Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging

Spatial and channel attention-based conditional Wasserstein GAN for direct and rapid image reconstruction in ultrasound computed tomography

Ultrasound computed tomography (USCT) is an emerging technology that offers a noninvasive and radiation-free imaging approach with high sensitivity, making it promising for the early detection and diagnosis of breast cancer. The speed-of-sound (SOS) parameter plays a crucial role in distinguishing between benign masses and breast cancer. However, traditional SOS reconstruction methods face challenges in achieving a balance between resolution and computational efficiency, which hinders their clinical applications due to high computational complexity and long reconstruction times. In this paper, we propose a novel and efficient approach for direct SOS image reconstruction based on an improved conditional generative adversarial network. The generator directly reconstructs SOS images from time-of-flight information, eliminating the need for intermediate steps. Residual spatial-channel attention blocks are integrated into the generator to adaptively determine the relevance of arrival time from the transducer pair corresponding to each pixel in the SOS image. An ablation study verified the effectiveness of this module. Qualitative and quantitative evaluation results on breast phantom datasets demonstrate that this method is capable of rapidly reconstructing high-quality SOS images, achieving better generation results and image quality. Therefore, we believe that the proposed algorithm represents a new direction in the research area of USCT SOS reconstruction.

Quantitative photoacoustic tomography: modeling and inverse problems

Significance

Quantitative photoacoustic tomography (QPAT) exploits the photoacoustic effect with the aim of estimating images of clinically relevant quantities related to the tissue's optical absorption. The technique has two aspects: an acoustic part, where the initial acoustic pressure distribution is estimated from measured photoacoustic time-series, and an optical part, where the distributions of the optical parameters are estimated from the initial pressure.

Aim

Our study is focused on the optical part. In particular, computational modeling of light propagation (forward problem) and numerical solution methodologies of the image reconstruction (inverse problem) are discussed.

Approach

The commonly used mathematical models of how light and sound propagate in biological tissue are reviewed. A short overview of how the acoustic inverse problem is usually treated is given. The optical inverse problem and methods for its solution are reviewed. In addition, some limitations of real-life measurements and their effect on the inverse problems are discussed.

Results

An overview of QPAT with a focus on the optical part was given. Computational modeling and inverse problems of QPAT were addressed, and some key challenges were discussed. Furthermore, the developments for tackling these problems were reviewed. Although modeling of light transport is well-understood and there is a well-developed framework of inverse mathematics for approaching the inverse problem of QPAT, there are still challenges in taking these methodologies to practice.

Conclusions

Modeling and inverse problems of QPAT together were discussed. The scope was limited to the optical part, and the acoustic aspects were discussed only to the extent that they relate to the optical aspect.

Mitigating Under-Sampling Artifacts in 3D Photoacoustic Imaging Using Res-UNet Based on Digital Breast Phantom

In recent years, photoacoustic (PA) imaging has rapidly grown as a non-invasive screening technique for breast cancer detection using three-dimensional (3D) hemispherical arrays due to their large field of view. However, the development of breast imaging systems is hindered by a lack of patients and ground truth samples, as well as under-sampling problems caused by high costs. Most research related to solving these problems in the PA field were based on 2D transducer arrays or simple regular shape phantoms for 3D transducer arrays or images from other modalities. Therefore, we demonstrate an effective method for removing under-sampling artifacts based on deep neural network (DNN) to reconstruct high-quality PA images using numerical digital breast simulations. We constructed 3D digital breast phantoms based on human anatomical structures and physical properties, which were then subjected to 3D Monte-Carlo and K-wave acoustic simulations to mimic acoustic propagation for hemispherical transducer arrays. Finally, we applied a 3D delay-and-sum reconstruction algorithm and a Res-UNet network to achieve higher resolution on sparsely-sampled data. Our results indicate that when using a 757 nm laser with uniform intensity distribution illuminated on a numerical digital breast, the imaging depth can reach 3 cm with 0.25 mm spatial resolution. In addition, the proposed DNN can significantly enhance image quality by up to 78.4%, as measured by MS-SSIM, and reduce background artifacts by up to 19.0%, as measured by PSNR, even at an under-sampling ratio of 10%. The post-processing time for these improvements is only 0.6 s. This paper suggests a new 3D real time DNN method addressing the sparse sampling problem based on numerical digital breast simulations, this approach can also be applied to clinical data and accelerate the development of 3D photoacoustic hemispherical transducer arrays for early breast cancer diagnosis.

Review of imaging test phantoms

Significance

Photoacoustic tomography has emerged as a prominent medical imaging technique that leverages its hybrid nature to provide deep penetration, high resolution, and exceptional optical contrast with notable applications in early cancer detection, functional brain imaging, drug delivery monitoring, and guiding interventional procedures. Test phantoms are pivotal in accelerating technology development and commercialization, specifically in photoacoustic (PA) imaging, and can be optimized to achieve significant advancements in PA imaging capabilities.

Aim

The analysis of material properties, structural characteristics, and manufacturing methodologies of test phantoms from existing imaging technologies provides valuable insights into their applicability to PA imaging. This investigation enables a deeper understanding of how phantoms can be effectively employed in the context of PA imaging.

Approach

Three primary categories of test phantoms (simple, intermediate, and advanced) have been developed to differentiate complexity and manufacturing requirements. In addition, four sub-categories (tube/channel, block, test target, and naturally occurring phantoms) have been identified to encompass the structural variations within these categories, resulting in a comprehensive classification system for test phantoms.

Results

Based on a thorough examination of literature and studies on phantoms in various imaging modalities, proposals have been put forth for the development of multiple PA-capable phantoms, encompassing considerations related to the material composition, structural design, and specific applications within each sub-category.

Conclusions

The advancement of novel and sophisticated test phantoms within each sub-category is poised to foster substantial progress in both the commercialization and development of PA imaging. Moreover, the continued refinement of test phantoms will enable the exploration of new applications and use cases for PA imaging.

A review of a strategic roadmapping exercise to advance clinical translation of photoacoustic imaging: From current barriers to future adoption

Photoacoustic imaging (PAI), also referred to as optoacoustic imaging, has shown promise in early-stage clinical trials in a range of applications from inflammatory diseases to cancer. While the first PAI systems have recently received regulatory approvals, successful adoption of PAI technology into healthcare systems for clinical decision making must still overcome a range of barriers, from education and training to data acquisition and interpretation. The International Photoacoustic Standardisation Consortium (IPASC) undertook an community exercise in 2022 to identify and understand these barriers, then develop a roadmap of strategic plans to address them. Here, we outline the nature and scope of the barriers that were identified, along with short-, medium- and long-term community efforts required to overcome them, both within and beyond the IPASC group.

