Superresolution ultrasound imaging using back-projected reconstruction

Super-resolution of 2D ultrasound images and videos

This paper proposes a novel deep-learning framework for super-resolution ultrasound images and videos in terms of spatial resolution and line reconstruction. To this end, we up-sample the acquired low-resolution image through a vision-based interpolation method; then, we train a learning-based model to improve the quality of the up-sampling. We qualitatively and quantitatively test our model on different anatomical districts (e.g., cardiac, obstetric) images and with different up-sampling resolutions (i.e., 2X, 4X). Our method improves the PSNR median value with respect to SOTA methods of [Formula: see text] on obstetric 2X raw images, [Formula: see text] on cardiac 2X raw images, and [Formula: see text] on abdominal raw 4X images; it also improves the number of pixels with a low prediction error of [Formula: see text] on obstetric 4X raw images, [Formula: see text] on cardiac 4X raw images, and [Formula: see text] on abdominal 4X raw images. The proposed method is then applied to the spatial super-resolution of 2D videos, by optimising the sampling of lines acquired by the probe in terms of the acquisition frequency. Our method specialises trained networks to predict the high-resolution target through the design of the network architecture and the loss function, taking into account the anatomical district and the up-sampling factor and exploiting a large ultrasound data set. The use of deep learning on large data sets overcomes the limitations of vision-based algorithms that are general and do not encode the characteristics of the data. Furthermore, the data set can be enriched with images selected by medical experts to further specialise the individual networks. Through learning and high-performance computing, the proposed super-resolution is specialised to different anatomical districts by training multiple networks. Furthermore, the computational demand is shifted to centralised hardware resources with a real-time execution of the network's prediction on local devices.

A 3-D Ultrasound Wearable Array Prognosis System With Advanced Imaging Capabilities

In the last few decades, the medical and healthcare scientific communities have focused their attention on the use or development of real-time monitoring devices and remote control systems. New generations of wearable, portable, and implantable devices offer better and more accurate measurements/prognosis for those that suffer from diseases and/or disabilities. Thus, there are still challenging issues of current ultrasound imaging (USI) systems, such as low-quality ultrasound images, slow time response to emergencies, and location-based operation. Thus, in response to these challenges, we present a new low-cost, portable/wearable 3-D array ultrasound prognosis system with advanced imaging capabilities that offer high-resolution (HR) accurate results in a near real-time response. The USI unique features are based on 2-D array transducers with 3-D overlapping capabilities and a new image enhancement methodology compatible with the system's structural characteristics to compensate for any loss of image quality. This system will offer an alternative way of ultrasound examination, independent of the radiologist's skills, that is, a system to be capable of automatic scanning of the volume of interest (VOI) without the guidance of the radiologist.

High-Resolution Ultrasound Imaging Enabled by Random Interference and Joint Image Reconstruction

In ultrasound, wave interference is an undesirable effect that degrades the resolution of the images. We have recently shown that a wavefront of random interference can be used to reconstruct high-resolution ultrasound images. In this study, we further improve the resolution of interference-based ultrasound imaging by proposing a joint image reconstruction scheme. The proposed reconstruction scheme utilizes radio frequency (RF) signals from all elements of the sensor array in a joint optimization problem to directly reconstruct the final high-resolution image. By jointly processing array signals, we significantly improved the resolution of interference-based imaging. We compare the proposed joint reconstruction method with popular beamforming techniques and the previously proposed interference-based compound method. The simulation study suggests that, among the different reconstruction methods, the joint reconstruction method has the lowest mean-squared error (MSE), the best peak signal-to-noise ratio (PSNR), and the best signal-to-noise ratio (SNR). Similarly, the joint reconstruction method has an exceptional structural similarity index (SSIM) of 0.998. Experimental studies showed that the quality of images significantly improved when compared to other image reconstruction methods. Furthermore, we share our simulation codes as an open-source repository in support of reproducible research.

