Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net Approach

Magnetic inversion is one of the popular methods to obtain information about the subsurface structure. However, many of the conventional methods have a serious problem, that is, the linear equations to be solved become ill-posed, under-determined, and thus, the uniqueness of the solution is not guaranteed. As a result, several different models fit the observed magnetic data with the same accuracy. To reduce the non-uniqueness of the model, conventional studies introduced regularization method based on the quadratic solution norm. However, these regularization methods impose a certain level of smoothness, and as the result, the resultant model is likely to be blurred. To obtain a focused magnetic model, I introduce L1 norm regularization. As is widely known, L1 norm regularization promotes sparseness of the model. So, it is expected that, the resulting model is constructed only with the features truly required to reconstruct data and, as a result, a simple and focused model is obtained. However, by using L1 norm regularization solely, an excessively concentrated model is obtained due to the nature of the L1 norm regularization and a lack of linear independence of the magnetic equations. To overcome this problem, I use a combination of L1 and L2 norm regularization. To choose a feasible regularization parameter, I introduce a regularization parameter selection method based on the L-curve criterion with fixing the mixing ratio of L1 and L2 norm regularization. This inversion method is applied to a real magnetic anomaly data observed on Hokkaido Island, northern Japan and reveals the subsurface magnetic structure on this area.

BackgroundThe ability to generate, visualize, and analyze motions of biomolecules has made a significant impact upon modern biology. Molecular Dynamics has gained substantial use, but remains computationally demanding and difficult to setup for many biologists. Elastic network models (ENMs) are an alternative and have been shown to generate the dominant equilibrium motions of biomolecules quickly and efficiently. These dominant motions have been shown to be functionally relevant and also to indicate the likely direction of conformational changes. Most structures have a small number of dominant motions. Comparing computed motions to the structure's conformational ensemble derived from a collection of static structures or frames from an MD trajectory is an important way to understand functional motions as well as evaluate the models. Modes of motion computed from ENMs can be visualized to gain functional and mechanistic understanding and to compute useful quantities such as average positional fluctuations, internal distance changes, collectiveness of motions, and directional correlations within the structure.ResultsOur new software, MAVEN, aims to bring ENMs and their analysis to a broader audience by integrating methods for their generation and analysis into a user friendly environment that automates many of the steps. Models can be constructed from raw PDB files or density maps, using all available atomic coordinates or by employing various coarse-graining procedures. Visualization can be performed either with our software or exported to molecular viewers. Mixed resolution models allow one to study atomic effects on the system while retaining much of the computational speed of the coarse-grained ENMs. Analysis options are available to further aid the user in understanding the computed motions and their importance for its function.ConclusionMAVEN has been developed to simplify ENM generation, allow for diverse models to be used, and facilitate useful analyses, all on the same platform. This represents an integrated approach that incorporates all four levels of the modeling process - generation, evaluation, analysis, visualization - and also brings to bear multiple ENM types. The intension is to provide a versatile modular suite of programs to a broader audience. MAVEN is available for download at http://maven.sourceforge.net.

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-tonoise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform. We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images. The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising. The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.

Photoacoustic imaging is a hybrid imaging modality, where ultrasound waves are generated from the interaction between light and biological tissue. Therefore, the image contrast is based on the optical absorption properties of tissues. A useful approach for studying photoacoustic imaging methods is through tissue-mimicking phantom experiments. Water-based materials are commonly used in phantoms for photoacoustic imaging. However, these materials have disadvantages such as easy degradation and low temporal stability. In this paper, copolymer-in-oil phantoms are presented for ultrasound and photoacoustic techniques, with the advantage of presenting low temporal degradation. Oil-based materials made of mixtures of styrene-ethylene/butylene-styrene (SEBS) and low-density polyethylene (LDPE) using paraffinic mineral oil as solvent was prepared, and annatto dye was used as optical absorber. Acoustic and optical characterization of the material was performed, where speed of sound, attenuation coefficient, optical absorption and scattering coefficients were evaluated. The phantom material showed tissue-mimicking optical and ultrasonic properties. A cubic phantom with a spherical absorber inclusion for photoacoustic imaging was manufactured. The photoacoustic signal from the inclusion showed the highest intensities at 532 nm with consistent variation for other wavelengths according to the measured absorption spectrum. In this case, the absorption spectrum acquired for the material containing annatto showed band between 350–550nm. Therefore, the results suggest the phantoms of SEBS and LDPE are promising for photoacoustic imaging.