A joint method of coherence factor and nonlinear beamforming for synthetic aperture imaging with a ring array

Ultrasound computed tomography (USCT) with a ring array is an emerging diagnostic method for breast cancer. In the literature, synthetic aperture (SA) imaging has employed the delay-and-sum (DAS) beamforming technique for ring-array USCT to obtain isotropic resolution reflection images. However, the images obtained by the conventional DAS beamformer suffer from off-axis clutter and low resolution due to inhomogeneity of the medium and phase distortion. To address these issues, researchers have developed adaptive beamforming methods, such as coherence factor (CF) and convolutional beamforming algorithm (COBA), that improve image quality. In this study, we propose a joint method that combines CF with short-lag COBA (SLCOBA). First, we estimate the average sound speed using CF to address tissue inhomogeneity. Based on the corrected sound speed map, SLCOBA effectively suppresses side lobes and enhances image quality. Numerical results show that the proposed method reduces clutter and noise, improving resolution performance. These findings suggest that the proposed method may be a practical option for breast imaging in inhomogeneous mediums in the future.

Synthetic dataset for visco-acoustic imaging

We provide computationally generated dataset simulating propagation of ultrasonic waves in viscous tissues in two and three dimensional domains. The dataset contains physical parameters of a human breast with a high-contrast inclusion, the acquisition setup with positions of sources and receivers, and the associated pressure-wave data at ultrasonic frequencies. We simulated the wave propagation based on seven different viscous models using the physical parameters of the breast. Furthermore, different choices of conditions for the medium's boundaries are given, namely absorbing and reflecting boundaries. The dataset allows to evaluate the performance of reconstruction methods for ultrasound imaging under attenuation model uncertainty, that is, when the precise attenuation law that characterizes the medium is unknown. In addition, the dataset enables to evaluate the robustness of inverse scheme in the context of reflecting boundary conditions where multiple reflections illuminate the sample, and/or the performance of data-processing to suppress these multiple reflections.

Photoacoustic digital brain and deep-learning-assisted image reconstruction

Photoacoustic tomography (PAT) is a newly developed medical imaging modality, which combines the advantages of pure optical imaging and ultrasound imaging, owning both high optical contrast and deep penetration depth. Very recently, PAT is studied in human brain imaging. Nevertheless, while ultrasound waves are passing through the human skull tissues, the strong acoustic attenuation and aberration will happen, which causes photoacoustic signals' distortion. In this work, we use 180 T1 weighted magnetic resonance imaging (MRI) human brain volumes along with the corresponding magnetic resonance angiography (MRA) brain volumes, and segment them to generate the 2D human brain numerical phantoms for PAT. The numerical phantoms contain six kinds of tissues, which are scalp, skull, white matter, gray matter, blood vessel and cerebrospinal fluid. For every numerical phantom, Monte-Carlo based optical simulation is deployed to obtain the photoacoustic initial pressure based on optical properties of human brain. Then, two different k-wave models are used for the skull-involved acoustic simulation, which are fluid media model and viscoelastic media model. The former one only considers longitudinal wave propagation, and the latter model takes shear wave into consideration. Then, the PA sinograms with skull-induced aberration is taken as the input of U-net, and the skull-stripped ones are regarded as the supervision of U-net to train the network. Experimental result shows that the skull's acoustic aberration can be effectively alleviated after U-net correction, achieving conspicuous improvement in quality of PAT human brain images reconstructed from the corrected PA signals, which can clearly show the cerebral artery distribution inside the human skull.

Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer

Significance

When developing a new quantitative optoacoustic computed tomography (OAT) system for diagnostic imaging of breast cancer, objective assessments of various system designs through human trials are infeasible due to cost and ethical concerns. In prototype stages, however, different system designs can be cost-efficiently assessed via virtual imaging trials (VITs) employing ensembles of digital breast phantoms, i.e., numerical breast phantoms (NBPs), that convey clinically relevant variability in anatomy and optoacoustic tissue properties.

Aim

The aim is to develop a framework for generating ensembles of realistic three-dimensional (3D) anatomical, functional, optical, and acoustic NBPs and numerical lesion phantoms (NLPs) for use in VITs of OAT applications in the diagnostic imaging of breast cancer.

Approach

The generation of the anatomical NBPs was accomplished by extending existing NBPs developed by the U.S. Food and Drug Administration. As these were designed for use in mammography applications, substantial modifications were made to improve blood vasculature modeling for use in OAT. The NLPs were modeled to include viable tumor cells only or a combination of viable tumor cells, necrotic core, and peripheral angiogenesis region. Realistic optoacoustic tissue properties were stochastically assigned in the NBPs and NLPs.

Results

To advance optoacoustic and optical imaging research, 84 datasets have been released; these consist of anatomical, functional, optical, and acoustic NBPs and the corresponding simulated multi-wavelength optical fluence, initial pressure, and OAT measurements. The generated NBPs were compared with clinical data with respect to the volume of breast blood vessels and spatially averaged effective optical attenuation. The usefulness of the proposed framework was demonstrated through a case study to investigate the impact of acoustic heterogeneity on OAT images of the breast.

Conclusions

The proposed framework will enhance the authenticity of virtual OAT studies and can be widely employed for the investigation and development of advanced image reconstruction and machine learning-based methods, as well as the objective evaluation and optimization of the OAT system designs.

Clinical photoacoustic/ultrasound dual-modal imaging: Current status and future trends

Photoacoustic tomography (PAT) is an emerging biomedical imaging modality that combines optical and ultrasonic imaging, providing overlapping fields of view. This hybrid approach allows for a natural integration of PAT and ultrasound (US) imaging in a single platform. Due to the similarities in signal acquisition and processing, the combination of PAT and US imaging creates a new hybrid imaging for novel clinical applications. Over the recent years, particular attention is paid to the development of PAT/US dual-modal systems highlighting mutual benefits in clinical cases, with an aim of substantially improving the specificity and sensitivity for diagnosis of diseases. The demonstrated feasibility and accuracy in these efforts open an avenue of translating PAT/US imaging to practical clinical applications. In this review, the current PAT/US dual-modal imaging systems are discussed in detail, and their promising clinical applications are presented and compared systematically. Finally, this review describes the potential impacts of these combined systems in the coming future.

Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging

SIGNIFICANCE

Quantitative optoacoustic imaging (QOAI) continues to be a challenge due to the influence of nonlinear optical fluence distribution, which distorts the optoacoustic image representation. Nonlinear optical fluence correction in OA imaging is highly ill-posed, leading to the inaccurate recovery of optical absorption maps. This work aims to recover the optical absorption maps using deep learning (DL) approach by correcting for the fluence effect.

AIM

Different DL models were compared and investigated to enable optical absorption coefficient recovery at a particular wavelength in a nonhomogeneous foreground and background medium.

APPROACH

Data-driven models were trained with two-dimensional (2D) Blood vessel and three-dimensional (3D) numerical breast phantom with highly heterogeneous/realistic structures to correct for the nonlinear optical fluence distribution. The trained DL models such as U-Net, Fully Dense (FD) U-Net, Y-Net, FD Y-Net, Deep residual U-Net (Deep ResU-Net), and generative adversarial network (GAN) were tested to evaluate the performance of optical absorption coefficient recovery (or fluence compensation) with in-silico and in-vivo datasets.

RESULTS

The results indicated that FD U-Net-based deconvolution improves by about 10% over reconstructed optoacoustic images in terms of peak-signal-to-noise ratio. Further, it was observed that DL models can indeed highlight deep-seated structures with higher contrast due to fluence compensation. Importantly, the DL models were found to be about 17 times faster than solving diffusion equation for fluence correction.

CONCLUSIONS

The DL methods were able to compensate for nonlinear optical fluence distribution more effectively and improve the optoacoustic image quality.

Video-rate full-ring ultrasound and photoacoustic computed tomography with real-time sound speed optimization

Full-ring dual-modal ultrasound and photoacoustic imaging provide complementary contrasts, high spatial resolution, full view angle and are more desirable in pre-clinical and clinical applications. However, two long-standing challenges exist in achieving high-quality video-rate dual-modal imaging. One is the increased data processing burden from the dense acquisition. Another one is the object-dependent speed of sound variation, which may cause blurry, splitting artifacts, and low imaging contrast. Here, we develop a video-rate full-ring ultrasound and photoacoustic computed tomography (VF-USPACT) with real-time optimization of the speed of sound. We improve the imaging speed by selective and parallel image reconstruction. We determine the optimal sound speed via co-registered ultrasound imaging. Equipped with a 256-channel ultrasound array, the dual-modal system can optimize the sound speed and reconstruct dual-modal images at 10 Hz in real-time. The optimized sound speed can effectively enhance the imaging quality under various sample sizes, types, or physiological states. In animal and human imaging, the system shows co-registered dual contrasts, high spatial resolution (140 µm), single-pulse photoacoustic imaging (< 50 µs), deep penetration (> 20 mm), full view, and adaptive sound speed correction. We believe VF-USPACT can advance many real-time biomedical imaging applications, such as vascular disease diagnosing, cancer screening, or neuroimaging.

Tutorial on Development of 3D Vasculature Digital Phantoms for Evaluation of Photoacoustic Image Reconstruction Algorithms

A synthetic phantom model is typically utilized to evaluate the initial performance of a photoacoustic image reconstruction algorithm. The characteristics of the phantom model (structural, optical, and acoustic) are required to be very similar to those of the biological tissue. Typically, generic two-dimensional shapes are used as imaging targets to calibrate reconstruction algorithms. However, these structures are not representative of complex biological tissue, and therefore the artifacts that exist in reconstructed images of biological tissue vasculature are ignored. Real data from 3D MRI/CT volumes can be extrapolated to create high-quality phantom models; however, these sometimes involve complicated pre-processing and mostly are challenging, due to the inaccessibility of these datasets or the requirement for approval to utilize the data. Therefore, it is necessary to develop a 3D tissue-mimicking phantom model consisting of different compartments with characteristics that can be easily modified. In this tutorial, we present an optimized development process of a generic 3D complex digital vasculature phantom model in Blender. The proposed workflow is such that an accurate and easily editable digital phantom can be developed. Other workflows for creating the same phantom will take much longer to set up and require more time to edit. We have made a few examples of editable 3D phantom models, which are publicly available to test and modify.

Feasibility of ultrasound tomography-guided localized mild hyperthermia using a ring transducer: Ex vivo and in silico studies

BACKGROUND

As of 2022, breast cancer continues to be the most diagnosed cancer worldwide. This problem persists within the United States as well, as the American Cancer Society has reported that ∼12.5% of women will be diagnosed with invasive breast cancer over the course of their lifetime. Therefore, a clinical need continues to exist to address this disease from a treatment and therapeutic perspective. Current treatments for breast cancer and cancers more broadly include surgery, radiation, and chemotherapy. Adjuncts to these methods have been developed to improve the clinical outcomes for patients. One such adjunctive treatment is mild hyperthermia therapy (MHTh), which has been shown to be successful in the treatment of cancers by increasing effectiveness and reduced dosage requirements for radiation and chemotherapies. MHTh-assisted treatments can be performed with invasive thermal devices, noninvasive microwave induction, heating and recirculation of extracted patient blood, or whole-body hyperthermia with hot blankets.

PURPOSE

One common method for inducing MHTh is by using microwave for heat induction and magnetic resonance imaging for temperature monitoring. However, this leads to a complex, expensive, and inaccessible therapy platform. Therefore, in this work we aim to show the feasibility of a novel all-acoustic MHTh system that uses focused ultrasound (US) to induce heating while also using US tomography (UST) to provide temperature estimates. Changes in sound speed (SS) have been shown to be strongly correlated with temperature changes and can therefore be used to indirectly monitor heating throughout the therapy. Additionally, these SS estimates allow for heterogeneous SS-corrected phase delays when heating complex and heterogeneous tissue structures.

METHODS

Feasibility to induce localized heat in tissue was investigated in silico with a simulated breast model, including an embedded tumor using continuous wave US. Here, both heterogenous acoustic and thermal properties were modeled in addition to blood perfusion. We further demonstrate, with ex vivo tissue phantoms, the feasibility of using ring-based UST to monitor temperature by tracking changes in SS. Two phantoms (lamb tissue and human abdominal fat) with latex tubes containing varied temperature flowing water were imaged. The measured SS of the water at each temperature were compared against values that are reported in literature.

