AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow

Shimron, Efrat; Perlman, Or

doi:10.3390/bioengineering10040492

Cited by 7 publications

(2 citation statements)

References 93 publications

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“…Furthermore, to go beyond, the involvement of artificial intelligence (AI) practices in these medical treatments contributes to reducing the complexity of information acquisition and post-processing in MRI through the use of strategy acceleration and offering faster analysis times with easier image processing [145,154]. AI can be used also to execute planed recurrent training jobs in IG robots.…”

Section: Discussionmentioning

confidence: 99%

Image-Guided Surgical and Pharmacotherapeutic Routines as Part of Diligent Medical Treatment

Razek

2023

Applied Sciences

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This contribution is part of the objective of diligent universal care that ensures the well-being of a patient. It aims to analyze and propose enriched image-guided procedures for surgical interventions and restricted delivery of implanted drugs in minimally invasive and non-ionizing circumstances. This analysis is supported by a literature review conducted in two ways. The first aims to illustrate the importance of recent research and applications involved in different topics of the subject; this is mainly the case for the introduction’s literature. The second concerns the literature dedicated to having more detailed information in context; this mainly concerns the citations in the different sections of the article. The universal goals of medical treatments are intended to involve the well-being of the patient and allow medical personnel to test new therapies and carry out therapeutic training without risk to the patient. First, the various functionalities involved in these procedures and the concerns of the magnetic resonance imaging technique (MRI) and ultrasound imaging technique (USI), recent contributions to the subject are reviewed. Second, the intervention procedures guided by the image and the implemented actions are analyzed. Third, the components of the fields involved in MRI are examined. Fourth, the MRI control of the treatments, its performance and its compliance are analyzed. Compatibility with MRI via electromagnetic compatibility (EMC) is conferred and demonstrated for an actuation example. Fifth, the extension of the concepts mentioned in the article, in the context of patient comfort and the training of medical staff is proposed. The main contribution of this article is the identification of the different strategic aids needed in healthcare related to image-assisted robotics, non-ionized, minimally invasive and locally restrictive means. Furthermore, it highlights the benefits of using phantoms based on real biological properties of the body, digital twins under human control, artificial intelligence tools and augmented reality-assisted robotics.

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Section: Discussionmentioning

confidence: 99%

Image-Guided Surgical and Pharmacotherapeutic Routines as Part of Diligent Medical Treatment

Razek

2023

Applied Sciences

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“…Notably, numerous review articles have surfaced discussing AI applications in magnetic resonance, particularly focusing on data processing, structure elucidation through the nuclear magnetic resonance spectroscopy (NMR) spectra, and magnetic resonance imaging (MRI) pattern recognition [1][2][3][4][5][6]. Recently, special issues of the NMR in Biomedicine [7] and Bioengineering [8] journals were specifically devoted to AI methods applied to magnetic resonance techniques. Despite the insights provided by these articles, the topic of AI-assisted generation of shaped pulses or pulse sequences remains largely unexplored in the existing reviews.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Powered Pulse Sequences in Nuclear Magnetic Resonance and Magnetic Resonance Imaging: Historical Trends, Current Innovations and Perspectives

Tokarz

2024

Sci. Rad.

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This review article explores the historical background and recent advances in the application of artificial intelligence (AI) in the development of radiofrequency pulses and pulse sequences in nuclear magnetic resonance spectroscopy (NMR) and imaging (MRI). The introduction of AI into this field, which traces back to the late 1970s, has recently witnessed remarkable progress, leading to the design of specialized frameworks and software solutions such as DeepRF, MRzero, and GENETICS-AI. Through an analysis of literature and case studies, this review tracks the transformation of AI-driven pulse design from initial proof-of-concept studies to comprehensive scientific programs, shedding light on the potential implications for the broader NMR and MRI communities. The fusion of artificial intelligence and magnetic resonance pulse design stands as a promising frontier in spectroscopy and imaging, offering innovative enhancements in data acquisition, analysis, and interpretation across diverse scientific domains.

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Revolutionary Point‐of‐Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Haghayegh,

Norouziazad,

Haghani

et al. 2024

Advanced Science

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Early‐stage disease detection, particularly in Point‐Of‐Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision‐medicine. Public benefits of early detection extend beyond cost‐effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground‐breaking innovations enabling automation of operations, conducting advanced large‐scale data analysis, generating predictive models, and facilitating remote and guided clinical decision‐making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

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AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow

Abstract: Over the last decade, artificial intelligence (AI) has made an enormous impact on a wide range of fields, including science, engineering, informatics, finance, and transportation [...]

Cited by 7 publications

References 93 publications

Image-Guided Surgical and Pharmacotherapeutic Routines as Part of Diligent Medical Treatment

Image-Guided Surgical and Pharmacotherapeutic Routines as Part of Diligent Medical Treatment

Artificial Intelligence-Powered Pulse Sequences in Nuclear Magnetic Resonance and Magnetic Resonance Imaging: Historical Trends, Current Innovations and Perspectives

Revolutionary Point‐of‐Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

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