618 research outputs found
Xampling in Ultrasound Imaging
Recent developments of new medical treatment techniques put challenging
demands on ultrasound imaging systems in terms of both image quality and raw
data size. Traditional sampling methods result in very large amounts of data,
thus, increasing demands on processing hardware and limiting the exibility in
the post-processing stages. In this paper, we apply Compressed Sensing (CS)
techniques to analog ultrasound signals, following the recently developed
Xampling framework. The result is a system with significantly reduced sampling
rates which, in turn, means significantly reduced data size while maintaining
the quality of the resulting images.Comment: 17 pages, 9 Figures. Introduced in SPIE Medical Imaging Conference,
Orlando Florida, 201
High-speed, high-frequency ultrasound, \u3ci\u3ein utero\u3c/i\u3e vector-flow imaging of mouse embryos
Real-time imaging of the embryonic murine cardiovascular system is challenging due to the small size of the mouse embryo and rapid heart rate. High-frequency, linear-array ultrasound systems designed for small-animal imaging provide high-frame-rate and Doppler modes but are limited in regards to the field of view that can be imaged at fine-temporal and -spatial resolution. Here, a plane-wave imaging method was used to obtain high-speed image data from in utero mouse embryos and multi-angle, vector-flow algorithms were applied to the data to provide information on blood flow patterns in major organs. An 18-MHz linear array was used to acquire plane-wave data at absolute frame rates ≥10 kHz using a set of fixed transmission angles. After beamforming, vector-flow processing and image compounding, effective frame rates were on the order of 2 kHz. Data were acquired from the embryonic liver, heart and umbilical cord. Vector-flow results clearly revealed the complex nature of blood-flow patterns in the embryo with fine-temporal and -spatial resolution
Eigenspace-Based Minimum Variance Combined with Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging
In Photoacoustic imaging, Delay-and-Sum (DAS) algorithm is the most commonly
used beamformer. However, it leads to a low resolution and high level of
sidelobes. Delay-Multiply-and-Sum (DMAS) was introduced to provide lower
sidelobes compared to DAS. In this paper, to improve the resolution and
sidelobes of DMAS, a novel beamformer is introduced using Eigenspace-Based
Minimum Variance (EIBMV) method combined with DMAS, namely EIBMV-DMAS. It is
shown that expanding the DMAS algebra leads to several terms which can be
interpreted as DAS. Using the EIBMV adaptive beamforming instead of the
existing DAS (inside the DMAS algebra expansion) is proposed to improve the
image quality. EIBMV-DMAS is evaluated numerically and experimentally. It is
shown that EIBMV-DMAS outperforms DAS, DMAS and EIBMV in terms of resolution
and sidelobes. In particular, at the depth of 11 mm of the experimental images,
EIBMV-DMAS results in about 113 dB and 50 dB sidelobe reduction, compared to
DMAS and EIBMV, respectively. At the depth of 7 mm, for the experimental
images, the quantitative results indicate that EIBMV-DMAS leads to improvement
in Signal-to-Noise Ratio (SNR) of about 75% and 34%, compared to DMAS and
EIBMV, respectively.Comment: arXiv admin note: substantial text overlap with arXiv:1709.0796
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