RESULTS

Results from ex vivo tissue studies indicate successful tracking of temperature under various phantom configurations and ranges of water temperature. The results of in silico studies show that the proposed system can heat an acoustically and thermally heterogenous breast model to the clinically relevant temperature of 42°C while accounting for a reasonable time needed to image the current cross section (200 ms). Further, we have performed an initial in silico study demonstrating the feasibility of adjusting the transmit waveform frequency to modify the effective heating height at the focused region. Lastly, we have shown in a simpler 2D breast model that MHTh level temperatures can be maintained by adjusting the transmit pressure intensity of the US ring.

CONCLUSIONS

This work has demonstrated the feasibility of using a 256-element ring array transducer for temperature monitoring; however, future work will investigate minimizing the difference between measured SS and the values shown in literature. A hypothesis attributes this bias to potential volumetric average artifacts from the ray-based SS inversion algorithm that was used, and that moving to a waveform-based SS inversion algorithm will greatly improve the SS estimates. Additionally, we have shown that an all-acoustic MHTh system is feasible via in silico studies. These studies have indicated that the proposed system can heat a tumor within a heterogenous breast model to 42°C within a narrow time frame. This holds great promise for increasing the accessibility and reducing the complexity of a future all-acoustic MHTh system.

Stride: A flexible software platform for high-performance ultrasound computed tomography

BACKGROUND AND OBJECTIVE

Advanced ultrasound computed tomography techniques like full-waveform inversion are mathematically complex and orders of magnitude more computationally expensive than conventional ultrasound imaging methods. This computational and algorithmic complexity, and a lack of open-source libraries in this field, represent a barrier preventing the generalised adoption of these techniques, slowing the pace of research, and hindering reproducibility. Consequently, we have developed Stride, an open-source Python library for the solution of large-scale ultrasound tomography problems.

METHODS

On one hand, Stride provides high-level interfaces and tools for expressing the types of optimisation problems encountered in medical ultrasound tomography. On the other, these high-level abstractions seamlessly integrate with high-performance wave-equation solvers and with scalable parallelisation routines. The wave-equation solvers are generated automatically using Devito, a domain-specific language, and the parallelisation routines are provided through the custom actor-based library Mosaic.

RESULTS

We demonstrate the modelling accuracy achieved by our wave-equation solvers through a comparison (1) with analytical solutions for a homogeneous medium, and (2) with state-of-the-art modelling software applied to a high-contrast, complex skull section. Additionally, we show through a series of examples how Stride can handle realistic numerical and experimental tomographic problems, in 2D and 3D, and how it can scale robustly from a local multi-processing environment to a multi-node high-performance cluster.

CONCLUSIONS

Stride enables researchers to rapidly and intuitively develop new imaging algorithms and to explore novel physics without sacrificing performance and scalability. This will lead to faster scientific progress in this field and will significantly ease clinical translation.

SIMPA: an open-source toolkit for simulation and image processing for photonics and acoustics

SIGNIFICANCE

Optical and acoustic imaging techniques enable noninvasive visualisation of structural and functional properties of tissue. The quantification of measurements, however, remains challenging due to the inverse problems that must be solved. Emerging data-driven approaches are promising, but they rely heavily on the presence of high-quality simulations across a range of wavelengths due to the lack of ground truth knowledge of tissue acoustical and optical properties in realistic settings.

AIM

To facilitate this process, we present the open-source simulation and image processing for photonics and acoustics (SIMPA) Python toolkit. SIMPA is being developed according to modern software design standards.

APPROACH

SIMPA enables the use of computational forward models, data processing algorithms, and digital device twins to simulate realistic images within a single pipeline. SIMPA's module implementations can be seamlessly exchanged as SIMPA abstracts from the concrete implementation of each forward model and builds the simulation pipeline in a modular fashion. Furthermore, SIMPA provides comprehensive libraries of biological structures, such as vessels, as well as optical and acoustic properties and other functionalities for the generation of realistic tissue models.

RESULTS

To showcase the capabilities of SIMPA, we show examples in the context of photoacoustic imaging: the diversity of creatable tissue models, the customisability of a simulation pipeline, and the degree of realism of the simulations.

CONCLUSIONS

SIMPA is an open-source toolkit that can be used to simulate optical and acoustic imaging modalities. The code is available at: https://github.com/IMSY-DKFZ/simpa, and all of the examples and experiments in this paper can be reproduced using the code available at: https://github.com/IMSY-DKFZ/simpa_paper_experiments.

Normalization of optical fluence distribution for three-dimensional functional optoacoustic tomography of the breast

SIGNIFICANCE

In three-dimensional (3D) functional optoacoustic tomography (OAT), wavelength-dependent optical attenuation and nonuniform incident optical fluence limit imaging depth and field of view and can hinder accurate estimation of functional quantities, such as the vascular blood oxygenation. These limitations hinder OAT of large objects, such as a human female breast.

AIM

We aim to develop a measurement-data-driven method for normalization of the optical fluence distribution and to investigate blood vasculature detectability and accuracy for estimating vascular blood oxygenation.

APPROACH

The proposed method is based on reasonable assumptions regarding breast anatomy and optical properties. The nonuniform incident optical fluence is estimated based on the illumination geometry in the OAT system, and the depth-dependent optical attenuation is approximated using Beer-Lambert law.

RESULTS

Numerical studies demonstrated that the proposed method significantly enhanced blood vessel detectability and improved estimation accuracy of the vascular blood oxygenation from multiwavelength OAT measurements, compared with direct application of spectral linear unmixing without optical fluence compensation. Experimental results showed that the proposed method revealed previously invisible structures in regions deeper than 15 mm and/or near the chest wall.

CONCLUSIONS

The proposed method provides a straightforward and computationally inexpensive approximation of wavelength-dependent effective optical attenuation and, thus, enables mitigation of the spectral coloring effect in functional 3D OAT imaging.

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.

Denoising of pre-beamformed photoacoustic data using generative adversarial networks

We have trained generative adversarial networks (GANs) to mimic both the effect of temporal averaging and of singular value decomposition (SVD) denoising. This effectively removes noise and acquisition artifacts and improves signal-to-noise ratio (SNR) in both the radio-frequency (RF) data and in the corresponding photoacoustic reconstructions. The method allows a single frame acquisition instead of averaging multiple frames, reducing scan time and total laser dose significantly. We have tested this method on experimental data, and quantified the improvement over using either SVD denoising or frame averaging individually for both the RF data and the reconstructed images. We achieve a mean squared error (MSE) of 0.05%, structural similarity index measure (SSIM) of 0.78, and a feature similarity index measure (FSIM) of 0.85 compared to our ground-truth RF results. In the subsequent reconstructions using the denoised data we achieve a MSE of 0.05%, SSIM of 0.80, and a FSIM of 0.80 compared to our ground-truth reconstructions.

