Image analysis research in neuroradiology: bridging clinical and technical domains.
Authors
Affiliations (9)
Affiliations (9)
- Neuroradiology Section, Radiology Department (IDI), Vall Hebron University Hospital, Psg Vall Hebron 119-129, 08035, Barcelona, Spain. [email protected].
- Neuroradiology Group, Vall Hebron Research Institute, Barcelona, Spain. [email protected].
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain.
- Institut Diagnòstic Per La Imatge (IDI), Centre Bellvitge, L'Hospitalet de Llobregat, Spain.
- Diagnostic Imaging and Nuclear Medicine Research Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain.
- Neuroradiology Section, Radiology Department, Diagnostic Image Center, Hospital Clinic of Barcelona, University of Barcelona, Barcelona, Spain.
- Institut Diagnòstic Per La Imatge (IDI), Serveis Corporatius, Parc Sanitaria Pere Virgili, Barcelona, Spain.
- Neuroradiology Section, Radiology Department (IDI), Vall Hebron University Hospital, Psg Vall Hebron 119-129, 08035, Barcelona, Spain.
- Neuroradiology Group, Vall Hebron Research Institute, Barcelona, Spain.
Abstract
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. 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. 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. 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.