Numerical method for tilt compensation in scanning acoustic microscopy

Kumar, Prakhar; Yadav, N.K.; Shamsuzzaman, Mohammad; Agarwal, Krishna; Melandsø, Frank; Habib, Anowarul

doi:10.1016/j.measurement.2021.110306

Cited by 12 publications

(13 citation statements)

References 16 publications

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“…5), integrated with a Standa (8MTF-200-Motorized XY Microscope Stage) high precision scanning stage which controlled by LabVIEW [28]. A similar SAM was employed earlier by our group to determine and correct on the inclined sample [29]. The acoustic microscopic features were implemented using National Instruments' PXIe FPGA modules and FlexRIO hardware.…”

Section: Methodsmentioning

confidence: 99%

High-Resolution Imaging in Acoustic Microscopy Using Deep Learning

et al. 2022

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Section: Methodsmentioning

confidence: 99%

High-Resolution Imaging in Acoustic Microscopy Using Deep Learning

et al. 2022

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“…Our group had previously utilised a similar SAM to identify and correct inclined samples, as reported in a previous study. 21 National Instruments' PXIe FPGA modules and FlexRIO hardware were utilised to implement the acoustic microscopic features. These were housed in a PXIe chassis (PXIe-1082), which included an arbitrary waveform generator (AT-1212).…”

Section: F I G U R Ementioning

confidence: 99%

“…Prakhar et al proposed a tilt correction method that involves selecting three points on the same plane of the object, finding the angle of inclination, and correcting for artefacts caused by the inclination. 21 The correction of sample surface inclination is performed during the postprocessing phase of each A-scan in the time domain. However, this method has a limitation that it relies on prior knowledge of the sample's surface or shape and the exact position of the specimen's surface in space.…”

Section: Introductionmentioning

confidence: 99%

Automated tilt compensation in acoustic microscopy

Gupta,

Habib,

Kumar

et al. 2023

Journal of Microscopy

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Scanning acoustic microscopy (SAM) is a potent and nondestructive technique capable of producing three‐dimensional topographic and tomographic images of specimens. This is achieved by measuring the differences in time of flight (ToF) of acoustic signals emitted from various regions of the sample. The measurement accuracy of SAM strongly depends on the ToF measurement, which is affected by tilt in either the scanning stage or the sample stage. Hence, compensating for the ToF shift resulting from sample tilt is imperative for obtaining precise topographic and tomographic profiles of the samples in a SAM. In the present work, we propose an automated tilt compensation in ToF of acoustic signal based on proposed curve fitting method. Unlike the conventional method, the proposed approach does not demand manually choosing three separate coordinate points from SAM's time domain data. The effectiveness of the proposed curve fitting method is demonstrated by compensating time shifts in ToF data of a coin due to the presence of tilt. The method is implemented for the correction of different amounts of tilt in the coin corresponding to angles 6.67°, 12.65° and 15.95°. It is observed that the present method can perform time offset correction in the time domain data of SAM with an accuracy of 45 arcsec. The experimental results confirm the effectiveness of the suggested tilt compensation technique in SAM, indicating its potential for future applications.

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“…The image of SAM, as depicted in Figure 2, has been annotated to highlight its key components or operational 3/13 settings and serves as a reference for image acquisition. More comprehensive explanations of the operational principles for these modes can be found in the following literature 21,22 . In this article, our emphasis lies in the utilization of the reflection mode for scanning samples.…”

Section: Scanning Acoustic Microscopic Imagingmentioning

confidence: 99%

“…This SAM system featured a Standa high-precision scanning stage (8MTF-200-Motorized XY Microscope Stage) for gathering experimental data. Acoustic imaging capabilities were enabled using National Instruments' PXIe FPGA modules and FlexRIO hardware housed in a PXIe chassis (PXIe-1082) with an integrated arbitrary waveform generator (AT-1212) [21][22][23] . The transducer was excited with Mexican hat signals and amplified using an RF amplifier (AMP018032-T).…”

Section: Xy Manual Stage Controllermentioning

confidence: 99%

Spatial mapping of acoustic impedance of shrimp scale using multiple overlap discrete wavelet transform (moDWT)

Ojha,

Agarwal,

Khan

et al. 2023

Preprint

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There are lots of challenges associated with conventional optical observation of biological tissues, where specimens are typically sliced and stained for better contrast. In contrast, Scanning Acoustic Microscopy (SAM) is a versatile label-free imaging technology widely applied in various domains, including biomedical imaging, non-destructive testing, and material research. It excels in offering precise visualization of both surface and subsurface structures, providing valuable insights through visual inspection and quantitative analysis. Acknowledging the SAM, this paper presents acoustic impedance microscopy of the shrimp scale in a novel manner. The proposed technique aims to image the local distribution of cross-sectional acoustic impedance in biological tissue, which is a parameter closely related to sound speed and potentially valuable for tissue characterization. The study leverages advanced signal processing techniques, maximal overlap discrete wavelet transform (moDWT), to decompose acoustic responses effectively. The moDWT, with its ability to handle signals of various lengths without constraints, is highlighted as a promising approach. To determine shrimp scale impedance, we first establish the accuracy of the proposed algorithm using PVDF as the target and polyimide as reference material. The results indicate an algorithm accuracy exceeding 90%. An impedance map is generated through Gaussian process regression (GPR), which predicts the impedance over the complete domain, addressing spatial variations in biological specimens. The resulting acoustic impedance maps provide in-depth insights into the functional framework and advance our understanding of shrimp biomechanics.

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Numerical method for tilt compensation in scanning acoustic microscopy

Cited by 12 publications

References 16 publications

High-Resolution Imaging in Acoustic Microscopy Using Deep Learning

High-Resolution Imaging in Acoustic Microscopy Using Deep Learning

Automated tilt compensation in acoustic microscopy

Spatial mapping of acoustic impedance of shrimp scale using multiple overlap discrete wavelet transform (moDWT)

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