Image analysis research in neuroradiology: bridging clinical and technical domains.

Purpose: Advancements in magnetic resonance imaging (MRI) analysis over the past decades have significantly reshaped the field of neuroradiology. The ability to extract multiple quantitative measures from each MRI scan, alongside the development of extensive data repositories, has been fundamental t...

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Published in:Neuroradiology Vol. 67; no. 7; pp. 1649 - 1667
Main Authors: Pareto, Deborah, Naval-Baudin, Pablo, Pons-Escoda, Albert, Bargalló, Núria, Garcia-Gil, María, Majós, Carlos, Rovira, Àlex
Format: diagnostic images pictorial review tables/charts Journal Article
Published: Springer Nature Jul2025
Online Access:View this record in EBSCOhost
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      pub: Springer Nature
      place: New York, New York
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        atl: Image analysis research in neuroradiology: bridging clinical and technical domains.
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          Pareto, Deborah
          Naval-Baudin, Pablo
          Pons-Escoda, Albert
          Bargalló, Núria
          Garcia-Gil, María
          Majós, Carlos
          Rovira, Àlex
        affil: https://ror.org/03ba28x55 Neuroradiology Section, Radiology Department (IDI), Vall Hebron University Hospital, Psg Vall Hebron 119-129, 08035, Barcelona, Spain
      sug:
        subj:
          Magnetic Resonance Imaging
          Neuroradiography
          Image Processing, Computer Assisted
          Radiomics
          Artificial Intelligence
          Research, Radiologic Technology
          Image Interpretation, Computer Assisted
          Paradigms
          Medical Practice
          Neurologists
          Radiologists
          Multidisciplinary Care Team
          Interprofessional Relations
          Collaboration
          Workflow
          Data Security
          Privacy and Confidentiality
      ab: Purpose: Advancements in magnetic resonance imaging (MRI) analysis over the past decades have significantly reshaped the field of neuroradiology. The ability to extract multiple quantitative measures from each MRI scan, alongside the development of extensive data repositories, has been fundamental to the emergence of advanced methodologies such as radiomics and artificial intelligence (AI). This educational review aims to delineate the importance of image analysis, highlight key paradigm shifts, examine their implications, and identify existing constraints that must be addressed to facilitate integration into clinical practice. Particular attention is given to aiding junior neuroradiologists in navigating this complex and evolving landscape. Methods: A comprehensive review of the available analysis toolboxes was conducted, focusing on major technological advancements in MRI analysis, the evolution of data repositories, and the rise of AI and radiomics in neuroradiology. Stakeholders within the field were identified and their roles examined. Additionally, current challenges and barriers to clinical implementation were analyzed. Results: The analysis revealed several pivotal shifts, including the transition from qualitative to quantitative imaging, the central role of large datasets in developing AI tools, and the growing importance of interdisciplinary collaboration. Key stakeholders—including academic institutions, industry partners, regulatory bodies, and clinical practitioners—were identified, each playing a distinct role in advancing the field. However, significant barriers remain, particularly regarding standardization, data sharing, regulatory approval, and integration into clinical workflows. Conclusions: While advancements in MRI analysis offer tremendous potential to enhance neuroradiology practice, realizing this potential requires overcoming technical, regulatory, and practical barriers. Education and structured support for junior neuroradiologists are essential to ensure they are well-equipped to participate in and drive future developments. A coordinated effort among stakeholders is crucial to facilitate the seamless translation of these technological innovations into everyday clinical practice.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        pictorial
        review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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