Ultrasound segmentation-guided edge artifact reduction in diffuse optical tomography using connected component analysis

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential value for breast cancer diagnosis and treatment response assessment. However, in clinical use, the chest wall, poor probe-tissue contact, and tissue heterogeneity can all cause image artifacts. These image artifacts, appearing commonly as hot spots in the non-lesion regions (edge artifacts), can decrease the reconstruction accuracy and cause misinterpretation of lesion images. Here we introduce an iterative, connected component analysis-based image artifact reduction algorithm. A convolutional neural network (CNN) is used to segment co-registered US images to extract the lesion location and size to guide the artifact reduction. We demonstrate its performance using Monte Carlo simulations on VICTRE digital breast phantoms and breast patient images. In simulated tissue mismatch models, this algorithm successfully reduces edge artifacts without significantly changing the reconstructed target absorption coefficients. With clinical data it improves the optical contrast between malignant and benign groups, from 1.55 without artifact reduction to 1.91 with artifact reduction. The proposed algorithm has a broad range of applications in other modality-guided DOT imaging.

Biopsy marker localization with thermo-acoustic ultrasound for lumpectomy guidance

PURPOSE

Almost one in four lumpectomies fails to fully remove cancerous tissue from the breast, requiring reoperation. This high failure rate suggests that existing lumpectomy guidance methods are inadequate for allowing surgeons to consistently identify the proper volume of tissue for excision. Current guidance techniques either provide little information about the tumor position or require surgeons to frequently switch between making incisions and manually probing for a marker placed at the lesion site. This article explores the feasibility of thermo-acoustic ultrasound (TAUS) to enable hands-free localization of metallic biopsy markers throughout surgery, which would allow for continuous visualization of the lesion site in the breast without the interruption of surgery. In a TAUS-based localization system, microwave excitations would be transmitted into the breast, and the amplification in microwave absorption around the metallic markers would generate acoustic signals from the marker sites through the thermo-acoustic effect. Detection and ranging of these signals by multiple acoustic receivers on the breast could then enable marker localization through acoustic multilateration.

METHODS

Physics simulations were used to characterize the TAUS signals generated from different markers by microwave excitations. First, electromagnetic simulations determined the spatial pattern of the amplification in microwave absorption around the markers. Then, acoustic simulations characterized the acoustic fields generated from these markers at various acoustic frequencies. TAUS-based one-dimensional (1D) ranging of two metallic markers-including a biopsy marker that is FDA-approved for clinical use-immersed in saline was also performed using a bench-top setup. To perform TAUS acquisitions, a microwave applicator was driven by 2.66 GHz microwave signals that were amplitude-modulated by chirps at the desired acoustic excitation frequencies, and the resulting TAUS signal from the markers was detected by an ultrasonic transducer.

RESULTS

The simulation results show that the geometry of the marker strongly impacts the quantity and spatial pattern of both the microwave absorption around the marker and the resulting TAUS signal generated from the marker. The simulated TAUS signal maps and acoustic frequency responses also make clear that the marker geometry plays an important role in determining the overall system response. Using the bench-top setup, TAUS detection and 1D localization of the markers were successfully demonstrated for multiple different combinations of microwave applicator and metallic marker. These initial results indicate that TAUS-based localization of biopsy markers is feasible.

CONCLUSIONS

Through microwave excitations and acoustic detection, TAUS can be used to localize metallic biopsy markers. With further development, TAUS opens new avenues to enable a more intuitive lumpectomy guidance system that could help to achieve better lumpectomy outcomes.

Deep-Learning-Driven Full-Waveform Inversion for Ultrasound Breast Imaging

Ultrasound breast imaging is a promising alternative to conventional mammography because it does not expose women to harmful ionising radiation and it can successfully image dense breast tissue. However, conventional ultrasound imaging only provides morphological information with limited diagnostic value. Ultrasound computed tomography (USCT) uses energy in both transmission and reflection when imaging the breast to provide more diagnostically relevant quantitative tissue properties, but it is often based on time-of-flight tomography or similar ray approximations of the wave equation, resulting in reconstructed images with low resolution. Full-waveform inversion (FWI) is based on a more accurate approximation of wave-propagation phenomena and can consequently produce very high resolution images using frequencies below 1 megahertz. These low frequencies, however, are not available in most USCT acquisition systems, as they use transducers with central frequencies well above those required in FWI. To circumvent this problem, we designed, trained, and implemented a two-dimensional convolutional neural network to artificially generate missing low frequencies in USCT data. Our results show that FWI reconstructions using experiment data after the application of the proposed method successfully converged, showing good agreement with X-ray CT and reflection ultrasound-tomography images.

Deep-Learning-Driven Full-Waveform Inversion for Ultrasound Breast Imaging

Ultrasound breast imaging is a promising alternative to conventional mammography because it does not expose women to harmful ionising radiation and it can successfully image dense breast tissue. However, conventional ultrasound imaging only provides morphological information with limited diagnostic value. Ultrasound computed tomography (USCT) uses energy in both transmission and reflection when imaging the breast to provide more diagnostically relevant quantitative tissue properties, but it is often based on time-of-flight tomography or similar ray approximations of the wave equation, resulting in reconstructed images with low resolution. Full-waveform inversion (FWI) is based on a more accurate approximation of wave-propagation phenomena and can consequently produce very high resolution images using frequencies below 1 megahertz. These low frequencies, however, are not available in most USCT acquisition systems, as they use transducers with central frequencies well above those required in FWI. To circumvent this problem, we designed, trained, and implemented a two-dimensional convolutional neural network to artificially generate missing low frequencies in USCT data. Our results show that FWI reconstructions using experiment data after the application of the proposed method successfully converged, showing good agreement with X-ray CT and reflection ultrasound-tomography images.

Deep learning in photoacoustic imaging: a review

SIGNIFICANCE

Photoacoustic (PA) imaging can provide structural, functional, and molecular information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue. In recent years, deep learning (DL) has shown outstanding performance when implemented in PAI, with applications in image reconstruction, quantification, and understanding.

AIM

We provide (i) a comprehensive overview of the DL techniques that have been applied in PAI, (ii) references for designing DL models for various PAI tasks, and (iii) a summary of the future challenges and opportunities.

APPROACH

Papers published before November 2020 in the area of applying DL in PAI were reviewed. We categorized them into three types: image understanding, reconstruction of the initial pressure distribution, and QPAI.

RESULTS

When applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis.

