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Caudoux M, Demeulenaere O, Poree J, Sauvage J, Mateo P, Ghaleh B, Flesch M, Ferin G, Tanter M, Deffieux T, Papadacci C, Pernot M. Curved Toroidal Row Column Addressed Transducer for 3D Ultrafast Ultrasound Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:3279-3291. [PMID: 38640053 DOI: 10.1109/tmi.2024.3391689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Abstract
3D Imaging of the human heart at high frame rate is of major interest for various clinical applications. Electronic complexity and cost has prevented the dissemination of 3D ultrafast imaging into the clinic. Row column addressed (RCA) transducers provide volumetric imaging at ultrafast frame rate by using a low electronic channel count, but current models are ill-suited for transthoracic cardiac imaging due to field-of-view limitations. In this study, we proposed a mechanically curved RCA with an aperture adapted for transthoracic cardiac imaging ( 24×16 mm2). The RCA has a toroidal curved surface of 96 elements along columns (curvature radius rC = 4.47 cm) and 64 elements along rows (curvature radius rR = 3 cm). We implemented delay and sum beamforming with an analytical calculation of the propagation of a toroidal wave which was validated using simulations (Field II). The imaging performance was evaluated on a calibrated phantom. Experimental 3D imaging was achieved up to 12 cm deep with a total angular aperture of 30° for both lateral dimensions. The Contrast-to-Noise ratio increased by 12 dB from 2 to 128 virtual sources. Then, 3D Ultrasound Localization Microscopy (ULM) was characterized in a sub-wavelength tube diameter. Finally, 3D ULM was demonstrated on a perfused ex-vivo swine heart to image the coronary microcirculation.
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Zhang G, Hu X, Ren X, Zhou B, Li B, Li Y, Luo J, Liu X, Ta D. In vivo ultrasound localization microscopy for high-density microbubbles. ULTRASONICS 2024; 143:107410. [PMID: 39084108 DOI: 10.1016/j.ultras.2024.107410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 07/04/2024] [Accepted: 07/18/2024] [Indexed: 08/02/2024]
Abstract
Ultrasound Localization Microscopy (ULM) surpasses the constraints imposed by acoustic diffraction, achieving sub-wavelength resolution visualization of microvasculature through the precise localization of minute microbubbles (MBs). Nonetheless, the analysis of densely populated regions with overlapping MB point spread responses introduces significant localization errors, limiting the use of technique to low-concentration conditions. This raises a trade-off issue between localization efficiency and MB density. In this work, we present a new deep learning framework that combines Transformer and U-Net architectures, termed ULM-TransUNet. As a non-linear model, it is able to learn the complex data patterns of overlapping MBs in dense conditions for accurate localization. To evaluate the performance of ULM-TransUNet, a series of numerical simulations and in vivo experiments are carried out. Numerical simulation results indicate that ULM-TransUNet achieves high-quality ULM imaging, with improvements of 21.93 % in detection rate, 17.36 % in detection precision, and 20.53 % in detection sensitivity, compared to previous state-of-the-art deep learning (DL) method (e.g., ULM-UNet). For the in vivo experiments, ULM-TransUNet achieves the highest spatial resolution (9.4 μm) and rapid inference speed (26.04 ms/frame). Furthermore, it consistently detects more small vessels and resolves closely spaced vessels more effectively. The outcomes of this work imply that ULM-TransUNet can potentially enhance the microvascular imaging performance on high-density MB conditions.
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Affiliation(s)
- Gaobo Zhang
- Department of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China
| | - Xing Hu
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai 201907, China
| | - Xuan Ren
- Academy for Engineering and Technology, Fudan University, Shanghai 200438, China
| | - Boqian Zhou
- Academy for Engineering and Technology, Fudan University, Shanghai 200438, China
| | - Boyi Li
- Academy for Engineering and Technology, Fudan University, Shanghai 200438, China
| | - Yifang Li
- Academy for Engineering and Technology, Fudan University, Shanghai 200438, China
| | - Jianwen Luo
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xin Liu
- Academy for Engineering and Technology, Fudan University, Shanghai 200438, China; State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai 200032, China.
| | - Dean Ta
- Department of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China; Academy for Engineering and Technology, Fudan University, Shanghai 200438, China.
