Tackling the Bottleneck of Delay Tables in 3D Ultrasound Imaging

3D ultrasound imaging is quickly becoming a refer- ence technique for high-quality, accurate, expressive diagnostic medical imaging. Unfortunately, its computation requirements are huge and, today, demand expensive, power-hungry, bulky processing resources. A key bottleneck is the receive beamforming operation, which requires the application of many permutations of fine-grained delays among the digitized received echoes. To apply these delays in the digital domain, in principle large tables (billions of coefficients) are needed, and the access bandwidth to these tables can reach multiple TB/s, meaning that their storage both on-chip and off-chip is impractical. However, smarter implementations of the delay generation function, including forgoing the tables altogether, are possible. In this paper we explore efficient strategies to compute the delay function that controls the reconstruction of the image, and present a feasibility analysis for an FPGA platform

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

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

Apodization Scheme for Hardware-Efficient Beamformer

3D ultrasound is an emerging diagnostic technique that extends standard ultrasound imaging by capturing volumes, instead of planes. This brings completely new diagnostic opportunities, among which the possibility of disjoining image acquisition and analysis, thus enabling remote diagnosis, which would bring obvious medical and economic benefits. Unfortunately, 3D ultrasound is several orders of magnitude more computationally complex than 2D imaging. Therefore, algorithmic improvements to simplify the processing are mandatory in order to conceive cheap, portable, low-power imagers. The kernel of the 3D imaging process, called beamforming, consists essentially of computing delay and apodization profiles. We have previously devised an approximation of the delay calculation stage, which dramatically reduces hardware complexity. Unfortunately, this approximation introduces an intrinsic degree of inaccuracy that can be characterized as added image noise. In this paper, we identify an efficient approximated approach to the calculation of apodization profiles, that additionally minimizes (-76%) the error introduced during delay calculation. Together, these two techniques enable an efficient computation of 3D ultrasound images

Inexpensive 1024-Channel 3D Telesonography System on FPGA

Volumetric ultrasound (US) is a very promising development of medical US imaging. An under-exploited advantage of volumetric US is the mitigation of the strict probe positioning constrains necessary to acquire 2D scans, potentially allowing the decoupling of US image acquisition and diagnosis. However, today’s 3D US systems are large and beset by high power and cost requirements, making them only available in well-equipped hospitals. In this study, we propose the first telesonography-capable medical imaging system that supports up to 1024 channels, on par with the state of the art. As a first embodiment, we have implemented our design in a single development FPGA board of 26.7cm×14cm×0.16cm, with an estimated power consumption of 6.1 W. Moreover, we have equipped our platform with an automatic positioning module to help any operator defining the scan location, hence allowing for better remote diagnosis. Our design supports two types of data inputs: real-time via an optical connection and offline over Ethernet. The reconstructed images can be visualized on an HDMI screen. The estimated cost of the proposed prototype materials is less than 4000e

Assessment of Image Quality vs. Computation Cost for Different Parameterizations of Ultrasound Imaging Pipelines

Ultrasound imaging is a technique widely used in medicine to visualize organs and other body structures, capturing their position, size, morphology and any pathological lesions. Its use is unfortunately limited to specialized centers with trained personnel, and it would be beneficial to expand its applicability to environments like on-the-sheld emergency response and family physician cabinets. This requires the development of new ultrasound platforms that must be faster, lower-power, easier to use, safe and reliable. One of the major challenges to be met is to dynamically manage a myriad of different imaging options and configuration parameters, which impact image quality and computation cost at the same time. Focusing on this challenge, in this paper we first give an overview of ultrasound imaging techniques and of their possible configuration and parametrization options. We then discuss the impact of these options on computation cost and image quality, showing outcomes from a prototype Matlab ultrasound imaging pipeline

Streaming Architectures for Medical Image Reconstruction

Non-invasive imaging modalities have recently seen increased use in clinical diagnostic procedures. Unfortunately, emerging computational imaging techniques, such as those found in 3D ultrasound and iterative magnetic resonance imaging (MRI), are severely limited by the high computational requirements and poor algorithmic efficiency in current arallel hardware---often leading to significant delays before a doctor or technician can review the image, which can negatively impact patients in need of fast, highly accurate diagnosis. To make matters worse, the high raw data bandwidth found in 3D ultrasound requires on-chip volume reconstruction with a tight power dissipation budget---dissipation of more than 5~W may burn the skin of the patient. The tight power constraints and high volume rates required by emerging applications require orders of magnitude improvement over state-of-the-art systems in terms of both reconstruction time and energy efficiency. The goal of the research outlined in this dissertation is to reduce the time and energy required to perform medical image reconstruction through software/hardware co-design. By analyzing algorithms with a hardware-centric focus, we develop novel algorithmic improvements which simultaneously reduce computational requirements and map more efficiently to traditional hardware architectures. We then design and implement hardware accelerators which push the new algorithms to their full potential. In the first part of this dissertation, we characterize the performance bottlenecks of high-volume-rate 3D ultrasound imaging. By analyzing the 3D plane-wave ultrasound algorithm, we reduce computational and storage requirements in Delay Compression. Delay Compression recognizes additional symmetry in the planar transmission scheme found in 2D, 3D, and 3D-Separable plane-wave ultrasound implementations, enabling on-chip storage of the reconstruction constants for the first time and eliminating the ost power-intensive component of the reconstruction process. We then design and implement Tetris, a streaming hardware accelerator for 3D-Separable plane-wave ultrasound. Tetris is enabled by the Tetris Reserveration Station, a novel 2D register file that buffers incomplete voxels and eliminates the need for a traditional load-and-store memory interface. Utilizing a fully pipelined architecture, Tetris reconstructs volumes at physics-limited rates (i.e., limited by the physical propagation speed of sound through tissue). Next, we review a core component of several computational imaging modalities, the Non-uniform Fast Fourier Transform (NuFFT), focusing on its use in MRI reconstruction. We find that the non-uniform interpolation step therein requires over 99% of the reconstruction time due to poor spatial and temporal memory locality. While prior work has made great strides in improving the performance of the NuFFT, the most common algorithmic optimization severely limits the available parallelism, causing it to map poorly to the massively parallel processing available in modern GPUs and FPGAs. To this end, we create Slice-and-Dice, a processing model which enables efficient mapping of the NuFFT's most computationally-intensive component onto traditional parallel architectures. We then demonstrate the full acceleration potential of Slice-and-Dice with Jigsaw, a custom hardware accelerator which performs the non-uniform interpolations found in the NuFFT in time approximately linear in the number of non-uniform samples, rrespective of sampling pattern, uniform grid size, or interpolation kernel width. The algorithms and architectures herein enable faster, more efficient medical image reconstruction, without sacrificing image quality. By decreasing the time and energy required for image reconstruction, our work opens the door for future exploration into higher-resolution imaging and emerging, computationally complex reconstruction algorithms which improve the speed and quality of patient diagnosis.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167986/1/westbl_1.pd