Dataset Information

Cerebrovascular Reactivity Measurement Using Magnetic Resonance Imaging: A Systematic Review.

ABSTRACT: Cerebrovascular reactivity (CVR) magnetic resonance imaging (MRI) probes cerebral haemodynamic changes in response to a vasodilatory stimulus. CVR closely relates to the health of the vasculature and is therefore a key parameter for studying cerebrovascular diseases such as stroke, small vessel disease and dementias. MRI allows in vivo measurement of CVR but several different methods have been presented in the literature, differing in pulse sequence, hardware requirements, stimulus and image processing technique. We systematically reviewed publications measuring CVR using MRI up to June 2020, identifying 235 relevant papers. We summarised the acquisition methods, experimental parameters, hardware and CVR quantification approaches used, clinical populations investigated, and corresponding summary CVR measures. CVR was investigated in many pathologies such as steno-occlusive diseases, dementia and small vessel disease and is generally lower in patients than in healthy controls. Blood oxygen level dependent (BOLD) acquisitions with fixed inspired CO₂ gas or end-tidal CO₂ forcing stimulus are the most commonly used methods. General linear modelling of the MRI signal with end-tidal CO₂ as the regressor is the most frequently used method to compute CVR. Our survey of CVR measurement approaches and applications will help researchers to identify good practice and provide objective information to inform the development of future consensus recommendations.

SUBMITTER: Sleight E

PROVIDER: S-EPMC7947694 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Cerebrovascular Reactivity Measurement Using Magnetic Resonance Imaging: A Systematic Review.

Sleight Emilie E Stringer Michael S MS Marshall Ian I Wardlaw Joanna M JM Thrippleton Michael J MJ

Frontiers in physiology 20210225

Cerebrovascular reactivity (CVR) magnetic resonance imaging (MRI) probes cerebral haemodynamic changes in response to a vasodilatory stimulus. CVR closely relates to the health of the vasculature and is therefore a key parameter for studying cerebrovascular diseases such as stroke, small vessel disease and dementias. MRI allows <i>in vivo</i> measurement of CVR but several different methods have been presented in the literature, differing in pulse sequence, hardware requirements, stimulus and im ...[more]

PMID: 33716793

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Project description:(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer's diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.

Dataset Information

Cerebrovascular Reactivity Measurement Using Magnetic Resonance Imaging: A Systematic Review.

Publications

Cerebrovascular Reactivity Measurement Using Magnetic Resonance Imaging: A Systematic Review.

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