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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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Wang H, Zhao X, Liu W, Li LC, Ma J, Guo L. Texture-Aware Dual Domain Mapping Model for Low Dose CT Reconstruction. Med Phys 2022; 49:3860-3873. [PMID: 35297051 DOI: 10.1002/mp.15607] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/10/2022] [Accepted: 03/10/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Remarkable progress has been made for low-dose CT reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low dose CT reconstruction. Purpose Most of the existing deep learning-based low dose CT reconstruction methods are derived from popular frameworks, and most models focus on the image domain. Even few existing methods start with dual domains (sinogram and image) by considering the processing of the data itself, the final performances are limited due to the lack of perception of textures. With this in mind, we propose a method for texture perception on dual domains, so that the reconstruction process can be uniformly driven by visual effects. METHODS The proposed method involves the processing of two domains: the sinogram domain and the image domain. For the sinogram domain, we have designed a novel dilated residual network (S-DRN) which aims to increase the receptive field to obtain multi-scale information. For the image domain, we propose a self attention (SA) residual encoder&decoder network (SRED-Net) as the denoising network for obtaining much acceptable edges and textures. In addition, the composite loss function composed of the feature loss constructed by the proposed boundary and texture feature aware network (BTFAN) and the mean square error (MSE) can obtain a higher image quality while retaining more details and fewer artifacts, thereby obtaining better visual image quality. RESULTS The proposed method was validated using both the AAPM-Mayo clinic low-dose CT datasets and a real clinic data. Experimental results demonstrated that the new method has achieved the state-of-the-art performance on objective indicators and visual metrics in terms of denoising and texture restoration. CONCLUSIONS Compared with single-domain or existing dual-domain processing strategies, the proposed texture-aware dual domain mapping network(TADDM-Net) can much better improve the visual effect of reconstructed CT images. Meantime, we also provide much intuitive evidence in terms of model interpretability. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Huafeng Wang
- North China University of Technology, Department of Radiology, Stony Brook University, Rm 067,HSC, T8, New York, 11790, China
| | - Xuemei Zhao
- School of Information Technology, North China University of Technology, Beijing, 100041, China
| | - Wanquan Liu
- School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, 510335, China
| | - Lihong C Li
- City University of New York at College of Staten Island, Engineering and Environmental Science, Room 1N-225, 2800 Victory Blvd, Staten Island, New York, NY, 10314, USA
| | - Jianhua Ma
- Southern Medical University, Department of Biomedical Engineering, Shatai Road 1023, BAIYUN, Tonghe 1838, Guangzhou, Guangdong, 510515, China
| | - Lei Guo
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
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Ren X, Lee SJ. Super-resolution Image Reconstruction from Projections Using Non-local and Local Regularizations. J Imaging Sci Technol 2021. [DOI: 10.2352/j.imagingsci.technol.2021.65.1.010502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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Tavitian B, Perez-Liva M. Hybrid PET-CT-Ultrasound Imaging. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00020-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Perez-Liva M, Yoganathan T, Herraiz JL, Porée J, Tanter M, Balvay D, Viel T, Garofalakis A, Provost J, Tavitian B. Ultrafast Ultrasound Imaging for Super-Resolution Preclinical Cardiac PET. Mol Imaging Biol 2020; 22:1342-1352. [PMID: 32602084 PMCID: PMC7497458 DOI: 10.1007/s11307-020-01512-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/13/2020] [Accepted: 05/27/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE Physiological motion and partial volume effect (PVE) significantly degrade the quality of cardiac positron emission tomography (PET) images in the fast-beating hearts of rodents. Several Super-resolution (SR) techniques using a priori anatomical information have been proposed to correct motion and PVE in PET images. Ultrasound is ideally suited to capture real-time high-resolution cine images of rodent hearts. Here, we evaluated an ultrasound-based SR method using simultaneously acquired and co-registered PET-CT-Ultrafast Ultrasound Imaging (UUI) of the beating heart in closed-chest rodents. PROCEDURES The method was tested with numerical and animal data (n = 2) acquired with the non-invasive hybrid imaging system PETRUS that acquires simultaneously PET, CT, and UUI. RESULTS We showed that ultrasound-based SR drastically enhances the quality of PET images of the beating rodent heart. For the simulations, the deviations between expected and mean reconstructed values were 2 % after applying SR. For the experimental data, when using Ultrasound-based SR correction, contrast was improved by a factor of two, signal-to-noise ratio by 11 %, and spatial resolution by 56 % (~ 0.88 mm) with respect to static PET. As a consequence, the metabolic defect following an acute cardiac ischemia was delineated with much higher anatomical precision. CONCLUSIONS Our results provided a proof-of-concept that image quality of cardiac PET in fast-beating rodent hearts can be significantly improved by ultrasound-based SR, a portable low-cost technique. Improved PET imaging of the rodent heart may allow new explorations of physiological and pathological situations related with cardiac metabolism.
