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Deep Learning in Ultrasound Imaging Deep Learning Is Taking an Ever More Prominent Role in Medical Imaging

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Deep Learning in Ultrasound Imaging Deep Learning Is Taking an Ever More Prominent Role in Medical Imaging 1 Deep learning in Ultrasound Imaging Deep learning is taking an ever more prominent role in medical imaging. This paper discusses applications of this powerful approach in ultrasound imaging systems along with domain-specific opportunities and challenges. RUUD J.G. VAN SLOUN1,REGEV COHEN2 AND YONINA C. ELDAR3 ABSTRACT j We consider deep learning strategies in ultra- a highly cost-effective modality that offers the clinician an sound systems, from the front-end to advanced applications. unmatched and invaluable level of interaction, enabled by its Our goal is to provide the reader with a broad understand- real-time nature. Its portability and cost-effectiveness permits ing of the possible impact of deep learning methodologies point-of-care imaging at the bedside, in emergency settings, on many aspects of ultrasound imaging. In particular, we rural clinics, and developing countries. Ultrasonography is discuss methods that lie at the interface of signal acquisi- increasingly used across many medical specialties, spanning tion and machine learning, exploiting both data structure from obstetrics to cardiology and oncology, and its market (e.g. sparsity in some domain) and data dimensionality (big share is globally growing. data) already at the raw radio-frequency channel stage. On the technological side, ultrasound probes are becoming As some examples, we outline efficient and effective deep increasingly compact and portable, with the market demand learning solutions for adaptive beamforming and adaptive for low-cost ‘pocket-sized’ devices expanding [2], [3]. Trans- spectral Doppler through artificial agents, learn compressive ducers are miniaturized, allowing e.g. in-body imaging for encodings for color Doppler, and provide a framework for interventional applications. At the same time, there is a strong structured signal recovery by learning fast approximations trend towards 3D imaging [4] and the use of high-frame- of iterative minimization problems, with applications to rate imaging schemes [5]; both accompanied by dramatically clutter suppression and super-resolution ultrasound. These increasing data rates that pose a heavy burden on the probe- emerging technologies may have considerable impact on ul- system communication and subsequent image reconstruction trasound imaging, showing promise across key components algorithms. Systems today offer a wealth of advanced appli- in the receive processing chain. cations and methods, including shear wave elasticity imaging [6], ultra-sensitive Doppler [7], and ultrasound localization KEYWORDS j Deep learning; ultrasound imaging; image microscopy for super-resolution microvascular imaging [8]. reconstruction; beamforming, Doppler, compression, deep With the demand for high-quality image reconstruction and unfolding, super resolution. signal extraction from unfocused planar wave transmissions that facilitate fast imaging, and a push towards miniaturization, I. INTRODUCTION modern ultrasound imaging leans heavily on innovations in powerful receive channel processing. In this paper, we discuss Diagnostic imaging plays a critical role in healthcare, serving how artificial intelligence and deep learning methods can play as a fundamental asset for timely diagnosis, disease staging a compelling role in this process, and demonstrate how these arXiv:1907.02994v2 [eess.SP] 29 Jul 2019 and management as well as for treatment choice, planning, data-driven systems can be leveraged across the ultrasound guidance, and follow-up. Among the diagnostic imaging op- imaging chain. We aim to provide the reader with a broad tions, ultrasound imaging [1] is uniquely positioned, being understanding of the possible impact of deep learning on R. J. G. van Sloun is with the department of Electrical En- a variety of ultrasound imaging aspects, placing particular gineering, Eindhoven University of Technology, Eindhoven, The emphasis on methods that exploit both the power of data and Netherlands (email: [email protected]) signal structure (for instance sparsity in some domain) to yield R. Cohen is with the department of Electrical Engineering, Technion, Israel robust and data-efficient solutions. We believe that methods Y. C. Eldar is with the Faculty of Mathematics and Computer that exploit models and structure together with learning from science, Weizmann Institute of Science, Rehovot, Israel (email: data can pave the way to interpretable and powerful processing [email protected]) methods from limited training sets. As such, throughout the Accepted for publication in the Proceedings of the