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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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Lee HS, Park JH, Lee SJ. Artificial intelligence-based speckle featurization and localization for ultrasound speckle tracking velocimetry. ULTRASONICS 2024; 138:107241. [PMID: 38232448 DOI: 10.1016/j.ultras.2024.107241] [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: 09/15/2023] [Revised: 12/25/2023] [Accepted: 01/02/2024] [Indexed: 01/19/2024]
Abstract
Deep learning-based super-resolution ultrasound (DL-SRU) framework has been successful in improving spatial resolution and measuring the velocity field information of a blood flows by localizing and tracking speckle signals of red blood cells (RBCs) without using any contrast agents. However, DL-SRU can localize only a small part of the speckle signals of blood flow owing to ambiguity problems encountered in the classification of blood flow signals from ultrasound B-mode images and the building up of suitable datasets required for training artificial neural networks, as well as the structural limitations of the neural network itself. An artificial intelligence-based speckle featurization and localization (AI-SFL) framework is proposed in this study. It includes a machine learning-based algorithm for classifying blood flow signals from ultrasound B-mode images, dimensionality reduction for featurizing speckle patterns of the classified blood flow signals by approximating them with quantitative values. A novel and robust neural network (ResSU-net) is trained using the online data generation (ODG) method and the extracted speckle features. The super-resolution performance of the proposed AI-SFL and ODG method is evaluated and compared with the results of previous U-net and conventional data augmentation methods under in silico conditions. The predicted locations of RBCs by the AI-SFL and DL-SRU for speckle patterns of blood flow are applied to a PTV algorithm to measure quantitative velocity fields of the flow. Finally, the feasibility of the proposed AI-SFL framework for measuring real blood flows is verified under in vivo conditions.
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Affiliation(s)
- Hyo Seung Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk, South Korea.
| | - Jun Hong Park
- Department of Radiology, Stanford University 450 Jane Stanford Way Stanford, CA 94305-2004, United States.
| | - Sang Joon Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk, South Korea.
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Sharahi HJ, Acconcia CN, Li M, Martel A, Hynynen K. A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging. SENSORS (BASEL, SWITZERLAND) 2023; 23:8760. [PMID: 37960460 PMCID: PMC10650508 DOI: 10.3390/s23218760] [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: 08/11/2023] [Revised: 10/18/2023] [Accepted: 10/20/2023] [Indexed: 11/15/2023]
Abstract
Convolutional neural networks (CNNs), initially developed for image processing applications, have recently received significant attention within the field of medical ultrasound imaging. In this study, passive cavitation imaging/mapping (PCI/PAM), which is used to map cavitation sources based on the correlation of signals across an array of receivers, is evaluated. Traditional reconstruction techniques in PCI, such as delay-and-sum, yield high spatial resolution at the cost of a substantial computational time. This results from the resource-intensive process of determining sensor weights for individual pixels in these methodologies. Consequently, the use of conventional algorithms for image reconstruction does not meet the speed requirements that are essential for real-time monitoring. Here, we show that a three-dimensional (3D) convolutional network can learn the image reconstruction algorithm for a 16×16 element matrix probe with a receive frequency ranging from 256 kHz up to 1.0 MHz. The network was trained and evaluated using simulated data representing point sources, resulting in the successful reconstruction of volumetric images with high sensitivity, especially for single isolated sources (100% in the test set). As the number of simultaneous sources increased, the network's ability to detect weaker intensity sources diminished, although it always correctly identified the main lobe. Notably, however, network inference was remarkably fast, completing the task in approximately 178 s for a dataset comprising 650 frames of 413 volume images with signal duration of 20μs. This processing speed is roughly thirty times faster than a parallelized implementation of the traditional time exposure acoustics algorithm on the same GPU device. This would open a new door for PCI application in the real-time monitoring of ultrasound ablation.
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Affiliation(s)
- Hossein J. Sharahi
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada (A.M.)
| | - Christopher N. Acconcia
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada (A.M.)
| | - Matthew Li
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada (A.M.)
| | - Anne Martel
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada (A.M.)
