Following works can be found in non-learning based ultrasound imaging:
- 2D array design
- Gaussian Function Fit
- Fourier Domain Low Rank Model
- Lasso for Micro bubble Super resolution
- Kalman filter for Micro bubble Super resolution
3-D Super-Resolution Ultrasound (SR-US) Imaging using a 2-D Sparse Array with High Volumetric Imaging Rate ( arXiv:1902.02631 )(Design of 2D array for super resolution )
Abstract—Super-resolution ultrasound imaging has been so far achieved in 3-D by mechanically scanning a volume with a linear probe, by co-aligning multiple linear probes, by using multiplexed 3-D clinical ultrasound systems, or by using 3- D ultrasound research systems. In this study, a 2-D sparse array was designed with 512 elements according to a densitytapered 2-D spiral layout and optimized to reduce the side lobes of the transmitted beam profile. High frame rate volumetric imaging with compounded plane waves was performed using two synchronized ULA-OP256 systems. Localization-based 3-D super-resolution images of two touching sub-wavelength tubes were generated from a 120 second acquisition.
Introduction …”If motion is present and subsequently corrected post-acquisition, then the motion correction accuracy can also limit the achievable spatial resolution ” … ” First, super-resolution cannot be achieved in the elevational direction. Second, out-of-plane motion can only be compensated for movements smaller than the elevational beam width of the transducer “…” In this study, a density-tapered sparse array method was chosen instead of a full 2-D array to reduce the number of channels and hence the amount of data while maintaining the frame rat “
Materials and Methods ” SVD was used to separate the micro bubble signals from the echoes originating from the tube and the assembly holding the tube. Localization of isolated micro bubbles was performed on every acquired volume to generate the 3-D super-resolved volumes “
We can try random sampling for 2 D arrays. We can try to find best 2d array configuration for super resolution by explaining limited degree of freedom
3-D Super-Resolution Ultrasound (SR-US) Imaging with a 2-D Sparse Array(arXiv:1902.01608 )(Design of 2D array for super resolution )
Abstract—High frame rate 3-D ultrasound imaging technology combined with super-resolution processing method can visualize 3-D microvascular structures by overcoming the diffraction limited resolution in every spatial direction. However, 3-D superresolution ultrasound imaging using a full 2-D array requires a system with large number of independent channels, the design of which might be impractical due to the high cost, complexity, and volume of data produced. In this study, a 2-D sparse array was designed and fabricated with 512 elements chosen from a density-tapered 2-D spiral layout. High frame rate volumetric imaging was performed using two synchronized ULA-OP 256 research scanners. Volumetric images were constructed by coherently compounding 9-angle plane waves acquired in 3 milliseconds at a pulse repetition frequency of 3000 Hz. To allow microbubbles sufficient time to move between consequent compounded volumetric frames, a 7- millisecond delay was introduced after each volume acquisition. This reduced the effective volume acquisition speed to 100 Hz and the total acquired data size by 3.3-fold. Localization-based 3- D super-resolution images of two touching sub-wavelength tubes were generated from 6000 volumes acquired in 60 seconds. In conclusion, this work demonstrates the feasibility of 3D superresolution imaging and super-resolved velocity mapping using a customized 2D sparse array transducer.
Microbubble Axial Localization Errors in Ultrasound Super-Resolution Imaging(Gaussian Fit for Micro bubble)
Abstract— Acoustic super-resolution imaging has allowed the visualization of microvascular structure and flow beyond the diffraction limit using standard clinical ultrasound systems through the localization of many spatially isolated microbubble signals. The determination of each microbubble position is typically performed by calculating the centroid, finding a local maximum, or finding the peak of a 2-D Gaussian function fit to the signal. However, the backscattered signal from a microbubble depends not only on diffraction characteristics of the waveform, but also on the microbubble behavior in the acoustic field. Here, we propose a new axial localization method by identifying the onset of the backscattered signal. We compare the accuracy of localization methods using in vitro experiments performed at 7-cm depth and 2.3-MHz center frequency. We corroborate these findings with simulation results based on the Marmottant model. We show experimentally and in simulations that detecting the onset of the returning signal provides considerably increased accuracy for super-resolution. Resulting experimental crosssectional profiles in super-resolution images demonstrate at least 5.8 times improvement in contrast ratio and more than 1.8 times reduction in spatial spread (provided by 90% of the localizations) for the onset method over centroiding, peak detection, and 2-D Gaussian fitting methods. Simulations estimate that these latter methods could create errors in relative bubble positions as high as 900 µm at these experimental settings, while the onset method reduced the interquartile range of these errors by a factor of over 2.2. Detecting the signal onset is, therefore, expected to considerably improve the accuracy of super-resolution.
