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

Abstract

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.

Single Sensor Compressive Ultrasound Imaging: A Study of Affecting Factors

“…Each of these approaches can be characterized by the details of their algorithms and reconstruction techniques, keeping in view their different attributes. There are several main ways of classifying US imaging using CS techniques, which can fall into the following categories of general CSbased reconstruction algorithms [7][8][9][10][11][12][13][14][15][16][17], CS recovery based on sparsifying transforms [11,[18][19][20], CS recovery based on compression ratio [7-9, 21, 22], CS reconstruction of 3D ultrasound [23][24][25], CS reconstruction based on deep learning [26,27], and CS reconstruction based on single-element [25,28].…”

Accelerated brain tumor dynamic contrast‐enhanced MRI using Adaptive Pharmaco‐Kinetic Model Constrained method

“…8,9 By exploiting the spatial and temporal correlations in the image sequence, CS methods can substantially accelerate dynamic MRI acquisition. 10,11 Based on CS theory, various methods have been developed for DCE-MRI reconstruction by using different choices of sparsifying transforms, such as temporal Fourier transform, 12 spatial wavelet transform, 13 temporal and spatial total variation (TV). 14,15 Recently, the low-rank approximation has been introduced as a special sparse model and usually combined with other sparsifying transforms to further improve the reconstruction quality.…”

“…In the past fifteen years, compressed sensing (CS) methods have shown great potential in improving the imaging speed of MRI through high‐quality reconstruction from undersampled k‐space data 8,9 . By exploiting the spatial and temporal correlations in the image sequence, CS methods can substantially accelerate dynamic MRI acquisition 10,11 …”