Source Density Apodization: Image Artifact Suppression Through Source Pitch Nonuniformity

Non-iterative model-based inversion for low channel-count optical ultrasound imaginga)

Ultrasound image reconstruction is typically performed using the computationally efficient delay-and-sum algorithm. However, this algorithm is suboptimal for systems of low channel counts, where it causes significant image artefacts. These artefacts can be suppressed through model-based inversion approaches; however, their computational costs typically prohibit real-time implementations. In this work, the emerging optical ultrasound (OpUS) modality is considered, where ultrasound waves are both generated and detected using light. With this modality, imaging probes comprise very low channel counts, resulting in significant image artefacts that limit the imaging dynamic range. However, this low channel counts offer an opportunity for non-iterative ("direct") model-based inversion (DMI) on modest computational resources available in a typical workstation. When applied to both synthetic and experimental OpUS data, the presented DMI method achieved substantial reduction in image artefacts and noise, improved recovery of image amplitudes, and-after one-off pre-computation of the system matrices-significantly reduced reconstruction time, even in imaging scenarios exhibiting mild spatial inhomogeneity. Whilst here applied to an OpUS imaging system, DMI can be applied to other low channel-count imaging systems, and is therefore expected to achieve better image quality, reduce system complexity, or both, in a wide range of settings.

Towards clinical application of freehand optical ultrasound imaging

Freehand optical ultrasound (OpUS) imaging is an emerging ultrasound imaging paradigm that uses an array of fibre-optic, photoacoustic ultrasound sources and a single fibre-optic ultrasound detector to perform ultrasound imaging without the need for electrical components in the probe head. Previous freehand OpUS devices have demonstrated capability for real-time, video-rate imaging of clinically relevant targets, but have been hampered by poor ultrasound penetration, significant imaging artefacts and low frame rates, and their designs limited their clinical applicability. In this work we present a novel freehand OpUS imaging platform, including a fully mobile and compact acquisition console and an improved probe design. The novel freehand OpUS probe presented utilises optical waveguides to shape the generated ultrasound fields for improved ultrasound penetration depths, an extended fibre-optic bundle to improve system versatility and an overall ruggedised design with protective elements to improve probe handling and protect the internal optical components. This probe is demonstrated with phantoms and the first multi-participant in vivo imaging study conducted with freehand OpUS imaging probes, this represents several significant steps towards the clinical translation of freehand OpUS imaging.

Online evolution of a phased array for ultrasonic imaging by a novel adaptive data acquisition method

Ultrasonic imaging, using ultrasonic phased arrays, has an enormous impact in science, medicine and society and is a widely used modality in many application fields. The maximum amount of information which can be captured by an array is provided by the data acquisition method capturing the complete data set of signals from all possible combinations of ultrasonic generation and detection elements of a dense array. However, capturing this complete data set requires long data acquisition time, large number of array elements and transmit channels and produces a large volume of data. All these reasons make such data acquisition unfeasible due to the existing phased array technology or non-applicable to cases requiring fast measurement time. This paper introduces the concept of an adaptive data acquisition process, the Selective Matrix Capture (SMC), which can adapt, dynamically, to specific imaging requirements for efficient ultrasonic imaging. SMC is realised experimentally using Laser Induced Phased Arrays (LIPAs), that use lasers to generate and detect ultrasound. The flexibility and reconfigurability of LIPAs enable the evolution of the array configuration, on-the-fly. The SMC methodology consists of two stages: a stage for detecting and localising regions of interest, by means of iteratively synthesising a sparse array, and a second stage for array optimisation to the region of interest. The delay-and-sum is used as the imaging algorithm and the experimental results are compared to images produced using the complete generation-detection data set. It is shown that SMC, without a priori knowledge of the test sample, is able to achieve comparable results, while preforming ∼ 10 times faster data acquisition and achieving ∼ 10 times reduction in data size.

Neural Network Kalman Filtering for 3-D Object Tracking From Linear Array Ultrasound Data

Many interventional surgical procedures rely on medical imaging to visualize and track instruments. Such imaging methods not only need to be real time capable but also provide accurate and robust positional information. In ultrasound (US) applications, typically, only 2-D data from a linear array are available, and as such, obtaining accurate positional estimation in three dimensions is nontrivial. In this work, we first train a neural network, using realistic synthetic training data, to estimate the out-of-plane offset of an object with the associated axial aberration in the reconstructed US image. The obtained estimate is then combined with a Kalman filtering approach that utilizes positioning estimates obtained in previous time frames to improve localization robustness and reduce the impact of measurement noise. The accuracy of the proposed method is evaluated using simulations, and its practical applicability is demonstrated on experimental data obtained using a novel optical US imaging setup. Accurate and robust positional information is provided in real time. Axial and lateral coordinates for out-of-plane objects are estimated with a mean error of 0.1 mm for simulated data and a mean error of 0.2 mm for experimental data. The 3-D localization is most accurate for elevational distances larger than 1 mm, with a maximum distance of 6 mm considered for a 25-mm aperture.

Freehand and video-rate all-optical ultrasound imaging

All-optical ultrasound (AOUS) imaging, which uses light to both generate and detect ultrasound, is an emerging alternative to conventional electronic ultrasound imaging. To date, AOUS imaging has been performed using paradigms that either resulted in long acquisition times or employed bench-top imaging systems that were impractical for clinical use. In this work, we present a novel AOUS imaging paradigm where scanning optics are used to rapidly synthesise an imaging aperture. This paradigm enabled the first AOUS system with a flexible, handheld imaging probe, which represents a critical step towards clinical translation. This probe, which provides video-rate imaging and a real-time display, is demonstrated with phantoms and in vivo human tissue.

Sparse channel sampling for ultrasound localization microscopy (SPARSE-ULM)

Ultrasound localization microscopy (ULM) has recently enabled the mapping of the cerebral vasculaturein vivowith a resolution ten times smaller than the wavelength used, down to ten microns. However, with frame rates up to 20000 frames per second, this method requires large amount of data to be acquired, transmitted, stored, and processed. The transfer rate is, as of today, one of the main limiting factors of this technology. Herein, we introduce a novel reconstruction framework to decrease this quantity of data to be acquired and the complexity of the required hardware by randomly subsampling the channels of a linear probe. Method performance evaluation as well as parameters optimization were conductedin silicousing the SIMUS simulation software in an anatomically realistic phantom and then compared toin vivoacquisitions in a rat brain after craniotomy. Results show that reducing the number of active elements deteriorates the signal-to-noise ratio and could lead to false microbubbles detections but has limited effect on localization accuracy. In simulation, the false positive rate on microbubble detection deteriorates from 3.7% for 128 channels in receive and 7 steered angles to 11% for 16 channels and 7 angles. The average localization accuracy ranges from 10.6μm and 9.93μm for 16 channels/3 angles and 128 channels/13 angles respectively. These results suggest that a compromise can be found between the number of channels and the quality of the reconstructed vascular network and demonstrate feasibility of performing ULM with a reduced number of channels in receive, paving the way for low-cost devices enabling high-resolution vascular mapping.