Mitigation of artifacts due to isolated acoustic heterogeneities in photoacoustic computed tomography using a variable data truncation-based reconstruction method

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the absorbed optical energy density within tissue. If the object possesses spatially variant acoustic properties that are unaccounted for by the reconstruction method, the estimated image can contain distortions. While reconstruction methods have recently been developed to compensate for this effect, they generally require the object's acoustic properties to be known a priori. To circumvent the need for detailed information regarding an object's acoustic properties, we previously proposed a half-time reconstruction method for PACT. A half-time reconstruction method estimates the PACT image from a data set that has been temporally truncated to exclude the data components that have been strongly aberrated. However, this method can be improved upon when the approximate sizes and locations of isolated heterogeneous structures, such as bones or gas pockets, are known. To address this, we investigate PACT reconstruction methods that are based on a variable data truncation (VDT) approach. The VDT approach represents a generalization of the half-time approach, in which the degree of temporal truncation for each measurement is determined by the distance between the corresponding ultrasonic transducer location and the nearest known bone or gas void location. Computer-simulated and experimental data are employed to demonstrate the effectiveness of the approach in mitigating artifacts due to acoustic heterogeneities

Microwave imaging techniques for biomedical applications

Microwaves have been considered for medical applications involving the detection of organ movements and changes in tissue water content. More particularly cardiopulmonary interrogation via microwaves has resulted in various sensors monitoring ventricular volume change or movement, arterial wall motion, respiratory movements, pulmonary oedema, etc. In all these applications, microwave sensors perform local measurements and need to be displaced for obtaining an image reproducing the spatial variations of a given quantity. Recently, advances in the area of inverse scattering theory and microwave technology have made possible the development of microwave imaging and tomographic instruments. This paper provides a review of such equipment developed at Suplec and UPC Barcelona, within the frame of successive French-Spanish PICASSO cooperation programs. It reports the most significant results and gives some perspectives for future developments. Firstly, a brief historical survey is given. Then, both technological and numerical aspects are considered. The results of preliminary pre-clinical assessments and in-lab experiments allow to illustrate the capabilities of the existing equipment, as well as its difficulty in dealing with clinical situations. Finally, some remarks on the expected development of microwave imaging techniques for biomedical applications are given.Peer ReviewedPostprint (published version

Far-field optical microscope with nanometer-scale resolution based on in-plane surface plasmon imaging

A new far-field optical microscopy technique capable of reaching nanometer-scale resolution has been developed recently using the in-plane image magnification by surface plasmon polaritons. This microscopy is based on the optical properties of a metal-dielectric interface that may, in principle, provide extremely large values of the effective refractive index n up to 100-1000 as seen by the surface plasmons. Thus, the theoretical diffraction limit on resolution becomes lambda/2n, and falls into the nanometer-scale range. The experimental realization of the microscope has demonstrated the optical resolution better than 50 nm for 502 nm illumination wavelength. However, the theory of such surface plasmon-based far-field microscope presented so far gives an oversimplified picture of its operation. For example, the imaginary part of the metal dielectric constant severely limits the surface-plasmon propagation and the shortest attainable wavelength in most cases, which in turn limits the microscope magnification. Here I describe how this limitation has been overcome in the experiment, and analyze the practical limits on the surface plasmon microscope resolution. In addition, I present more experimental results, which strongly support the conclusion of extremely high spatial resolution of the surface plasmon microscope.Comment: 23 pages, 9 figures, will be published in the topical issue on Nanostructured Optical Metamaterials of the Journal of Optics A: Pure and Applied Optics, Manuscript revised in response to referees comment

Body MRI artifacts in clinical practice: a physicist\u27s and radiologist\u27s perspective.

