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Shin M, Seo M, Lee K, Yoon K. Super-resolution techniques for biomedical applications and challenges. Biomed Eng Lett 2024; 14:465-496. [PMID: 38645589 PMCID: PMC11026337 DOI: 10.1007/s13534-024-00365-4] [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: 12/11/2023] [Revised: 02/12/2024] [Accepted: 02/18/2024] [Indexed: 04/23/2024] Open
Abstract
Super-resolution (SR) techniques have revolutionized the field of biomedical applications by detailing the structures at resolutions beyond the limits of imaging or measuring tools. These techniques have been applied in various biomedical applications, including microscopy, magnetic resonance imaging (MRI), computed tomography (CT), X-ray, electroencephalogram (EEG), ultrasound, etc. SR methods are categorized into two main types: traditional non-learning-based methods and modern learning-based approaches. In both applications, SR methodologies have been effectively utilized on biomedical images, enhancing the visualization of complex biological structures. Additionally, these methods have been employed on biomedical data, leading to improvements in computational precision and efficiency for biomedical simulations. The use of SR techniques has resulted in more detailed and accurate analyses in diagnostics and research, essential for early disease detection and treatment planning. However, challenges such as computational demands, data interpretation complexities, and the lack of unified high-quality data persist. The article emphasizes these issues, underscoring the need for ongoing development in SR technologies to further improve biomedical research and patient care outcomes.
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Affiliation(s)
- Minwoo Shin
- School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722 Republic of Korea
| | - Minjee Seo
- School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722 Republic of Korea
| | - Kyunghyun Lee
- School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722 Republic of Korea
| | - Kyungho Yoon
- School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722 Republic of Korea
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2
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Minopoulou I, Kleyer A, Yalcin-Mutlu M, Fagni F, Kemenes S, Schmidkonz C, Atzinger A, Pachowsky M, Engel K, Folle L, Roemer F, Waldner M, D'Agostino MA, Schett G, Simon D. Imaging in inflammatory arthritis: progress towards precision medicine. Nat Rev Rheumatol 2023; 19:650-665. [PMID: 37684361 DOI: 10.1038/s41584-023-01016-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/31/2023] [Indexed: 09/10/2023]
Abstract
Imaging techniques such as ultrasonography and MRI have gained ground in the diagnosis and management of inflammatory arthritis, as these imaging modalities allow a sensitive assessment of musculoskeletal inflammation and damage. However, these techniques cannot discriminate between disease subsets and are currently unable to deliver an accurate prediction of disease progression and therapeutic response in individual patients. This major shortcoming of today's technology hinders a targeted and personalized patient management approach. Technological advances in the areas of high-resolution imaging (for example, high-resolution peripheral quantitative computed tomography and ultra-high field MRI), functional and molecular-based imaging (such as chemical exchange saturation transfer MRI, positron emission tomography, fluorescence optical imaging, optoacoustic imaging and contrast-enhanced ultrasonography) and artificial intelligence-based data analysis could help to tackle these challenges. These new imaging approaches offer detailed anatomical delineation and an in vivo and non-invasive evaluation of the immunometabolic status of inflammatory reactions, thereby facilitating an in-depth characterization of inflammation. By means of these developments, the aim of earlier diagnosis, enhanced monitoring and, ultimately, a personalized treatment strategy looms closer.
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Affiliation(s)
- Ioanna Minopoulou
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Arnd Kleyer
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Melek Yalcin-Mutlu
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Filippo Fagni
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Stefan Kemenes
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Christian Schmidkonz
- Department of Nuclear Medicine, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Institute for Medical Engineering, University of Applied Sciences Amberg-Weiden, Weiden, Germany
| | - Armin Atzinger
- Department of Nuclear Medicine, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Milena Pachowsky
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | | | - Lukas Folle
- Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frank Roemer
- Institute of Radiology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Maximilian Waldner
- Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Department of Internal Medicine 1, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Maria-Antonietta D'Agostino
- Division of Rheumatology, Catholic University of the Sacred Heart, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Université Paris-Saclay, UVSQ, Inserm U1173, Infection et Inflammation, Laboratory of Excellence Inflamex, Montigny-Le-Bretonneux, France
| | - Georg Schett
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany
| | - David Simon
- Department of Internal Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
- Deutsches Zentrum Immuntherapie (DZI), Friedrich-Alexander-University Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, Germany.
