Regularized Dual Averaging Image Reconstruction for Full-Wave Ultrasound Computed Tomography

Unveiling the potential of ultrasound in brain imaging: Innovations, challenges, and prospects

Within medical imaging, ultrasound serves as a crucial tool, particularly in the realms of brain imaging and disease diagnosis. It offers superior safety, speed, and wider applicability compared to Magnetic Resonance Imaging (MRI) and X-ray Computed Tomography (CT). Nonetheless, conventional transcranial ultrasound applications in adult brain imaging face challenges stemming from the significant acoustic impedance contrast between the skull bone and soft tissues. Recent strides in ultrasound technology encompass a spectrum of advancements spanning tissue structural imaging, blood flow imaging, functional imaging, and image enhancement techniques. Structural imaging methods include traditional transcranial ultrasound techniques and ultrasound elastography. Transcranial ultrasound assesses the structure and function of the skull and brain, while ultrasound elastography evaluates the elasticity of brain tissue. Blood flow imaging includes traditional transcranial Doppler (TCD), ultrafast Doppler (UfD), contrast-enhanced ultrasound (CEUS), and ultrasound localization microscopy (ULM), which can be used to evaluate the velocity, direction, and perfusion of cerebral blood flow. Functional ultrasound imaging (fUS) detects changes in cerebral blood flow to create images of brain activity. Image enhancement techniques include full waveform inversion (FWI) and phase aberration correction techniques, focusing on more accurate localization and analysis of brain structures, achieving more precise and reliable brain imaging results. These methods have been extensively studied in clinical animal models, neonates, and adults, showing significant potential in brain tissue structural imaging, cerebral hemodynamics monitoring, and brain disease diagnosis. They represent current hotspots and focal points of ultrasound medical research. This review provides a comprehensive summary of recent developments in brain imaging technologies and methods, discussing their advantages, limitations, and future trends, offering insights into their prospects.

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography

Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for large-scale problems. Deep learning-based image-to-image learned reconstruction (IILR) methods can offer computationally efficient alternatives. This study investigates the impact of the chosen input modalities on IILR methods for high-resolution SOS reconstruction in USCT. The selected modalities are traveltime tomography (TT) and reflection tomography (RT), which produce a low-resolution SOS map and a reflectivity map, respectively. These modalities have been chosen for their lower computational cost relative to FWI and their capacity to provide complementary information: TT offers a direct SOS measure, while RT reveals tissue boundary information. Systematic analyses were facilitated by employing a virtual USCT imaging system with anatomically realistic numerical breast phantoms (NBPs). Within this testbed, a supervised convolutional neural network (CNN) was trained to map dual-channel (TT and RT images) to a high-resolution SOS map. Single-input CNNs were trained separately using inputs from each modality alone (TT or RT) for comparison. The accuracy of the methods was systematically assessed using normalized root-mean-squared error (NRMSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). For tumor detection performance, receiver operating characteristic (ROC) analysis was employed. The dual-channel IILR method was also tested on clinical human breast data. Ensemble average of the NRMSE, SSIM, and PSNR evaluated on this clinical dataset was 0.2355, 0.8845, and 28.33 dB, respectively.

LUFT: A low-frequency ultrasound tomography system designed for lung imaging

Pulmonary imaging with ultrasound in the conventional MHz range suffers from significant artifacts, as the high frequency acoustic waves primarily reflect off of the lung pleura with little to no penetration through the lung tissue. Furthermore, B-mode ultrasound images are difficult to interpret and require a skilled technician to obtain. Motivated by the finding that acoustic frequencies in the kHz penetrate lung tissue, a low-frequency tomographic ultrasound system is presented. A Verasonics Vantage 64 low-frequency ultrasound system is programmed to work with a novel low-frequency Tonpilz transducers set arranged in a circular array on a belt. After signal processing, a time-of-flight (TOF) tomographic reconstruction algorithm with refraction correction is applied to estimate the sound speed in the plane of the transducer array. Data were collected on experimental phantoms made of ballistic gel containing targets of varying sound speeds, and reconstructions TOF were computed. Results demonstrate that the low-frequency ultrasound tomography system effectively detects targets in an experimental phantom with the ability to resolve multiple targets and distinguish between targets of high and low sound speed compared to the background medium.

