with encapsulated microbubbles is being widely investigated for its ability to elicit bioeffects in the microvasculature, such as transient permeabilization for drug delivery or at higher pressures to achieve 'antivascular' effects. While it is well established that the behaviors of microbubbles are altered when they are situated within sufficiently small vessels, there is a paucity of data examining how the bubble population dynamics and emissions change as a function of channel (vessel) diameter over a size range relevant to therapeutic ultrasound, particularly at pressures relevant to antivascular ultrasound. Here we use acoustic emissions detection and high-speed microscopy (10 kframes/s) to examine the behavior of a polydisperse clinically employed agent (Definity®) in wall-less channels as their diameters are scaled from 1200 to 15 µm. Pressures are varied from 0.1 to 3 MPa using either a 5 ms pulse or a sequence of 0.1 ms pulses spaced at 1 ms, both of which have been previously employed in an in vivo context. With increasing pressure, the 1200 µm channel - on the order of small arteries and veins - exhibited inertial cavitation, 1/2 subharmonics and 3/2 ultraharmonics, consistent with numerous previous reports. The 200 and 100 µm channels - in the size range of larger microvessels less affected by therapeutic focused ultrasound - exhibited a distinctly different behavior, having muted development of 1/2 subharmonics and 3/2 ultraharmonics and reduced persistence. These were associated with radiation forces displacing bubbles to the distal wall and inducing clusters that then rapidly dissipated along with emissions. As the diameter transitioned to 50 and then 15 µm - a size regime that is most relevant to therapeutic focused ultrasound - there was a higher threshold for the onset of inertial cavitation as well as subharmonics and ultraharmonics, which importantly had more complex orders that are not normally reported. Clusters also occurred in these channels (e.g. at 3 MPa, the mean lateral and axial sizes were 23 and 72 µm in the 15 µm channel; 50 and 90 µm in the 50 µm channel), however in this case they occupied the entire lumens and displaced the wall boundaries. Damage to the 15 µm channel was observed for both pulse types, but at a lower pressure for the long pulse. Experiments conducted with a 'nanobubble' (<0.45 µm) subpopulation of Definity followed broadly similar features to 'native' Definity, albeit at a higher pressure threshold for inertial cavitation. These results provide new insights into the behavior of microbubbles in small vessels at higher pressures and have implications for therapeutic focused ultrasound cavitation monitoring and control.

Passive acoustic mapping with absolute time-of-flight information and delay-multiply-sum beamforming

BACKGROUND

Passive acoustic mapping (PAM) is showing increasing application potential in monitoring ultrasound therapy by spatially resolving cavitation activity. PAM with the relative time-of-flight information leads to poor axial resolution when implemented with ultrasound diagnostic transducers. Through utilizing the absolute time-of-flight information preserved by the transmit-receive synchronization and applying the common delay-sum (DS) beamforming algorithm, PAM axial resolution can be greatly improved in the short-pulse excitation scenario, as with active ultrasound imaging. However, PAM with the absolute time-of-flight information (referred as AtPAM) suffers from low imaging resolution and weak interference suppression when the DS algorithm is applied.

PURPOSE

This study aims to propose an enhanced AtPAM algorithm based on delay-multiply-sum (DMS) beamforming, to address the shortcomings of the DS-based AtPAM algorithm.

METHODS

In DMS beamforming, the element signals delayed by the absolute time delays are first processed with a signed square-root operation and then multiplied in pairs and finally summed, the resulting beamformed output is further band-pass filtered. The performances of DS- and DMS-based AtPAMs are compared by experiments, in which an ultrasound diagnostic transducer (a linear array) is employed to passively sense the wire signals generated by an unfocused ultrasound transducer and the cavitation signals generated by a focused therapeutic ultrasound transducer in a flow phantom. The AtPAM image quality is assessed by main-lobe width (MLW), intensity valley value (IVV), area of pixels (AOP), signal-to-interference ratio (SIR), and signal-to-noise ratio (SNR).

