Phase Aberration Research Articles

Characterization of weakly nonlinear effects in relationship to transducer parameters in focused ultrasound therapy.

Focused ultrasound therapy has been widely used for the treatment of various diseases, employing different types of transducers. The focused ultrasound pressure fields inevitably exhibit nonlinear effects, which can influence the ablation region. However, the nonlinear effects exhibit noticeable variations across different applications. The characterization of the nonlinear pressure fields of ultrasound is important for the effective implementation of focused ultrasound therapy. The traditional angular spectrum method (ASM) was extended to accurately and efficiently simulate the propagation of weakly nonlinear ultrasound in heterogeneous mediums of clinical model. The nonlinear effects were further analyzed in relationship to the transducer parameters that are different in various applications. The pressure fields were simulated using the extended ASM, incorporating calculations for phase aberration in the frequency domain and magnitude compensation in the spatial domain to account for heterogeneous acoustic impedance mismatch. Validation was performed by comparison to k-Wave simulation results using two simplified clinical models, an abdominal soft tissue and a transcranial skull model. The nonlinear effects were then analyzed in relation to the transducer parameters of f-number and effective source area based on the same acoustic output power. The analysis of nonlinear effects was conducted under both homogeneous medium and the clinical models. The simulation results demonstrated a maximum error of 3.93% in the calculated harmonic pressure of the abdominal model, and a maximum error of 4.89% within the transcranial model when comparing the extended ASM simulation results to those obtained from k-Wave simulations. The characterization of the nonlinear effects reveals a strong correlation with the transducer parameters. Specifically, the results indicate that the nonlinear effects intensify with an increase in the effective source area and f-number, under the same acoustic output power of the transducer. However, the clinical model also showed an influence on the nonlinear effects in relation to the f-number. The extended ASM was demonstrated as an accurate and efficient simulation tool, and the simulation results provide a reference for evaluating the intensity of nonlinear effects in various transducer designs.

Transducer module apodization to reduce bone heating during focused ultrasound uterine fibroid ablation with phased arrays: A numerical study.

During magnetic resonance-guided focused ultrasound (MRgFUS) surgery for uterine fibroids, ablation of fibrous tissues in proximity to the hips and spine is challenging due to heating within the bone that can cause patients to experience pain and potentially damage nerves. This far-field bone heating limits the volume of fibroid tissue that is treatable viaMRgFUS. To investigate transducer module apodization for improving the ratio of focal-to-bone heating ( ) when targeting fibroid tissue close to the hips and spine, to enable MRgFUS treatments closer to thebone. Acoustic and thermal simulations were performed using 3D magnetic resonance imaging (MRI)-derived anatomies of ten patients who underwent MRgFUS ablation for uterine fibroids using a low-frequency ( ) 6144-element flat fully-populated modular phased array system (Arrayus Technologies Inc., Burlington, Canada) at our institution as part of a larger clinical trial (NCT03323905). Transducer modules ( per module) whose beams intersected with no-pass zones delineated within the field were identified, their output power levels were reduced by varying blocking percentage levels, and the resulting temperature field distributions were evaluated across multiple sonications near the hip and spine bones in each patient. Acoustic and thermal simulations took approximately ( ) and ( ) to run for a single near-spine (near-hip) target,respectively. For all simulated sonications, transducer module blocking improved compared to the no blocking case. In just over half of sonications, full module blocking maximized (increase of 82% 38% in 50% of hip targets and 49% 30% in 62% of spine targets vs. no blocking; mean±SD), at the cost of more diffuse focusing (focal heating volumes increased by 13%±13% for hip targets and 39%±27% for spine targets) and thus requiring elevated total (hip: 6%±17%, spine: 37%±17%) and peak module-wise (hip: 65%±36%, spine: 101%±56%) acoustic power levels to achieve equivalent focal heating as the no blocking control case. In the remaining sonications, partial module blocking provided further improvements in both (increased by 29%±25% in the hip and 15%±12% in the spine) and focal heating volume (decrease of 20%±10% in the hip and 34%±17% in the spine) relative to the full blocking case. The optimal blocking percentage value was dependent on the specific patient geometry and target location of interest. Although not all individual target locations saw the benefit, element-wise phase aberration corrections improved the average compared to the no correction case (increase of 52%±47% in the hip, 35%±24% in the spine) and impacted the optimal blocking percentage value. Transducer module blocking enabled ablative treatments to be carried out closer to both hip and spine without overheating or damaging the bone (no blocking: / , full blocking: / , optimal partial blocking: / for hip/spine). The proposed transducer apodization scheme shows promise for improving MRgFUS treatments of uterine fibroids, and may ultimately increase the effective treatment envelope of MRgFUS surgery in the body by enabling tissue ablation closer to bonystructures.

