Approximate k-space models and Deep Learning for fast photoacoustic reconstruction

We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography. The approximate model introduces aliasing artefacts in the gradient information for the iterative reconstruction, but these artefacts are highly structured and we can train a CNN that can use the approximate information to perform an iterative reconstruction. We show feasibility of the method for human in-vivo measurements in a limited-view geometry. The proposed method is able to produce superior results to total variation reconstructions with a speed-up of 32 times

Extended source imaging, a unifying framework for seismic & medical imaging

We present three imaging modalities that live on the crossroads of seismic and medical imaging. Through the lens of extended source imaging, we can draw deep connections among the fields of wave-equation based seismic and medical imaging, despite first appearances. From the seismic perspective, we underline the importance to work with the correct physics and spatially varying velocity fields. Medical imaging, on the other hand, opens the possibility for new imaging modalities where outside stimuli, such as laser or radar pulses, can not only be used to identify endogenous optical or thermal contrasts but that these sources can also be used to insonify the medium so that images of the whole specimen can in principle be created.Comment: Submitted to the Society of Exploration Geophysicists Annual Meeting 202

Single-pixel camera photoacoustic tomography

Since it was first demonstrated more than a decade ago, the single-pixel camera concept has been used in numerous applications in which it is necessary or advantageous to reduce the channel count, cost, or data volume. Here, three-dimensional (3-D), compressed-sensing photoacoustic tomography (PAT) is demonstrated experimentally using a single-pixel camera. A large area collimated laser beam is reflected from a planar Fabry-Pérot ultrasound sensor onto a digital micromirror device, which patterns the light using a scrambled Hadamard basis before it is collected into a single photodetector. In this way, inner products of the Hadamard patterns and the distribution of thickness changes of the FP sensor-induced by the photoacoustic waves-are recorded. The initial distribution of acoustic pressure giving rise to those photoacoustic waves is recovered directly from the measured signals using an accelerated proximal gradient-type algorithm to solve a model-based minimization with total variation regularization. Using this approach, it is shown that 3-D PAT of imaging phantoms can be obtained with compression rates as low as 10%. Compressed sensing approaches to photoacoustic imaging, such as this, have the potential to reduce the data acquisition time as well as the volume of data it is necessary to acquire, both of which are becoming increasingly important in the drive for faster imaging systems giving higher resolution images with larger fields of view

A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentation

In an inhomogeneously illuminated photoacoustic image, important information like vascular geometry is not readily available when only the initial pressure is reconstructed. To obtain the desired information, algorithms for image segmentation are often applied as a post-processing step. In this work, we propose to jointly acquire the photoacoustic reconstruction and segmentation, by modifying a recently developed partially learned algorithm based on a convolutional neural network. We investigate the stability of the algorithm against changes in initial pressures and photoacoustic system settings. These insights are used to develop an algorithm that is robust to input and system settings. Our approach can easily be applied to other imaging modalities and can be modified to perform other high-level tasks different from segmentation. The method is validated on challenging synthetic and experimental photoacoustic tomography data in limited angle and limited view scenarios. It is computationally less expensive than classical iterative methods and enables higher quality reconstructions and segmentations than state-of-the-art learned and non-learned methods.Comment: "copyright 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Mitigating the limited view problem in photoacoustic tomography for a planar detection geometry by regularised iterative reconstruction

The use of a planar detection geometry in photoacoustic tomography results in the so-called limited-view problem due to the finite extent of the acoustic detection aperture. When images are reconstructed using one-step reconstruction algorithms, image quality is compromised by the presence of streaking artefacts, reduced contrast, image distortion and reduced signal-to-noise ratio. To mitigate this, model-based iterative reconstruction approaches based on least squares minimisation with and without total variation regularisation were evaluated using in-silico , experimental phantom, ex vivo and in vivo data. Compared to one-step reconstruction methods, it has been shown that iterative methods provide better image quality in terms of enhanced signal-to-artefact ratio, signal-to-noise ratio, amplitude accuracy and spatial fidelity. For the total variation approaches, the impact of the regularisation parameter on image feature scale and amplitude distribution was evaluated. In addition, the extent to which the use of Bregman iterations can compensate for the systematic amplitude bias introduced by total variation was studied. This investigation is expected to inform the practical application of model-based iterative image reconstruction approaches for improving photoacoustic image quality when using finite aperture planar detection geometries