RESULTS

The single-wire experimental results show that compared to the DS algorithm, the DMS algorithm leads to an enhanced AtPAM image with a decreased transverse MLW of 0.15 mm and an improved SIR and SNR of 31.50 and 18.77 dB. For the four-wire images, the transverse (axial) IVV is decreased by 18.37 dB (13.11 dB) and the SIR (the SNR) is increased by 26.13 dB (18.47 dB) when using the DMS algorithm. The cavitation activity is better highlighted by DMS-based AtPAM, which decreases the AOP by 0.81 mm² (-10-dB level) and 4.43 mm² (-20-dB level) and increases the SIR and SNR by 20.14 and 10.48 dB respectively. The pixel distributions of AtPAM images of both wires and cavitation activity also indicate a better suppression of the DMS algorithm in sidelobe and noise.

CONCLUSIONS

The experimental results illustrate that the DMS algorithm can improve the image quality of AtPAM compared to the DS algorithm. DMS-based AtPAM is beneficial for detecting cavitation activity during short-pulse ultrasound exposure with high resolution, and further for monitoring short-pulse ultrasound therapy.

Cerebral microcirculation mapped by echo particle tracking velocimetry quantifies the intracranial pressure and detects ischemia

Affecting 1.1‰ of infants, hydrocephalus involves abnormal accumulation of cerebrospinal fluid, resulting in elevated intracranial pressure (ICP). It is the leading cause for brain surgery in newborns, often causing long-term neurologic disabilities or even death. Since conventional invasive ICP monitoring is risky, early neurosurgical interventions could benefit from noninvasive techniques. Here we use clinical contrast-enhanced ultrasound (CEUS) imaging and intravascular microbubble tracking algorithms to map the cerebral blood flow in hydrocephalic pediatric porcine models. Regional microvascular perfusions are quantified by the cerebral microcirculation (CMC) parameter, which accounts for the concentration of micro-vessels and flow velocity in them. Combining CMC with hemodynamic parameters yields functional relationships between cortical micro-perfusion and ICP, with correlation coefficients exceeding 0.85. For cerebral ischemia cases, the nondimensionalized cortical micro-perfusion decreases by an order of magnitude when ICP exceeds 50% of the MAP. These findings suggest that CEUS-based CMC measurement is a plausible noninvasive method for assessing the ICP and detecting ischemia.

Super-Resolution Ultrasound Localization Microscopy Through Deep Learning

Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents (microbubbles) across numerous imaging frames. However, analysis of high-density regions with significant overlaps among the microbubble point spread responses yields high localization errors, constraining the technique to low-concentration conditions. As such, long acquisition times are required to sufficiently cover the vascular bed. In this work, we present a fast and precise method for obtaining super-resolution vascular images from high-density contrast-enhanced ultrasound imaging data. This method, which we term Deep Ultrasound Localization Microscopy (Deep-ULM), exploits modern deep learning strategies and employs a convolutional neural network to perform localization microscopy in dense scenarios, learning the nonlinear image-domain implications of overlapping RF signals originating from such sets of closely spaced microbubbles. Deep-ULM is trained effectively using realistic on-line synthesized data, enabling robust inference in-vivo under a wide variety of imaging conditions. We show that deep learning attains super-resolution with challenging contrast-agent densities, both in-silico as well as in-vivo. Deep-ULM is suitable for real-time applications, resolving about 70 high-resolution patches ( 128×128 pixels) per second on a standard PC. Exploiting GPU computation, this number increases to 1250 patches per second.

Super-resolution Ultrasound Imaging

The majority of exchanges of oxygen and nutrients are performed around vessels smaller than 100 μm, allowing cells to thrive everywhere in the body. Pathologies such as cancer, diabetes and arteriosclerosis can profoundly alter the microvasculature. Unfortunately, medical imaging modalities only provide indirect observation at this scale. Inspired by optical microscopy, ultrasound localization microscopy has bypassed the classic compromise between penetration and resolution in ultrasonic imaging. By localization of individual injected microbubbles and tracking of their displacement with a subwavelength resolution, vascular and velocity maps can be produced at the scale of the micrometer. Super-resolution ultrasound has also been performed through signal fluctuations with the same type of contrast agents, or through switching on and off nano-sized phase-change contrast agents. These techniques are now being applied pre-clinically and clinically for imaging of the microvasculature of the brain, kidney, skin, tumors and lymph nodes.

