Single-FPGA 3D Ultrasound Beamformer

In medical diagnosis, ultrasound (US) imaging is one of the most common, safe, and powerful techniques. Volumetric (3D) US imaging, an emerging technique, is even more attractive than standard 2D imaging, as it allows for imaging without the local presence of a trained sonographer finely positioning the probe. This would be particularly useful in rescue operations, remote areas and developing countries. Unfortunately, present-day 3D imagers are expensive, bulky and power-hungry, confining them to hospitals. There is therefore a strong motivation to develop efficient electronics to enable a portable US platform that is small, cheap, and battery- operated. Beamforming (BF) is the most computationally expensive of 3D imaging. Both commercial [1] and research [2] imagers have dealt with the challenge by reducing the number of receive channels, hence simplifying the computation through the usage of far fewer elements. This comes at the cost of image quality, and the resulting machines are nonetheless still non-portable and expensive. In turn, the bottleneck of the BF process is the calculation of acoustic delays, which requires up to trillions of square roots per second. We propose a drastically more efficient architecture [3]. With geometric considerations, each delay is calculated from a small set of square roots (mapped onto CORDICs), plus two additions. In this demo, we will show the reconstruction of a 2.5M-voxel volume, supporting a transducer with 32×32 receive channels. We have fitted the architecture into a single Kintex UltraScale KU040 [4], which is unprecedented. We also extrapolated the utilization of a 80×80 instance on a Virtex UltraScale XCVU190 [4]. Table I shows the implementation results. Fig. 1 shows our beamformer custom block connected to the other FPGA subsystems. The delay calculation architecture is shown in Fig. 2. The demo setup is presented in Fig. 3, where the 3D beamformer is implemented on the FPGA, while the pre- and post-processing stages are currently performed on Matlab.

Demo: Efficient Delay and Apodization for on-FPGA 3D Ultrasound

Federico Angiolini, Marcel Arditi, Giovanni De Micheli, Aya Mohamed Naguib Ahmed Ibrahim, Jean-Philippe Thiran, Ahmet Caner Yüzügüler

In medical diagnosis, ultrasound (US) imaging is one of the most common, safe, and powerful techniques. Volumetric (3D) US is potentially very attractive, compared to 2D US, because it might enable telesonography - decoupling the local image acquisition, by an untrained person, and the diagnosis, by the trained sonographer, who can be remote. Unfortunately, current 3D systems are hospital-oriented, bulky and expensive, and they cannot be available in emergency operations or rural areas. This motivates us to develop a portable US platform with cheap, battery-operated, more efficient electronics.

2016

1024-Channel Single 5W FPGA Towards High-quality Portable 3D Ultrasound Platform

Marcel Arditi, Giovanni De Micheli, Aya Mohamed Naguib Ahmed Ibrahim, Jean-Philippe Thiran, Ahmet Caner Yüzügüler

Volumetric Ultrasound (US) imaging is an emerging tech- nology for medical US applications. Typically, US imaging is 2D, where a number of vibrating elements, arranged in an array, are used to scan 2D cross-sections of the human body. In volumetric US a matrix probe of vibrating elements is used instead of the array, where conical volumes are reconstructed instead of 2D cross-sections. Today, cardiology and obstetrics are the most benefiting applications from 3D imaging, where better assessment of chamber volumes, and more expressive imaging are provided, respectively. 3D US allows the imaging of entire volumes using a single scan, unlike in 2D imaging, where multiple slices should be acquired precisely by a trained sonographer to be able to diagnose the entire structure. As a result, 3D US imaging speeds up the acquisition time, and eliminates the dependency on the presence of a trained operator during the scan. These characteristics make 3D US ideal for situations where the presence of a trained sonographer is an issue and the need to speed up the acquisition time is paramount, such as battlefields and rescue environments. How- ever, todays 3D systems [1] are bulky, expensive, and power hungry because the processing load of 3D US is orders of magnitude higher compared to conventional 2D imaging. For this reason, 3D systems are currently only available in well- equipped hospitals, and not in rural areas and underdeveloped regions where even electricity supply is an issue.

2017

Ultrasound Imaging: From Physical Modeling to Deep Learning

Dimitris Perdios

Among the medical imaging modalities, ultrasound (US) imaging is one of the safest, most widespread, and least expensive method used in medical diagnosis. In the past decades, several technological advances enabled the advent of ultrafast US imaging, an acquisition technique capable of imaging large tissue zones at very high frame rates of multiple kilohertz. Achieving such frame rates on large tissue zones enables the analysis of complex physical phenomena occurring in the human body. For instance, such capability enables estimating both very fast and very slow flows occurring in the cardiovascular system, with high sensitivity. High frame rates also enable estimating micrometer tissue displacements induced by naturally occurring or externally induced shear waves that propagate through tissue at a few meters per second. Ultrafast US imaging is already at the origin of several breakthrough imaging modes such as shear-weave elastography and functional neuroimaging. One of the main advantages of pulse-echo US imaging is that it is a dynamic imaging modality. Conventional US images reconstructed using the well-known delay-and-sum algorithm are characterized by speckle patterns. Despite being an "illusion" of the imaging system, these patterns react coherently to underlying physical phenomena, thus containing positional information of the tissue being imaged that can be exploited by displacement estimation techniques. Because ultrafast acquisitions are performed using unfocused wavefronts, resulting images are of low quality, characterized by broad main lobes (low resolution) and high diffraction artifacts (low contrast) caused by grating lobes, side lobes, and edge waves. Such artifacts can be detrimental to both lesion detection and displacement estimation techniques, the latter being the core objectives of most ultrafast US imaging modes. A natural way of increasing the image quality of consecutive frames consists of averaging coherently multiple low-quality images obtained from differently steered unfocused wavefronts, at the expense of a reduced frame rate and possible motion artifacts. This thesis aims at answering the increasing need for US image reconstruction methods capable of producing high-quality images from single ultrafast acquisitions, may it be to improve the accuracy and robustness of ultrafast imaging modes such as shear-wave elastography, to reduce the cost and complexity of 3-D ultrasound scanners, or to mitigate the power and data transfer rate requirements of portable systems. This work builds in the context of inverse problems, with an efficient modeling of the physical measurement process (forward model) involved in ultrasound acquisitions. It leverages recent deep-learning-based projection methods to overcome a crucial limitation of regularized ultrasound imaging: conventional image-processing regularizers are not well suited to the high dynamic range and statistical properties of radio frequency ultrasound images, especially in the presence of speckle patterns. Physical modeling is fundamental to this work. It was crucial to derive a computationally tractable forward model for image reconstruction, but also to develop a highly efficient, spline-based, spatial impulse response ultrasound simulator that allowed generating sufficiently large datasets to train convolutional neural networks. Applications were carried out in single-plane-wave imaging, ultrafast displacement estimation, and sparse-array imaging.

EPFL2021

Ultrasound Imaging: From Physical Modeling to Deep Learning

Dimitris Perdios

EPFL2021