High-Resolution Ultrasound Imaging Using Random Interference

Spatial resolution in conventional sonography is achieved through focusing and steering of an ultrasound beam. However, due to acoustic diffraction, the ability to focus an ultrasound beam is limited which leads to low spatial and contrast resolutions. We aim to propose a new method wherein the array elements are simultaneously excited with signals coded with random sequences, which yields an unfocused ultrasound wavefront of random interference. When such a wavefront propagates through the medium, its energy reflects back from the tissue, causing individual scatterers to have unique impulse responses. In such a case, we can reconstruct high-resolution ultrasound images using a priori measurements of spatial impulse responses and the l₁ -norm minimization algorithm. In a simulation study, we achieved a spatial resolution of 0.25 mm, which constitutes a four-fold improvement over conventional methods that use delay-and-sum beamforming. In the experimental study, we demonstrate the accuracy of the proposed interference-based method using a tissue-mimicking phantom with 0.1- and 0.08-mm-diameter nylon wires.

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.

Portable ultrasound imaging system with super-resolution capabilities

This paper discusses an ultrasound technique where the echo signals from the array of transducer elements are compressed to as few as two RF channels while still in analog domain, with a much simplified front-end electronics. The method can achieve resolutions well beyond the diffraction limit, which is set by the excitation signal wavelength and numerical aperture of the imaging system. The fundamental principle that underlies this model based imaging technique is the preservation of the spatial frequency information content of the recorded echo signals with the help of pseudo-random apodization function followed by summation. A Verasonics V1 ultrasonic scanner is used to conduct experiments using an anechoic cyst made from gel phantom, immersed in degassed water. The estimated images were compared to those obtained using traditional B-mode delay-and-sum imaging available with the Verasonics V1 ultrasound machine. The estimated images using the proposed imaging technique showed a contrast ratio of 0.96 and Full-Width-Half-Maximum (FWHM) of about half the wavelength at a depth of 9.1 cm and at 1.875 MHz center frequency while the traditional delay and sum images had a contrast ratio of 0.62 and FWHM of about 5.5 wavelengths.

High Spatial-Temporal Resolution Reconstruction of Plane-Wave Ultrasound Images With a Multichannel Multiscale Convolutional Neural Network

In recent years, plane-wave imaging (PWI) has attracted considerable attention because of its high temporal resolution. However, the low spatial resolution of PWI limits its clinical applications, which has inspired various studies on the high spatial resolution reconstruction of PW ultrasound images. Although compounding methods and traditional high spatial resolution reconstruction approaches can improve the image quality, these techniques decrease the temporal resolution. Since learning methods can fully reserve the high temporal resolution of PW ultrasounds, a novel convolutional neural network (CNN) model for the high spatial-temporal resolution reconstruction of PW ultrasound images is proposed in this paper. Considering the multiangle form of PW data, a multichannel model is introduced to produce balanced training. To combine local and contextual information, the multiscale model is adopted. These two innovations constitute our multichannel and multiscale CNN (MMCNN) model. Compared with traditional CNN methods, the proposed model uses a two-stage structure in which a cascading wavelet postprocessing stage is combined with the trained MMCNN model. Cascading wavelet postprocessing aims to preserve speckle information. Furthermore, a feedback system is appended to the iteration process of the network training to solve the overfitting problem and help produce convergence. Based on these improvements, an end-to-end mapping is established between a single-angle B-mode PW image and its corresponding multiangle compounded, high-resolution image. The experiments were conducted on simulated, phantom, and real human data. The advantages of our proposed method were compared with a coherent PW compounding method, a conventional maximum a posteriori-based high spatial resolution reconstruction method, and a 2-D CNN compounding method, and the results verified that our approach is capable of attaining a better temporal resolution and comparable spatial resolution. In clinical usage, the proposed method is equipped to satisfy with many ultrafast imaging applications, which require high spatial-temporal resolution. i.

