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High-Intensity Focused Ultrasound Ablation

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Review of Robot-Assisted HIFU Therapy.

Sensors (Basel, Switzerland)
This paper provides an overview of current robot-assisted high-intensity focused ultrasound (HIFU) systems for image-guided therapies. HIFU is a minimally invasive technique that relies on the thermo-mechanical effects of focused ultrasound waves to ...

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

Physics in medicine and biology
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 p...

Development of Convolutional Neural Network to Segment Ultrasound Images of Histotripsy Ablation.

IEEE transactions on bio-medical engineering
OBJECTIVE: Histotripsy is a focused ultrasound therapy that ablates tissue via the action of bubble clouds. It is under investigation to treat a number of ailments, including renal tumors. Ultrasound imaging is used to monitor histotripsy, though the...

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

Computer methods and programs in biomedicine
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 ultr...

Development and validation of a deep learning-based method for automatic measurement of uterus, fibroid, and ablated volume in MRI after MR-HIFU treatment of uterine fibroids.

European journal of radiology
INTRODUCTION: The non-perfused volume divided by total fibroid load (NPV/TFL) is a predictive outcome parameter for MRI-guided high-intensity focused ultrasound (MR-HIFU) treatments of uterine fibroids, which is related to long-term symptom relief. I...

Predicting the Prognosis of HIFU Ablation of Uterine Fibroids Using a Deep Learning-Based 3D Super-Resolution DWI Radiomics Model: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: To assess the feasibility and efficacy of a deep learning-based three-dimensional (3D) super-resolution diffusion-weighted imaging (DWI) radiomics model in predicting the prognosis of high-intensity focused ultrasound (HIFU)...

Model based deep learning method for focused ultrasound pathway scanning.

Scientific reports
The primary purpose of high-intensity focused ultrasound (HIFU), a non-invasive medical therapy, is to precisely target and ablate tumors by focusing high-frequency ultrasound from an external power source. A series of ablations must be performed in ...

A holistic physics-informed neural network solution for precise destruction of breast tumors using focused ultrasound on a realistic breast model.

Mathematical biosciences and engineering : MBE
This study presented a novel approach for the precise ablation of breast tumors using focused ultrasound (FUS), leveraging a physics-informed neural network (PINN) integrated with a realistic breast model. FUS has shown significant promise in treatin...

Spherical lesion formation in HIFU using robotic assistance for controlled focal point manipulation.

Scientific reports
We propose a robot-assisted method to generate spherical thermal lesions by high-intensity focused ultrasound (HIFU) ablation. Typically, HIFU-induced thermal lesions are cigar-shaped because the acoustic field in the focal area has a similar elongat...

Machine learning models for prediction of NPVR ≥80% with HIFU ablation for uterine fibroids.

International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group
BACKGROUND: Currently high-intensity focused ultrasound (HIFU) is widely used to treat uterine fibroids (UFs). The aim of this study is to develop a machine learning model that can accurately predict the efficacy of HIFU ablation for UFs, assisting t...