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Wang F, Yang Z, Peng W, Song L, Luo Y, Zhao Z, Huang L. RPCA-based thermoacoustic imaging for microwave ablation monitoring. PHOTOACOUSTICS 2024; 38:100622. [PMID: 38911132 PMCID: PMC11192794 DOI: 10.1016/j.pacs.2024.100622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/11/2024] [Accepted: 05/17/2024] [Indexed: 06/25/2024]
Abstract
Microwave ablation (MWA) is a potent cancer treatment tool, but its effectiveness can be hindered by the lack of visual feedback. This paper validates the feasibility of using microwave-induced thermoacoustic imaging (TAI) technique to monitor the MWA process. A feasibility analysis was conducted at the principle level and a high-performance real-time TAI system was introduced. To address the interference caused by MWA, a robust principal component analysis (RPCA)-based method for TAI was proposed. This method leverages the correlation between multiple signal frames to eliminate interference. RPCA's effectiveness in TAI was demonstrated through three sets of different experiments. Experiments demonstrated that TAI can effectively monitors the MWA process. This work represents the first application of RPCA-related matrix decomposition methods in TAI, paving the way for the application of TAI in more complex clinical scenarios. By providing rapid and accurate visual feedback, this research advances MWA technology.
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Affiliation(s)
- Fuyong Wang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China
| | - Zeqi Yang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China
| | - Wanting Peng
- School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, Sichuan, China
| | - Ling Song
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yan Luo
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Zhiqin Zhao
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China
| | - Lin Huang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China
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He B, Lei J, Lang X, Li Z, Cui W, Zhang Y. Ultra-fast ultrasound blood flow velocimetry for carotid artery with deep learning. Artif Intell Med 2023; 144:102664. [PMID: 37783552 DOI: 10.1016/j.artmed.2023.102664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 07/22/2023] [Accepted: 09/14/2023] [Indexed: 10/04/2023]
Abstract
Accurate measurement of blood flow velocity is important for the prevention and early diagnosis of atherosclerosis. However, due to the uncertainty of parameter settings, the autocorrelation velocimetry methods based on clutter filtering are prone to incorrectly filter out the near-wall blood flow signal, resulting in poor velocimetric accuracy. In addition, the Doppler coherent compounding acts as a low-pass filter, which also leads to low values of blood flow velocity estimated by the above methods. Motivated by this status quo, here we propose a deep learning estimator that combines clutter filtering and blood flow velocimetry based on the adaptive property of one-dimensional convolutional neural network (1DCNN). The estimator is operated by first extracting the blood flow signal from the original Doppler echo signal through an affine transformation of the 1D convolution, and then converting the extracted signal into the desired blood flow velocity using a linear transformation function. The effectiveness of the proposed method is verified by simulation as well as in vivo carotid artery data. Compared with typical velocimetry methods such as high-pass filtering (HPF) and singular value decomposition (SVD), the results show that the normalized root means square error (NRMSE) obtained by 1DCNN is reduced by 54.99 % and 53.50 % for forward blood flow velocimetry, and 70.99 % and 69.50 % for reverse blood flow velocimetry, respectively. Consistently, the in vivo measurements demonstrate that the goodness-of-fit of the proposed estimator is improved by 8.72 % and 4.74 % for five subjects. Moreover, the estimation time consumed by 1DCNN is greatly reduced, which costs only 2.91 % of the time of HPF and 12.83 % of the time of SVD. In conclusion, the proposed estimator is a better alternative to the current blood flow velocimetry, and is capable of providing more accurate diagnosis information for vascular diseases in clinical applications.
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Affiliation(s)
- Bingbing He
- Department of Electronic Engineering, Information School, Yunnan University, Kunming 650091, China
| | - Jian Lei
- Department of Electronic Engineering, Information School, Yunnan University, Kunming 650091, China
| | - Xun Lang
- Department of Electronic Engineering, Information School, Yunnan University, Kunming 650091, China.