A Low-Rank and Joint-Sparse Model for Ultrasound Signal Reconstruction (arXiv:1812.04843)( Fourier Domain Approach, Low Rank Property)
Abstract— With the introduction of very dense sensor arrays in ultrasound (US) imaging, data transfer rate and data storage became a bottleneck in ultrasound system design. To reduce the amount of sampled channel data, we propose to use a low-rank and joint-sparse model to represent US signals and exploit the correlations between adjacent receiving channels. Results show that the proposed method is adapted to the ultrasound signals and can recover high quality image approximations from as low as 10% of the samples.
Introduction ” The aim of the current study was therefore to further reduce data rates by exploiting the low-rank property of US signals “
Methodology ” According to the fact that the prebeamformed US signals from different transducer elements are joint-sparse in the Fourier domain “
Sparsity-driven super-resolution in clinical contrast-enhanced ultrasound ( https://ieeexplore.ieee.org/document/8092945 )( Solves Lasso then motion compensation by calculating the affine transformation on tissue signal )
Abstract—Super-resolution ultrasound enables detailed assessment of the fine vascular network by pinpointing individual microbubbles, using ultrasound contrast agents. The fidelity and achieved resolution of this technique is determined by the density of localized microbubbles and their localization accuracy. To obtain high densities, one can evaluate extremely sparse subsets of microbubbles across thousands of frames by using a very low microbubble dose and imaging for a very long time, which is impractical for clinical routine. While ultrafast imaging somewhat alleviates this problem, long acquisition times are still required to enhance the full vascular bed. As a result, localization accuracy remains hampered by patient motion. Recently, sparsity-based ultrasonic super resolution hemodynamic imaging was proposed, featuring a high spatial as well as temporal resolution by exploiting the temporal correlation structure of flowing microbubbles. However, when using clinical scanners operating at low framerates, this pixel-wise correlation across imaging frames may vanish. The aim of this work is hence twofold. First, to attain high microbubble localization accuracy on dense contrast-enhanced ultrasound data using a clinical dose of ultrasound contrast agents and a standard clinical scanner. Second, to retain a high resolution by adequate motion compensation.
Sparse Recovery
Motion Compensation ” To this end, we perform a singular value decomposition (SVD) on the full space-time CEUS data ” ,”… first k singular values to tissue …”, ” For each subregion/patch, we determine the affine transformation that maps the image data back to the first frame in the loop, by minimizing the mean squared error among those patches. ” “Motion compensation is performed after MB localization to avoid distortion of the system PSF following the affine transformation “
Exploiting flow dynamics for super-resolution in contrast-enhanced ultrasound(arXiv:1804.03134 )( Kalman filter for bubble tracking )
Abstract—Ultrasound localization microscopy offers new radiation-free diagnostic tools for vascular imaging deep within the tissue. Sequential localization of echoes returned from inert micro bubbles with low-concentration within the bloodstream reveal the vasculature with capillary resolution. Despite its high spatial resolution, low micro bubble concentrations dictate the acquisition of tens of thousands of images, over the course of several seconds to tens of seconds, to produce a single super-resolved image. Such long acquisition times and stringent constraints on micro bubble 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 micro bubble overlap. Here, we further improve sparsity-based super resolution ultrasound imaging by exploiting the inherent flow of micro bubbles and utilize their motion kinematics. While doing so, we also provide quantitative measurements of micro bubble velocities. Our method relies on simultaneous tracking and super localization of individual micro bubbles in a frame-by-frame manner, and as such, may be suitable for real-time implementation. We demonstrate the effectiveness of the proposed approach on both simulations and in-vivo contrast enhanced human prostate scans, acquired with a clinically approved scanner.
Introduction ” Our method combines weighted sparse recovery with simultaneous tracking of the individual MBs in the imaging plane “… ” In our method, each MB track is used to estimate the position of the MBs in the next frame “… “The accumulated position estimates are then used to form a weighting matrix for weighted sparse recovery which locates the MBs ” …” We refer to our method as simultaneous sparsity-based super-resolution and tracking, or Triple-SAT “
Weighted Sparse Recovery