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Huang X, Zhang Y, Zhou Q, Deng Q. Value of Ultrasound Super-Resolution Imaging for the Assessment of Renal Microcirculation in Patients with Acute Kidney Injury: A Preliminary Study. Diagnostics (Basel) 2024; 14:1192. [PMID: 38893718 PMCID: PMC11171740 DOI: 10.3390/diagnostics14111192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 05/31/2024] [Accepted: 05/31/2024] [Indexed: 06/21/2024] Open
Abstract
The present study aimed to explore the clinical applicability of ultrasound super-resolution imaging (US SRI) for assessing renal microcirculation in patients with acute kidney injury (AKI). A total of 62 patients with sepsis were enrolled in the present study-38 with AKI and 24 control patients-from whom renal ultrasounds and clinical data were obtained. SonoVue contrast (1.5 mL) was administered through the elbow vein and contrast-enhanced ultrasound (CEUS) images were obtained on a Mindray Resona A20 ultrasound unit for 2 min. The renal perfusion time-intensity curve (TIC) was analyzed and, after 15 min, additional images were obtained to create a microscopic blood flow map. Microvascular density (MVD) was calculated and its correlation with serum creatinine (Scr) levels was analyzed. There were significant differences in heart rate, Scr, blood urea nitrogen, urine volume at 24 h, and glomerular filtration rate between the two groups (p < 0.01), whereas other characteristics, such as renal morphology, did not differ significantly between the AKI group and control group (p > 0.05). The time to peak and mean transit times of the renal cortex in the AKI group were prolonged compared to those in the control group (p < 0.01), while the peak intensity and area under the TIC were lower than those in the control group (p < 0.05). The MVD of the renal cortex in the AKI group was lower than that in the control group (18.46 ± 5.90% vs. 44.93 ± 11.65%; p < 0.01) and the MVD in the AKI group showed a negative correlation with Scr (R = -0.84; p < 0.01). Based on the aforementioned results, US SRI can effectively assess renal microcirculation in patients with AKI and is a noninvasive technique for the diagnosis of AKI and quantitative evaluation of renal microcirculation.
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Affiliation(s)
| | | | - Qing Zhou
- Department of Ultrasound, Renmin Hospital of Wuhan University, Wuhan 430060, China; (X.H.); (Y.Z.)
| | - Qing Deng
- Department of Ultrasound, Renmin Hospital of Wuhan University, Wuhan 430060, China; (X.H.); (Y.Z.)
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Tuccio G, Afrakhteh S, Iacca G, Demi L. Time Efficient Ultrasound Localization Microscopy Based on A Novel Radial Basis Function 2D Interpolation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1690-1701. [PMID: 38145542 DOI: 10.1109/tmi.2023.3347261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Ultrasound localization microscopy (ULM) allows for the generation of super-resolved (SR) images of the vasculature by precisely localizing intravenously injected microbubbles. Although SR images may be useful for diagnosing and treating patients, their use in the clinical context is limited by the need for prolonged acquisition times and high frame rates. The primary goal of our study is to relax the requirement of high frame rates to obtain SR images. To this end, we propose a new time-efficient ULM (TEULM) pipeline built on a cutting-edge interpolation method. More specifically, we suggest employing Radial Basis Functions (RBFs) as interpolators to estimate the missing values in the 2-dimensional (2D) spatio-temporal structures. To evaluate this strategy, we first mimic the data acquisition at a reduced frame rate by applying a down-sampling (DS = 2, 4, 8, and 10) factor to high frame rate ULM data. Then, we up-sample the data to the original frame rate using the suggested interpolation to reconstruct the missing frames. Finally, using both the original high frame rate data and the interpolated one, we reconstruct SR images using the ULM framework steps. We evaluate the proposed TEULM using four in vivo datasets, a Rat brain (dataset A), a Rat kidney (dataset B), a Rat tumor (dataset C) and a Rat brain bolus (dataset D), interpolating at the in-phase and quadrature (IQ) level. Results demonstrate the effectiveness of TEULM in recovering vascular structures, even at a DS rate of 10 (corresponding to a frame rate of sub-100Hz). In conclusion, the proposed technique is successful in reconstructing accurate SR images while requiring frame rates of one order of magnitude lower than standard ULM.