The Correction Method for Wavefront Aberration Caused by Spectrum-Splitting Filters in Multi-Modal Optical Imaging System

In current biomedical and environmental detection, multi-modal optical imaging technology is playing an increasingly important role. By utilizing information from dimensions such as spectra and polarization, it reflects the detailed characteristics and material properties of the targets. However, as detection system performance becomes more complex, issues such as aberrations introduced by multilayered lenses, signal attenuation, decreased polarization sensitivity, and latency can no longer be ignored. These factors directly affect the assessment of image details, influencing subsequent analyses. In this paper, we propose a method for designing and optimizing spectrum-splitting filters that considers the wavefront aberration and transmittance of the multi-modal optical imaging system. The method of optimizing coating phases to minimize scalar phase aberrations while maximizing system transmission leads to substantially improved imaging performance. Simulation and experimental results demonstrate that the method can improve the imaging performance. The proposed approach has potential applications in fields such as biomedical field, multi-spectral, remote sensing and microscopy.

Model-based wavefront measurement and compensation in atmospheric turbulence.

The model-based method can measure phase aberration without special wavefront detectors. However, the influence of non-uniform beam intensity distribution was not considered, leading to non-negligible system errors. Moreover, no experiments were employed to verify its capability and practicability in atmospheric turbulence. This paper proposes the aberrated pupil method, which can enhance the accuracy of phase recovery by introducing an aberrated pupil function into the traditional model-based method. The effectiveness of the model-based phase measurement and compensation method was experimentally investigated and verified in the application of free-space optical communication. Compared with the traditional model-based method, the aberrated pupil method can reduce the coupling power loss caused by turbulence from 2.59 to 0.87 dB, resulting in a reduction of 1.72 dB. With coherent communication experiments, the model-based phase recovery method significantly improved the communication power budget by about 13.5 dB. The proposed method can measure and compensate for the phase aberration to significantly improve the communication quality of free-space optical communication.

Simultaneous phase aberration compensation and denoising for quantitative phase imaging in digital holographic microscopy with deep learning

In digital holographic microscopy, the quantitative phase image suffers from phase aberrations and coherent noises. To solve these problems, two independent steps are applied sequentially in the reconstruction procedure to compensate for the phase aberrations and denoising. Here we demonstrate for the first time, to the best of our knowledge, that the reconstruction process can be simplified by replacing the two step methods with a deep learning-based algorithm. A convolutional neural network is trained simultaneously for phase aberration correction and denoising from an only wrapped phase map. In order to train the network, a database consists of massive wrapped phase maps as input, and noise-free sample phase maps as labels are constructed. The generated wrapped phase maps include a variety of phase aberrations and faithful coherent noises that are reconstructed from a practical apparatus. The trained network is applied to correct phase aberrations and denoise of both simulated and experimental data for the quantitative phase image. It exhibits excellent performance with output comparable to that reconstructed from the double exposure method for phase aberration correction followed with block-matching and 3D filtering for denoising, while outperforming other conventional two step methods.