Ultrasound Localization Microscopy and Super-Resolution: A State of the Art

Because it drives the compromise between resolution and penetration, the diffraction limit has long represented an unreachable summit to conquer in ultrasound imaging. Within a few years after the introduction of optical localization microscopy, we proposed its acoustic alter ego that exploits the micrometric localization of microbubble contrast agents to reconstruct the finest vessels in the body in-depth. Various groups now working on the subject are optimizing the localization precision, microbubble separation, acquisition time, tracking, and velocimetry to improve the capacity of ultrasound localization microscopy (ULM) to detect and distinguish vessels much smaller than the wavelength. It has since been used in vivo in the brain, the kidney, and tumors. In the clinic, ULM is bound to improve drastically our vision of the microvasculature, which could revolutionize the diagnosis of cancer, arteriosclerosis, stroke, and diabetes.

Multiaccess In Vivo Biotelemetry Using Sonomicrometry and M-Scan Ultrasound Imaging

Objective: In this paper, we investigate the use of commercial off-the-shelf diagnostic ultrasound readers to achieve multiaccess wireless in vivo telemetry with millimeter-sized sonomicrometry crystal transducers.

METHODS

The sonomicrometry crystals generate ultrasonic pulses that supersede the echoes generated at the tissue interfaces in response to M-scan interrogation pulses. The traces of these synthetic pulses are captured on an M-scan image and the transmitted data are decoded using image deconvolution and deblurring algorithms.

RESULTS

Using a chicken phantom and 1.3 MHz sonomicrometry crystals of diameter 1 mm, we first demonstrate that a standard ultrasound reader can achieve biotelemetry data rates up to 1 Mb/s for implantation depths greater than 10 cm. For this experiment the maximum power dissipation at the crystals was measured to be 20 and bit-error-rate of the telemetry link was shown to be . We also demonstrate the use of this method for multiaccess biotelemetry where several sonomicrometry crystals simultaneously transmit the data using different modulation and coding techniques. Using a live ovine model, we demonstrate a sonomicrometry crystal implanted in the sheep 's tricuspid valve can maintain a continuous, reliable telemetry link at data rates up tob 800 Kb/s in the presence of respiratory and cardiac motion artifacts.

CONCLUSION

Compared to existing radio-frequency and ultrasound based biotelemetry devices, the reported data-rates are significantly higher considering the transducer's form-factor and its implantation depth.

SIGNIFICANCE

The proposed technique thus validates the feasibility of establishing reliable communication link with multiple in vivo implants using M-scan-based ultrasound imaging.

Passive Acoustic Mapping with the Angular Spectrum Method

In the present proof of principle study, we evaluated the homogenous angular spectrum method for passive acoustic mapping (AS-PAM) of microbubble oscillations using simulated and experimental data. In the simulated data we assessed the ability of AS-PAM to form 3D maps of a single and multiple point sources. Then, in the two dimensional limit, we compared the 2D maps from AS-PAM with alternative frequency and time domain passive acoustic mapping (FD- and TD-PAM) approaches. Finally, we assessed the ability of AS-PAM to visualize microbubble activity in vivo with data obtained during 8 different experiments of FUS-induced blood-brain barrier disruption in 3 nonhuman primates, using a clinical MR-guided FUS system. Our in silico results demonstrate AS-PAM can be used to perform 3D passive acoustic mapping. 2D AS-PAM as compared to FD- PAM and TD-PAM is 10 and 200 times faster respectively and has similar sensitivity, resolution, and localization accuracy, even when the noise was 10-fold higher than the signal. In-vivo, the AS-PAM reconstructions of emissions at frequency bands pertinent to the different types of microbubble oscillations were also found to be more sensitive than TD-PAM. AS-PAM of harmonic-only components predicted safe blood-brain barrier disruption, whereas AS-PAM of broadband emissions correctly identified MR-evident tissue damage. The disparity (3.2 mm) in the location of the cavitation activity between the three methods was within their resolution limits. These data clearly demonstrate that AS-PAM is a sensitive and fast approach for PAM, thus providing a clinically relevant method to guide therapeutic ultrasound procedures.