| | - Zhiyao Li
- Third Affiliated Hospital of Kunming Medical University, Kunming 650031, China
| | - Wang Cui
- Department of Electronic Engineering, Information School, Yunnan University, Kunming 650091, China
| | - Yufeng Zhang
- Department of Electronic Engineering, Information School, Yunnan University, Kunming 650091, China
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Insana MF, Dai B, Babaei S, Abbey CK. Combining Spatial Registration With Clutter Filtering for Power-Doppler Imaging in Peripheral Perfusion Applications. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2022; 69:3243-3254. [PMID: 36191097 PMCID: PMC9741924 DOI: 10.1109/tuffc.2022.3211469] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Power-Doppler ultrasonic (PD-US) imaging is sensitive to echoes from blood cell motion in the microvasculature but generally nonspecific because of difficulties with filtering nonblood-echo sources. We are studying the potential for using PD-US imaging for routine assessments of peripheral blood perfusion without contrast media. The strategy developed is based on an experimentally verified computational model of tissue perfusion that simulates typical in vivo conditions. The model considers directed and diffuse blood perfusion states in a field of moving clutter and noise. A spatial registration method is applied to minimize tissue motion prior to clutter and noise filtering. The results show that in-plane clutter motion is effectively minimized. While out-of-plane motion remains a strong source of clutter-filter leakage, those registration errors are readily minimized by straightforward modification of scanning techniques and spatial averaging.
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Vilimek D, Kubicek J, Golian M, Jaros R, Kahankova R, Hanzlikova P, Barvik D, Krestanova A, Penhaker M, Cerny M, Prokop O, Buzga M. Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images. PLoS One 2022; 17:e0270745. [PMID: 35797331 PMCID: PMC9262246 DOI: 10.1371/journal.pone.0270745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 06/16/2022] [Indexed: 11/19/2022] Open
Abstract
Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images.
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Affiliation(s)
- Dominik Vilimek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic
| | - Jan Kubicek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic
| | - Milos Golian
- Human Motion Diagnostic Center, Department of Human Movement Studies, University of Ostrava, Ostrava, Czech Republic
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic
| | - Radana Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic
- * E-mail:
| | - Pavla Hanzlikova
- Department of Imaging Method, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
| | - Daniel Barvik
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic
| | - Alice Krestanova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic
| | - Marek Penhaker
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic
| | - Martin Cerny
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic
| | | | - Marek Buzga
- Human Motion Diagnostic Center, Department of Human Movement Studies, University of Ostrava, Ostrava, Czech Republic
- Deparment of Physiology and Pathophysiology, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
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Zhang N, Nguyen MB, Mertens L, Barron DJ, Villemain O, Baranger J. Improving coronary ultrafast Doppler angiography using fractional moving blood volume and motion-adaptive ensemble length. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 05/27/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Coronary microperfusion assessment is a key parameter for understanding cardiac function. Currently, coronary ultrafast Doppler angiography is the only non-invasive clinical imaging technique able to assess coronary microcirculation quantitatively in humans. In this study, we propose to use fractional moving blood volume (FMBV), proportional to the red blood cell concentration, as a metric for perfusion. FMBV compares the power Doppler in a region of interest (ROI) inside the myocardium to the power Doppler of a reference area in the heart chamber, fully filled with blood. This normalization gives then relative values of the ROI blood filling. However, due to the impact of ultrasound attenuation and elevation focus on power Doppler values, the reference area and the ROI need to be at the same depth to allow this normalization. This condition is rarely satisfied in vivo due to the cardiac anatomy. Hereby, we propose to locally compensate the attenuation between the ROI and the reference, by measuring the attenuation law on a phantom. We quantified the efficiency of this approach by comparing FMBV with and without compensation on a flow phantom. Compensated FMBV was able to estimate the ground-truth FMBV with less than 5% variation. This method was then adapted to the in vivo case of myocardial perfusion imaging during heart surgery on human neonates. The translation from in vitro to in vivo required an additional clutter filtering step to ensure that blood signals could be correctly identified in the fast-moving myocardium. We applied the singular value decomposition filter on temporal sliding windows whose lengths were a function of myocardium motion. This motion-adaptive temporal sliding window approach was able to improve blood and tissue separation in terms of contrast-to-noise ratio, as compared to well-established constant-length sliding window approaches. Therefore, compensated FMBV and singular value decomposition assisted with motion-adaptive temporal sliding windows improves the quantification of blood volume in coronary ultrafast Doppler angiography.
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