.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.

Multi-trace acoustic impedance inversion with multiplicative regularization

.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.

The International Photoacoustic Standardisation Consortium (IPASC) emerged from SPIE 2018, established to drive consensus on photoacoustic system testing. As photoacoustic imaging (PAI) matures from research laboratories into clinical trials, it is essential to establish best-practice guidelines for photoacoustic image acquisition, analysis and reporting, and a standardised approach for technical system validation. The primary goal of the IPASC is to create widely accepted phantoms for testing preclinical and clinical PAI systems. To achieve this, the IPASC has formed five working groups (WGs). The first and second WGs have defined optical and acoustic properties, suitable materials, and configurations of photoacoustic image quality phantoms. These phantoms consist of a bulk material embedded with targets to enable quantitative assessment of image quality characteristics including resolution and sensitivity across depth. The third WG has recorded details such as illumination and detection configurations of PAI instruments available within the consortium, leading to proposals for system-specific phantom geometries. This PAI system inventory was also used by WG4 in identifying approaches to data collection and sharing. Finally, WG5 investigated means for phantom fabrication, material characterisation and PAI of phantoms. Following a pilot multi-centre phantom imaging study within the consortium, the IPASC settled on an internationally agreed set of standardised recommendations and imaging procedures. This leads to advances in: (1) quantitative comparison of PAI data acquired with different data acquisition and analysis methods; (2) provision of a publicly available reference data set for testing new algorithms; and (3) technical validation of new and existing PAI devices across multiple centres.

.As photoacoustic imaging (PAI) technology matures, computational modeling will increasingly represent a critical tool for facilitating clinical translation through predictive simulation of real-world performance under a wide range of device and biological conditions. While modeling currently offers a rapid, inexpensive tool for device development and prediction of fundamental image quality metrics (e.g., spatial resolution and contrast ratio), rigorous verification and validation will be required of models used to provide regulatory-grade data that effectively complements and/or replaces in vivo testing. To address methods for establishing model credibility, we developed an integrated computational model of PAI by coupling a previously developed three-dimensional Monte Carlo model of tissue light transport with a two-dimensional (2D) acoustic wave propagation model implemented in the well-known k-Wave toolbox. We then evaluated ability of the model to predict basic image quality metrics by applying standardized verification and validation principles for computational models. The model was verified against published simulation data and validated against phantom experiments using a custom PAI system. Furthermore, we used the model to conduct a parametric study of optical and acoustic design parameters. Results suggest that computationally economical 2D acoustic models can adequately predict spatial resolution, but metrics such as signal-to-noise ratio and penetration depth were difficult to replicate due to challenges in modeling strong clutter observed in experimental images. Parametric studies provided quantitative insight into complex relationships between transducer characteristics and image quality as well as optimal selection of optical beam geometry to ensure adequate image uniformity. Multidomain PAI simulation tools provide high-quality tools to aid device development and prediction of real-world performance, but further work is needed to improve model fidelity, especially in reproducing image noise and clutter.

Low frequency normal mode approach to describe conformational fluctuations of globular proteins has helped to characterize their biological functions. Various theoretical and experimental methods have been employed to determine the magnitudes of those internal motions. Those motions can be characterized by the Debye-Waller factor (Bfactor), corresponding to the local mobility of the residue at the atomic level. Normal mode analysis using elastic network models (ENM) has demonstrated to be a robust technique. Experimental B-factors has been reproduced theoretically by means of this technique in a short computational time and it has been shown to be competitive with more sophisticated techniques. The ENM is a coarse-grained approach in which the protein is represented by a three-dimensional elastic network of alpha-carbon atoms connected by springs. Springs represent bonded and non-bonded interactions between the alpha-carbon atoms. In this work, we study the elastic network models known in the literature. Next, we perform a comparative study between them. We show that the pfGNM and pfANM models present better correlation with experimental B-factors than the traditional GNM and ANM models. We also develop a new approach, which we entitled anisotropic weighted contact number (AWCN). We show that it presents results significantly better than the traditional anisotropic elastic network model. Finally, we perform a study of investigative character of the behavior for the weight of the interactions between residues. This study revealed that, for the WCN and AWCN models, the correlation exhibits its maximum value for weighted interactions R -p ij , between residues i and j, for values of p around 2. In the pfGNM and pfANM models the correlation is maximized for two values of p, the first one around 2 and the second one around 4.75. This indicates that the weighting by the reciprocal of the square of the distance, usually employed in the literature, may not be appropriate to obtain the best correlation.