A Framework for Iterative Phase Retrieval Technique Integration into Atmospheric Adaptive Optics—Part I: Wavefront Sensing in Strong Scintillations

The objective of this study, which is divided into two parts, is twofold: to address long-standing challenges in the sensing of atmospheric turbulence-induced wavefront aberrations under strong scintillation conditions via a comparative analysis of several basic scintillation-resistant wavefront sensing (SR-WFS) architectures and iterative phase retrieval (IPR) techniques (Part I, this paper), and to develop a framework for the potential integration of SR-WFS techniques into practical closed-loop non-astronomical atmospheric adaptive optics (AO) systems (Part II). In this paper, we consider basic SR-WFS mathematical models and phase retrieval algorithms, tradeoffs in sensor design and phase retrieval technique implementation, and methodologies for WFS parameter optimization and performance assessment. The analysis is based on wave-optics numerical simulations imitating realistic turbulence-induced phase aberrations and intensity scintillations, as well as optical field propagation inside the SR-WFSs. Several potential issues important for the practical implementation of SR-WFS and IPR techniques, such as the requirements for phase retrieval computational grid resolution, tolerance with respect to optical element misalignments, and the impact of camera noise and input light non-monochromaticity, are also considered. The results demonstrate that major wavefront sensing requirements desirable for AO operation under strong intensity scintillations can potentially be achieved by transitioning to novel SR-WFS architectures, based on iterative phase retrieval techniques.

Transcranial ultrafast ultrasound Doppler imaging: A phantom study

Ultrafast ultrasound Doppler imaging facilitates the assessment of cerebral hemodynamics with high spatio-temporal resolution. However, the significant acoustic impedance mismatch between the skull and soft tissue results in phase aberrations, which can compromise the quality of transcranial imaging and introduce biases in velocity and direction quantification of blood flow. This paper proposed an aberration correction method that combines deep learning-based skull sound speed modelling with ray theory to realize transcranial plane-wave imaging and ultrafast Doppler imaging. The method was validated through phantom experiments using a linear array with a center frequency of 6.25 MHz, 128 elements, and a pitch of 0.3 mm. The results demonstrated an improvement in the imaging quality of intracranial targets when using the proposed method. After aberration correction, the average locating deviation decreased from 1.40 mm to 0.27 mm in the axial direction, from 0.50 mm to 0.20 mm in the lateral direction, and the average full-width-at-half-maximum (FWHM) decreased from 1.37 mm to 0.97 mm for point scatterers. For circular inclusions, the average contrast-to-noise ratio (CNR) improved from 8.1 dB to 11.0 dB, and the average eccentricity decreased from 0.36 to 0.26. Furthermore, the proposed method was applied to transcranial ultrafast Doppler flow imaging. The results showed a significant improvement in accuracy and quality for blood Doppler flow imaging. The results in the absence of the skull were considered as the reference, and the average normalized root-mean-square errors of the axial velocity component on the five selected axial profiles were reduced from 17.67% to 8.02% after the correction.

Iterative outlier detection and refinement rule of compensation for phase aberrations in digital holographic microscopy.

We propose a robust and accurate compensation method for phase aberrations based on the iterative outlier detection and refinement (ODR) rule. This method does not require additional steps to select the known flat region manually or by image segmentation. Based on the proposed method, the phase aberration in regions of a specimen can be detected and refined iteratively. Then, the least squares fitting can be carried out to estimate the coefficients of Zernike polynomials and obtain the accurate phase aberration finally. Computer simulations and real experiments validate the feasibility and effectiveness, and the results show that the proposed method is robust to noise and has superior accuracy even when the specimen occupies half of the field of view.