In Vivo Visualization of Eye Vasculature Using Super-Resolution Ultrasound Microvessel Imaging

OBJECTIVE

The choroidal vessels, which supply oxygen and nutrient to the retina, may play a pivotal role in eye disease pathogenesis such as diabetic retinopathy and glaucoma. In addition, the retrobulbar circulation that feeds the choroid shows an important pathophysiologic role in myopia and degenerative myopia. Owing to the light-absorbing retinal pigment epithelium (RPE) and optically opaque sclera, choroidal and retrobulbar vasculature were difficult to be observed using clinically accepted optical coherence tomography angiography (OCT-A) technique. Here, we have developed super-resolution ultrasound microvessel imaging technique to visualize the deep ocular vasculature.

METHODS

An 18-MHz linear array transducer with compounding plane wave imaging technique and contrast agent - microbubble was implemented in this study. The centroid intensity of each microbubble was detected using image deconvolution algorithm with spatially variant point spread function, and then accumulated in successive frames in order to reconstruct microvasculature. The image deconvolution technique was first evaluated in a simulation study and experimental flow phantoms. The performance was then validated on normal rabbit eyes in vivo.

RESULTS

The image deconvolution based super-resolution ultrasound microvessel imaging technique shows good performance on either simulation study or flow phantoms. In vivo rabbit eye study indicated that the micron-level choroidal and retrobulbar vessels around the optic nerve head were successfully reconstructed in multiple 2D views and 3D volume imaging.

CONCLUSIONS

Our results demonstrate the capability of using super-resolution ultrasound microvessel imaging technique to image the microvasculature of the posterior pole of the eye. This efficient approach can potentially lead to a routinely performed diagnostic procedure in the field of ophthalmology.

Enhancement of Acoustic Microscopy Lateral Resolution: A Comparison Between Deep Learning and Two Deconvolution Methods

Scanning acoustic microscopy (SAM) provides high-resolution images of biological tissues. Since higher transducer frequencies limit penetration depth, image resolution enhancement techniques could help in maintaining sufficient lateral resolution without sacrificing penetration depth. Compared with existing SAM research, this work introduces two novelties. First, deep learning (DL) is used to improve lateral resolution of 180-MHz SAM images, comparing it with two deconvolution-based approaches. Second, 316-MHz images are used as ground truth in order to quantitatively evaluate image resolution enhancement. The samples used were mouse and rat brain sections. The results demonstrate that DL can closely approximate ground truth (NRMSE = 0.056 and PSNR = 28.4 dB) even with a relatively limited training set (four images, each smaller than 1 mm ×1 mm). This study suggests the high potential of using DL as a single image superresolution method in SAM.

Computed Tomography (CT) Image Quality Enhancement via a Uniform Framework Integrating Noise Estimation and Super-Resolution Networks

Computed tomography (CT) imaging technology has been widely used to assist medical diagnosis in recent years. However, noise during the process of imaging, and data compression during the process of storage and transmission always interrupt the image quality, resulting in unreliable performance of the post-processing steps in the computer assisted diagnosis system (CADs), such as medical image segmentation, feature extraction, and medical image classification. Since the degradation of medical images typically appears as noise and low-resolution blurring, in this paper, we propose a uniform deep convolutional neural network (DCNN) framework to handle the de-noising and super-resolution of the CT image at the same time. The framework consists of two steps: Firstly, a dense-inception network integrating an inception structure and dense skip connection is proposed to estimate the noise level. The inception structure is used to extract the noise and blurring features with respect to multiple receptive fields, while the dense skip connection can reuse those extracted features and transfer them across the network. Secondly, a modified residual-dense network combined with joint loss is proposed to reconstruct the high-resolution image with low noise. The inception block is applied on each skip connection of the dense-residual network so that the structure features of the image are transferred through the network more than the noise and blurring features. Moreover, both the perceptual loss and the mean square error (MSE) loss are used to restrain the network, leading to better performance in the reconstruction of image edges and details. Our proposed network integrates the degradation estimation, noise removal, and image super-resolution in one uniform framework to enhance medical image quality. We apply our method to the Cancer Imaging Archive (TCIA) public dataset to evaluate its ability in medical image quality enhancement. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on de-noising and super-resolution by providing higher peak signal to noise ratio (PSNR) and structure similarity index (SSIM) values.