Marta Betcke

Accelerated high-resolution photoacoustic tomography via compressed sensing.

Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue (4D PAT). A particular example is the planar Fabry-Pérot (FP) photoacoustic scanner, which yields high-resolution 3D images but takes several minutes to sequentially map the incident photoacoustic field on the 2D sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining model-based, variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: first, we describe and model two general spatial sub-sampling schemes. Then, we discuss how to implement them using the FP interferometer and demonstrate the potential of these novel compressed sensing PAT devices through simulated data from a realistic numerical phantom and through measured data from a dynamic experimental phantom as well as from in vivo experiments. Our results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction techniques that describe the tissues structures with suitable sparsity-constraints are used. In particular, we examine the use of total variation (TV) regularization enhanced by Bregman iterations. These novel reconstruction strategies offer new opportunities to dramatically increase the acquisition speed of photoacoustic scanners that employ point-by-point sequential scanning as well as reducing the channel count of parallelized schemes that use detector arrays.

Single-pixel optical camera for video rate ultrasonic imaging

A coherent-light single-pixel camera was used to interrogate a Fabry–Perot polymer film ultrasound sensor, thereby serially encoding a time-varying 2D ultrasonic field onto a single optical channel. By facilitating compressive sensing, this device enabled video rate imaging of ultrasound fields. In experimental demonstrations, this compressed sensing capability was exploited to reduce motion blur and capture dynamic features in the data. This relatively simple and inexpensive proof-of-principle device offers a route to high pixel count, high frame rate, broadband 2D ultrasound field mapping.

Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography

Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artifacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung computed tomography scans and then applied to in-vivo photoacoustic measurement data.

Iterated preconditioned LSQR method for inverse problems on unstructured grids

This article presents a method for solving large-scale linear inverse imaging problems regularized with a nonlinear, edge-preserving penalty term such as total variation or the Perona–Malik technique. Our method is aimed at problems defined on unstructured meshes, where such regularizers naturally arise in unfactorized form as a stiffness matrix of an anisotropic diffusion operator and factorization is prohibitively expensive. In the proposed scheme, the nonlinearity is handled with lagged diffusivity fixed point iteration, which involves solving a large-scale linear least squares problem in each iteration. Because the convergence of Krylov methods for problems with discontinuities is notoriously slow, we propose to accelerate it by means of priorconditioning (Bayesian preconditioning). priorconditioning is a technique that, through transformation to the standard form, embeds the information contained in the prior (Bayesian interpretation of a regularizer) directly into the forward operator and thence into the solution space. We derive a factorization-free preconditioned LSQR algorithm (MLSQR), allowing implicit application of the preconditioner through efficient schemes such as multigrid. The resulting method is also matrix-free i.e. the forward map can be defined through its action on a vector. We illustrate the performance of the method on two numerical examples. Simple 1D-deblurring problem serves to visualize the discussion throughout the paper. The effectiveness of the proposed numerical scheme is demonstrated on a three-dimensional problem in fluorescence diffuse optical tomography with total variation regularization derived algebraic multigrid preconditioner, which is the type of large scale, unstructured mesh problem, requiring matrix-free and factorization-free approaches that motivated the work here.

A real-time ultrasonic field mapping system using a Fabry Pérot single pixel camera for 3D photoacoustic imaging

A system for dynamic mapping of broadband ultrasound fields has been designed, with high frame rate photoacoustic imaging in mind. A Fabry-Pérot interferometric ultrasound sensor was interrogated using a coherent light single-pixel camera. Scrambled Hadamard measurement patterns were used to sample the acoustic field at the sensor, and either a fast Hadamard transform or a compressed sensing reconstruction algorithm were used to recover the acoustic pressure data. Frame rates of 80 Hz were achieved for 32x32 images even though no specialist hardware was used for the on-the-fly reconstructions. The ability of the system to obtain photocacoustic images with data compressions as low as 10% was also demonstrated.

Patterned interrogation scheme for compressed sensing photoacoustic imaging using a Fabry Perot planar sensor

Photoacoustic tomography (PAT) has become a powerful tool for biomedical imaging, particularly pre-clinical small animal imaging. Several different measurement systems have been demonstrated, in particular, optically addressed Fabry-Perot interferometer (FPI) sensors have been shown to provide exquisite images when a planar geometry is suitable. However, in its current incarnation the measurements must be made at each point sequentially, so these devices therefore suffer from slow data acquisition time. An alternative to this point-by-point interrogation scheme, is to interrogate the whole sensor with a series of independent patterns, so each measurement is the spatial integral of the product of the pattern and the acoustic field (as in the single-pixel Rice camera). Such an interrogation scheme allows compressed sensing to be used. This enables the number of measurements to be reduced significantly, leading to much faster data acquisition. An experimental implementation will be described, which employs a wide NIR tunable laser beam to interrogate the FPI sensor. The reflected beam is patterned by a digital micro-mirror device, and then focused to a single photodiode. To demonstrate the idea of patterned and compressed sensing for ultrasound detection, a scrambled Hadamard operator is used in the experiments. Photoacoustic imaging experiments of phantoms shows good reconstructed results with 20% compression.

Sub-sampled Fabry-Perot photoacoustic scanner for fast 3D imaging

The planar Fabry Perot (FP) photoacoustic scanner provides exquisite high resolution 3D images of soft tissue structures for sub-cm penetration depths. However, as the FP sensor is optically addressed by sequentially scanning an interrogation laser beam over its surface, the acquisition speed is low. To address this, a novel scanner architecture employing 8 interrogation beams and an optimised sub-sampling framework have been developed that increase the data acquisition speed significantly. With a 200Hz repetition rate excitation laser, full 3D images can be obtained within 10 seconds. Further increases in imaging speed with only minor decreases in image quality can be obtained by applying sub-sampling techniques with rates as low as 12.5%. This paper shows 3D images reconstructed from sub-sampled data for an ex vivo dataset, and results from a dynamic phantom imaging experiment.

Acoustic Wave Field Reconstruction From Compressed Measurements With Application in Photoacoustic Tomography

We present a method for the recovery of compressively sensed acoustic fields using patterned, instead of point-by-point, detection. From a limited number of such compressed measurements, we propose to reconstruct the field on the sensor plane in each time step independently assuming its sparsity in a Curvelet frame. A modification of the Curvelet frame is proposed to account for the smoothing effects of data acquisition and motivated by a frequency domain model for photoacoustic tomography. An ADMM type algorithm, split augmented Lagrangian shrinkage algorithm, is used to recover the pointwise data in each individual time step from the patterned measurements. For photoacoustic applications, the photoacoustic image of the initial pressure is reconstructed using time reversal in

Multi-sheet surface rebinning methods for reconstruction from asymmetrically truncated cone beam projections: I. Approximation and optimality

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Multiple-view diffuse optical tomography system based on time-domain compressive measurements

-Wave Toolbox.