Coherence-based phase aberration correction and beamforming for ring-array ultrasound imaging

Degradation of image quality caused by aberrations and off-axis interferences is common in medical ultrasound imaging. The ring-array echo imaging also suffers from these constraints, where phase aberrations caused by tissue heterogeneity create overlapping of interest target and worsen the interpretability of anatomical structures; while off-axis interferences reduce the spatial resolution and contrast of the reconstructed images. To address them, the signal coherence is available since they can be considered as incoherent interference and noise. The coherence factor (CF) is sensitive to phase aberrations and is thus adopted in this study. Moreover, the convolutional beamforming algorithm (COBA) specifically exploits the autocorrelation operation to improve the signal coherence and effectively reduce noise. Therefore, we first utilize the CF to detect the local coherence and its maximization criterion as a way to estimate the sound speed to correct for phase aberration. Then, CF is used as an adaptive weighting factor to suppress the noise, and based on this, we develop a short-lag COBA to further enhance the image quality, referred to as the CF_SLCOBA method. Simulations and experiments were performed to evaluate the performance of the proposed methods. Results show that, the improvements of the CF_SLCOBA method in resolution is about 26.8% compared with CF; while about 92.0% in contrast ratio (CR) compared with COBA, in the experiments. Meanwhile, the proposed method yields a good performance in phase aberration correction. This demonstrates that the proposed method may provide benefits for rebuilding high-quality images, which also reveals that it offers a certain potential for practical applications.

A novel transcranial MR Guided focused ultrasound method based on the ultrashort echo time skull acoustic model and phase retrieval techniques

Transcranial ultrasound stimulation (TUS) has been clinically applied as a neuromodulation tool. Particularly, the phase array ultrasound can be applied in TUS to non-invasively focus on the cortex or deep brain. However, the vital phase distortion of the ultrasound induced by the skull limits its clinical application. In the current study, we aimed to develop a hybrid method that combines the ultrashort echo time (UTE) magnetic resonance imaging (MRI) sequences with the prDeep technique to achieve focusing ventral intermediate thalamic nucleus (VIM). The time-reversal (TR) approach of the UTE numerical acoustic model of the skull combined with the prDeep algorithm was used to reduce the number of iterations. The skull acoustic model simulation therapy process was establish to valid this method’s prediction and focus performance, and the classical TR method were considered as the gold standard (GS). Our approach could restore 75% of the GS intensity in 25 iteration steps, with a superior the noise immunity. Our findings demonstrate that the phase aberration caused by the skull can be estimated using phase retrieval techniques to achieve a fast and accurate transcranial focus. The method has excellent adaptability and anti-noise capacity for satisfying complex and changeable scenarios.

Transcranial volumetric imaging using a conformal ultrasound patch.

Accurate and continuous monitoring of cerebral blood flow is valuable for clinical neurocritical care and fundamental neurovascular research. Transcranial Doppler (TCD) ultrasonography is a widely used non-invasive method for evaluating cerebral blood flow1, but the conventional rigid design severely limits the measurement accuracy of the complex three-dimensional (3D) vascular networks and the practicality for prolonged recording2. Here we report a conformal ultrasound patch for hands-free volumetric imaging and continuous monitoring of cerebral blood flow. The 2 MHz ultrasound waves reduce the attenuation and phase aberration caused by the skull, and the copper mesh shielding layer provides conformal contact to the skin while improving the signal-to-noise ratio by 5 dB. Ultrafast ultrasound imaging based on diverging waves can accurately render the circle of Willis in 3D and minimize human errors during examinations. Focused ultrasound waves allow the recording of blood flow spectra at selected locations continuously. The high accuracy of the conformal ultrasound patch was confirmed in comparison with a conventional TCD probe on 36 participants, showing a mean difference and standard deviation of difference as -1.51 ± 4.34 cm s-1, -0.84 ± 3.06 cm s-1 and -0.50 ± 2.55 cm s-1 for peak systolic velocity, mean flow velocity, and end diastolic velocity, respectively. The measurement success rate was 70.6%, compared with 75.3% for a conventional TCD probe. Furthermore, we demonstrate continuous blood flow spectra during different interventions and identify cascades of intracranial B waves during drowsiness within 4 h of recording.

