Toward high-intensity focused ultrasound lesion quantification using compressive sensing theory

Abstract

Compressive sensing theory has in recent years been increasingly used in various pattern recognition applications. Compressive sensing theory makes it possible, under certain assumptions, to recover a signal or an image sampled below the Nyquist sampling limit. In this work, a new application of compressive sensing based on the threshold algorithm, in the area of controlling and monitoring of high-intensity focused ultrasound therapy, was investigated. In this work, a new method of high-intensity focused ultrasound lesion detection is presented based on a modified compressive sensing method in combination with the threshold algorithm and the wavelet transforms. In this study, analysis of the suggested method is performed using two sets of data: simulated and experimental ultrasound radio frequency data. The results of processing the data show that the proposed algorithm results in enhancement of the high-intensity focused ultrasound lesion contrast in comparison with the ultrasound B-mode and standard compressive sensing imaging methods. The results of the study show that the modified compressive sensing method could effectively detect thermal lesions in vitro. Comparing the estimated size of the thermal lesion (8.3 mm × 8.4 mm) using the proposed algorithm with the actual size of that from physical examination (10.1 mm × 9 mm) shows that we could detect high-intensity focused ultrasound thermal lesions with the difference of 0.8 mm × 0.5 mm.

Keywords

High-intensity focused ultrasound compressive sensing radio frequency signal sparse representation wavelet transform

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