A Forward Model Incorporating Elevation-Focused Transducer Properties for 3-D Full-Waveform Inversion in Ultrasound Computed Tomography

Ultrasound computed tomography (USCT) is an emerging medical imaging modality that holds great promise for improving human health. Full-waveform inversion (FWI)-based image reconstruction methods account for the relevant wave physics to produce high spatial resolution images of the acoustic properties of the breast tissues. A practical USCT design employs a circular ring-array comprised of elevation-focused ultrasonic transducers, and volumetric imaging is achieved by translating the ring-array orthogonally to the imaging plane. In commonly deployed slice-by-slice (SBS) reconstruction approaches, the 3-D volume is reconstructed by stacking together 2-D images reconstructed for each position of the ring-array. A limitation of the SBS reconstruction approach is that it does not account for 3-D wave propagation physics and the focusing properties of the transducers, which can result in significant image artifacts and inaccuracies. To perform 3-D image reconstruction when elevation-focused transducers are employed, a numerical description of the focusing properties of the transducers should be included in the forward model. To address this, a 3-D computational model of an elevation-focused transducer is developed to enable 3-D FWI-based reconstruction methods to be deployed in ring-array-based USCT. The focusing is achieved by applying a spatially varying temporal delay to the ultrasound pulse (emitter mode) and recorded signal (receiver mode). The proposed numerical transducer model is quantitatively validated and employed in computer simulation studies that demonstrate its use in image reconstruction for ring-array USCT.

Full-Waveform Inversion for Breast Ultrasound Tomography Using Line-Shape Modeled Elements

OBJECTIVE

The objective of the work described here was to incorporate the spatial shapes of the transducer elements into the framework of the full-waveform inversion.

METHODS

An element is treated as its cross-section in the 2-D imaging plane, that is, a line segment. The elements are not simply modeled as a set of point sources on their surface to avoid staircasing artifacts. By use of the Fourier collocation method, an element is spatially represented as the discrete convolution between its spatial distribution and a band-limited delta function. The excitation pulses on the emitters and recorded signals on the receivers are then weighted based on the discrete convolution results. Digital and physical experiments are implemented to validate the method.

DISCUSSION

It is meaningful to model the shapes of the elements if their spatial sizes are similar to or larger than the acoustic wavelengths. It should, however, be noted that because this article focuses on 2-D imaging, the inter-plane effects are not considered.

CONCLUSION

The approach helps reduce the root mean square errors and increase the structural similarity of the reconstructed images. It also helps to improve the stability of convergence and to accelerate the convergence speed.

Full Wave Inversion and Inverse Scattering in Ultrasound Tomography/Volography

Ultrasound breast tomography has been around for more than 40 years. Early approaches to reconstruction focused on simple algebraic reconstructions and bent ray techniques. These approaches were not able to provide high-quality and high spatial-resolution images. The advent of inverse scattering approaches resulted in a shift in image reconstruction approaches for breast tomography and a subsequent improvement in image quality. Full wave inverse solvers were developed to improve the reconstruction times without sacrificing image quality. The development of GPUs has markedly decreased the time for reconstruction using inverse scatting approaches. The development of fully 3D image solvers and hardware capable of capturing out of plane scattering have resulted in further improvement in breast tomography. This chapter discusses the state-of-the-art in ultrasound breast tomography, its history, the theory behind inverse scattering, approximations that are included to improve convergence, 3D image reconstruction, and hardware implementation of the constructions.

Deep-Learning-Based Ultrasound Sound-Speed Tomography Reconstruction with Tikhonov Pseudo-Inverse Priori