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3
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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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Brown KG, Li J, Margolis R, Trinh B, Eisenbrey JR, Hoyt K. Assessment of Transarterial Chemoembolization Using Super-resolution Ultrasound Imaging and a Rat Model of Hepatocellular Carcinoma. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:1318-1326. [PMID: 36868958 DOI: 10.1016/j.ultrasmedbio.2023.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/23/2023] [Accepted: 01/25/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVE Hepatocellular carcinoma (HCC) is a highly prevalent form of liver cancer diagnosed annually in 600,000 people worldwide. A common treatment is transarterial chemoembolization (TACE), which interrupts the blood supply of oxygen and nutrients to the tumor mass. The need for repeat TACE treatments may be assessed in the weeks after therapy with contrast-enhanced ultrasound (CEUS) imaging. Although the spatial resolution of traditional CEUS has been restricted by the diffraction limit of ultrasound (US), this physical barrier has been overcome by a recent innovation known as super-resolution US (SRUS) imaging. In short, SRUS enhances the visible details of smaller microvascular structures on the 10 to 100 µm scale, which unlocks a host of new clinical opportunities for US. METHODS In this study, a rat model of orthotopic HCC is introduced and TACE treatment response (to a doxorubicin-lipiodol emulsion) is assessed using longitudinal SRUS and magnetic resonance imaging (MRI) performed at 0, 7 and 14 d. Animals were euthanized at 14 d for histological analysis of excised tumor tissue and determination of TACE response, that is, control, partial response or complete response. CEUS imaging was performed using a pre-clinical US system (Vevo 3100, FUJIFILM VisualSonics Inc.) equipped with an MX201 linear array transducer. After administration of a microbubble contrast agent (Definity, Lantheus Medical Imaging), a series of CEUS images were collected at each tissue cross-section as the transducer was mechanically stepped at 100 μm increments. SRUS images were formed at each spatial position, and a microvascular density metric was calculated. Microscale computed tomography (microCT, OI/CT, MILabs) was used to confirm TACE procedure success, and tumor size was monitored using a small animal MRI system (BioSpec 3T, Bruker Corp.). RESULTS Although there were no differences at baseline (p > 0.15), both microvascular density levels and tumor size measures from the complete responder cases at 14 d were considerably lower and smaller, respectively, than those in the partial responder or control group animals. Histological analysis revealed tumor-to-necrosis levels of 8.4%, 51.1% and 100%, for the control, partial responder and complete responder groups, respectively (p < 0.005). CONCLUSION SRUS imaging is a promising modality for assessing early changes in microvascular networks in response to tissue perfusion-altering interventions such as TACE treatment of HCC.
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Affiliation(s)
- Katherine G Brown
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Junjie Li
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Ryan Margolis
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Brian Trinh
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - John R Eisenbrey
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Kenneth Hoyt
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA.
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Khairalseed M, Hoyt K. Generalized mathematical framework for contrast-enhanced ultrasound imaging with pulse inversion spectral deconvolution. ULTRASONICS 2023; 129:106913. [PMID: 36528905 DOI: 10.1016/j.ultras.2022.106913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/30/2022] [Accepted: 12/04/2022] [Indexed: 06/03/2023]
Abstract
A generalized mathematical framework for performing contrast-enhanced ultrasound (CEUS) imaging is introduced. Termed pulse inversion spectral deconvolution (PISD), this CEUS approach is founded on Gaussian derivative functions (GDFs). PISD pulses are used to form two inverted pulse sequences, which are then used to filter backscattered ultrasound (US) data for isolation of the nonlinear (NL) microbubble (MB) signal component. An US scanner equipped with a linear array transducer was used for data acquisition. With a vascular flow phantom perfused with MBs, data was collected using PISD and NL-based CEUS imaging. The role of wide-beam transmit aperture size (32 or 64 elements) was also evaluated using an US pulse frequency of 6.25 MHz. Image enhancement was quantified by a contrast-to-noise ratio (CNR). Preliminary in vivo data was collected in the hindlimb and kidney of healthy rats. Overall, in vitro wide-beam CEUS imaging using an aperture size of 64 elements yielded improved CNR values. Specifically, PISD-based CEUS imaging produced CNR values of 37.3 dB. For comparison, CNR values obtained using B-mode US or NL approaches were 2.1 and 12.1 dB, respectively. In vivo results demonstrated that PISD-based CEUS imaging improved vascular visualization compared to the NL imaging strategy.
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Affiliation(s)
- Mawia Khairalseed
- Department of Biomedical Engineering, University of Texas at Dallas, Richardson, TX, USA
| | - Kenneth Hoyt
- Department of Biomedical Engineering, University of Texas at Dallas, Richardson, TX, USA.