CONCLUSION

DL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.

Development of a digital breast phantom for photoacoustic computed tomography

Photoacoustic (PA) imaging provides morphological and functional information about angiogenesis and thus is potentially suitable for breast cancer diagnosis. However, the development of PA breast imaging has been hindered by inadequate patients and a lack of ground truth images. Here, we report a digital breast phantom with realistic acoustic and optical properties, with which a digital PA-ultrasound imaging pipeline is developed to create a diverse pool of virtual patients with three types of masses: ductal carcinoma in situ, invasive breast cancer, and fibroadenoma. The experimental results demonstrate that our model is realistic, flexible, and can be potentially useful for accelerating the development of PA breast imaging technology.

Review of deep learning for photoacoustic imaging

Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks. Later, it was widely used in academia and industry. Ranging from image analysis to natural language processing, it fully exerted its magic and now become the state-of-the-art machine learning models. Deep neural networks have great potential in medical imaging technology, medical data analysis, medical diagnosis and other healthcare issues, and is promoted in both pre-clinical and even clinical stages. In this review, we performed an overview of some new developments and challenges in the application of machine learning to medical image analysis, with a special focus on deep learning in photoacoustic imaging. The aim of this review is threefold: (i) introducing deep learning with some important basics, (ii) reviewing recent works that apply deep learning in the entire ecological chain of photoacoustic imaging, from image reconstruction to disease diagnosis, (iii) providing some open source materials and other resources for researchers interested in applying deep learning to photoacoustic imaging.

Tunable blood oxygenation in the vascular anatomy of a semi-anthropomorphic photoacoustic breast phantom

SIGNIFICANCE

Recovering accurate oxygenation estimations in the breast with quantitative photoacoustic tomography (QPAT) is not straightforward. Accurate light fluence models are required, but the unknown ground truth of the breast makes it difficult to validate them. Phantoms are often used for the validation, but most reported phantoms have a simple architecture. Fluence models developed in these simplistic objects are not accurate for application on the complex tissues of the breast.

AIM

We present a sophisticated breast phantom platform for photoacoustic (PA) and ultrasound (US) imaging in general, and specifically for QPAT. The breast phantom is semi-anthropomorphic in distribution of optical and acoustic properties and contains wall-less channels with blood.

APPROACH

3D printing approaches are used to develop the solid 3D breast phantom from custom polyvinyl chloride plastisol formulations and additives for replicating the tissue optical and acoustic properties. A flow circuit was developed to flush the channels with bovine blood with a controlled oxygen saturation level. To showcase the phantom's functionality, PA measurements were performed on the phantom with two oxygenation levels. Image reconstructions with and without fluence compensation from Monte Carlo simulations were analyzed for the accuracy of oxygen saturation estimations.

RESULTS

We present design aspects of the phantom, demonstrate how it is developed, and present its breast-like appearance in PA and US imaging. The oxygen saturations were estimated in two regions of interest with and without using the fluence models. The fluence compensation positively influenced the SO2 estimations in all cases and confirmed that highly accurate fluence models are required to minimize estimation errors.

CONCLUSIONS

This phantom allows studies to be performed in PA in carefully controlled laboratory settings to validate approaches to recover both qualitative and quantitative features sought after in in-vivo studies. We believe that testing with phantoms of this complexity can streamline the transition of new PA technologies from the laboratory to studies in the clinic.

Deep Neural Network-Based Sinogram Super-Resolution and Bandwidth Enhancement for Limited-Data Photoacoustic Tomography

Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for photoacoustic (PA) signals require a large number of data points for accurate image reconstruction. However, in practical scenarios, data are collected using the limited number of transducers along with data being often corrupted with noise resulting in only qualitative images. Furthermore, the collected boundary data are band-limited due to limited bandwidth (BW) of the transducer, making the PA imaging with limited data being qualitative. In this work, a deep neural network-based model with loss function being scaled root-mean-squared error was proposed for super-resolution, denoising, as well as BW enhancement of the PA signals collected at the boundary of the domain. The proposed network has been compared with traditional as well as other popular deep-learning methods in numerical as well as experimental cases and is shown to improve the collected boundary data, in turn, providing superior quality reconstructed PA image. The improvement obtained in the Pearson correlation, structural similarity index metric, and root-mean-square error was as high as 35.62%, 33.81%, and 41.07%, respectively, for phantom cases and signal-to-noise ratio improvement in the reconstructed PA images was as high as 11.65 dB for in vivo cases compared with reconstructed image obtained using original limited BW data. Code is available at https://sites.google.com/site/sercmig/home/dnnpat.

Ultrasound transmission tomography image reconstruction with a fully convolutional neural network

Image reconstruction of ultrasound computed tomography based on the wave equation is able to show much more structural details than simpler ray-based image reconstruction methods. However, to invert the wave-based forward model is computationally demanding. To address this problem, we develop an efficient fully learned image reconstruction method based on a convolutional neural network. The image is reconstructed via one forward propagation of the network given input sensor data, which is much faster than the reconstruction using conventional iterative optimization methods. To transform the ultrasound measured data in the sensor domain into the reconstructed image in the image domain, we apply multiple down-scaling and up-scaling convolutional units to efficiently increase the number of hidden layers with a large receptive and projective field that can cover all elements in inputs and outputs, respectively. For dataset generation, a paraxial approximation forward model is used to simulate ultrasound measurement data. The neural network is trained with a dataset derived from natural images in ImageNet and tested with a dataset derived from medical images in OA-Breast Phantom dataset. Test results show the superior efficiency of the proposed neural network to other reconstruction algorithms including popular neural networks. When compared with conventional iterative optimization algorithms, our neural network can reconstruct a 110 × 86 image more than 20 times faster on a CPU and 1000 times faster on a GPU with comparable image quality and is also more robust to noise.

Human Breast Numerical Model Generation Based on Deep Learning for Photoacoustic Imaging

Photoacoustic imaging which combines high contrast of optical imaging and high resolution of ultrasound imaging, can provide functional information, potentially playing a crucial role in the study of breast cancer diagnostics. However, open source dataset for PA imaging research is insufficient on account of lacking clinical data. To tackle this problem, we propose a method to automatically generate breast numerical model for photoacoustic imaging. The different type of tissues is automatically extracted first by employing deep learning and other methods from mammography. And then the tissues are combined by mathematical set operation to generate a new breast image after being assigned optical and acoustic parameters. Finally, breast numerical model with proper optical and acoustic properties are generated, which are specifically suitable for PA imaging studies, and the experiment results indicate that our method is feasible with high efficiency.