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Shin Y, Lowerison MR, Wang Y, Chen X, You Q, Dong Z, Anastasio MA, Song P. Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy. Nat Commun 2024; 15:2932. [PMID: 38575577 PMCID: PMC10995206 DOI: 10.1038/s41467-024-47154-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 03/20/2024] [Indexed: 04/06/2024] Open
Abstract
Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated microbubbles, resulting in prolonged imaging time to obtain detailed microvascular maps. Here, we introduce LOcalization with Context Awareness (LOCA)-ULM, a deep learning-based microbubble simulation and localization pipeline designed to enhance localization performance in high microbubble concentrations. In silico, LOCA-ULM enhanced microbubble detection accuracy to 97.8% and reduced the missing rate to 23.8%, outperforming conventional and deep learning-based localization methods up to 17.4% in accuracy and 37.6% in missing rate reduction. In in vivo rat brain imaging, LOCA-ULM revealed dense cerebrovascular networks and spatially adjacent microvessels undetected by conventional ULM. We further demonstrate the superior localization performance of LOCA-ULM in functional ULM (fULM) where LOCA-ULM significantly increased the functional imaging sensitivity of fULM to hemodynamic responses invoked by whisker stimulations in the rat brain.
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Affiliation(s)
- YiRang Shin
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Matthew R Lowerison
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Yike Wang
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Xi Chen
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Qi You
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Zhijie Dong
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Mark A Anastasio
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Pengfei Song
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, IL, USA.
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Wei L, Wahyulaksana G, Te Lintel Hekkert M, Beurskens R, Boni E, Ramalli A, Noothout E, Duncker DJ, Tortoli P, van der Steen AFW, de Jong N, Verweij M, Vos HJ. High-Frame-Rate Volumetric Porcine Renal Vasculature Imaging. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:2476-2482. [PMID: 37704558 DOI: 10.1016/j.ultrasmedbio.2023.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/02/2023] [Accepted: 08/08/2023] [Indexed: 09/15/2023]
Abstract
OBJECTIVE The aim of this study was to assess the feasibility and imaging options of contrast-enhanced volumetric ultrasound kidney vasculature imaging in a porcine model using a prototype sparse spiral array. METHODS Transcutaneous freehand in vivo imaging of two healthy porcine kidneys was performed according to three protocols with different microbubble concentrations and transmission sequences. Combining high-frame-rate transmission sequences with our previously described spatial coherence beamformer, we determined the ability to produce detailed volumetric images of the vasculature. We also determined power, color and spectral Doppler, as well as super-resolved microvasculature in a volume. The results were compared against a clinical 2-D ultrasound machine. RESULTS Three-dimensional visualization of the kidney vasculature structure and blood flow was possible with our method. Good structural agreement was found between the visualized vasculature structure and the 2-D reference. Microvasculature patterns in the kidney cortex were visible with super-resolution processing. Blood flow velocity estimations were within a physiological range and pattern, also in agreement with the 2-D reference results. CONCLUSION Volumetric imaging of the kidney vasculature was possible using a prototype sparse spiral array. Reliable structural and temporal information could be extracted from these imaging results.
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Affiliation(s)
- Luxi Wei
- Department of Cardiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
| | - Geraldi Wahyulaksana
- Department of Cardiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | | | - Robert Beurskens
- Department of Cardiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Enrico Boni
- Department of Information Engineering, University of Florence, Florence, Italy
| | - Alessandro Ramalli
- Department of Information Engineering, University of Florence, Florence, Italy
| | - Emile Noothout
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Dirk J Duncker
- Department of Cardiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Piero Tortoli
- Department of Information Engineering, University of Florence, Florence, Italy
| | - Antonius F W van der Steen
- Department of Cardiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands; Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Nico de Jong
- Department of Cardiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands; Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Martin Verweij
- Department of Cardiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands; Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Hendrik J Vos
- Department of Cardiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands; Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
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Li J, Wei C, Ma X, Ying T, Sun D, Zheng Y. Maximum intensity projection based on high frame rate contrast-enhanced ultrasound for the differentiation of breast tumors. Front Oncol 2023; 13:1274716. [PMID: 37965464 PMCID: PMC10642959 DOI: 10.3389/fonc.2023.1274716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/16/2023] [Indexed: 11/16/2023] Open
Abstract
Objective We explored the role of maximum intensity projection (MIP) based on high frame rate contrast-enhanced ultrasound (H-CEUS) for the differentiation of breast tumors. Methods MIP imaging was performed in patients with breast tumors who underwent H-CEUS examinations. The microvasculature morphology of breast tumors was assessed. The receiver operating characteristic curve was plotted to evaluate the diagnostic performance of MIP. Results Forty-three breast tumors were finally analyzed, consisting of 19 benign and 24 malignant tumors. For the ≤30-s and >30-s phases, dot-, line-, or branch-like patterns were significantly more common in benign tumors. A tree-like pattern was only present in the benign tumors. A crab claw-like pattern was significantly more common in the malignant tumors. Among the tumors with crab claw-like patterns, three cases of malignant tumors had multiple parallel small spiculated vessels. There were significant differences in the microvasculature morphology for the ≤30-s and >30-s phases between the benign and malignant tumors (all p < 0.001). The area under the curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the ≤30-s phase were all higher than those of the >30-s phase for the classification of breast tumors. Conclusion MIP based on H-CEUS can be used for the differentiation of breast tumors, and the ≤30-s phase had a better diagnostic value. Multiple parallel small spiculated vessels were a new finding, which could provide new insight for the subsequent study of breast tumors.
