Abstract
Medical ultrasonic imaging has been utilized in a variety of clinical diagnoses for many years. Recently, because of the needs of portable and mobile medical ultrasonic diagnoses, the development of real-time medical ultrasonic imaging algorithms on embedded computing platforms is a rising research direction. Typically, delay-and-sum beamforming algorithm is implemented on embedded medical ultrasonic scanners. Such algorithm is the easiest to implement at real-time frame rate, but the image quality of this algorithm is not high enough for complicated diagnostic cases. As a result, minimum-variance adaptive beamforming algorithm for medical ultrasonic imaging is considered in this paper, which shows much higher image quality than that of delay-and-sum beamforming algorithm. However, minimum-variance adaptive beamforming algorithm is a complicated algorithm with O(n3) computational complexity. Consequently, it is not easy to implement such algorithm on embedded computing platform at real-time frame rate. On the other hand, GPU is a well-known parallel computing platform for image processing. Therefore, embedded GPU computing platform is considered as a potential real-time implementation platform of minimum-variance beamforming algorithm in this paper. By applying the described effective implementation strategies, the GPU implementation of minimum-variance beamforming algorithm performed more than 100 times faster than the ARM implementation on the same heterogeneous embedded platform. Furthermore, platform power consumptions, computation energy efficiency, and platform cost efficiency of the experimental heterogeneous embedded platforms were also evaluated, which demonstrated that the investigated heterogeneous embedded computing platforms were suitable for real-time portable or mobile high-quality medical ultrasonic imaging device constructions.
Highlights
There are several useful medical imaging modalities for clinical diagnoses, which are radiography [1], magnetic resonance imaging [2], nuclear imaging [3], ultrasonic imaging [4], and computed tomography [5]
A medical ultrasonic imaging algorithm can be used for real-world clinical diagnostic applications only if its implementation can run at a real-time video frame rate
Delay-and-sum (DAS) beamforming [6] is the most widespread imaging algorithm among various medical ultrasonic imaging algorithms, which is the easiest to implement at real-time frame rate
Summary
There are several useful medical imaging modalities for clinical diagnoses, which are radiography [1], magnetic resonance imaging [2], nuclear imaging [3], ultrasonic imaging [4], and computed tomography [5]. A medical ultrasonic imaging algorithm can be used for real-world clinical diagnostic applications only if its implementation can run at a real-time video frame rate. Delay-and-sum (DAS) beamforming [6] is the most widespread imaging algorithm among various medical ultrasonic imaging algorithms, which is the easiest to implement at real-time frame rate. High-quality medical ultrasonic imaging algorithms (e.g., minimumvariance (MV) adaptive beamforming algorithm [11], synthetic aperture imaging algorithm [12]) provide much better image quality, as compared to DAS beamforming algorithm, which can present more anatomical details for complicated diagnostic cases. Field II simulator is a widespread tool to generate reliable medical ultrasonic imaging input data, especially for quantitative evaluation of the output image quality and verification of imaging algorithm correctness, which is its major benefit over real scenario. On the other hand, the two very close point targets at 30-mm imaging depth were clearly distinguished using MV adaptive beamforming algorithm
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