Research Topic

Artificial Intelligence and MRI: Boosting Clinical Diagnosis

About this Research Topic

We are currently experiencing an unprecedented explosion of Artificial Intelligence (AI) applications in our day-to-day lives, which are also becoming increasingly prevalent in medical imaging. Particularly, AI is intensively employed in Magnetic Resonance Imaging (MRI) due to MRI intrinsic soft-tissue contrast, a broad spectrum of structural and physiological acquisition protocols, and its diagnostic potential. In the upcoming years, AI will revolutionize MRI, transforming its largely qualitative clinical applications into a new era of quantitative imaging that fully utilizes these large structures of data. MR images, more than any other methodologies, contain a large amount of information and AI has all prerequisites to be the tool that might be able to push boundaries of conventional MRI reads to the next level.

Whilst promising new methodological research is constantly being published, there is currently a lack of standardization and reproducibility testing of these approaches on larger multivendor/multicentre datasets that decelerate clinical AI applications. The ever increasing large numbers of publications ensure that reviews become quickly out-of-date, and therefore it is important to regularly update the cutting edge AI reports in translational medicine.

Thus, our goal is to present an overview of the latest AI applications in MRI and to collate original articles with the most recent methodological advances in machine and deep learning for improving MR image acquisition, processing, and its clinical applications.
This Research Topic aims to provide the reader with a general comprehension of these techniques and an understanding of the challenges behind them through topical Review papers that analyze the state of the art techniques. In addition, Original Research papers on the topics below will challenge the most onerous boundaries in diagnostic MRI that AI might be able to address.

This Research Topic focuses on MR imaging only, may include adult and pediatric studies, and welcomes Reviews and Original Research articles on the following topics:

● Clinical applications of AI using MR images
● Machine learning applications for prediction, detection, classification, registration, and segmentation
● Radiomics and radiogenomics
● Use of multiparametric and non-conventional advanced MR protocols
● Applications in neuroscience, oncology, cardiovascular, MSK, and body imaging
● Big Data: data augmentation strategies, synthetic MR images, multicentre studies
● Reliability, reproducibility, challenges, and weaknesses of AI
● Reviews on the history and latest methods.

This Research Topic has been realized in collaboration with Luca Pasquini, Neuro-Oncology Imaging Fellow at Memorial Sloan Kettering Cancer Center and a Ph.D. Student at La Sapienza University in Rome.


Keywords: deep learning, machine learning, classification, predictive models, multi-omics, detection, radiomics, image segmentation, convolutional neuronal networks


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

We are currently experiencing an unprecedented explosion of Artificial Intelligence (AI) applications in our day-to-day lives, which are also becoming increasingly prevalent in medical imaging. Particularly, AI is intensively employed in Magnetic Resonance Imaging (MRI) due to MRI intrinsic soft-tissue contrast, a broad spectrum of structural and physiological acquisition protocols, and its diagnostic potential. In the upcoming years, AI will revolutionize MRI, transforming its largely qualitative clinical applications into a new era of quantitative imaging that fully utilizes these large structures of data. MR images, more than any other methodologies, contain a large amount of information and AI has all prerequisites to be the tool that might be able to push boundaries of conventional MRI reads to the next level.

Whilst promising new methodological research is constantly being published, there is currently a lack of standardization and reproducibility testing of these approaches on larger multivendor/multicentre datasets that decelerate clinical AI applications. The ever increasing large numbers of publications ensure that reviews become quickly out-of-date, and therefore it is important to regularly update the cutting edge AI reports in translational medicine.

Thus, our goal is to present an overview of the latest AI applications in MRI and to collate original articles with the most recent methodological advances in machine and deep learning for improving MR image acquisition, processing, and its clinical applications.
This Research Topic aims to provide the reader with a general comprehension of these techniques and an understanding of the challenges behind them through topical Review papers that analyze the state of the art techniques. In addition, Original Research papers on the topics below will challenge the most onerous boundaries in diagnostic MRI that AI might be able to address.

This Research Topic focuses on MR imaging only, may include adult and pediatric studies, and welcomes Reviews and Original Research articles on the following topics:

● Clinical applications of AI using MR images
● Machine learning applications for prediction, detection, classification, registration, and segmentation
● Radiomics and radiogenomics
● Use of multiparametric and non-conventional advanced MR protocols
● Applications in neuroscience, oncology, cardiovascular, MSK, and body imaging
● Big Data: data augmentation strategies, synthetic MR images, multicentre studies
● Reliability, reproducibility, challenges, and weaknesses of AI
● Reviews on the history and latest methods.

This Research Topic has been realized in collaboration with Luca Pasquini, Neuro-Oncology Imaging Fellow at Memorial Sloan Kettering Cancer Center and a Ph.D. Student at La Sapienza University in Rome.


Keywords: deep learning, machine learning, classification, predictive models, multi-omics, detection, radiomics, image segmentation, convolutional neuronal networks


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

03 May 2021 Abstract
06 September 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

03 May 2021 Abstract
06 September 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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