In B-mode images from dual-sided ultrasound, it has been shown that by delineating structures suspected of being
relatively homogeneous, one can enhance limited angle tomography to produce speed of sound images in the same view
as X-ray Digital Breast Tomography (DBT). This could allow better breast cancer detection and discrimination, as well
as improved registration of the ultrasound and X-ray images, because of the similarity of SOS and X-ray contrast in the
breast. However, this speed of sound reconstruction method relies strongly on B-mode or other reflection mode
segmentation. If that information is limited or incorrect, artifacts will appear in the reconstructed images. Therefore, the
iterative speed of sound reconstruction algorithm has been modified in a manner of simultaneously utilizing the image
segmentations and removing most artifacts. The first step of incorporating a priori information is solved by any nonlinearnonconvex
optimization method while artifact removal is accomplished by employing the fast split Bregman method to
perform total-variation (TV) regularization for image denoising. The proposed method was demonstrated in simplified
simulations of our dual-sided ultrasound scanner. To speed these computations two opposed 40-element ultrasound linear
arrays with 0.5 MHz center frequency were simulated for imaging objects in a uniform background. The proposed speed
of sound reconstruction method worked well with both bent-ray and full-wave inversion methods. This is also the first
demonstration of successful full-wave medical ultrasound tomography in the limited angle geometry. Presented results
lend credibility to a possible translation of this method to clinical breast imaging.
Rungroj Jintamethasawat, Yunhao Zhu, Oliver D. Kripfgans, Jie Yuan, Mitchell M. Goodsitt, and Paul L. Carson, "Limited angle breast ultrasound tomography with a priori information and artifact removal," Proc. SPIE 10139, Medical Imaging 2017: Ultrasonic Imaging and Tomography, 101390N (Presented at SPIE Medical Imaging: February 16, 2017; Published: 13 March 2017); https://doi.org/10.1117/12.2253911.
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