Golay-Encoded Ultrasound Monitoring of Simultaneous High-Intensity Focused Ultrasound Treatment: A Phantom Study

Suppressing the HIFU interference in ultrasound guiding images with a diffusion-based deep learning model

BACKGROUND AND OBJECTIVES

In ultrasound guided high-intensity focused ultrasound (HIFU) surgery, it is necessary to transmit sound waves at different frequencies simultaneously using two transducers: one for the HIFU therapy and another for the ultrasound imaging guidance. In this specific setting, real-time monitoring of non-invasive surgery is challenging due to severe contamination of the ultrasound guiding images by strong acoustic interference from the HIFU sonication.

METHODS

This paper proposed the use of a deep learning (DL) solution, specifically a diffusion implicit model, to suppress the HIFU interference. We considered the images contaminated with HIFU interference as low-resolution images, and those free from interference as high-resolution. While suppressing HIFU interference using the diffusion implicit (HIFU-Diff) model, the task was transformed into generating a high-resolution image through a series of forward diffusion steps and reverse sampling. A series of ex-vivo and in-vivo experiments, conducted under various parameters, were designed to validate the performance of the proposed network.

RESULTS

Quantitative evaluation and statistical analysis demonstrated that the HIFU-Diff network achieved superior performance in reconstructing interference-free images under a variety of ex-vivo and in-vivo conditions, compared to the most commonly used notch filtering and the recent 1D FUS-Net deep learning network. The HIFU-Diff maintains high performance with 'unseen' datasets from separate experiments, and its superiority is more pronounced under strong HIFU interferences and in complex in-vivo situations. Furthermore, the reconstructed interference-free images can also be used for quantitative attenuation imaging, indicating that the network preserves acoustic characteristics of the ultrasound images.

CONCLUSIONS

With the proposed technique, HIFU therapy and the ultrasound imaging can be conducted simultaneously, allowing for real-time monitoring of the treatment process. This capability could significantly enhance the safety and efficacy of the non-invasive treatment across various clinical applications. To the best of our knowledge, this is the first diffusion-based model developed for HIFU interference suppression.

Frequency-Domain Robust PCA for Real-Time Monitoring of HIFU Treatment

High intensity focused ultrasound (HIFU) is a thriving non-invasive technique for thermal ablation of tumors, but significant challenges remain in its real-time monitoring with medical imaging. Ultrasound imaging is one of the main imaging modalities for monitoring HIFU surgery in organs other than the brain, mainly due to its good temporal resolution. However, strong acoustic interference from HIFU irradiation severely obscures the B-mode images and compromises the monitoring. To address this problem, we proposed a frequency-domain robust principal component analysis (FRPCA) method to separate the HIFU interference from the contaminated B-mode images. Ex-vivo and in-vivo experiments were conducted to validate the proposed method based on a clinical HIFU therapy system combined with an ultrasound imaging platform. The performance of the FRPCA method was compared with the conventional notch filtering method. Results demonstrated that the FRPCA method can effectively remove HIFU interference from the B-mode images, which allowed HIFU-induced grayscale changes at the focal region to be recovered. Compared to notch-filtered images, the FRPCA-processed images showed an 8.9% improvement in terms of the structural similarity (SSIM) index to the uncontaminated B-mode images. These findings demonstrate that the FRPCA method presents an effective signal processing framework to remove the strong HIFU acoustic interference, obtains better dynamic visualization in monitoring the HIFU irradiation process, and offers great potential to improve the efficacy and safety of HIFU treatment and other focused ultrasound related applications.

Suppressing HIFU interference in ultrasound images using 1D U-Net-based neural networks