Ultrasound sound-speed tomography (USST) is a promising technology for breast imaging and breast cancer detection. Its reconstruction is a complex non-linear mapping from the projection data to the sound-speed image (SSI). The traditional reconstruction methods include mainly the ray-based methods and the waveform-based methods. The ray-based methods with linear approximation have low computational cost but low reconstruction quality; the full wave-based methods with the complex non-linear model have high quality but high cost. To achieve both high quality and low cost, we introduced traditional linear approximation as prior knowledge into a deep neural network and treated the complex non-linear mapping of USST reconstruction as a combination of linear mapping and non-linear mapping. In the proposed method, the linear mapping was seamlessly implemented with a fully connected layer and initialized using the Tikhonov pseudo-inverse matrix. The non-linear mapping was implemented using a U-shape Net (U-Net). Furthermore, we proposed the Tikhonov U-shape net (TU-Net), in which the linear mapping was done before the non-linear mapping, and the U-shape Tikhonov net (UT-Net), in which the non-linear mapping was done before the linear mapping. Moreover, we conducted simulations and experiments for evaluation. In the numerical simulation, the root-mean-squared error was 6.49 and 4.29 m/s for the UT-Net and TU-Net, the peak signal-to-noise ratio was 49.01 and 52.90 dB, the structural similarity was 0.9436 and 0.9761 and the reconstruction time was 10.8 and 11.3 ms, respectively. In this study, the SSIs obtained with the proposed methods exhibited high sound-speed accuracy. Both the UT-Net and the TU-Net achieved high quality and low computational cost.

DeepUCT: Complex cascaded deep learning network for improved ultrasound tomography

Ultrasound computed tomography is an inexpensive and radiation-free medical imaging technique used to quantify the tissue acoustic properties for advanced clinical diagnosis. Image reconstruction in ultrasound tomography is often modeled as an optimization scheme solved by iterative methods like full-waveform inversion. These iterative methods are computationally expensive, while the optimization problem is ill-posed and nonlinear. To address this problem, we propose to use deep learning to overcome the computational burden and ill-posedness, and achieve near real-time image reconstruction in ultrasound tomography. We aim to directly learn the mapping from the recorded time-series sensor data to a spatial image of acoustical properties. To accomplish this, we develop a deep learning model using two cascaded convolutional neural networks with an encoder–decoder architecture. We achieve a good representation by first extracting the intermediate mapping-knowledge and later utilizing this knowledge to reconstruct the image. This approach is evaluated on synthetic phantoms where simulated ultrasound data are acquired from a ring of transducers surrounding the region of interest. The measurement data is acquired by forward modeling the wave equation using the k-wave toolbox. Our simulation results demonstrate that our proposed deep-learning method is robust to noise and significantly outperforms the state-of-the-art traditional iterative method both quantitatively and qualitatively. Furthermore, our model takes substantially less computational time than the conventional full-wave inversion method.

Quantitative Sound Speed Imaging of Cortical Bone and Soft Tissue: Results From Observational Data Sets

This work presents the first quantitative ultrasonic sound speed images of ex vivo limb cross-sections containing both soft tissue and bone using Full Waveform Inversion (FWI) with level set (LS) and travel time regularization. The estimated bulk sound speed of bone and soft tissue are within 10% and 1%, respectively, of ground truth estimates. The sound speed imagery shows muscle, connective tissue and bone features. Typically, ultrasound tomography (UST) using FWI is applied to imaging breast tissue properties (e.g. sound speed and density) that correlate with cancer. With further development, UST systems have the potential to deliver volumetric operator independent tissue property images of limbs with non-ionizing and portable hardware platforms. This work addresses the algorithmic challenges of imaging the sound speed of bone and soft tissue by combining FWI with LS regularization and travel time methods to recover soft tissue and bone sound speed with improved accuracy and reduced soft tissue artifacts when compared to conventional FWI. The value of leveraging LS and travel time methods is realized by evidence of improved bone geometry estimates as well as promising convergence properties and reduced risk of final model errors due to un-modeled shear wave propagation. Ex vivo bulk measurements of sound speed and MRI cross-sections validates the final inversion results.

3-D Stochastic Numerical Breast Phantoms for Enabling Virtual Imaging Trials of Ultrasound Computed Tomography