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.

Semi-anthropomorphic photoacoustic breast phantom

Imaging parameters of photoacoustic breast imaging systems such as the spatial resolution and imaging depth are often characterized with phantoms. These objects usually contain simple structures in homogeneous media such as absorbing wires or spherical objects in scattering gels. While these kinds of basic phantoms are uncluttered and useful, they do not challenge the system as much as a breast does, and can thereby overestimate the system's performance. The female breast is a complex collection of tissue types, and the acoustic and optical attenuation of these tissues limit the imaging depth, the resolution and the ability to extract quantitative information. For testing and challenging photoacoustic breast imaging systems to the full extent before moving to in vivo studies, a complex breast phantom which simulates the breast's most prevalent tissues is required. In this work we present the first three dimensional multi-layered semi-anthropomorphic photoacoustic breast phantom. The phantom aims to simulate skin, fat, fibroglandular tissue and blood vessels. The latter three are made from custom polyvinyl chloride plastisol (PVCP) formulations and are appropriately doped with additives to obtain tissue realistic acoustic and optical properties. Two tumors are embedded, which are modeled as clusters of small blood vessels. The PVCP materials are surrounded by a silicon layer mimicking the skin. The tissue mimicking materials were cast into the shapes and sizes expected in the breast using 3D-printed moulds developed from a magnetic resonance imaging segmented numerical breast model. The various structures and layers were assembled to obtain a realistic breast morphology. We demonstrate the phantom's appearance in both ultrasound imaging as photoacoustic tomography and make a comparison with a photoacoustic image of a real breast. A good correspondence is observed, which confirms the phantom's usefulness.

Fractional Regularization to Improve Photoacoustic Tomographic Image Reconstruction

Photoacoustic tomography involves reconstructing the initial pressure rise distribution from the measured acoustic boundary data. The recovery of the initial pressure rise distribution tends to be an ill-posed problem in the presence of noise and when limited independent data is available, necessitating regularization. The standard regularization schemes include Tikhonov, l₁ -norm, and total-variation. These regularization schemes weigh the singular values equally irrespective of the noise level present in the data. This paper introduces a fractional framework to weigh the singular values with respect to a fractional power. This fractional framework was implemented for Tikhonov, l₁ -norm, and total-variation regularization schemes. Moreover, an automated method for choosing the fractional power was also proposed. It was shown theoretically and with numerical experiments that the fractional power is inversely related to the data noise level for fractional Tikhonov scheme. The fractional framework outperforms the standard regularization schemes, Tikhonov, l₁ -norm, and total-variation by 54% in numerical simulations, experimental phantoms, and in vivo rat data in terms of observed contrast/signal-to-noise-ratio of the reconstructed images.

Breast Density Assessment in Young Women with Ultrasound based on Speed of Sound: Influence of the Menstrual Cycle

To investigate changes in breast density (BD) during the menstrual cycle in young women in comparison to inter-breast and -segment changes as well as reproducibility of a novel Speed-of-Sound (SoS) Ultrasound (US) method.SoS-US uses a conventional US system with a reflector and a software add-on to quantify SoS in the retro-mammillary, inner and outer segments of both breasts. Twenty healthy women (18-40 years) with regular menstrual cycles were scanned twice with two weeks in-between. Three of these were additionally measured twice per week for 25 days. Average SoS (m/s) and ΔSoS (segment-variation SoS; m/s) were measured. Variations between follicular and luteal phases and changes over the four-week period were assessed. Inter-examiner and inter-reader agreements were also evaluated. Variances between cycle phases, examiners and readers were compared.No significant SoS difference was observed between follicular and luteal phases for the twenty women (P = .126), and between all different days for the three more frequently measured women (P = .892). Inter-reader (ICC = 0.999) and inter-examiner (ICC = 0.990) agreements were high. The SoS variance due to menstrual variations was not significantly larger than the inter-examiner uncertainty (P = .461). Inter-reader variations were significantly smaller than menstrual and examiner variations (P < .001).SoS-US showed high inter-examiner and inter-reader reproducibility. The alterations during the menstrual cycles were not significantly larger than the confidence interval of measurements.

Maximum Entropy Based Non-Negative Optoacoustic Tomographic Image Reconstruction

OBJECTIVE

Optoacoustic (photoacoustic) tomography is aimed at reconstructing maps of the initial pressure rise induced by the absorption of light pulses in tissue. In practice, due to inaccurate assumptions in the forward model, noise, and other experimental factors, the images are often afflicted by artifacts, occasionally manifested as negative values. The aim of this work is to develop an inversion method which reduces the occurrence of negative values and improves the quantitative performance of optoacoustic imaging.

METHODS

We present a novel method for optoacoustic tomography based on an entropy maximization algorithm, which uses logarithmic regularization for attaining non-negative reconstructions. The reconstruction image quality is further improved using structural prior-based fluence correction.

RESULTS

We report the performance achieved by the entropy maximization scheme on numerical simulation, experimental phantoms, and in-vivo samples.

CONCLUSION

The proposed algorithm demonstrates superior reconstruction performance by delivering non-negative pixel values with no visible distortion of anatomical structures.

SIGNIFICANCE

Our method can enable quantitative optoacoustic imaging, and has the potential to improve preclinical and translational imaging applications.

Speed-of-Sound Imaging Based on Reflector Delineation

OBJECTIVE

Speed-of-sound (SoS) has large potential for tissue and pathology differentiation. We aim to develop a novel Ultrasound Computed Tomography (USCT) technique that can reconstruct local SoS in tissue on conventional ultrasound machines with hand-held linear arrays.

METHODS

A passive reflector is placed opposite the tissue sample as an echogenic reference to measure the time-of-flight (ToF) of ultrasound wave- fronts. A Dynamic Programming algorithm provides a robust ToF measurements based on global optimization of all transmit- receive echo data. An Anisotropically-Weighted Total Variation (AWTV) algorithm allows sharp delineation of focal lesions based on limited-angle USCT data.

RESULTS

Inclusions, which are not visible in conventional ultrasound, could be delineated in SoS images. AWTV allows to reconstruct focal lesions with a contrast-ratio of 93.7% of their nominal value, compared to that of 31.5% with conventional least-squares based algebraic tomographic reconstruction. In full-wave simulations of realistic heterogeneous breast models, a high CR of 84.3% is observed, with the reconstruction filtering out background heterogeneity. In experiments, our proposed method quantifies SoS in a homogeneous background with an accuracy of 0.93ms, allowing to differentiate several tissue types.