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Affiliation(s)
| | | | | | - Tao Ying
- Department of Ultrasound in Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Di Sun
- Department of Ultrasound in Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuanyi Zheng
- Department of Ultrasound in Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Zhang G, Liao C, Hu JR, Hu HM, Lei YM, Harput S, Ye HR. Nanodroplet-Based Super-Resolution Ultrasound Localization Microscopy. ACS Sens 2023; 8:3294-3306. [PMID: 37607403 DOI: 10.1021/acssensors.3c00418] [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] [Indexed: 08/24/2023]
Abstract
Over the past decade, super-resolution ultrasound localization microscopy (SR-ULM) has revolutionized ultrasound imaging with its capability to resolve the microvascular structures below the ultrasound diffraction limit. The introduction of this imaging technique enables the visualization, quantification, and characterization of tissue microvasculature. The early implementations of SR-ULM utilize microbubbles (MBs) that require a long image acquisition time due to the requirement of capturing sparsely isolated microbubble signals. The next-generation SR-ULM employs nanodroplets that have the potential to significantly reduce the image acquisition time without sacrificing the resolution. This review discusses various nanodroplet-based ultrasound localization microscopy techniques and their corresponding imaging mechanisms. A summary is given on the preclinical applications of SR-ULM with nanodroplets, and the challenges in the clinical translation of nanodroplet-based SR-ULM are presented while discussing the future perspectives. In conclusion, ultrasound localization microscopy is a promising microvasculature imaging technology that can provide new diagnostic and prognostic information for a wide range of pathologies, such as cancer, heart conditions, and autoimmune diseases, and enable personalized treatment monitoring at a microlevel.
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Affiliation(s)
- Ge Zhang
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Wuhan University of Science and Technology, Wuhan 430080, People's Republic of China
- Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan 430065, People's Republic of China
- Physics for Medicine Paris, Inserm U1273, ESPCI Paris, PSL University, CNRS, Paris 75015, France
| | - Chen Liao
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Wuhan University of Science and Technology, Wuhan 430080, People's Republic of China
- Medical College, Wuhan University of Science and Technology, Wuhan 430065, People's Republic of China
| | - Jun-Rui Hu
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
| | - Hai-Man Hu
- Department of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, People's Republic of China
| | - Yu-Meng Lei
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Wuhan University of Science and Technology, Wuhan 430080, People's Republic of China
| | - Sevan Harput
- Department of Electrical and Electronic Engineering, London South Bank University, London SE1 0AA, U.K
| | - Hua-Rong Ye
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Wuhan University of Science and Technology, Wuhan 430080, People's Republic of China
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Dencks S, Schmitz G. Ultrasound localization microscopy. Z Med Phys 2023; 33:292-308. [PMID: 37328329 PMCID: PMC10517400 DOI: 10.1016/j.zemedi.2023.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 01/24/2023] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Ultrasound Localization Microscopy (ULM) is an emerging technique that provides impressive super-resolved images of microvasculature, i.e., images with much better resolution than the conventional diffraction-limited ultrasound techniques and is already taking its first steps from preclinical to clinical applications. In comparison to the established perfusion or flow measurement methods, namely contrast-enhanced ultrasound (CEUS) and Doppler techniques, ULM allows imaging and flow measurements even down to the capillary level. As ULM can be realized as a post-processing method, conventional ultrasound systems can be used for. ULM relies on the localization of single microbubbles (MB) of commercial, clinically approved contrast agents. In general, these very small and strong scatterers with typical radii of 1-3 µm are imaged much larger in ultrasound images than they actually are due to the point spread function of the imaging system. However, by applying appropriate methods, these MBs can be localized with sub-pixel precision. Then, by tracking MBs over successive frames of image sequences, not only the morphology of vascular trees but also functional information such as flow velocities or directions can be obtained and visualized. In addition, quantitative parameters can be derived to describe pathological and physiological changes in the microvasculature. In this review, the general concept of ULM and conditions for its applicability to microvessel imaging are explained. Based on this, various aspects of the different processing steps for a concrete implementation are discussed. The trade-off between complete reconstruction of the microvasculature and the necessary measurement time as well as the implementation in 3D are reviewed in more detail, as they are the focus of current research. Through an overview of potential or already realized preclinical and clinical applications - pathologic angiogenesis or degeneration of vessels, physiological angiogenesis, or the general understanding of organ or tissue function - the great potential of ULM is demonstrated.
