The influence of speckle statistics on contrast metrics in ultrasound imaging

Hverven, Stine M.; Rindal, Ole Marius Hoel; Rodríguez-Molares, Alfonso; Austeng, Andreas

doi:10.1109/ultsym.2017.8091875

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…For this reason, the p-value can be adjusted to enhance the resolution and the CR (useful for lesion detectability), while preserving the speckle structure and CNR (used for texture analysis, and so, tissue characterization) [19]. Such a trade-off was identified as common behavior for adaptive beamformers by [20].…”

Section: Single Plane Wave Imagingmentioning

confidence: 99%

A Nonlinear Beamformer Based on p-th Root Compression—Application to Plane Wave Ultrasound Imaging

et al. 2018

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Ultrafast medical ultrasound imaging is necessary for 3D and 4D ultrasound imaging, and it can also achieve high temporal resolution (thousands of frames per second) for monitoring of transient biological phenomena. However, reaching such frame rates involves reduction of image quality compared with that obtained with conventional ultrasound imaging, since the latter requires each image line to be reconstructed separately with a thin ultrasonic focused beam. There are many techniques to simultaneously acquire several image lines, although at the expense of resolution and contrast, due to interference from echoes from the whole medium. In this paper, a nonlinear beamformer is applied to plane wave imaging to improve resolution and contrast of ultrasound images. The method consists of the introduction of nonlinear operations in the conventional delay-and-sum (DAS) beamforming algorithm. To recover the value of each pixel, the raw radiofrequency signals are first dynamically focused and summed on the plane wave dimension. Then, their amplitudes are compressed using the signed p th root. After summing on the element dimension, the signed p-power is applied to restore the original dimensionality in volts. Finally, a band-pass filter is used to remove artificial harmonics introduced by these nonlinear operations. The proposed method is referred to as p-DAS, and it has been tested here on numerical and experimental data from the open access platform of the Plane wave Imaging Challenge in Medical UltraSound (PICMUS). This study demonstrates that p-DAS achieves better resolution and artifact rejection than the conventional DAS (for p = 2 with eleven plane wave imaging on experimental phantoms, the lateral resolution is improved by 21%, and contrast ratio (CR) by 59%). However, like many coherence-based beamformers, it tends to distort the conventional speckle structure (contrast-to-noise-ratio (CNR) decreased by 45%). It is demonstrated that p-DAS, for p = 2, is very similar to the nonlinear filtered-delay-multiply-and-sum (FDMAS) beamforming, but also that its impact on image quality can be tuned changing the value of p.

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Section: Single Plane Wave Imagingmentioning

confidence: 99%

A Nonlinear Beamformer Based on p-th Root Compression—Application to Plane Wave Ultrasound Imaging

et al. 2018

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“…This noise originates as a result of variations in the backscattered waves from the objects that are random in nature and also due to the errors in transmission. Although beamformers aim to improve resolution and contrast in ultrasound images, certain beamformers affect the speckle statistics thereby changing the contrast in the ultrasound images [1]. Speckle noise impairs the quality of the image as it hides small differences in grey level.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Convolutional Neural Network-based Speckle Removal From Undersampled K-space Data

Manimala¹,

Naidu

2022

Preprint

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This paper presents the framework for accelerating MR Imaging (MRI) by adopting Compressive Sampling/Sensing (CS) strategy. The major drawback is the extensive time involved in recovering MR Images from the limited k -space data which is often affected by various noises. Specifically speckle noise is seen to affect medical imaging modalities like ultrasound and MR scanning. This work presents the mathematical analysis for the distribution that arises due to the Speckle scattering. Further, a method has been devised to eliminate the Speckle noise to reconstruct MR images from the sub-sampled k -space (spatial frequency) data, while preserving the visual quality. The proposed sparse reconstruction method constructs on a Deep Convolutional Neural Network (DCNN) to reduce the reconstruction time. The CNN training involves the pairs of images which are generated from sub-sampled noisy spatial frequency points and the corresponding fully sampled k -space data. The performance of the proposed method has been evaluated with various sub-sampling schemes. The results show a remarkable reduction in the computation time along with high image quality for various undersampling strategies.

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An Information-Theoretic Spatial Resolution Criterion for Qualitative Images

Hyun

2021

2021 IEEE International Ultrasonics Symposium (IUS)

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The influence of speckle statistics on contrast metrics in ultrasound imaging

Cited by 5 publications

References 18 publications

A Nonlinear Beamformer Based on p-th Root Compression—Application to Plane Wave Ultrasound Imaging

A Nonlinear Beamformer Based on p-th Root Compression—Application to Plane Wave Ultrasound Imaging

Adaptive Convolutional Neural Network-based Speckle Removal From Undersampled K-space Data

An Information-Theoretic Spatial Resolution Criterion for Qualitative Images

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