Ultrasound computed tomography (USCT) is an emerging imaging modality for breast imaging that can produce quantitative images that depict the acoustic properties of tissues. Computer-simulation studies, also known as virtual imaging trials, provide researchers with an economical and convenient route to systematically explore imaging system designs and image reconstruction methods. When simulating an imaging technology intended for clinical use, it is essential to employ realistic numerical phantoms that can facilitate the objective, or task-based, assessment of image quality (IQ). Moreover, when computing objective IQ measures, an ensemble of such phantoms should be employed, which displays the variability in anatomy and object properties that are representative of the to-be-imaged patient cohort. Such stochastic phantoms for clinically relevant applications of USCT are currently lacking. In this work, a methodology for producing realistic 3-D numerical breast phantoms for enabling clinically relevant computer-simulation studies of USCT breast imaging is presented. By extending and adapting an existing stochastic 3-D breast phantom for use with USCT, methods for creating ensembles of numerical acoustic breast phantoms are established. These breast phantoms will possess clinically relevant variations in breast size, composition, acoustic properties, tumor locations, and tissue textures. To demonstrate the use of the phantoms in virtual USCT studies, two brief case studies are presented, which addresses the development and assessment of image reconstruction procedures. Examples of breast phantoms produced by use of the proposed methods and a collection of 52 sets of simulated USCT measurement data have been made open source for use in image reconstruction development.

Impact of image averaging on vessel detection using optical coherence tomography angiography in eyes with macular oedema and in healthy eyes

Purpose

To assess the repeatability of multiple automatic vessel density (VD) measurements and the effect of image averaging on vessel detection by optical coherence tomography angiography (OCTA).

Methods

An observational study was conducted in a series of healthy volunteers and patients with macular oedema. Five sequential OCTA images were acquired for each eye using the OptoVue HD device. The effect of the averaging of the 5 acquisitions on vessel detection was analysed quantitatively using a pixel-by-pixel automated analysis. In addition, two independent retina experts qualitatively assessed the change in vessel detection in averaged images segmented in 9 boxes and compared to the first non-averaged image.

Results

The automatic VD measurement in OCTA images showed a good repeatability with an overall mean intra-class correlation coefficient (ICC) of 0.924. The mean ICC was higher in healthy eyes compared to eyes with macular oedema (0.877 versus 0.960; p < 0.001) and in the superficial vascular plexus versus the deep vascular complex (0.967 versus 0.888; p = 0.001). The quantitative analysis of the effect of the averaging showed that averaged images had a mean gain of 790.4 pixels/box, located around or completing interruptions in the vessel walls, and a mean loss of 727.2 pixels/box. The qualitative analysis of the averaged images showed that 99.6% of boxes in the averaged images had a gain in vessel detection (i.e., vessels detected in the averaged image but not in the non-averaged image). The loss of pixels was due to a reduction in background noise and motion artifacts in all cases and no case of loss of vessel detection was observed.

Conclusion

The automatic VD measurement using the OptoVue HD device showed a good repeatability in 5 acquisitions in a row setting. Averaging images increased vessel detection, and in about a third of boxes, decreased the background noise, both in healthy eyes and, in a greater proportion, in eyes with macular oedema.

Zone-Shrinking Fresnel Zone Travel-Time Tomography for Sound Speed Reconstruction in Breast USCT

Many studies have been carried out on ultrasound computed tomography (USCT) for its potential application in breast imaging. The sound speed (SS) image modality in USCT can help doctors diagnose the breast cancer, as the tumor usually has a higher sound speed than normal tissues. Travel time is commonly used to reconstruct SS image. Raypath travel-time tomography (RTT) assumes that the sound wave travels through a raypath. RTT is computationally efficient but with low contrast to noise ratio (CNR). Fresnel zone travel-time tomography (FZTT) is based on the assumption that the sound wave travels through an area called the Fresnel zone. FZTT can provide SS image with high CNR but low accuracy due to the wide Fresnel zone. Here, we propose a zone-shrinking Fresnel zone travel-time tomography (ZSFZTT), where a weighting factor is adopted to shrink the Fresnel zone during the inversion process. Numerical phantom and in vivo breast experiments were performed with ZSFZTT, FZTT, and RTT. In the numerical experiment, the reconstruction biases of size by ZSFZTT, FZTT, and RTT were 0.2%~8.3%, 2.3%~31.7%, and 1.8%~25%; the reconstruction biases of relative SS value by ZSFZTT, FZTT, and RTT were 24.7%~42%, 53%~60.8%, and 30.3%~47.8%; and the CNR by ZSFZTT, FZTT, and RTT were 67.7~96.6, 68.5~98, and 1.7~2.7. In the in vivo breast experiment, ZSFZTT provided the highest CNR of 8.6 compared to 8.1 by FZTT and 1.9 by RTT. ZSFZTT improved the reconstruction accuracy of size and the relative reconstruction accuracy of SS value compared to FZTT and RTT while maintaining a high CNR similar to that of FZTT.