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Affiliation(s)
- Stefanie Dencks
- Lehrstuhl für Medizintechnik, Fakultät für Elektrotechnik und Informationstechnik, Ruhr-Universität Bochum, Bochum, Germany.
| | - Georg Schmitz
- Lehrstuhl für Medizintechnik, Fakultät für Elektrotechnik und Informationstechnik, Ruhr-Universität Bochum, Bochum, Germany
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Ghanbarzadeh-Dagheyan A, Nili VA, Ejtehadi M, Savabi R, Kavehvash Z, Ahmadian MT, Vahdat BV. Time-domain ultrasound as prior information for frequency-domain compressive ultrasound for intravascular cell detection: A 2-cell numerical model. ULTRASONICS 2022; 125:106791. [PMID: 35809517 DOI: 10.1016/j.ultras.2022.106791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 05/05/2022] [Accepted: 06/08/2022] [Indexed: 06/15/2023]
Abstract
This study proposes a new method for the detection of a weak scatterer among strong scatterers using prior-information ultrasound (US) imaging. A perfect application of this approach is in vivo cell detection in the bloodstream, where red blood cells (RBCs) serve as identifiable strong scatterers. In vivo cell detection can help diagnose cancer at its earliest stages, increasing the chances of survival for patients. This work combines time-domain US with frequency-domain compressive US imaging to detect a 20-μ MCF-7 circulating tumor cell (CTC) among a number of RBCs within a simulated venule inside the mouth. The 2D image reconstructed from the time-domain US is employed to simulate the reflected and scattered pressure field from the RBCs, which is then measured at the location of the receivers. The RBCs are tagged one time by a human operator and another time, automatically, by template-based computer vision. Next, the resulting signal from the RBCs is subtracted from the measured total signal in frequency domain to generate the scattered-field data, coming from the CTC alone. Feeding that signal and the background pressure field into a norm-one-based compressive sensing code enables detecting the CTC at various locations. As errors could arise in determining the location of the RBCs and their acoustic properties in the real world, small errors (up to 10% in the former and 5% in the latter) are purposefully introduced to the model, to which the proposed method is shown to be resilient. Localization errors are smaller than 12 μ when a human tags the RBCs and smaller than 25 μ when computer vision is applied. Despite its limitations, this study, for the first time, reports the results of combining two US modalities aimed at cell detection and introduces a unique and useful application for ultrahigh-frequency US imaging. It should be noted that this method can be used in detecting weak scatterers with ultrasound waves in other applications as well.
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Affiliation(s)
- Ashkan Ghanbarzadeh-Dagheyan
- Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran; Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
| | - Vahid Amin Nili
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Mehdi Ejtehadi
- Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
| | - Reza Savabi
- School of Mechanical Engineering, University of Tehran, Tehran, Iran
| | - Zahra Kavehvash
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
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Felipe VB, Ananya B, Ying T, Qiang L, Ji-Bin L, John RE. Renal Contrast-enhanced Ultrasound: Clinical Applications and Emerging Researc. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY 2022; 6:129. [DOI: 10.37015/audt.2022.220036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2024] Open
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