Objective.One big challenge with high-intensity focused ultrasound (HIFU) is that the intense acoustic interference generated by HIFU irradiation overwhelms the B-mode monitoring images, compromising monitoring effectiveness. This study aims to overcome this problem using a one-dimensional (1D) deep convolutional neural network.Approach. U-Net-based networks have been proven to be effective in image reconstruction and denoising, and the two-dimensional (2D) U-Net has already been investigated for suppressing HIFU interference in ultrasound monitoring images. In this study, we propose that the one-dimensional (1D) convolution in U-Net-based networks is more suitable for removing HIFU artifacts and can better recover the contaminated B-mode images compared to 2D convolution.Ex vivoandinvivoHIFU experiments were performed on a clinically equivalent ultrasound-guided HIFU platform to collect image data, and the 1D convolution in U-Net, Attention U-Net, U-Net++, and FUS-Net was applied to verify our proposal.Main results.All 1D U-Net-based networks were more effective in suppressing HIFU interference than their 2D counterparts, with over 30% improvement in terms of structural similarity (SSIM) to the uncontaminated B-mode images. Additionally, 1D U-Nets trained usingex vivodatasets demonstrated better generalization performance ininvivoexperiments.Significance.These findings indicate that the utilization of 1D convolution in U-Net-based networks offers great potential in addressing the challenges of monitoring in ultrasound-guided HIFU systems.

Ultrasound Monitoring of Simultaneous high-intensity focused ultrasound (HIFU) therapy using minimum-peak-sidelobe coded excitation

Bipolar sequences can be readily transmitted by ultrasound (US) pulser hardware with the full driving voltage to boost the echo magnitude in B-mode monitoring of HIFU treatment. In this study, a novel single-transmit bipolar sequence with minimum-peak-sidelobe (MPS) level is developed not only to restore the image quality of US monitoring but also remove acoustic interference from simultaneous HIFU transmission. The proposed MPS code is designed with an equal number of positive and negative bits and the bit duration should be an integer multiple of the period of the HIFU waveform. In addition, different permutations of code sequence are searched in order to obtain the optimal encoding. The received imaging echo is firstly decoded by matched filtering to cancel HIFU interference and to enhance the echo magnitude of US monitoring. Then, Wiener filtering is applied as the second-stage pulse compression to improve the final image quality. Simulations and phantom experiments are performed to compare the single-transmit MPS decoding with conventional two-transmit methods such as pulse-inversion subtraction (PIS) and Golay decoding for their performance in simultaneous US monitoring of HIFU treatment. Results show that the MPS decoding effectively removes HIFU interference even in the presence of tissue motion. The image quality of PIS and Golay decoding, on the other hand, is compromised by the uncancelled HIFU components due to tissue motion. Simultaneous US monitoring of tissue ablation using the proposed MPS decoding has also demonstrated to be feasible in ex-vivo experiments. Compared to the notch filtering that also allows single-transmit HIFU elimination, the MPS decoding is preferrable because it does not suffer from the tradeoff between residual HIFU and speckle deterioration in US monitoring images.

Improved source localization in passive acoustic mapping using delay-multiply-and-sum beamforming with virtually augmented aperture

High-intensity focused ultrasound (HIFU) is a promising non-invasive treatment method whose applications include tissue ablation, hemostasis, thrombolysis and blood-brain barrier opening etc. Its therapeutic effects come from the thermal necrosis and the mechanical destruction associated with acoustic cavitation. Passive acoustic mapping (PAM) is capable of simultaneous monitoring of HIFU-induced cavitation events using only receive beamforming. Nonetheless, conventional time exposure acoustics (TEA) algorithm has poor spatial resolution and suffers from the X-shaped artifacts. These factors lead to difficulties in precise localization of cavitation source. In this study, we proposed a novel adaptive PAM method which combines Delay-Multiply-and-Sum (DMAS) beamforming with virtual augmented aperture (VA) to overcome the problem. In DMAS-VA beamforming, the magnitude of each channel waveform is scaled by p-th root while the phase is multiplied by L. The p and L correspond respectively to the degree of signal coherence in DMAS beamforming and the augmentation factor of aperture size. After channel sum, p-th power is applied to restore the dimensionality of source strength and then the PAM image is reconstructed by accumulating the signal power over the observation time. Based on simulation and experimental results, the proposed DMAS-VA has better image resolution and image contrast compared with the conventional TEA. Moreover, since the VA method may introduce grating lobes into PAM because of the virtually augmented pitch size, DMAS coherent factor (DCF) is further developed to alleviate these image artifacts. Results indicate that, with DCF weighting, the PAM image of DMAS-VA beamforming could be constructed without detectable image artifacts from grating lobes and false main lobes.