IEEE. ©2019 IEEE. paper, we will typically first discuss an appropriate model- Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, includ- based solution for the problems considered, and then follow ing reprinting/republishing this material for advertising or promotional by a data-driven deep learning solution derived from it. purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in We start by briefly describing a standard ultrasound imaging other works. chain in Section II. We then elaborate on several dedicated 2 deep learning solutions that aim at improving key components switching and processing already takes place in the probe in this processing pipeline, covering adaptive beamforming head, e.g. in the form of multiplexing or microbeamforming. (Section III-A), adaptive spectral Doppler (Section III-B), Slow-time1 multiplexing distributes the received channel data compressive tissue Doppler (Section III-C), and clutter sup- across multiple transmits, by only communicating a subset pression (Section III-D). In Section IV, we show how the of the number of channels to the back-end for each such synergetic exploitation of deep learning and signal struc- transmit. This consequently reduces the achieved frame rate. ture enables robust super-resolution microvascular ultrasound In microbeamforming, an analog pre-beamforming step is imaging. Finally, we discuss future perspectives, opportunities, performed to compress channel data from multiple (adjacent) and challenges for the holistic integration of artificial intelli- elements into a single focused line. This however impairs gence and deep learning methods in ultrasound systems. flexibility in subsequent digital beamforming, limiting the achievable image quality. Other approaches aim at mixing II. THE ULTRASOUND IMAGING CHAIN multiple channels through analog modulation with chipping AT A GLANCE sequences [9]. Additional analog processing includes signal A. Transmit schemes amplification by a low-noise amplifier (LNA) as well as depth (i.e. fast-time) dependent gain compensation (TGC) for The resolution, contrast, and overall fidelity of ultrasound attenuation correction. pulse-echo imaging relies on careful optimization across its Digital receive beamforming in ultrasound imaging is dy- entire imaging chain. At the front-end, imaging starts with the namic, i.e. receive focusing is dynamically optimized based design of appropriate transmit schemes. on the scan depth. The industry standard is delay-and-sum At this stage, crucial trade-offs are made, in which the beamforming, where depth-dependent channel tapering (or frame rate, imaging depth, and attainable axial and lateral apodization) is optimized and fine-tuned based on the system resolution are weighted carefully against each other: improved and application. Delay-and-sum beamforming is commonplace resolution can be achieved through the use of higher pulse due to its low complexity, providing real-time image recon- modulation frequencies; yet, these shorter wavelengths suffer struction, albeit at a high sampling rate and non-optimal image from increased absorption and thus lead to reduced penetration quality. depth. Likewise, high frame rate can be reached by exploiting Performing beamforming in the digital domain requires parallel transmission schemes based on e.g. planar or diverging sampling the signals received at the transducer elements and waves. However, use of such unfocused transmissions comes transmitting the samples to a back-end processing unit. To at the cost of loss in lateral resolution compared to line- achieve sufficient delay resolution for focusing, the received based scanning with tightly focused beams. As such, optimal signals are typically sampled at 4-10 times their bandwidth, transmit schemes depend on the application. i.e., the sampling rate may severely exceed the Nyquist Today, an increasing amount of ultrasound applications rely rate. A possible approach for sampling rate reduction is to on high frame-rate (dubbed ultrafast) imaging. Among these consider the received signals within the framework of finite are e.g. ultrasound localization microscopy (see Section IV), rate of innovation (FRI) [10], [11]. Tur et al. [12] modeled highly-sensitive Doppler, and shear wave elastography. Where the received signal at each element as a finite sum of replicas the former two mostly exploit the incredible vastness of data to of the transmitted pulse backscattered from reflectors. The obtain accurate signal statistics, the later leverages high-speed replicas are fully described by their unknown amplitudes and imaging to track ultrasound-induced shear waves propagating delays, which can be recovered from the signals’ Fourier at several meters per second. series coefficients. The latter can be computed from low- With the expanding use of ultrafast
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