Advanced post-processing for scanned ultrasonic arrays: Application to defect detection and classification in non-destructive evaluation

Holmes, C.; Drinkwater, Bruce W.; Wilcox, Paul D.

doi:10.1016/j.ultras.2008.07.019

Cited by 110 publications

(56 citation statements)

References 20 publications

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“…Commands for easily defining specific SAFT sequences of emission/reception are given. Anyway, the library comes with some predefined techniques, like 2R-SAFT [6], and TFM [9].  2D/3D imaging.…”

Section: Cheetah Core Featuresmentioning

confidence: 99%

“…Then, a beamforming algorithm is applied to focus the image dynamically in emission and reception obtaining the maximum quality at each image point. One of the most common beamforming methods is Total Focusing Method (TFM) [9] [11] and it is based on Full Matrix Array (FMA), which is the complete data matrix X(t) created by any transmitter-receiver combination:…”

Section: Tfm Imaging Principlesmentioning

confidence: 99%

See 1 more Smart Citation

Cheetah: A Library for Parallel Ultrasound Beamforming in Multi-Core Systems

Romero-Laorden¹,

Martín-Arguedas²,

Villazón-Terrazas³

et al. 2015

JAMP

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Developing new imaging methods needs to establish some proofs of concept before implementing them on real-time scenarios. Nowadays, the high computational power reached by multi-core CPUs and GPUs have driven the development of software-based beamformers. Taking this into account, a library for the fast generation of ultrasound images is presented. It is based on Synthetic Aperture Imaging Techniques (SAFT) and it is fast because of the use of parallel computing techniques. Any kind of transducers as well as SAFT techniques can be defined although it includes some pre-built SAFT methods like 2R-SAFT and TFM. Furthermore, 2D and 3D imaging (slicebased or full volume computation) is supported along with the ability to generate both rectangular and angular images. For interpolation, linear and polynomial schemes can be chosen. The versatility of the library is ensured by interfacing it to Matlab, Python and any programming language over different operating systems. On a standard PC equipped with a single NVIDIA Quadro 4000 (256 cores), the library is able to calculate 262,144 pixels in ≈105 ms using a linear transducer with 64 elements, and 2,097,152 voxels in ≈ 5 seconds using a matrix transducer with 121 elements when TFM is applied.

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Section: Cheetah Core Featuresmentioning

confidence: 99%

Section: Tfm Imaging Principlesmentioning

confidence: 99%

Cheetah: A Library for Parallel Ultrasound Beamforming in Multi-Core Systems

Romero-Laorden¹,

Martín-Arguedas²,

Villazón-Terrazas³

et al. 2015

JAMP

View full text Add to dashboard Cite

show abstract

“…Examination of this strategy reveals that conventional SAFT imaging can be obtained by taking the diagonal of the FMC dataset, and that by accounting for reciprocity of the pitch-catch time domain signals a reduced size of rx/2(rx+1) is possible [9] . That is, for each tx, rx pair, the corresponding rx, tx can be determined from the original tx, rx pair:…”

Section: Full Matrix Capturementioning

confidence: 99%

Virtual source aperture imaging for non-destructive testing

Sutcliffe¹,

Weston²,

Charlton³

et al. 2012

insight

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“…Since limited positioning and scanning is assumed, the accumulation of all of the data volume available at that spot may be crucial. The method of Total Matrix developed by C. Holmes 4 provides a method for collection of all possible array combinations by firing elements of the transducer individually and collecting response from each of the individual elements. In post processing any combination of focal delays may be applied to the data set.…”

Section: In Situ Inspection Of Storage Casksmentioning

confidence: 99%