Update on 2-D array transducers for medical ultrasound

Smith, Stephen W.; Davidsen, R.E.; Emery, Charles D.; Goldberg, Richard J.; Light, Edward D.

doi:10.1109/ultsym.1995.495789

Cited by 22 publications

(6 citation statements)

References 16 publications

(6 reference statements)

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“…Despite its advantages, the lack of 3D information in traditional 2D ultrasound imaging prevents reproductivity of examinations, longitudinal follow-up and precise quantitative measurements. To overcome these limits and produce a 3D representation of the scanned organs, severals techniques exist: mechanicallyswept acquisitions, freehand imaging [3], mechanical built-in probes and 2D phased-array probes [4]. The two first approaches are based on the reconstruction of a 3D regular lattice from 2D B-scans and their positions, whereas 3D probes directly acquire 3D images.…”

Section: Introductionmentioning

confidence: 99%

Probe trajectory interpolation for 3D reconstruction of freehand ultrasound

Coupé

Hellier

Morandi

et al. 2007

Medical Image Analysis

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Three-dimensional (3D) freehand ultrasound uses the acquisition of non-parallel B-scans localized in 3D by a tracking system (optic, mechanical or magnetic). Using the positions of the irregularly spaced B-scans, a regular 3D lattice volume can be reconstructed, to which conventional 3D computer vision algorithms (registration and segmentation) can be applied. This paper presents a new 3D reconstruction method which explicitly accounts for the probe trajectory. Experiments were conducted on phantom and intra-operative datasets using various probe motion types and varied slice-to-slice B-scan distances. Results suggest that this technique improves on classical methods at the expense of computational time.

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Section: Introductionmentioning

confidence: 99%

Probe trajectory interpolation for 3D reconstruction of freehand ultrasound

Coupé

Hellier

Morandi

et al. 2007

Medical Image Analysis

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“…For the pulsed case and away from the focal point, this approach gave even better results than a random sparse array. Others [12] have reported that in 2D array simulations the random-like and periodic thinning approaches have given comparable performance.…”

Section: A Sparse Array Optimizationmentioning

confidence: 93%

Properties of the beampattern of weight- and layout-optimized sparse arrays

Holm

Elgetun

Dahl

1997

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

127

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Theory for random arrays predicts a mean sidelobe level given by the inverse of the number of elements. In practice, however, the sidelobe level fluctuates much around this mean. In this paper two optimization methods for thinned arrays are given: one is for optimizing the weights of each element, and the other one optimizes both the layout and the weights. The weight optimization algorithm is based on linear programming and minimizes the peak sidelobe level for a given beamwidth. It is used to investigate the conditions for finding thinned arrays with peak sidelobe level at or below the inverse of the number of elements. With optimization of the weights of a randomly thinned array, it is possible to come quite close and even below this value, especially for 1D arrays. Even for 2D sparse arrays a large reduction in peak sidelobe level is achieved. Even better solutions are found when the thinning pattern is optimized also. This requires an algorithm that uses mixed integer linear programming. In this case solutions with lower peak sidelobe level than the inverse number of elements can be found both in the 1D and the 2D cases.

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“…Numerous solutions for the acquisition problem and a large number of algorithms for processing the sensor time series have been proposed recently [25,11,2,15,18,24]. This work has examined, among other, issues related to transducers and their relation to beamforming techniques for ultrasound systems.…”

Section: Related Workmentioning

confidence: 99%

“…With the advent of high performance computing facilities and the availability of transducer crystal technology, ultrasound imaging systems have emerged as efficient methods to extract and reproduce relevant medical diagnostic information. Generally, a digital ultrasound system consists of a set of sensors [25,11,2] that perform data acquisition and a backend computing architecture, responsible for processing the raw data and reconstructing the ultrasonic images [24]. Figure 1 depicts a typical ultrasound system.…”

Section: Introductionmentioning

confidence: 99%

Parallelization and performance of 3D ultrasound imaging beamforming algorithms on modern clusters

Zhang

Bilas

Dhanantwari

et al. 2002

Proceedings of the 16th International Conference on Supercomputing

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Recently there has been a lot of interest in improving the infrastructure used in medical applications. In particular, there is renewed interest on non-invasive, high-resolution diagnostic methods. One such method is digital, 3D ultrasound medical imaging. Current state-of-the-art ultrasound systems use specialized hardware for performing advanced processing of input data to improve the quality of the generated images. Such systems are limited in their capabilities by the underlying computing architecture and they tend to be expensive due to the specialized nature of the solutions they employ.Our goal in this work is twofold: (i) To understand the behavior of this class of emerging medical applications in order to provide an efficient parallel implementation and (ii) to introduce a new benchmark for parallel computer architectures from a novel and important class of applications. We address the limitations faced by modern ultrasound systems by investigating how all processing required by advanced beamforming algorithms can be performed on modern clusters of high-end PCs connected with low-latency, high-bandwidth system area networks. We investigate the computational characteristics of a state-of-the-art algorithm and demonstrate that today's commodity architectures are capable of providing almost-real-time performance without compromising image quality significantly.

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Update on 2-D array transducers for medical ultrasound

Cited by 22 publications

References 16 publications

Probe trajectory interpolation for 3D reconstruction of freehand ultrasound

Probe trajectory interpolation for 3D reconstruction of freehand ultrasound

Properties of the beampattern of weight- and layout-optimized sparse arrays

Parallelization and performance of 3D ultrasound imaging beamforming algorithms on modern clusters

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