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Luijten B, Chennakeshava N, Eldar YC, Mischi M, van Sloun RJG. Ultrasound Signal Processing: From Models to Deep Learning. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:677-698. [PMID: 36635192 DOI: 10.1016/j.ultrasmedbio.2022.11.003] [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: 03/10/2022] [Revised: 11/02/2022] [Accepted: 11/05/2022] [Indexed: 06/17/2023]
Abstract
Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Conventionally, reconstruction algorithms have been derived from physical principles. These algorithms rely on assumptions and approximations of the underlying measurement model, limiting image quality in settings where these assumptions break down. Conversely, more sophisticated solutions based on statistical modeling or careful parameter tuning or derived from increased model complexity can be sensitive to different environments. Recently, deep learning-based methods, which are optimized in a data-driven fashion, have gained popularity. These model-agnostic techniques often rely on generic model structures and require vast training data to converge to a robust solution. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning and exploiting domain knowledge. These model-based solutions yield high robustness and require fewer parameters and training data than conventional neural networks. In this work we provide an overview of these techniques from the recent literature and discuss a wide variety of ultrasound applications. We aim to inspire the reader to perform further research in this area and to address the opportunities within the field of ultrasound signal processing. We conclude with a future perspective on model-based deep learning techniques for medical ultrasound.
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Affiliation(s)
- Ben Luijten
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | - Nishith Chennakeshava
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Yonina C Eldar
- Faculty of Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ruud J G van Sloun
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Philips Research, Eindhoven, The Netherlands
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Harmon JS, Khaing ZZ, Hyde JE, Hofstetter CP, Tremblay-Darveau C, Bruce MF. Quantitative tissue perfusion imaging using nonlinear ultrasound localization microscopy. Sci Rep 2022; 12:21943. [PMID: 36536012 PMCID: PMC9763240 DOI: 10.1038/s41598-022-24986-w] [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: 08/26/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022] Open
Abstract
Ultrasound localization microscopy (ULM) is a recent advancement in ultrasound imaging that uses microbubble contrast agents to yield vascular images that break the classical diffraction limit on spatial resolution. Current approaches cannot image blood flow at the tissue perfusion level since they rely solely on differences in velocity to separate tissue and microbubble signals; lower velocity microbubble echoes are removed during high pass wall filtering. To visualize blood flow in the entire vascular tree, we have developed nonlinear ULM, which combines nonlinear pulsing sequences with plane-wave imaging to segment microbubble signals independent of their velocity. Bubble localization and inter-frame tracking produces super-resolved images and, with parameters derived from the bubble tracks, a rich quantitative feature set that can describe the relative quality of microcirculatory flow. Using the rat spinal cord as a model system, we showed that nonlinear ULM better resolves some smaller branching vasculature compared to conventional ULM. Following contusion injury, both gold-standard histological techniques and nonlinear ULM depicted reduced in-plane vessel length between the penumbra and contralateral gray matter (-16.7% vs. -20.5%, respectively). Here, we demonstrate that nonlinear ULM uniquely enables investigation and potential quantification of tissue perfusion, arguably the most important component of blood flow.
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Affiliation(s)
- Jonah S. Harmon
- grid.34477.330000000122986657Department of Neurological Surgery, University of Washington, Seattle, WA 98105 USA
| | - Zin Z. Khaing
- grid.34477.330000000122986657Department of Neurological Surgery, University of Washington, Seattle, WA 98105 USA
| | - Jeffrey E. Hyde
- grid.34477.330000000122986657Department of Neurological Surgery, University of Washington, Seattle, WA 98105 USA
| | - Christoph P. Hofstetter
- grid.34477.330000000122986657Department of Neurological Surgery, University of Washington, Seattle, WA 98105 USA
| | | | - Matthew F. Bruce
- grid.34477.330000000122986657Applied Physics Laboratory, University of Washington, Seattle, WA 98105 USA
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Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4431536. [DOI: 10.1155/2022/4431536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/20/2022] [Accepted: 09/28/2022] [Indexed: 12/13/2022]
Abstract
Computerized tomography (CT) is widely used for clinical screening and treatment planning. In this study, we aimed to reduce X-ray radiation and achieve high-quality CT imaging by using low-intensity X-rays because CT radiation is damaging to the human body. An innovative vision transformer for medical image super-resolution (SR) is applied to establish a high-definition image target. To achieve this, we proposed a method called swin transformer and attention network (STAN) that uses the swin transformer network, which employs an attention method to overcome the long-range dependency difficulties encountered in CNNs and RNNs to enhance and restore the quality of medical CT images. We adopted the peak signal-to-noise ratio for performance comparison with other mainstream SR reconstruction models used in medical CT imaging. Experimental results revealed that the proposed STAN model yields superior medical CT imaging results than the existing SR techniques based on CNNs. The proposed STAN model employs a self-attention mechanism to more effectively extract critical features and long-range information, hence enhancing the quality of medical CT image reconstruction.
