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

Ghasemifard, Hadi; Behnam, Hamid; Tavakkoli, Jahan

doi:10.1177/0954411917735557

Cited by 4 publications

(12 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An ultrasound imaging system (Sonix RP scanner, Ultrasonix Inc., Richmond, BC, Canada), with an endocavity array probe of 128 elements, operating at a center frequency of 7 MHz and bandwidth of 3 MHz, was used to record B-mode images and RF backscattered data. [18]…”

Section: Methodsmentioning

confidence: 99%

“…The data acquisition sampling frequency was 40 MHz. [18] The total HIFU treatment time was 40 s for total acoustic power (TAP) levels of 34, 37, 39, 44, and 49 W. Figure 1 illustrates the thermal lesions induced at TAPs ranging from 34 to 49. As illustrated in Figure 1, the depth of lesions from the tissue surface was measured.…”

Section: Methodsmentioning

confidence: 99%

“…The results of the study showed that the modified CS method could effectively detect HIFU thermal lesions in vitro . [18] In the current work for enhancement of lesion detection, a new application of CS-EMD is presented. One method of multidecomposition that does not require preselection functions unlike wavelet transform is the EMD method.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, we have witnessed increasing development of utilizing the concept of CS and sparse representation in various signal and image processing applications, including pattern recognition and machine vision. [18][19][20][21] Generally, signal reconstruction from compressive measurements is done by optimization algorithms. In many engineering applications, the signal is contaminated with a variety of noises.…”

Section: Introductionmentioning

confidence: 99%

High-Intensity Focused Ultrasound Lesion Detection Using Adaptive Compressive Sensing Based on Empirical Mode Decomposition

Ghasemifard¹,

Behnam²,

Tavakkoli³

2023

Preprint

View full text Add to dashboard Cite

<p>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. </p>

show abstract

Automatic feature extraction by supervised and contrastive self-supervised learning based on wavelet and hard negatives to detect HIFU lesion area

Zavar,

Ghaffary,

Tabatabaee

2023

Preprint

View full text Add to dashboard Cite

The adoption of Deep Neural Networks has surged due to their ability to automatically extract features and employ diverse approaches in data analysis. This research proposes a novel feature extraction method that doesn't rely on labeled training data, particularly considering the utilization of hard negatives. Given the remarkable success of DNN-based models in analyzing various medical images, including disease diagnosis and detection, this paper delves into diagnosing the lesion area against the normal area, particularly in the context of the non-invasive treatment of HIFU. Monitoring and analyzing inputs related to the lesion area are crucial to prevent damage to normal tissue during the heating process. However, several challenges exist in ultrasound medical imaging, including small sample sizes, data lacking labels, and the time-intensive nature of deep supervised training. These challenges have motivated the introduction of a new self-supervised deep learning method. While supervised learning excels in accuracy, unlabeled data holds valuable information discarded in supervised approaches. Conversely, ultrasonic data's nature lies in the RF signal, offering a detailed acoustic structure of tissue. Acknowledging the limitations and advantages of each method, an effective approach leveraging both signal and image simultaneously is presented. This integrated method enhances diagnostic capabilities and contributes to improve monitoring of HIFU procedures. The proposed methodology for classifying HIFU lesion areas attained high performance metrics: 95% accuracy, 94% precision, 96% recall, and a 95% F1-score. These outcomes underscore the efficacy of the proposed method in accurately classifying HIFU lesion areas.

show abstract

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

Cited by 4 publications

References 32 publications

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

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

High-Intensity Focused Ultrasound Lesion Detection Using Adaptive Compressive Sensing Based on Empirical Mode Decomposition

Automatic feature extraction by supervised and contrastive self-supervised learning based on wavelet and hard negatives to detect HIFU lesion area

Contact Info

Product

Resources

About