Degraded image restoration of vortex beam array based on deep learning

The phase aberration and intensity fluctuation of the vortex beam array caused by atmospheric turbulence decrease the decoding accuracy of the optical communication system. This paper proposes an end-to-end turbulence-degraded image restoration method based on deep learning to solve the problem. The K-means clustering algorithm is employed to obtain the coordinate information of each beam in the array, and the distorted vortex beam array is segmented. The neural network constructed is used to restore the degraded image of a single vortex beam obtained by segmentation. Then the restored intensity image of the vortex beam array is obtained by combining the existing coordinate information. The simulation results show that the intensity correlation coefficients of the 3 × 3 rectangular distribution Laguerre–Gaussian beam arrays are increased to more than 0.99 after restoring from 1000 meters of transmission in both varied and unknown turbulence intensities, alongside differing CCD signal-to-noise ratios. This method does not require wavefront reconstruction, which further improves the restoration speed and saves computational resources, and has good generalization ability and robustness in quickly restoring the distorted light intensity of vortex beams. The results provide a theoretical basis for studying atmospheric turbulence influence mitigation techniques for vortex optical communication.

Aberration correction by polynomial approximation for synthetic aperture ultrasound imaging

AbstractBackgroundPreviously, there has been some work in the field of optical imaging on phase compensation by employing Legendre polynomials as an expansion of the phase function, and this seems to be an appealing unstudied area in the field of ultrasound imaging.PurposeThe paper is devoted to solving one of the problems of enhancing the authenticity of diagnostics data obtained in ultrasound visualization systems and presents a novel approach to aberration correction, which ensures a reliable eliminating of phase distortions. The novelty of the proposed approach consists in the use of the decomposition of the wave front by Legendre polynomials to approximate aberrations of the phase front of ultrasonic waves propagating through distorting layers.MethodsThe phase aberrations are corrected using a single sector probe that captures echoes in synthetic aperture mode. The proposed method approximates the changes in the wave front by means of the Legendre polynomials. The performance was investigated by placing a 13‐mm‐wide ultrasonic probe with 64 elements operating at 2 MHz on the surface of a commercial quality control phantom ATS Model 539 through a distorting layer. The following metrics were measured: peak value, root mean square (RMS), full width at half maximum (FWHM), contrast‐to‐noise ratio (CNR). During the experiments, three different aberrators were used interchangeably as distorting layers, two of which were made of photopolymer resin with RMS values of 39 and 97 ns and a speed of sound close to that of a human temporal bone tissue, and the third was an ex vivo human temporal bone with the RMS value of 44 ns. To test the correction, three regions inside the phantom were examined. Two‐sided nonparametric Wilcoxon rank‐sum test was used for assessing the statistical significance. The significance level for this study was set at α ≤ 0.05. To counteract the problem of multiple comparisons, the p‐values were adjusted by the Holm–Bonferroni correction method.ResultsThe statistically significant results have demonstrated the possibility of increasing peak intensity value up to 3.08 times, reducing the RMS width and FWHM of the intensity angular distribution down to 60% and 82%, respectively, and increasing CNR up to 2.12 times by using the proposed phase distortion correction method, compared with the case without correction. The results show that the method can be used as an effective aberration correction technique for all tested regions inside the phantom and distorting layers. Its usage allowed correcting up to 97% of the aberrations caused by the ex vivo temporal bone model.ConclusionThe results show that the usage of the proposed method for aberration correction can successfully increase the intensity and reduce the angular width of the ultrasound wave scattered by the point targets inside the phantoms. The method works with different distorting layers and is capable of correcting phase aberrations in multiple sections of the sonogram. The limitation of the proposed method consists in the fact that it requires the use of aperture synthesis and access to raw radiofrequency data, which restricts its application in common scanners.