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Affiliation(s)
- Mailyn Perez-Liva
- Université de Paris, PARCC, INSERM, 56, rue Leblanc, 75015, Paris, France.
| | | | - Joaquin L Herraiz
- Nuclear Physics Group and IPARCOS, Complutense University of Madrid, Plaza de las Ciencias, 1, 28020, Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Jonathan Porée
- Physics for Medicine Paris, Inserm/ESPCI Paris-PSL/PSL-University/CNRS, 17 rue Moreau, 75012, Paris, France
- Engineering physics department, Polytechnique Montréal, Montréal, Canada
| | - Mickael Tanter
- Physics for Medicine Paris, Inserm/ESPCI Paris-PSL/PSL-University/CNRS, 17 rue Moreau, 75012, Paris, France
| | - Daniel Balvay
- Université de Paris, PARCC, INSERM, 56, rue Leblanc, 75015, Paris, France
| | - Thomas Viel
- Université de Paris, PARCC, INSERM, 56, rue Leblanc, 75015, Paris, France
| | | | - Jean Provost
- Engineering physics department, Polytechnique Montréal, Montréal, Canada
- Montreal Heart Institute, Montréal, Canada
| | - Bertrand Tavitian
- Université de Paris, PARCC, INSERM, 56, rue Leblanc, 75015, Paris, France
- Service de Radiologie, APHP Centre, Hôpital Européen Georges Pompidou, Paris, France
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Perez-Liva M, Yoganathan T, Herraiz JL, Porée J, Tanter M, Balvay D, Viel T, Garofalakis A, Provost J, Tavitian B. Ultrafast Ultrasound Imaging for Super-Resolution Preclinical Cardiac PET. Mol Imaging Biol 2020. [DOI: https://doi.org/10.1007/s11307-020-01512-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Abstract
Purpose
Physiological motion and partial volume effect (PVE) significantly degrade the quality of cardiac positron emission tomography (PET) images in the fast-beating hearts of rodents. Several Super-resolution (SR) techniques using a priori anatomical information have been proposed to correct motion and PVE in PET images. Ultrasound is ideally suited to capture real-time high-resolution cine images of rodent hearts. Here, we evaluated an ultrasound-based SR method using simultaneously acquired and co-registered PET-CT-Ultrafast Ultrasound Imaging (UUI) of the beating heart in closed-chest rodents.
Procedures
The method was tested with numerical and animal data (n = 2) acquired with the non-invasive hybrid imaging system PETRUS that acquires simultaneously PET, CT, and UUI.
Results
We showed that ultrasound-based SR drastically enhances the quality of PET images of the beating rodent heart. For the simulations, the deviations between expected and mean reconstructed values were 2 % after applying SR. For the experimental data, when using Ultrasound-based SR correction, contrast was improved by a factor of two, signal-to-noise ratio by 11 %, and spatial resolution by 56 % (~ 0.88 mm) with respect to static PET. As a consequence, the metabolic defect following an acute cardiac ischemia was delineated with much higher anatomical precision.
Conclusions
Our results provided a proof-of-concept that image quality of cardiac PET in fast-beating rodent hearts can be significantly improved by ultrasound-based SR, a portable low-cost technique. Improved PET imaging of the rodent heart may allow new explorations of physiological and pathological situations related with cardiac metabolism.
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Learning deconvolutional deep neural network for high resolution medical image reconstruction. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.08.022] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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A review of GPU-based medical image reconstruction. Phys Med 2017; 42:76-92. [PMID: 29173924 DOI: 10.1016/j.ejmp.2017.07.024] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Revised: 07/06/2017] [Accepted: 07/30/2017] [Indexed: 11/20/2022] Open
Abstract
Tomographic image reconstruction is a computationally demanding task, even more so when advanced models are used to describe a more complete and accurate picture of the image formation process. Such advanced modeling and reconstruction algorithms can lead to better images, often with less dose, but at the price of long calculation times that are hardly compatible with clinical workflows. Fortunately, reconstruction tasks can often be executed advantageously on Graphics Processing Units (GPUs), which are exploited as massively parallel computational engines. This review paper focuses on recent developments made in GPU-based medical image reconstruction, from a CT, PET, SPECT, MRI and US perspective. Strategies and approaches to get the most out of GPUs in image reconstruction are presented as well as innovative applications arising from an increased computing capacity. The future of GPU-based image reconstruction is also envisioned, based on current trends in high-performance computing.
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