Temporal super resolution of ultrasound images using compressive sensing

Hosseinpour, Mina; Behnam, Hamid; Shojaeifard, Maryam

doi:10.1016/j.bspc.2019.03.003

Cited by 14 publications

(37 citation statements)

References 44 publications

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“…Hossenipour et al proposed a method for reconstructing ultrasound images using CS theory based on both spatial and temporal information. Their method led to the increased frame rate but at the cost of high computational complexity 19 …”

Section: Introductionmentioning

confidence: 99%

A fast and high frame rate adaptive beamforming using DCT‐based RF‐line recovery in line‐by‐line ultrasound imaging

Afrakhteh

Behnam

2020

Int J Imaging Syst Tech

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Ultrasound imaging is an important modality used in medical imaging. One of the significant stages in the ultrasound imaging is the beamforming process. This article proposes a new technique for reducing the overall computational time of adaptive linear ultrasound imaging. The method uses the discrete cosine transform‐based reconstruction for missing data imputation. The novelty of the paper is that we do not need to beam‐form the total scan lines, so the time of image construction can be saved significantly. In other words, a fraction of the total scan lines is selected for beamforming and the others are assumed to have values as Not‐a‐Number (NaN). The proposed reconstruction technique tries to assign appropriate values to the NaN ones. We applied the proposed method to the simulated and experimental radio frequency (RF) datasets for resolution and contrast evaluation. Results showed that the proposed technique is near to the minimum variance (MV) method in terms of resolution and contrast, and has less computational time for image formation compared to the MV. As some quantitative examples in some experiments we have formed only 50% and 33% of the total lines and reconstructed the rest, then we have been able to increase the frame rate twice and three times, respectively, which can be very useful in many applications, especially in echocardiography imaging. In addition, since the execution time of the reconstruction algorithm is not very significant, we were also able to increase the speed by two and three times while achieving an error of less than 10% compared to the case of using all image lines.

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Section: Introductionmentioning

confidence: 99%

A fast and high frame rate adaptive beamforming using DCT‐based RF‐line recovery in line‐by‐line ultrasound imaging

Afrakhteh

Behnam

2020

Int J Imaging Syst Tech

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“…[20][21][22][23] A variety of studies have been devoted to the application of the CS theory in the Doppler and B-mode US imaging up to now. [20][21][22][23][24][25] The CS theory has been used at various stages of US image formation in different applications. In one study, the reconstruction is performed on raw RF signals (pre-beamforming stage) to reduce data acquisition.…”

Section: Introductionmentioning

confidence: 99%

“…It has indirectly pointed out that this method can be used to improve the temporal resolution of the US images. Another study 25 offered a new approach based on an acquisition scheme for the temporal SR of the dynamic US imaging using the CS theory. This study shows that using only spatial information is suitable only for static images such as imaging from a liver.…”

Section: Introductionmentioning

confidence: 99%

“…However, for dynamic images such as heart images, the combination of the temporal and spatial information between RF image sequences can provide a better reconstruction with the quality of images, which is appropriate for the medical diagnosis. 25 One paper studies the US image reconstruction from a few randomly selected samples using the assumption of jointly sparse signals. They used a distributed CS framework in the post-beamforming stage.…”

Section: Introductionmentioning

confidence: 99%

“…The conventional fixed sparsity bases for sparsifying US signals are Fourier, 22,23,32,41,42 wavelet, 22,23,43 discrete cosine transform (DCT), 24 and wave atoms. 22,23 In the study, 25 wavelet, DCT, and Fourier transforms are investigated for the selection of the best sparsity bases for the sparsifying of the temporal and spatial signals in the US images. In this study, the idea of the combination of the fixed sparsity bases is introduced for the sparser representation of the US signals.…”

Section: Introductionmentioning

confidence: 99%

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Temporal Super-resolution of Ultrasound Imaging Using Matrix Completion

2020

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The temporal super-resolution of the dynamic ultrasound imaging, a means to observe rapid heart movements, is considered an important subject in medical diagnosis of cardiac conditions. Here, a new technique based on the acquisition scheme using the matrix completion (MC) theory is offered for the temporal super-resolution of the two-dimensional (2D) and three-dimensional (3D) ultrasound imaging. MC mentions the problem of completing a low-rank matrix when only a subset of its elements can be observed. Here, the lower scan lines are acquired. Whereby, the proposed method uses temporal and spatial information of the radio frequency (RF) image sequences for the reconstruction of skipped RF lines. This is performed using the construction of the MC images and then reconstruction of them by the MC theory. The results of the proposed method are compared with the compressive sensing (CS) reconstruction methods. The qualitative and quantitative evaluations of 2D and 3D data demonstrate that in the proposed method, which uses the spatial and temporal relation of RF images and the MC theory, the reconstruction is more accurate, and the reconstruction error is lower. The computational complexity of this method is very low. It also does not require hardware adjustments. Therefore, it can be easily implemented in current ultrasound-imaging devices with the frame-rate enhancement. For instance, the frame rate up to two times the original sequence is feasible using the proposed methods, while root mean square error is decreased by about 35% and 30% for 2D and 3D data, respectively, compared with the CS reconstruction method.

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Compressive sensing theory and neighborhood spatial‐temporal information for frame rate improvement of dynamic ultrasonic imaging

Hosseinpour

Behnam

Shojaeifard

2020

Int J Imaging Syst Tech

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The frame rate improvement is an essential issue in dynamic ultrasonic imaging for better displaying rapid heart movements. In this study, a new technique using the compressive sensing (CS) theory was introduced for the frame rate improvement of two-dimensional (2D) and three-dimensional (3D) dynamic ultrasonic imaging. In the suggested procedure, a fewer radio frequency (RF) lines were received. Subsequently, the CS theory by the recommended approach was used to reconstruct the nonacquired RF lines. The suggested process uses the temporal and spatial information in the neighborhood samples of the desired sample in RF frame sequences. We utilized the dictionary learning technique to construct the sparsifying over-complete dictionary. Here, we used the 2D in vivo carotid artery data and the 3D simulated echocardiography for the quality assessment of the suggested approach. According to the qualitative and quantitative results, reconstructions in the suggested approach are more precise, and the reconstruction errors are much lower than the conventional techniques. The tissues' structure in reconstructed videos by the suggested approach is preserved to be applicable for medical diagnosis goals. The frame rate can be improved by a factor of 10 with suitable quality for medical diagnosis goals. The implementation of the suggested approach does not require any more hardware or alternation in the devices' available hardware.The frame rate can be improved by only implementing effective programming on the ultrasound systems.

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Temporal super resolution of ultrasound images using compressive sensing

Cited by 14 publications

References 44 publications

A fast and high frame rate adaptive beamforming using DCT‐based RF‐line recovery in line‐by‐line ultrasound imaging

A fast and high frame rate adaptive beamforming using DCT‐based RF‐line recovery in line‐by‐line ultrasound imaging

Temporal Super-resolution of Ultrasound Imaging Using Matrix Completion

Compressive sensing theory and neighborhood spatial‐temporal information for frame rate improvement of dynamic ultrasonic imaging

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