Enhanced-Resolution Optical Coherence Tomography Imaging

PURPOSE

The effect of the enhanced-resolution imaging (ERI) technique on optical coherence tomography (OCT) images was evaluated.

METHODS

A total of 5 healthy subjects and 20 patients diagnosed with various eye diseases were recruited into the study. ERI, a novel image processing technique, was accomplished by using super-resolution technology, and was assessed by objectively and subjectively comparing the image quality among three different image groups: images enlarged without bicubic interpolation (NONE), with bicubic interpolation (IP), and with ERI.

RESULTS

ERI showed a higher ratio of the detailed variance to the background variance than NONE, whereas no significant difference was detected between NONE and IP. The mean opinion score of 5 experienced retinal specialists for ERI was significantly higher than that for IP.

CONCLUSIONS

ERI generated a sharper image and clearly visualized small objects. Additionally, it is effective in enhancing OCT image quality.

Motion estimation-based image enhancement in ultrasound imaging

High resolution medical ultrasound (US) imaging is an ongoing challenge in many diagnosis applications and can be achieved either by instrumentation or by post-processing. Though many works have considered the issue of resolution enhancement in optical imaging, very few works have investigated this issue in US imaging. In optics, several algorithms have been proposed to achieve super-resolution (SR) image reconstruction, which consists of merging several low resolution images to create a higher resolution image. However, the straightforward implementation of such techniques for US imaging is unsuccessful, due to the interaction of ultrasound with tissue and speckle. We show how to overcome the limit of SR in this framework by refining the registration part of common multiframe techniques. For this purpose, we investigate motion estimation methods adapted to US imaging. Performance of the proposed technique is evaluated on both realistic simulated US images (providing an estimated best-case performance) and real US sequences of phantom and in-vivo thyroid images. Compared to classical SR methods, our technique brings both quantitative and qualitative improvements. Resolution gain was found to be 1.41 for the phantom sequence and 1.12 for the thyroid sequence and a quantitative study using the phantom further confirmed the spatial resolution enhancement. Furthermore, the contrast-to-noise ratio was increased by 27% and 13% for simulated and experimental US images, respectively.

Resolution enhancement in medical ultrasound imaging

Image resolution enhancement is a problem of considerable interest in all medical imaging modalities. Unlike general purpose imaging or video processing, for a very long time, medical image resolution enhancement has been based on optimization of the imaging devices. Although some recent works purport to deal with image postprocessing, much remains to be done regarding medical image enhancement via postprocessing, especially in ultrasound imaging. We face a resolution improvement issue in the case of medical ultrasound imaging. We propose to investigate this problem using multidimensional autoregressive (AR) models. Noting that the estimation of the envelope of an ultrasound radio frequency (RF) signal is very similar to the estimation of classical Fourier-based power spectrum estimation, we theoretically show that a domain change and a multidimensional AR model can be used to achieve super-resolution in ultrasound imaging provided the order is estimated correctly. Here, this is done by means of a technique that simultaneously estimates the order and the parameters of a multidimensional model using relevant regression matrix factorization. Doing so, the proposed method specifically fits ultrasound imaging and provides an estimated envelope. Moreover, an expression that links the theoretical image resolution to both the image acquisition features (such as the point spread function) and a postprocessing feature (the AR model) order is derived. The overall contribution of this work is threefold. First, it allows for automatic resolution improvement. Through a simple model and without any specific manual algorithmic parameter tuning, as is used in common methods, the proposed technique simply and exclusively uses the ultrasound RF signal as input and provides the improved B-mode as output. Second, it allows for the a priori prediction of the improvement in resolution via the knowledge of the parametric model order before actual processing. Finally, to achieve the previous goal, while classical parametric methods would first estimate the model order and then the model parameters, our approach estimates the model parameters and the order simultaneously. The effectiveness of the methodology is validated using two-dimensional synthetic and in vivo data. We show that, compared to other techniques, our method provides better results from a qualitative and a quantitative viewpoint.