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Kullervo Hynynen
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada (A.M.)
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
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Yu X, Luan S, Lei S, Huang J, Liu Z, Xue X, Ma T, Ding Y, Zhu B. Deep learning for fast denoising filtering in ultrasound localization microscopy. Phys Med Biol 2023; 68:205002. [PMID: 37703894 DOI: 10.1088/1361-6560/acf98f] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 09/13/2023] [Indexed: 09/15/2023]
Abstract
Objective.Addition of a denoising filter step in ultrasound localization microscopy (ULM) has been shown to effectively reduce the error localizations of microbubbles (MBs) and achieve resolution improvement for super-resolution ultrasound (SR-US) imaging. However, previous image-denoising methods (e.g. block-matching 3D, BM3D) requires long data processing times, making ULM only able to be processed offline. This work introduces a new way to reduce data processing time through deep learning.Approach.In this study, we propose deep learning (DL) denoising based on contrastive semi-supervised network (CS-Net). The neural network is mainly trained with simulated MBs data to extract MB signals from noise. And the performances of CS-Net denoising are evaluated in bothin vitroflow phantom experiment andin vivoexperiment of New Zealand rabbit tumor.Main results.Forin vitroflow phantom experiment, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of single microbubble image are 26.91 dB and 4.01 dB, repectively. Forin vivoanimal experiment , the SNR and CNR were 12.29 dB and 6.06 dB. In addition, single microvessel of 24μm and two microvessels separated by 46μm could be clearly displayed. Most importantly,, the CS-Net denoising speeds forin vitroandin vivoexperiments were 0.041 s frame-1and 0.062 s frame-1, respectively.Significance.DL denoising based on CS-Net can improve the resolution of SR-US as well as reducing denoising time, thereby making further contributions to the clinical real-time imaging of ULM.
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Affiliation(s)
- Xiangyang Yu
- Shool of Integrated Circuit, Wuhan National Laboratory for optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Shunyao Luan
- Shool of Integrated Circuit, Wuhan National Laboratory for optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Shuang Lei
- Shool of Integrated Circuit, Wuhan National Laboratory for optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Jing Huang
- Shool of Integrated Circuit, Wuhan National Laboratory for optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Zeqing Liu
- Shool of Integrated Circuit, Wuhan National Laboratory for optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xudong Xue
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Teng Ma
- The Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, People's Republic of China
| | - Yi Ding
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Benpeng Zhu
- Shool of Integrated Circuit, Wuhan National Laboratory for optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of 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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6
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De Rosa L, L’Abbate S, Kusmic C, Faita F. Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals. Life (Basel) 2023; 13:1759. [PMID: 37629616 PMCID: PMC10455134 DOI: 10.3390/life13081759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/28/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND AND AIM Ultrasound (US) imaging is increasingly preferred over other more invasive modalities in preclinical studies using animal models. However, this technique has some limitations, mainly related to operator dependence. To overcome some of the current drawbacks, sophisticated data processing models are proposed, in particular artificial intelligence models based on deep learning (DL) networks. This systematic review aims to overview the application of DL algorithms in assisting US analysis of images acquired in in vivo preclinical studies on animal models. METHODS A literature search was conducted using the Scopus and PubMed databases. Studies published from January 2012 to November 2022 that developed DL models on US images acquired in preclinical/animal experimental scenarios were eligible for inclusion. This review was conducted according to PRISMA guidelines. RESULTS Fifty-six studies were enrolled and classified into five groups based on the anatomical district in which the DL models were used. Sixteen studies focused on the cardiovascular system and fourteen on the abdominal organs. Five studies applied DL networks to images of the musculoskeletal system and eight investigations involved the brain. Thirteen papers, grouped under a miscellaneous category, proposed heterogeneous applications adopting DL systems. Our analysis also highlighted that murine models were the most common animals used in in vivo studies applying DL to US imaging. CONCLUSION DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. However, in this scenario, these techniques are still in their early stages, and there is room for improvement, such as sample sizes, data preprocessing, and model interpretability.