Fast, Precise, High Contrast Laser Writing for Photonic Chips with Phase Aberrations

AbstractIntegrated photonic chips have significant potential in telecommunications, classic computing, quantum systems, and topological photonics. Direct laser writing offers unique capability for creating three‐dimensional photonic devices in an optical glass chip with quick prototyping. However, it is a challenge for existing laser writing schemes to create index‐modified structures in glass that precisely match the laser focal shape while also achieving high refractive index contrasts and high scanning speeds. Here, we introduce a refractive index modification scheme that combines the advantages of non‐thermal and thermal regime fabrication methods. We also propose a waveguide formation model that is verified through a thorough study on the effects of phase aberrations. The presented new photonic chip fabrication scheme uses a novel focal intensity distribution, where pulse energy is relocated to the bottom of a laser focus by manipulating primary and higher order spherical aberrations. The technique can produce index modifications with high scanning speed (can be 20 mm/s or higher), high index contrast (ranging from 0.009 to 0.021), and high precision to fabricate with arbitrary cross‐sections. This method has potential to expand the capabilities of photonic chips in applications that require small‐scale, high precision, or high contrast refractive index control.

Making the unmodulated Pyramid wavefront sensor smart

Almost all current and future high-contrast imaging instruments will use a Pyramid wavefront sensor (PWFS) as a primary or secondary wavefront sensor. The main issue with the PWFS is its nonlinear response to large phase aberrations, especially under strong atmospheric turbulence. Most instruments try to increase its linearity range by using dynamic modulation, but this leads to decreased sensitivity, most prominently for low-order modes, and makes it blind to petal-piston modes. In the push toward high-contrast imaging of fainter stars and deeper contrasts, there is a strong interest in using the PWFS in its unmodulated form. Here, we present closed-loop lab results of a nonlinear reconstructor for the unmodulated PWFS of the Magellan Adaptive Optics extreme (MagAO-X) system based on convolutional neural networks (CNNs). We show that our nonlinear reconstructor has a dynamic range of >600 nm root-mean-square (RMS), significantly outperforming the linear reconstructor that only has a 50 nm RMS dynamic range. The reconstructor behaves well in closed loop and can obtain >80% Strehl at 875 nm under a large variety of conditions and reaches higher Strehl ratios than the linear reconstructor under all simulated conditions. The CNN reconstructor also achieves the theoretical sensitivity limit of a PWFS, showing that it does not lose its sensitivity in exchange for dynamic range. The current CNN’s computational time is 690 µs, which enables loop speeds of >1 kHz. On-sky tests are foreseen soon and will be important for pushing future high-contrast imaging instruments toward their limits.

Improved beamforming by accounting for multiple domain shifts and sources of degradation

A number of mechanisms are known to degrade ultrasound images. To address these issues, many beamforming strategies have been proposed including a slew of recent approaches centered around deep learning techniques. Most deep learning methods target a single type of image degradation. Here, we will simultaneously address multiple sources of image degradation within the same beamformer. In particular, we will address phase aberration, reverberation and off-axis scatting within a single network. Within this context of training networks to address multiple sources of image degradation, we will highlight the problem of domain shift in ultrasound beamforming deep networks. Domain shift is the mismatch in the statistics of the training data and the data encountered in the intended use case that reduces the performance of the trained deep network. We will show that ultrasound beamformers struggle with domain shift on both the input side and the output side of the networks, and we will present strategies for addressing both domain shifts including the shift on the output side where there is no ground truth data to learn from.

The Advantages and Disadvantages of Using Structured High-Order but Single Laguerre–Gauss LGp0 Laser Beams

Most laser applications are based on the focusing of a Gaussian laser beam (GLB). When the latter is subject to a phase aberration such as the optical Kerr effect (OKE) or spherical aberration (SA), it is recognised that the focusing performance of the GLB is degraded. In this paper, it is demonstrated that high-order radial Laguerre–Gauss LGp0 beams are more resilient than the GLB when subject to the OKE or SA. This opens up opportunities to replace with advantages the usual GLB with a high-order LGp0 beam for some applications.