Source encoding for viscoacoustic ultrasound computed tomography

Ultrasound computed tomography (USCT) is a noninvasive imaging modality that has shown its clinical relevance for breast cancer diagnostics. As opposed to traveltime inversions, waveform-based inversions can exploit the full content of ultrasound data, thereby providing increased resolution. However, this is only feasible when modeling the full physics of wave propagation, accounting for 3D effects such as refraction and diffraction, and this comes at a significant computational cost. To mitigate this cost, a crosstalk-free source encoding method for explicit time-domain solvers is proposed. The gradient computation is performed with only two numerical "super" wave simulations, independent of the number of sources and receivers. Absence of crosstalk is achieved by considering orthogonal frequencies attributed to each source. By considering "double-difference" measurements, no a priori knowledge of the source time function is required. With this method, full-physics based 3D waveform inversions can be performed within minutes using reasonable computational resources, fitting clinical requirements.

Optimal experimental design for joint reflection-transmission ultrasound breast imaging: From ray- to wave-based methods

Ultrasound computed tomography (USCT) is an emerging modality to image the acoustic properties of the breast tissue for cancer diagnosis. With the need of improving the diagnostic accuracy of USCT, while maintaining the cost low, recent research is mainly focused on improving (1) the reconstruction methods and (2) the acquisition systems. D-optimal sequential experimental design (D-SOED) offers a method to integrate these aspects into a common systematic framework. The transducer configuration is optimized to minimize the uncertainties in the estimated model parameters, and to reduce the time to solution by identifying redundancies in the data. This work presents a formulation to jointly optimize the experiment for transmission and reflection data and, in particular, to estimate the speed of sound and reflectivity of the tissue using either ray-based or wave-based imaging methods. Uncertainties in the parameters can be quantified by extracting properties of the posterior covariance operator, which is analytically computed by linearizing the forward problem with respect to the prior knowledge about parameters. D-SOED is first introduced by an illustrative toy example, and then applied to real data. This shows that the time to solution can be substantially reduced, without altering the final image, by selecting the most informative measurements.

Therapeutic Systems and Technologies: State-of-the-Art Applications, Opportunities, and Challenges

In this review, we present current state-of-the-art developments and challenges in the areas of thermal therapy, ultrasound tomography, image-guided therapies, ocular drug delivery, and robotic devices in neurorehabilitation. Additionally, intellectual property and regulatory aspects pertaining to therapeutic systems and technologies are addressed.

Plane wave imaging combined with eigenspace-based minimum variance beamforming using a ring array in ultrasound computed tomography

BACKGROUND

Ultrasound computed tomography (USCT) is usually realized with a ring array. It can provide better imaging performance and more tissue information by emitting and receiving the ultrasound signal in different directions simultaneously. However, USCT imaging is usually applied with the synthetic aperture (SA) emission method, which leads to a long scanning time with a large number of elements on the ring array. The echo image can provide the structural information, and has a higher resolution than maps of other parameters in USCT. Hence, we proposed plane wave (PW) imaging for ring array to acquire the echo wave and reduce the scanning time considerably.

RESULTS

In this paper, an emitting and receiving process was proposed to realize plane wave imaging with a ring array. With the proposed scanning method, the number of emission events can be reduced greatly. A beamforming method based on the eigenspace-based minimum variance (ESBMV) was also combined with the scanning method. With ESBMV beamformer, the resolution and contrast ratio of reconstruction result can be maintained or even improved under a fewer-emissions condition. We validated the method using both computer simulations with Field II and phantom experiments with a ring array of 512 elements. The Verasonics^® system was used to transmit and receive the ultrasound signal in the phantom experiments.

CONCLUSIONS

According to the results of the experiments, the imaging results will have a better contrast ratio with a higher emitting energy. Additionally, the scanning time with the proposed method can be only one-tenth of that with the SA emission method, while the echo imaging performance still remains at a similar level or even better.