Three-dimensional super-resolution: theory, modeling, and field test results

Many flash lidar applications continue to demand higher three-dimensional image resolution beyond the current state-of-the-art technology of the detector arrays and their associated readout circuits. Even with the available number of focal plane pixels, the required number of photons for illuminating all the pixels may impose impractical requirements on the laser pulse energy or the receiver aperture size. Therefore, image resolution enhancement by means of a super-resolution algorithm in near real time presents a very attractive solution for a wide range of flash lidar applications. This paper describes a super-resolution technique and illustrates its performance and merits for generating three-dimensional image frames at a video rate.

Super-resolution imaging using multi- electrode CMUTs: theoretical design and simulation using point targets

This paper investigates a low computational cost, super-resolution ultrasound imaging method that leverages the asymmetric vibration mode of CMUTs. Instead of focusing on the broadband received signal on the entire CMUT membrane, we utilize the differential signal received on the left and right part of the membrane obtained by a multi-electrode CMUT structure. The differential signal reflects the asymmetric vibration mode of the CMUT cell excited by the nonuniform acoustic pressure field impinging on the membrane, and has a resonant component in immersion. To improve the resolution, we propose an imaging method as follows: a set of manifold matrices of CMUT responses for multiple focal directions are constructed off-line with a grid of hypothetical point targets. During the subsequent imaging process, the array sequentially steers to multiple angles, and the amplitudes (weights) of all hypothetical targets at each angle are estimated in a maximum a posteriori (MAP) process with the manifold matrix corresponding to that angle. Then, the weight vector undergoes a directional pruning process to remove the false estimation at other angles caused by the side lobe energy. Ultrasound imaging simulation is performed on ring and linear arrays with a simulation program adapted with a multi-electrode CMUT structure capable of obtaining both average and differential received signals. Because the differential signals from all receiving channels form a more distinctive temporal pattern than the average signals, better MAP estimation results are expected than using the average signals. The imaging simulation shows that using differential signals alone or in combination with the average signals produces better lateral resolution than the traditional phased array or using the average signals alone. This study is an exploration into the potential benefits of asymmetric CMUT responses for super-resolution imaging.

Generalizing the nonlocal-means to super-resolution reconstruction

Super-resolution reconstruction proposes a fusion of several low-quality images into one higher quality result with better optical resolution. Classic super-resolution techniques strongly rely on the availability of accurate motion estimation for this fusion task. When the motion is estimated inaccurately, as often happens for nonglobal motion fields, annoying artifacts appear in the super-resolved outcome. Encouraged by recent developments on the video denoising problem, where state-of-the-art algorithms are formed with no explicit motion estimation, we seek a super-resolution algorithm of similar nature that will allow processing sequences with general motion patterns. In this paper, we base our solution on the Nonlocal-Means (NLM) algorithm. We show how this denoising method is generalized to become a relatively simple super-resolution algorithm with no explicit motion estimation. Results on several test movies show that the proposed method is very successful in providing super-resolution on general sequences.

Two-dimensional ultrasound detection with unfocused frequency-randomized signals

A method is described for detecting scattering in two-dimensions using an unfocused ultrasound field created from a continuously driven source array. The frequency of each element on the array is unique, resulting in a field that is highly variant as a function of both time and position. The scattered signal is then received by a single receiving line. The method, as currently written, is valid under the first order Born approximation. To demonstrate the approach, a series of simulations within the frequency range of 0.10-1.25 MHz are performed and compared with a simulated B-Scan in the same frequency range. The method is found to be superior in resolving closely spaced objects, discerning 1.4 mm separation in the radial and 0.5-mm separation in the axial direction. The method was also better able to determine object size, resolving scatters less than 10% of wavelength associated with the center frequency.