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López-Ales E, Menchón-Lara RM, Simmross-Wattenberg F, Rodríguez-Cayetano M, Martín-Fernández M, Alberola-López C. Multi-Device Parallel MRI Reconstruction: Efficient Partitioning for Undersampled 5D Cardiac CINE. SENSORS (BASEL, SWITZERLAND) 2024; 24:1313. [PMID: 38400470 PMCID: PMC10891760 DOI: 10.3390/s24041313] [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: 12/29/2023] [Revised: 02/04/2024] [Accepted: 02/16/2024] [Indexed: 02/25/2024]
Abstract
Cardiac CINE, a form of dynamic cardiac MRI, is indispensable in the diagnosis and treatment of heart conditions, offering detailed visualization essential for the early detection of cardiac diseases. As the demand for higher-resolution images increases, so does the volume of data requiring processing, presenting significant computational challenges that can impede the efficiency of diagnostic imaging. Our research presents an approach that takes advantage of the computational power of multiple Graphics Processing Units (GPUs) to address these challenges. GPUs are devices capable of performing large volumes of computations in a short period, and have significantly improved the cardiac MRI reconstruction process, allowing images to be produced faster. The innovation of our work resides in utilizing a multi-device system capable of processing the substantial data volumes demanded by high-resolution, five-dimensional cardiac MRI. This system surpasses the memory capacity limitations of single GPUs by partitioning large datasets into smaller, manageable segments for parallel processing, thereby preserving image integrity and accelerating reconstruction times. Utilizing OpenCL technology, our system offers adaptability and cross-platform functionality, ensuring wider applicability. The proposed multi-device approach offers an advancement in medical imaging, accelerating the reconstruction process and facilitating faster and more effective cardiac health assessment.
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Affiliation(s)
- Emilio López-Ales
- Laboratorio de Procesado de Imagen, Universidad de Valladolid, Campus Miguel Delibes sn., 47011 Valladolid, Spain; (R.-M.M.-L.); (F.S.-W.); (M.R.-C.); (M.M.-F.)
| | | | | | | | | | - Carlos Alberola-López
- Laboratorio de Procesado de Imagen, Universidad de Valladolid, Campus Miguel Delibes sn., 47011 Valladolid, Spain; (R.-M.M.-L.); (F.S.-W.); (M.R.-C.); (M.M.-F.)
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Zhang M, Wang Y, Lv M, Sang L, Wang X, Yu Z, Yang Z, Wang Z, Sang L. Trends and Hotspots in Global Radiomics Research: A Bibliometric Analysis. Technol Cancer Res Treat 2024; 23:15330338241235769. [PMID: 38465611 DOI: 10.1177/15330338241235769] [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: 03/12/2024] Open
Abstract
Objectives: The purpose of this research is to summarize the structure of radiomics-based knowledge and to explore potential trends and priorities by using bibliometric analysis. Methods: Select radiomics-related publications from 2012 to October 2022 from the Science Core Collection Web site. Use VOSviewer (version 1.6.18), CiteSpace (version 6.1.3), Tableau (version 2022), Microsoft Excel and Rstudio's free online platforms (http://bibliometric.com) for co-writing, co-citing, and co-occurrence analysis of countries, institutions, authors, references, and keywords in the field. The visual analysis is also carried out on it. Results: The study included 6428 articles. Since 2012, there has been an increase in research papers based on radiomics. Judging by publications, China has made the largest contribution in this area. We identify the most productive institutions and authors as Fudan University and Tianjie. The top three magazines with the most publications are《FRONTIERS IN ONCOLOGY》, 《EUROPEAN RADIOLOGY》, and 《CANCERS》. According to the results of reference and keyword analysis, "deep learning, nomogram, ultrasound, f-18-fdg, machine learning, covid-19, radiogenomics" has been determined as the main research direction in the future. Conclusion: Radiomics is in a phase of vigorous development with broad prospects. Cross-border cooperation between countries and institutions should be strengthened in the future. It can be predicted that the development of deep learning-based models and multimodal fusion models will be the focus of future research. Advances in knowledge: This study explores the current state of research and hot spots in the field of radiomics from multiple perspectives, comprehensively, and objectively reflecting the evolving trends in imaging-related research and providing a reference for future research.