Image based automatic wind velocity profiler for adaptive optics

Abstract Adaptive optics (AO) systems correct optical phase aberrations of the incoming light generated by the atmosphere. To do so simultaneous estimators of the atmospheric turbulence parameters are required. For the family of wide-field AO systems (WFAO), this information must be stratified in altitude. Among these vectorized estimations, wind profiling in altitude is needed for the reduction of temporal errors in AO loops or for the estimation of turbulence coherence time. This paper proposes a turbulence wind profiler called image processing based peak tracking algorithm (IPTA). IPTA is an image-processing based approach that automatically and reliably estimates wind velocity for several turbulent layers along the line of sight. The estimation of each wind layer is achieved by tracking peaks produced in cross-correlation maps from pairs of wavefront sensors (WFSs) slopes using the technique known as SLODAR (slope detection and ranging). Results for simulated and on-sky WFS datasets demonstrate that IPTA outperforms one of the state of the art wind profiler methods (the profiler covariance parametrization of wind velocity (CAW)) in terms of accuracy and speed. Results also show that, in terms of execution time, our method scales better when the number of WFS lenslets is increased. Being an open source and reliable tool, we believe IPTA can be a useful wind profiler for the adaptive optics community.

Phase Aberration Correction for In Vivo Ultrasound Localization Microscopy Using a Spatiotemporal Complex-Valued Neural Network.

Ultrasound Localization Microscopy (ULM) can map microvessels at a resolution of a few micrometers ( [Formula: see text]). Transcranial ULM remains challenging in presence of aberrations caused by the skull, which lead to localization errors. Herein, we propose a deep learning approach based on recently introduced complex-valued convolutional neural networks (CV-CNNs) to retrieve the aberration function, which can then be used to form enhanced images using standard delay-and-sum beamforming. CV-CNNs were selected as they can apply time delays through multiplication with in-phase quadrature input data. Predicting the aberration function rather than corrected images also confers enhanced explainability to the network. In addition, 3D spatiotemporal convolutions were used for the network to leverage entire microbubble tracks. For training and validation, we used an anatomically and hemodynamically realistic mouse brain microvascular network model to simulate the flow of microbubbles in presence of aberration. The proposed CV-CNN performance was compared to the coherence-based method by using microbubble tracks. We then confirmed the capability of the proposed network to generalize to transcranial in vivo data in the mouse brain (n=3). Vascular reconstructions using a locally predicted aberration function included additional and sharper vessels. The CV-CNN was more robust than the coherence-based method and could perform aberration correction in a 6-month-old mouse. After correction, we measured a resolution of [Formula: see text] for younger mice, representing an improvement of 25.8%, while the resolution was improved by 13.9% for the 6-month-old mouse. This work leads to different applications for complex-valued convolutions in biomedical imaging and strategies to perform transcranial ULM.

Mitigating Aberration-Induced Noise: A Deep Learning-Based Aberration-to-Aberration Approach.

One of the primary sources of suboptimal image quality in ultrasound imaging is phase aberration. It is caused by spatial changes in sound speed over a heterogeneous medium, which disturbs the transmitted waves and prevents coherent summation of echo signals. Obtaining non-aberrated ground truths in real-world scenarios can be extremely challenging, if not impossible. This challenge hinders the performance of deep learning-based techniques due to the domain shift between simulated and experimental data. Here, for the first time, we propose a deep learning-based method that does not require ground truth to correct the phase aberration problem and, as such, can be directly trained on real data. We train a network wherein both the input and target output are randomly aberrated radio frequency (RF) data. Moreover, we demonstrate that a conventional loss function such as mean square error is inadequate for training such a network to achieve optimal performance. Instead, we propose an adaptive mixed loss function that employs both B-mode and RF data, resulting in more efficient convergence and enhanced performance. Finally, we publicly release our dataset, comprising over 180,000 aberrated single plane-wave images (RF data), wherein phase aberrations are modeled as near-field phase screens. Although not utilized in the proposed method, each aberrated image is paired with its corresponding aberration profile and the non-aberrated version, aiming to mitigate the data scarcity problem in developing deep learning-based techniques for phase aberration correction. Source code and trained model are also available along with the dataset at http://code.sonography.ai/main-aaa.