Open AccessRapid processing and quantitative evaluation of structural brain scans for adaptive multimodal imaging
Author(s) -
Váša František,
Hobday Harriet,
Stanyard Ryan A.,
Daws Richard E.,
Giampietro Vincent,
O'Daly Owen,
Lythgoe David J.,
Seidlitz Jakob,
Skare Stefan,
Williams Steven C. R.,
Marquand Andre F.,
Leech Robert,
Cole James H.
Publication year - 2022
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25755
Subject(s) - neuroimaging , fluid attenuated inversion recovery , computer science , artificial intelligence , voxel , contrast (vision) , pattern recognition (psychology) , psychology , magnetic resonance imaging , neuroscience , medicine , radiology
Current neuroimaging acquisition and processing approaches tend to be optimised for quality rather than speed. However, rapid acquisition and processing of neuroimaging data can lead to novel neuroimaging paradigms, such as adaptive acquisition, where rapidly processed data is used to inform subsequent image acquisition steps. Here we first evaluate the impact of several processing steps on the processing time and quality of registration of manually labelled T 1 ‐weighted MRI scans. Subsequently, we apply the selected rapid processing pipeline both to rapidly acquired multicontrast EPImix scans of 95 participants (which include T 1 ‐FLAIR, T 2 , T 2 *, T 2 ‐FLAIR, DWI and ADC contrasts, acquired in ~1 min), as well as to slower, more standard single‐contrast T 1 ‐weighted scans of a subset of 66 participants. We quantify the correspondence between EPImix T 1 ‐FLAIR and single‐contrast T 1 ‐weighted scans, using correlations between voxels and regions of interest across participants, measures of within‐ and between‐participant identifiability as well as regional structural covariance networks. Furthermore, we explore the use of EPImix for the rapid construction of morphometric similarity networks. Finally, we quantify the reliability of EPImix‐derived data using test–retest scans of 10 participants. Our results demonstrate that quantitative information can be derived from a neuroimaging scan acquired and processed within minutes, which could further be used to implement adaptive multimodal imaging and tailor neuroimaging examinations to individual patients.