Photoacoustic (PA) imaging has been gaining attention as a new imaging modality that can non-invasively visualize blood vessels inside biological tissues. In the process of imaging large body parts through multi-scan fusion, alignment turns out to be an important issue, since body motion degrades image quality. In this paper, we carefully examine the characteristics of PA images and propose a novel registration method that achieves better alignment while effectively decomposing the shot volumes into low-rank foreground (blood vessels), dense background (noise), and sparse complement (corruption) components on the basis of the PA characteristics. The results of experiments using a challenging real data-set demonstrate the efficacy of the proposed method, which significantly improved image quality, and had the best alignment accuracy among the state-of-the-art methods tested.

Photoacoustic microscopy is an in vivo imaging technology based on the photoacoustic effect. It is widely used in various biomedical studies because it can provide high-resolution images while being label-free, safe, and harmless to biological tissue. Polygon-scanning is an effective scanning method in photoacoustic microscopy that can realize fast imaging of biological tissue with a large field of view. However, in polygon-scanning, fluctuations of the rotating motor speed and the geometric error of the rotating mirror cause image distortions, which seriously affect the photoacoustic-microscopy imaging quality. To improve the image quality of photoacoustic microscopy using polygon-scanning, an image correction method is proposed based on accurate ultrasound positioning. In this method, the photoacoustic and ultrasound imaging data of the sample are simultaneously obtained, and the angle information of each mirror used in the polygon-scanning is extracted from the ultrasonic data to correct the photoacoustic images. Experimental results show that the proposed method can significantly reduce image distortions in photoacoustic microscopy, with the image dislocation offset decreasing from 24.774 to 10.365 μm.

Assessment of hessian-based Frangi vesselness filter in optoacoustic imaging

Ultrasound (US) image guidance is widely used for minimally invasive procedures, but the invasive medical devices (such as metallic needles), especially their tips, can be poorly visualised in US images, leading to significant complications. Photoacoustic (PA) imaging is promising for visualising invasive devices and peripheral tissue targets. Light-emitting diodes (LEDs) acting as PA excitation sources facilitate the clinical translation of PA imaging, but the image quality is degraded due to the low pulse energy leading to insufficient contrast with needles at deep locations. In this paper, photoacoustic visualisation of clinical needles was enhanced by elastomeric nanocomposite coatings with superficial and interstitial illumination. Candle soot nanoparticle-polydimethylsiloxane (CSNP-PDMS) composites with high optical absorption and large thermal expansion coefficients were applied onto the needle exterior and the end-face of an optical fibre placed in the needle lumen. The excitation light was delivered at the surface by LED arrays and through the embedded optical fibre by a pulsed diode laser to improve the visibility of the needle tip. The performance was validated using an ex-vivo tissue model. An LED-based PA/US imaging system was used for imaging the needle out-of-plane and in-plane insertions over approach angles of 20 deg to 55 deg. The CSNP-PDMS composite conferred substantial visual enhancements on both the needle shaft and the tip, with an average of 1.7- and 1.6-fold improvements in signal-to-noise ratios (SNRs), respectively. With the extended light field involving extracorporeal and interstitial illumination and the highly absorbing coatings, enhanced visualisation of the needle shaft and needle tip was achieved with PA imaging, which could be helpful in current US-guided minimally invasive surgeries.

Because optoacoustic tomography (OAT) can provide functional information based on hemoglobin contrast, it is a promising imaging modality for breast cancer diagnosis. Developing an effective OAT breast imaging system requires balancing multiple design constraints, which can be expensive and time-consuming. Therefore, computer- simulation studies are often conducted to facilitate this task. However, most existing computer-simulation studies of OAT breast imaging employ simple phantoms such as spheres or cylinders that over-simplify the complex anatomical structures in breasts, thus limiting the value of these studies in guiding real-world system design. In this work, we propose a method to generate realistic numerical breast phantoms for OAT research based on clinical magnetic resonance imaging (MRI) data. The phantoms include a skin layer that defines breast-air boundary, major vessel branches that affect light absorption in the breast, and fatty tissue and fibroglandular tissue whose acoustical heterogeneity perturbs acoustic wave propagation. By assigning realistic optical and acoustic parameters to different tissue types, we establish both optic and acoustic breast phantoms, which will be exported into standard data formats for cross-platform usage.