Paper Info
Title
Evaluation of ultrasound synthetic aperture imaging using bidirectional pixel-based focusing: preliminary phantom and in vivo breast study.
Abstract
In medical ultrasound imaging, lateral resolution is limited when using a fixed transmit focusing. Various synthetic aperture (SA) techniques, in which two-way dynamic focusing is enabled by utilizing prebeamformed radio-frequency (RF) data have been proposed for improving the spatial resolution. However, SA methods were not extensively evaluated in terms of their clinical performance. In this paper, a phantom and an in vivo evaluation of the SA method with bidirectional pixel-based focusing (BiPBF) is presented in comparison with the conventional beamforming. The performance of the proposed SA-BiPBF was assessed with a blind study and the established breast imaging-reporting and data system (BI-RADS), in addition to measuring contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). Prebeamformed RF data were acquired from a tissue mimicking phantom (Model 040, CIRS Inc., Norfolk, VA, USA) and from patients with breast lesions by using a commercial ultrasound scanning system with a linear array transducer equipped with a research package and parallel data acquisition system (SonixTouch, SonixDAQ, and L14-5/38, Ultrasonix Corp., Canada). In phantom and in vivo experiments, a default setting of a breast preset was applied (e.g., the center frequency of 10 MHz and acoustic output of MI = 0.66). In phantom experiment, the SA-BiPBF method showed higher CNR and SNR values compared to the conventional method (3.4 and 23.9 dB versus 3.1 and 15.8 dB, respectively). In addition, the lateral resolution and penetration depth were increased by 95.4% and 40.3%, respectively. Consistent with the phantom experiment, in the in vivo experiment with ten patients, the CNR value for the SA method was 3.3 ± 0.5 compared to 2.8 ± 0.8 for the conventional method. Similarly, the SNR values with the SA-BiPBF and conventional methods were 34.0 ± 3.6 and 27.2 ± 3.4 dB, respectively. From the experiments, it was shown in side-by-side comparisons that the image quality of the SA-BiPBF method was considerably improved in both phantom and in vivo breast images. However, the SA-BiPBF image showed different features compared to the conventional one in the in vivo experiments. These features are resulting from the increased image quality of the SA-BiPBF method but are not always perceived as improvements by the radiologists.
Year
DOI
Venue
2013
10.1109/TBME.2013.2263310
IEEE Trans. Biomed. Engineering
Keywords
Field
DocType
fixed transmit focusing,clinical evaluation,sa-bipbf method,noise figure 23.9 db,bi-rads,image quality,noise figure 3.4 db,sa method,tissue mimicking phantom,bidirectional pixel-based focusing,conventional beamforming,penetration depth,biomedical transducers,image resolution,breast ultrasound imaging,breast preset,noise figure 3.1 db,spatial resolution,biomedical ultrasonics,acoustic output,clinical performance,synthetic aperture (sa),data acquisition,linear array transducer,sa-bipbf image,ultrasound synthetic aperture imaging,lateral resolution,commercial ultrasound scanning system,cnr value,contrast-to-noise ratio,medical ultrasound imaging,frequency 10 mhz,tumours,blind study,ultrasonic transducers,synthetic aperture technique,bidirectional pixel-based focusing (bipbf),two-way dynamic focusing,breast lesion,parallel data acquisition system,breast imaging-reporting and data system,snr value,research package,phantoms,prebeamformed radio-frequency data,medical image processing,signal-to-noise ratio,noise figure 15.8 db
Biomedical engineering,Ultrasonic sensor,Synthetic aperture radar,Computer science,Imaging phantom,Image quality,Electronic engineering,Center frequency,Pixel,Image resolution,Ultrasound
Journal
Volume
Issue
ISSN
60
10
1558-2531
Citations 
PageRank 
References 
2
0.59
0
Authors
9
Name
Order
Citations
PageRank
Choye Kim181.10
Changhan Yoon2113.33
Jongho Park313016.29
Yuhwa Lee420.59
Won Hwa Kim5459.01
Jung Min Chang61013.74
Byung Ihn Choi7101.49
Tai-Kyong Song8115.27
Yang-Mo Yoo9847.43