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Affiliation(s)
- Minghui Zhang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Yan Wang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Mutian Lv
- Department of Nuclear Medicine, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Li Sang
- Department of Acupuncture and Massage, Shouguang Hospital of Traditional Chinese Medicine, Weifang, P. R. China
| | - Xuemei Wang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Zijun Yu
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Ziyi Yang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Zhongqing Wang
- Department of Information Center, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Liang Sang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
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McNaughton J, Fernandez J, Holdsworth S, Chong B, Shim V, Wang A. Machine Learning for Medical Image Translation: A Systematic Review. Bioengineering (Basel) 2023; 10:1078. [PMID: 37760180 PMCID: PMC10525905 DOI: 10.3390/bioengineering10091078] [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: 06/19/2023] [Revised: 07/30/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND CT scans are often the first and only form of brain imaging that is performed to inform treatment plans for neurological patients due to its time- and cost-effective nature. However, MR images give a more detailed picture of tissue structure and characteristics and are more likely to pick up abnormalities and lesions. The purpose of this paper is to review studies which use deep learning methods to generate synthetic medical images of modalities such as MRI and CT. METHODS A literature search was performed in March 2023, and relevant articles were selected and analyzed. The year of publication, dataset size, input modality, synthesized modality, deep learning architecture, motivations, and evaluation methods were analyzed. RESULTS A total of 103 studies were included in this review, all of which were published since 2017. Of these, 74% of studies investigated MRI to CT synthesis, and the remaining studies investigated CT to MRI, Cross MRI, PET to CT, and MRI to PET. Additionally, 58% of studies were motivated by synthesizing CT scans from MRI to perform MRI-only radiation therapy. Other motivations included synthesizing scans to aid diagnosis and completing datasets by synthesizing missing scans. CONCLUSIONS Considerably more research has been carried out on MRI to CT synthesis, despite CT to MRI synthesis yielding specific benefits. A limitation on medical image synthesis is that medical datasets, especially paired datasets of different modalities, are lacking in size and availability; it is therefore recommended that a global consortium be developed to obtain and make available more datasets for use. Finally, it is recommended that work be carried out to establish all uses of the synthesis of medical scans in clinical practice and discover which evaluation methods are suitable for assessing the synthesized images for these needs.
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Affiliation(s)
- Jake McNaughton
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
| | - Justin Fernandez
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
- Department of Engineering Science and Biomedical Engineering, University of Auckland, 3/70 Symonds Street, Auckland 1010, New Zealand
| | - Samantha Holdsworth
- Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
- Centre for Brain Research, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
- Mātai Medical Research Institute, 400 Childers Road, Tairāwhiti Gisborne 4010, New Zealand
| | - Benjamin Chong
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
- Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
- Centre for Brain Research, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
- Mātai Medical Research Institute, 400 Childers Road, Tairāwhiti Gisborne 4010, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand; (J.M.)
- Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
- Centre for Brain Research, University of Auckland, 85 Park Road, Auckland 1023, New Zealand
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Ma X, Liu J. Predictive value of MRI features on glioblastoma. Eur Radiol 2023; 33:4472-4474. [PMID: 37020071 PMCID: PMC10182105 DOI: 10.1007/s00330-023-09535-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/15/2022] [Accepted: 01/30/2023] [Indexed: 04/07/2023]
Affiliation(s)
- Xiaodong Ma
- Department of Neurosurgery, General Hospital of Chinese PLA, 28th Fuxing Road, 100853, Beijing, China.
| | - Jiayu Liu
- Department of Neurosurgery, General Hospital of Chinese PLA, 28th Fuxing Road, 100853, Beijing, China
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Moya-Sáez E, de Luis-García R, Alberola-López C. Toward deep learning replacement of gadolinium in neuro-oncology: A review of contrast-enhanced synthetic MRI. FRONTIERS IN NEUROIMAGING 2023; 2:1055463. [PMID: 37554645 PMCID: PMC10406200 DOI: 10.3389/fnimg.2023.1055463] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/04/2023] [Indexed: 08/10/2023]
Abstract
Gadolinium-based contrast agents (GBCAs) have become a crucial part of MRI acquisitions in neuro-oncology for the detection, characterization and monitoring of brain tumors. However, contrast-enhanced (CE) acquisitions not only raise safety concerns, but also lead to patient discomfort, the need of more skilled manpower and cost increase. Recently, several proposed deep learning works intend to reduce, or even eliminate, the need of GBCAs. This study reviews the published works related to the synthesis of CE images from low-dose and/or their native -non CE- counterparts. The data, type of neural network, and number of input modalities for each method are summarized as well as the evaluation methods. Based on this analysis, we discuss the main issues that these methods need to overcome in order to become suitable for their clinical usage. We also hypothesize some future trends that research on this topic may follow.
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Affiliation(s)
- Elisa Moya-Sáez
- Laboratorio de Procesado de Imagen, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
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