The high information content of MRI exams brings with it unintended effects, which we call artifacts. The purpose of this review is to promote understanding of these artifacts, so they can be prevented or properly interpreted to optimize diagnostic effectiveness. We begin by addressing static magnetic field uniformity, which is essential for many techniques, such as fat saturation. Eddy currents, resulting from imperfect gradient pulses, are especially problematic for new techniques that depend on high performance gradient switching. Nonuniformity of the transmit radiofrequency system constitutes another source of artifacts, which are increasingly important as magnetic field strength increases. Defects in the receive portion of the radiofrequency system have become a more complex source of problems as the number of radiofrequency coils, and the sophistication of the analysis of their received signals, has increased. Unwanted signals and noise spikes have many causes, often manifesting as zipper or banding artifacts. These image alterations become particularly severe and complex when they are combined with aliasing effects. Aliasing is one of several phenomena addressed in our final section, on artifacts that derive from encoding the MR signals to produce images, also including those related to parallel imaging, chemical shift, motion, and image subtraction

Ultrasonic Computed Tomography

Ultrasonic Computed Tomography (UCT) is a full digital imaging technique, which consists in numerically solving the inverse scattering problem associated to the forward scattering problem describing the interaction of ultrasonic waves with inhomogeneous media. For weakly inhomogeneous media such as soft tissues, various approximations of the solution of the forward problem (straight ray approximation, Born approximation...), leading to easy-to-implement approximations of the inverse scattering problem (back-projection or back-propagation algorithms) can be used. In the case of highly heterogeneous media such as bone surrounded by soft tissues, such approximations are no more valid. We present here two non-linear inversion schemes based on high-order approximations. These methods are conceived like the prolongation of the methods implemented in the weakly inhomogeneous case for soft tissues. The results show the feasibility of this UCT approach to bones and its potential to perform measurements in vivo

Distorted Born diffraction tomography: limits and applications to inverse the ultrasonic field scattered by an non-circular infinite elastic tube

International audienceThis study focuses on the application of ultrasonic diffraction tomography to noncircular 2D-cylindrical objects immersed in an infinite fluid. The distorted Born iterative method used to solve the inverse scattering problem be longs to the class of algebraic reconstruction algorithms. This method was developed to increase the order of application of the Born approximation (in the case of weakly-contrasted media) to higher orders. This yields quantitative in formation about the scatterer, such as the speed of sound and the attenuation. Quantitative ultrasonic imaging techniques of this kind are of great potential value in fields such as medicine, under water acoustics and non destructive testing

Learning to Interpret Fluid Type Phenomena via Images

Learning to interpret fluid-type phenomena via images is a long-standing challenging problem in computer vision. The problem becomes even more challenging when the fluid medium is highly dynamic and refractive due to its transparent nature. Here, we consider imaging through such refractive fluid media like water and air. For water, we design novel supervised learning-based algorithms to recover its 3D surface as well as the highly distorted underground patterns. For air, we design a state-of-the-art unsupervised learning algorithm to predict the distortion-free image given a short sequence of turbulent images. Specifically, we design a deep neural network that estimates the depth and normal maps of a fluid surface by analyzing the refractive distortion of a reference background pattern. Regarding the recovery of severely downgraded underwater images due to the refractive distortions caused by water surface fluctuations, we present the distortion-guided network (DG-Net) for restoring distortion-free underwater images. The key idea is to use a distortion map to guide network training. The distortion map models the pixel displacement caused by water refraction. Furthermore, we present a novel unsupervised network to recover the latent distortion-free image. The key idea is to model non-rigid distortions as deformable grids. Our network consists of a grid deformer that estimates the distortion field and an image generator that outputs the distortion-free image. By leveraging the positional encoding operator, we can simplify the network structure while maintaining fine spatial details in the recovered images. We also develop a combinational deep neural network that can simultaneously perform recovery of the latent distortion-free image as well as 3D reconstruction of the transparent and dynamic fluid surface. Through extensive experiments on simulated and real captured fluid images, we demonstrate that our proposed deep neural networks outperform the current state-of-the-art on solving specific tasks