Ultrasound Detection Arrays via Coded Hadamard Apertures

Hahamovich, Evgeny; Rosenthal, Amir

doi:10.1109/tuffc.2020.2993583

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…The notable research endeavors in the eld were highlighted, including the development of a single transducer-based US imaging setup and the use of coded apertures to enable CS in single-element imaging. we mentioned speci c studies by Kruizinga et al [25], Hahamovich et al [30], Ghanbarzadeh et al [31], Nie et al [32], and Choi et al [33], which had explored different methods and techniques in singleelement compressive US imaging.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we aim to develop and characterize a computationally-e cient method for simulating a single-element compressive US imaging system by utilizing a model for the coded aperture mask. While some previous works, such as Kruizinga et al [25] and Hahamovich et al [30], have taken an empirical approach and treated the coded aperture as a black box that must be measured experimentally, we instead use a theoretical digital model for the coded aperture that is as simple, fast, and e cient as possible. Although this approach may slightly compromise the level of physical accuracy, it captures the essential features of the system and elucidates its basic behavior in a computationally-e cient manner.…”

Section: Introductionmentioning

confidence: 99%

“…They stated that the need for just one sensor instead of thousands paves the way for simpler, cheaper, smaller, and faster sensing devices and possible clinical applications. In a study by Hahamovich et al [30], another approach to implement a coded aperture in CS ultrasound imaging was using an amplitude mask with Hadamard-encoding. They developed a method for transforming a single transducer into an effective detection array by spatially ltering the incoming acoustic elds using their binary acoustic mask.…”

Section: Introductionmentioning

confidence: 99%

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Single Sensor Compressive Ultrasound Imaging: A Study of Affecting Factors

Pasyar,

Makkiabadi,

Montazeriani

et al. 2023

Preprint

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Compressed sensing has enabled 2D and 3D ultrasound imaging using a single transducer by encoding lateral and elevation spatial information as temporal variations in the transmitted and received ultrasound signal through a coded aperture in the form of a pseudo-random delay mask. This technology has become increasingly important with the development of ultrasound techniques as it allows for a reduction in machinery size and power consumption. In this article, we develop a model for compressive ultrasound imaging using a single coded sensor to investigate the factors that affect image quality and enable computationally-efficient simulation of the system. We provide a step-by-step guide to creating synthetic data and demonstrate compressive ultrasound experiments of scenes with varying levels of sparseness generated according to a linear image formation model. We then calculate qualitative and quantitative measurements and solve the inverse problem using several sparse recovery solutions to achieve faithful reconstruction of the scene under different signal-to-noise ratios (SNR) and coded sensor geometries. Our model analysis reveals that failure to consider preferable conditions results in degraded peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity (SSIM) indexes related to the quality of the reconstruction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Single Sensor Compressive Ultrasound Imaging: A Study of Affecting Factors

Pasyar,

Makkiabadi,

Montazeriani

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In practice, various transforms are used for medical image fusion, denoising, and compression. Discrete Fourier transform (DFT) [25,26], Hadamard transform (HT) [27], and discrete wavelet transform (DWT) [28,29] are the most common of them. Both DFT and HT are widely used in the frequency domain, but the time domain characteristics disappear.…”

Section: Introductionmentioning

confidence: 99%

RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients

et al. 2022

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Medical imaging using different modalities has many problems. The main ones are low informativeness, various distortion noises, and a large amount of information. Fusion, denoising, and visual data compression are used to solve them in practice. Discrete wavelet transform is one way to implement various fusion, denoising, and compression methods for 2D and 3D medical image processing. Medical imaging systems produce increasingly accurate images with scanning technology and digital devices development. These images have improved quality using both higher spatial resolutions and color bit-depth. Processing a large volume of medical imaging data requires considerable resources and processing time. Modern wavelet-based devices for medical image processing do not meet the current performance demand. Hardware accelerators are being designed to solve this problem. This paper proposes new (fieldprogrammable gate array) FPGA accelerators using wavelet processing (WP) with scaled filter coefficients (SFC) and parallel computing in residue number system (RNS) to improve the performance of high-quality 3D medical image WP systems. The computational complexity is reduced using the developed WP method with SFC and the proposed wavelet filter coefficients scaling algorithm. Parallel computing is organized in RNS using moduli sets of a particular type. Hardware implementation of 3D medical image WP using the proposed FPGA accelerators increases device performance by 2.89-3.59 times, increasing the hardware resources by 1.18-3.29 times compared to state-of-the-art solutions. The device performance improvement is achieved while maintaining high-quality 3D medical image processing in peak signal-to-noise ratio terms.INDEX TERMS Medical image processing, discrete wavelet transform, scaled filter coefficients, residue number system, high-performance computing, hardware accelerator, field-programmable gate arrays.

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