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Affiliation(s)
- Laura De Rosa
- Institute of Clinical Physiology, National Research Council (CNR), 56124 Pisa, Italy; (L.D.R.); (F.F.)
- Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy
| | - Serena L’Abbate
- Institute of Life Sciences, Scuola Superiore Sant’Anna, 56124 Pisa, Italy;
| | - Claudia Kusmic
- Institute of Clinical Physiology, National Research Council (CNR), 56124 Pisa, Italy; (L.D.R.); (F.F.)
| | - Francesco Faita
- Institute of Clinical Physiology, National Research Council (CNR), 56124 Pisa, Italy; (L.D.R.); (F.F.)
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Chen X, Lowerison MR, Dong Z, Chandra Sekaran NV, Llano DA, Song P. Localization Free Super-Resolution Microbubble Velocimetry Using a Long Short-Term Memory Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:2374-2385. [PMID: 37028074 PMCID: PMC10461750 DOI: 10.1109/tmi.2023.3251197] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Ultrasound localization microscopy is a super-resolution imaging technique that exploits the unique characteristics of contrast microbubbles to side-step the fundamental trade-off between imaging resolution and penetration depth. However, the conventional reconstruction technique is confined to low microbubble concentrations to avoid localization and tracking errors. Several research groups have introduced sparsity- and deep learning-based approaches to overcome this constraint to extract useful vascular structural information from overlapping microbubble signals, but these solutions have not been demonstrated to produce blood flow velocity maps of the microcirculation. Here, we introduce Deep-SMV, a localization free super-resolution microbubble velocimetry technique, based on a long short-term memory neural network, that provides high imaging speed and robustness to high microbubble concentrations, and directly outputs blood velocity measurements at a super-resolution. Deep-SMV is trained efficiently using microbubble flow simulation on real in vivo vascular data and demonstrates real-time velocity map reconstruction suitable for functional vascular imaging and pulsatility mapping at super-resolution. The technique is successfully applied to a wide variety of imaging scenarios, include flow channel phantoms, chicken embryo chorioallantoic membranes, and mouse brain imaging. An implementation of Deep-SMV is openly available at https://github.com/chenxiptz/SR_microvessel_velocimetry, with two pre-trained models available at https://doi.org/10.7910/DVN/SECUFD.
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Microbubbles for human diagnosis and therapy. Biomaterials 2023; 294:122025. [PMID: 36716588 DOI: 10.1016/j.biomaterials.2023.122025] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 01/17/2023] [Accepted: 01/24/2023] [Indexed: 01/26/2023]
Abstract
Microbubbles (MBs) were observed for the first time in vivo as a curious consequence of quick saline injection during ultrasound (US) imaging of the aortic root, more than 50 years ago. From this serendipitous event, MBs are now widely used as contrast enhancers for US imaging. Their intrinsic properties described in this review, allow a multitude of designs, from shell to gas composition but also from grafting targeting agents to drug payload encapsulation. Indeed, the versatile MBs are deeply studied for their dual potential in imaging and therapy. As presented in this paper, new generations of MBs now opens perspectives for targeted molecular imaging along with the development of new US imaging systems. This review also presents an overview of the different therapeutic strategies with US and MBs for cancer, cardiovascular diseases, and inflammation. The overall aim is to overlap those fields in order to find similarities in the MBs application for treatment enhancement associated with US. To conclude, this review explores the new scales of MBs technologies with nanobubbles development, and along concurrent advances in the US imaging field. This review ends by discussing perspectives for the booming future uses of MBs.