CONCLUSION

We validate our method using numerical simulations with ray-tracing and full- wave models, and phantom and ex-vivo data. Preliminary in- vivo results show the potential of this new technique to detect and differentiate malignant and benign lesions in the breast.

SIGNIFICANCE

Breast cancer is the most common cancer in women. Ultrasound B-mode only provides qualitative information about breast lesions, whereas USCT can provide quantitative tissue imaging biomarkers, such as SoS. The proposed method can potentially be implemented as a complementary modality to ultrasound for tissue and disease differentiation.

Spatially variant regularization based on model resolution and fidelity embedding characteristics improves photoacoustic tomography

Photoacoustic tomography tends to be an ill-conditioned problem with noisy limited data requiring imposition of regularization constraints, such as standard Tikhonov (ST) or total variation (TV), to reconstruct meaningful initial pressure rise distribution from the tomographic acoustic measurements acquired at the boundary of the tissue. However, these regularization schemes do not account for nonuniform sensitivity arising due to limited detector placement at the boundary of tissue as well as other system parameters. For the first time, two regularization schemes were developed within the Tikhonov framework to address these issues in photoacoustic imaging. The model resolution, based on spatially varying regularization, and fidelity-embedded regularization, based on orthogonality between the columns of system matrix, were introduced. These were systematically evaluated with the help of numerical and in-vivo mice data. It was shown that the performance of the proposed spatially varying regularization schemes were superior (with at least 2 dB or 1.58 times improvement in the signal-to-noise ratio) compared to ST-/TV-based regularization schemes.

Efficient nonlinear beamformer based on P'th root of detected signals for linear-array photoacoustic tomography: application to sentinel lymph node imaging

In linear-array transducer-based photoacoustic (PA) imaging, B-scan PA images are formed using the raw channel PA signals. Delay-and-sum (DAS) is the most prevalent algorithm due to its simple implementation, but it leads to low-quality images. Delay-multiply-and-sum (DMAS) provides a higher image quality in comparison with DAS while it imposes a computational burden of O  (  M2  )  . We introduce a nonlinear (NL) beamformer for linear-array PA imaging, which uses the p'th root of the detected signals and imposes the complexity of DAS [O  (  M  )  ]. The proposed algorithm is evaluated numerically and experimentally [wire-target and in-vivo sentinel lymph node (SLN) imaging], and the effects of the parameter p are investigated. The results show that the NL algorithm, using a root of p (NL_p), leads to lower sidelobes and higher signal-to-noise ratio compared with DAS and DMAS, for (p  >  2). The sidelobes level (for the wire-target phantom), at the depth of 11.4 mm, are about -31, -52, -52, -67, -88, and -109  dB, for DAS, DMAS, NL_2, NL_3, NL_4, and NL_5, respectively, indicating the superiority of the NL_p algorithm. In addition, the best value of p for SLN imaging is reported to be 12.

Segmentation of vessel structures from photoacoustic images with reliability assessment

Photoacoustic imaging enables the imaging of soft biological tissue with combined optical contrast and ultrasound resolution. One of the targets of interest is tissue vasculature. However, the photoacoustic images may not directly provide the information on, for example, vasculature structure. Therefore, the images are improved by reducing noise and artefacts, and processed for better visualisation of the target of interest. In this work, we present a new segmentation method of photoacoustic images that also straightforwardly produces assessments of its reliability. The segmentation depends on parameters which have a natural tendency to increase the reliability as the parameter values monotonically change. The reliability is assessed by counting classifications of image voxels with different parameter values. The resulting segmentation with reliability offers new ways and tools to analyse photoacoustic images and new possibilities for utilising them as anatomical priors in further computations. Our MATLAB implementation of the method is available as an open-source software package [P. Raumonen, Matlab, 2018].

Image-guided filtering for improving photoacoustic tomographic image reconstruction

Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image. This approach act as a postprocessing step to improve commonly used Tikhonov or total variational regularization method. The result obtained from linear backprojection was used as a guiding image to improve these results. Using both numerical and experimental phantom cases, it was shown that the proposed guided filtering approach was able to improve (as high as 11.23 dB) the signal-to-noise ratio of the reconstructed images with the added advantage being computationally efficient. This approach was compared with state-of-the-art basis pursuit deconvolution as well as standard denoising methods and shown to outperform them.

Accelerated image reconstruction using extrapolated Tikhonov filtering for photoacoustic tomography

PURPOSE

Development of simple and computationally efficient extrapolated Tikhonov filtering reconstruction methods for photoacoustic tomography.

METHODS

The model-based reconstruction algorithms in photoacoustic tomography typically utilize Tikhonov regularization scheme for the reconstruction of initial pressure distribution from the measured boundary acoustic data. The automated choice of regularization parameter in these cases is computationally expensive. Moreover, the Tikhonov scheme promotes the smooth features in the reconstructed image due to the smooth regularizer, thus leading to loss of sharp features. The proposed extrapolation method estimates the solution at zero regularization assuming the solution being a function of regularization parameter and thus posing it as a zero value problem. Thus, the numerically computed zero regularization solution is expected to have better features compared to standard Tikhonov solution, with an added advantage of removing the necessity of automated choice of regularization. The reconstructed results using this method were shown in three variants (Lanczos, traditional, and exponential) of Tikhonov filtering and were compared with the standard error estimate technique.

RESULTS

Four numerical (including realistic breast phantom) and two experimental phantom data were utilized to show the effectiveness of the proposed method. It was shown that the proposed method performance was superior than the standard error estimate technique, being at least four times faster in terms of computation, and provides an improvement as high as 2.6 times in terms of standard figures of merit.

CONCLUSION

The developed extrapolated Tikhonov filtering methods overcome the difficulty of obtaining a suitable regularization parameter and shown to be reconstructing high-quality photoacoustic images with additional advantage of being computationally efficient, making it more appealing in real-time applications.

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.

Advances in Monte Carlo Simulation for Light Propagation in Tissue

Monte Carlo (MC) simulation for light propagation in tissue is the gold standard for studying the light propagation in biological tissue and has been used for years. Interaction of photons with a medium is simulated based on its optical properties. New simulation geometries, tissue-light interaction methods, and recording techniques recently have been designed. Applications, such as whole mouse body simulations for fluorescence imaging, eye modeling for blood vessel imaging, skin modeling for terahertz imaging, and human head modeling for sinus imaging, have emerged. Here, we review the technical advances and recent applications of MC simulation.