Big Data Era in Magnetic Resonance Imaging of the Human Brain

Ding, Xiaoyu; Caparelli, Elisabeth de Castro; Ross, Thomas J.

doi:10.1201/9781351061223-3

Cited by 1 publication

(2 citation statements)

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“…Data from routine MRI studies result in high volume, velocity (rate of data throughput), and variety: characteristics of big data. [34][35][36][37][38][39][40] These big data characteristics require efficient DL implementation strategies. Qiu et al 41 and Al-Jarrah et al 42 provide a review of the symbiotic interactions of the big data and ML areas to improve the efficiency of implementations.…”

Section: Prerequisites For Dl-based Neuro-mrimentioning

confidence: 99%

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Developing and deploying deep learning models in brain magnetic resonance imaging: A review

et al. 2023

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Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models and subsequent peer‐reviewed publications. However, the rate of deployment in clinical settings is low. Therefore, this review attempts to bring together the ideas from data collection to deployment in the clinic, building on the guidelines and principles that accreditation agencies have espoused. We introduce the need for and the role of DL to deliver accessible MRI. This is followed by a brief review of DL examples in the context of neuropathologies. Based on these studies and others, we collate the prerequisites to develop and deploy DL models for brain MRI. We then delve into the guiding principles to develop good machine learning practices in the context of neuroimaging, with a focus on explainability. A checklist based on the United States Food and Drug Administration's good machine learning practices is provided as a summary of these guidelines. Finally, we review the current challenges and future opportunities in DL for brain MRI.

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Section: Prerequisites For Dl-based Neuro-mrimentioning

confidence: 99%

“…Data from routine MRI studies result in high volume, velocity (rate of data throughput), and variety: characteristics of big data 34–40 . These big data characteristics require efficient DL implementation strategies.…”

Section: Prerequisites For Dl‐based Neuro‐mrimentioning

confidence: 99%