High-intensity focused ultrasound lesion detection using adaptive compressive sensing based on empirical mode decomposition

Abstract

Background:The main goal of ultrasound therapy is to have clinical effects in the tissue without damage to the intervening and surrounding tissues. Treatments have been developed for both in vitro and in clinical applications. HIFU therapy is one of these. Non-invasive surgeries, such as HIFU, have been developed to treat tumors or to stop bleeding. In this approach, an adequate imaging method for monitoring and controlling the treatment is required.Methods:In this paper, an adaptive compressive sensing representation of ultrasound RF echo signals is presented based on empirical mode decomposition (EMD). According to the different numbers of intrinsic mode functions (IMFs) produced by the EMD, the ultrasound signals is adaptively compressive sampled in the source and then adaptively reconstructed in the receiver domains. In this paper, a new application of compressive sensing based on EMD (CS-EMD) in the monitoring of high-intensity focused ultrasound (HIFU) treatment is presented. Non-invasive surgeries such as HIFU have been developed for various therapeutic applications. In this technique, a suitable imaging method is necessary for monitoring of the treatment to achieve adequate treatment safety and efficacy. So far, several methods have been proposed, such as ultrasound radiofrequency (RF) signal processing techniques, and imaging methods such as X-ray, MRI, and ultrasound to monitor HIFU lesions.Results:In this paper, a CS-EMD method is used to detect the HIFU thermal lesion dimensions using different types of wavelet transform. The results of the processing on the real data demonstrate the potential for this technique in image-guided HIFU therapy.Conclusions:In this study, a new application of compressive sensing in the field of monitoring of the HIFU treatment is presented. To the best of our knowledge, so far no studies on compressive sensing have been carried out in the monitoring of the HIFU. Based on the results obtained, it was showed that the number of measurements and Intrinsic Mode Functions have the function of noise reduction. In addition, results were shown that the successful reconstruction of the compressive sensing signals can be gained using a threshold based algorithm. To this end, in this paper it was shown that by selecting an suitable number of measurements, the sparse transform, and a thresholding algorithm, we can achieve a more accurate detection of the HIFU thermal lesion size.

Noninvasive Monitoring of Tissue Temperature Changes Induced by Focused Ultrasound Exposure using Sparse Expression of Ultrasonic Radio Frequency Echo Signals

“…Because of utilizing the same HIFU dataset, more details about data acquisition and equipment have been mentioned in references Ghasemifard et al . [ 8 9 ] and Rangraz et al . [ 10 ]…”

“…To this end, CS theory can be applied as a method for reducing noise if the sparse space of the signal is known. [ 9 ] This fact is attributable to two causes: signal and noise are relatively distinct in sparse space, and the number of measurements can only contain limited information about noise and artifacts. Multiple studies have demonstrated that CS can be used for two main purposes: (1) it can reduce the amount of data required, thereby increasing the speed of data acquisition and (2) it can be used in applications with data recording limitations.…”

“…[ 8 ] Ex vivo porcine muscle tissue was used for all experiments and results in the current study. [ 7 9 ]…”

“…In mentioned methods, the CS method (Ghasemifard et al . [ 8 9 ]) was applied for HIFU lesion detection. In addition, the entropy (Behnia et al .…”