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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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Wu Y, Zhang W, Shao X, Yang Y, Zhang T, Lei M, Wang Z, Gao B, Hu S. Research on the Multi-Element Synthetic Aperture Focusing Technique in Breast Ultrasound Imaging, Based on the Ring Array. MICROMACHINES 2022; 13:1753. [PMID: 36296106 PMCID: PMC9609697 DOI: 10.3390/mi13101753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/10/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
As a widely clinical detection method, ultrasonography (US) has been applied to the diagnosis of breast cancer. In this paper, the multi-element synthetic aperture focusing (M-SAF) is applied to the ring array of breast ultrasonography (US) imaging, which addresses the problem of low imaging quality due to the single active element for each emission and the reception in the synthetic aperture focusing. In order to determine the optimal sub-aperture size, the formula is derived for calculating the internal sound pressure of the ring array with a 200 mm diameter, and the sound pressure distribution is analyzed. The ring array with 1024 elements (1024 ring array) is established in COMSOL Multiphysics 5.6, and the optimal sub-aperture size is 16 elements, according to the sound field beam simulation and the directivity research. Based on the existing experimental conditions, the ring array with 256 elements (256 ring array) is simulated and verified by experiments. The simulation has a spatial resolution evaluation in the k-Wave toolbox, and the experiment uses nylon rope and breast model imaging. The results show that if the sub-aperture size has four elements, the imaging quality is the highest. Specifically, the spatial resolution is the best, and the sound pressure amplitude and signal-to-noise ratio (SNR) are maintained at a high level in the reconstructed image. The optimal sub-aperture theory is verified by the two kinds of ring arrays, which also provide a theoretical basis for the application of the multi-element synthetic aperture focusing technology (M-SAF) in ring arrays.
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Affiliation(s)
- Yang Wu
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Wendong Zhang
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Xingling Shao
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Yuhua Yang
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Tian Zhang
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Miao Lei
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Zhihao Wang
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Bizhen Gao
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Shumin Hu
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
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Oezdemir I, Li J, Song J, Hoyt K. 3-D Super-Resolution Ultrasound Imaging for Monitoring Early Changes in Breast Cancer after Treatment with a Vascular-Disrupting Agent. IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM : [PROCEEDINGS]. IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM 2021; 2021:10.1109/IUS52206.2021.9593426. [PMID: 38351971 PMCID: PMC10863700 DOI: 10.1109/ius52206.2021.9593426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
The purpose of this research project was to evaluate the use of 3-dimensional (3-D) super-resolution ultrasound (SR-US) imaging to assess any early changes in breast cancer after treatment with a vascular-disrupting agent (VDA). A Vevo 3100 ultrasound system (FUJIFILM VisualSonics Inc) equipped with an MX 201 transducer was used for image acquisition. A total of 2.5 × 107 microbubbles (MBs) were injected into the tail vein of anesthetized breast cancer-bearing mice using repeat bolus injections every 5 min. A total of 10 stacks of ultrasound images were collected as the transducer was mechanically moved across the tumor at 0.6 mm intervals yielding a 6-mm thick volume. At each tumor location, a stack contained 1 × 104 frames of ultrasound data that were acquired at 463 frames/sec and stored as in-phase/quadrature (IQ) format. After motion correction, each temporal stack of ultrasound images was processed separately for clutter signal removal, which was followed by MB localization and enumeration before generation of the final SR-US image. After reconstruction of the 3-D SR-US volume dataset, the tumor microvasculature was enhanced using a multiscale vessel enhancement filter. Vessels from the resultant microvascular network were then segmented using an adaptive thresholding method. Finally, mean microvascular density (MVD) measurements from each tumor volume were computed as a summarizing statistic. While no differences were found between baseline SR-US image-derived measures of MVD (p = 0.76), these same measurements were significantly lower at 24 h after VDA treatment (p < 0.001). Overall, 3-D SR-US imaging detected early tumor changes following treatment with a vascular-targeted drug.
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Affiliation(s)
- Ipek Oezdemir
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Junjie Li
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Jane Song
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Kenneth Hoyt
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
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