A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography

One of the major concerns in photoacoustic computed tomography (PACT) is obtaining a high-quality image using the minimum number of ultrasound transducers/view angles. This issue is of importance when a cost-effective PACT system is needed. On the other hand, analytical reconstruction algorithms such as back projection (BP) and time reversal, when a limited number of view angles is used, cause artifacts in the reconstructed image. Iterative algorithms provide a higher image quality, compared to BP, due to a model used for image reconstruction. The performance of the model can be further improved using the sparsity concept. In this paper, we propose using a novel sparse dictionary to capture important features of the photoacoustic signal and eliminate the artifacts while few transducers is used. Our dictionary is an optimum combination of Wavelet Transform (WT), Discrete Cosine Transform (DCT), and Total Variation (TV). We utilize two quality assessment metrics including peak signal-to-noise ratio and edge preservation index to quantitatively evaluate the reconstructed images. The results show that the proposed method can generate high-quality images having fewer artifacts and preserved edges, when fewer view angles are used for reconstruction in PACT.

Local speed of sound estimation in tissue using pulse-echo ultrasound: Model-based approach

A model and method to accurately estimate the local speed of sound in tissue from pulse-echo ultrasound data is presented. The model relates the local speeds of sound along a wave propagation path to the average speed of sound over the path, and allows one to avoid bias in the sound-speed estimates that can result from overlying layers of subcutaneous fat and muscle tissue. Herein, the average speed of sound using the approach by Anderson and Trahey is measured, and then the authors solve the proposed model for the local sound-speed via gradient descent. The sound-speed estimator was tested in a series of simulation and ex vivo phantom experiments using two-layer media as a simple model of abdominal tissue. The bias of the local sound-speed estimates from the bottom layers is less than 6.2 m/s, while the bias of the matched Anderson's estimates is as high as 66 m/s. The local speed-of-sound estimates have higher standard deviation than the Anderson's estimates. When the mean local estimate is computed over a 5-by-5 mm region of interest, its standard deviation is reduced to less than 7 m/s.

Joint reconstruction of the initial pressure and speed of sound distributions from combined photoacoustic and ultrasound tomography measurements

The initial pressure and speed of sound (SOS) distributions cannot both be stably recovered from photoacoustic computed tomography (PACT) measurements alone. Adjunct ultrasound computed tomography (USCT) measurements can be employed to estimate the SOS distribution. Under the conventional image reconstruction approach for combined PACT/USCT systems, the SOS is estimated from the USCT measurements alone and the initial pressure is estimated from the PACT measurements by use of the previously estimated SOS. This approach ignores the acoustic information in the PACT measurements and may require many USCT measurements to accurately reconstruct the SOS. In this work, a joint reconstruction method where the SOS and initial pressure distributions are simultaneously estimated from combined PACT/USCT measurements is proposed. This approach allows accurate estimation of both the initial pressure distribution and the SOS distribution while requiring few USCT measurements.

Efficient Segmentation of a Breast in B-Mode Ultrasound Tomography Using Three-Dimensional GrabCut (GC3D)

As an emerging modality for whole breast imaging, ultrasound tomography (UST), has been adopted for diagnostic purposes. Efficient segmentation of an entire breast in UST images plays an important role in quantitative tissue analysis and cancer diagnosis, while major existing methods suffer from considerable time consumption and intensive user interaction. This paper explores three-dimensional GrabCut (GC3D) for breast isolation in thirty reflection (B-mode) UST volumetric images. The algorithm can be conveniently initialized by localizing points to form a polygon, which covers the potential breast region. Moreover, two other variations of GrabCut and an active contour method were compared. Algorithm performance was evaluated from volume overlap ratios ( T O , target overlap; M O , mean overlap; F P , false positive; F N , false negative) and time consumption. Experimental results indicate that GC3D considerably reduced the work load and achieved good performance ( T O = 0.84; M O = 0.91; F P = 0.006; F N = 0.16) within an average of 1.2 min per volume. Furthermore, GC3D is not only user friendly, but also robust to various inputs, suggesting its great potential to facilitate clinical applications during whole-breast UST imaging. In the near future, the implemented GC3D can be easily automated to tackle B-mode UST volumetric images acquired from the updated imaging system.