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Andersen SB, Sørensen CM, Jensen JA, Nielsen MB. Microvascular Imaging with Super-Resolution Ultrasound. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2022; 43:543-547. [PMID: 36470255 DOI: 10.1055/a-1937-6868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
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10
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Lok UW, Huang C, Trzasko JD, Kim Y, Lucien F, Tang S, Gong P, Song P, Chen S. Three-Dimensional Ultrasound Localization Microscopy with Bipartite Graph-Based Microbubble Pairing and Kalman-Filtering-Based Tracking on a 256-Channel Verasonics Ultrasound System with a 32 × 32 Matrix Array. J Med Biol Eng 2022; 42:767-779. [PMID: 36712192 PMCID: PMC9881453 DOI: 10.1007/s40846-022-00755-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/05/2022] [Indexed: 02/02/2023]
Abstract
Three-dimensional (3D) ultrasound localization microscopy (ULM) using a 2-D matrix probe and microbubbles (MBs) has been recently proposed to visualize microvasculature beyond the ultrasound diffraction limit in three spatial dimensions. However, 3D ULM suffers from several limitations: (1) high system complexity due to numerous channel counts, (2) complex MB flow dynamics in 3D, and (3) extremely long acquisition time. To reduce the system complexity while maintaining high image quality, we used a sub-aperture process to reduce received channel counts. To address the second issue, a 3D bipartite graph-based method with Kalman filtering-based tracking was used in this study for MB tracking. An MB separation approach was incorporated to separate high concentration MB data into multiple, sparser MB datasets, allowing better MB localization and tracking for a limited acquisition time. The proposed method was first validated in a flow channel phantom, showing improved spatial resolutions compared with the contrasted enhanced power Doppler image. Then the proposed method was evaluated with an in vivo chicken embryo brain dataset. Results showed that the reconstructed 3D super-resolution image achieved a spatial resolution of around 52 μm (smaller than the wavelength of around 200 μm). Microvessels that cannot be resolved clearly using localization only, can be well identified with the tailored 3D pairing and tracking algorithms. To sum up, the feasibility of the 3D ULM is shown, indicating the great possibility in clinical applications.
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Affiliation(s)
- U-Wai Lok
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN
| | - Chengwu Huang
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN
| | - Joshua D. Trzasko
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN
| | - Yohan Kim
- Department of Urology, Mayo Clinic College of Medicine and Science, Rochester, MN
| | - Fabrice Lucien
- Department of Urology, Mayo Clinic College of Medicine and Science, Rochester, MN
| | - Shanshan Tang
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN
| | - Ping Gong
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN
| | - Pengfei Song
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL
| | - Shigao Chen
- Corresponding Author: Dr. Shigao Chen, Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905,
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Deep Learning-Based Image Analysis for the Quantification of Tumor-Induced Angiogenesis in the 3D In Vivo Tumor Model—Establishment and Addition to Laser Speckle Contrast Imaging (LSCI). Cells 2022; 11:cells11152321. [PMID: 35954165 PMCID: PMC9367525 DOI: 10.3390/cells11152321] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/17/2022] [Accepted: 07/26/2022] [Indexed: 12/10/2022] Open
Abstract
(1) Background: angiogenesis plays an important role in the growth and metastasis of tumors. We established the CAM assay application, an image analysis software of the IKOSA platform by KML Vision, for the quantification of blood vessels with the in ovo chorioallantoic membrane (CAM) model. We added this proprietary deep learning algorithm to the already established laser speckle contrast imaging (LSCI). (2) Methods: angiosarcoma cell line tumors were grafted onto the CAM. Angiogenesis was measured at the beginning and at the end of tumor growth with both measurement methods. The CAM assay application was trained to enable the recognition of in ovo CAM vessels. Histological stains of the tissue were performed and gluconate, an anti-angiogenic substance, was applied to the tumors. (3) Results: the angiosarcoma cells formed tumors on the CAM that appeared to stay vital and proliferated. An increase in perfusion was observed using both methods. The CAM assay application was successfully established in the in ovo CAM model and anti-angiogenic effects of gluconate were observed. (4) Conclusions: the CAM assay application appears to be a useful method for the quantification of angiogenesis in the CAM model and gluconate could be a potential treatment of angiosarcomas. Both aspects should be evaluated in further research.
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Madhavanunni A, Panicker MR. A nonlinear beamforming for enhanced spatiotemporal sensitivity in high frame rate ultrasound flow imaging. Comput Biol Med 2022; 147:105686. [DOI: 10.1016/j.compbiomed.2022.105686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 11/03/2022]
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