Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis

Wang, Wen; Poirion, Émilie; Bodini, Benedetta; Durrleman, Stanley; Colliot, Olivier; Stankoff, Bruno; Ayache, Nicholas

doi:10.1117/1.jmi.6.1.014005

Cited by 29 publications

(20 citation statements)

References 27 publications

(24 reference statements)

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“…To this purpose, Wei et al ( 73 ) used a 3D FCNN to predict FLAIR pulse sequence from other MRI protocols. For this study, 20 MS patients and 4 healthy controls were involved, including T1-w, T2-w, PD, FLAIR, T1 SE and double inversion echo sequences.…”

Section: Post Processing Techniques and Image Enhancement Methodsmentioning

confidence: 99%

Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)

et al. 2021

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Computer-aided diagnosis systems aim to assist clinicians in the early identification of abnormal signs in order to optimize the interpretation of medical images and increase diagnostic precision. Multiple sclerosis (MS) and clinically isolated syndrome (CIS) are chronic inflammatory, demyelinating diseases affecting the central nervous system. Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification of anatomic structures, as well as optimization of neuroimaging protocols. To this end, there are several publications presenting artificial intelligence-based predictive models aiming to increase diagnostic accuracy and to facilitate optimal clinical management in patients diagnosed with MS and/or CIS. The current study presents a thorough review covering DL techniques that have been applied in MS and CIS during recent years, shedding light on their current advances and limitations.

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Section: Post Processing Techniques and Image Enhancement Methodsmentioning

confidence: 99%

Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)

et al. 2021

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“… 20 , 21 The majority of studies to date used classic machine learning techniques such as support vector machines or random forests, where features have to be defined and extracted from the data a priori, while more recent studies also use deep learning methods, allowing automated detection of relevant features in the data. Deep learning has now been used not only to segment WM lesions 22 - 24 or their enhancing subset, 17 but also to quantify lesion changes, 25 , 26 detect the central vein sign, 27 classify different lesion types based on diffusion basis spectrum imaging, 28 predict gadolinium enhancement from other image types, 29 perform MRI-based diagnosis, 30 , 31 segment and analyze nonlesion structures, 32 , 33 analyze myelin water fraction 34 or quantitative susceptibility mapping data, 35 synthesize absent image types, 36 perform automatic QC, 37 improve image quality, 38 or correct intensity differences between scanners 39 (additional references in eAppendix 1).…”

Section: Artificial Intelligencementioning

confidence: 99%

Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence

et al. 2021

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Multiple sclerosis (MS) patients have heterogeneous clinical presentations, symptoms and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data-sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using magnetic resonance imaging (MRI).First, development of validated MS-specific image analysis methods can be boosted by verified reference, test and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy or functional network changes) to large multi-domain datasets (imaging, cognition, clinical disability, genetics, etc.).After reviewing data-sharing and artificial intelligence, this paper highlights three areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging and the understanding of MS.

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“…Even if the model prediction accuracy is high enough, a sufficient interpretable basis must be provided the final judgment. One way to make AI systems more interpretable is to predict different types of medical data and measurement results that characterize a patient's condition while predicting clinical outcomes (disease/health) [Wei, Poirion, Bodini et al (2019)]. However, the complexity of the algorithm is greatly increased due to this operation.…”

Section: Clinical Severity Predictionmentioning

confidence: 99%

An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

Yu¹,

Xia²,

Fei³

et al. 2020

Computers, Materials &Amp; Continua

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Coronaviruses are a well-known family of viruses that can infect humans or animals. Recently, the new coronavirus (COVID-19) has spread worldwide. All countries in the world are working hard to control the coronavirus disease. However, many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system, which leads to the mass spread of diseases. As a powerful tool, artificial intelligence (AI) has been successfully applied to solve various complex problems ranging from big data analysis to computer vision. In the process of epidemic control, many algorithms are proposed to solve problems in various fields of medical treatment, which is able to reduce the workload of the medical system. Due to excellent learning ability, AI has played an important role in drug development, epidemic forecast, and clinical diagnosis. This research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.

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Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis

Abstract: "Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using threedimensional fully convolutional networks for multiple sclerosis,"

Cited by 29 publications

References 27 publications

Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)

Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)

Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence

An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

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