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Record W3005706161 · doi:10.1101/2020.02.13.947382

Effects of MP2RAGE B <sub>1</sub> <sup>+</sup> sensitivity on inter-site T <sub>1</sub> reproducibility and morphometry at 7T

2020· preprint· en· W3005706161 on OpenAlexaff
Roy A.M. Haast, Jonathan C. Lau, Dimo Ivanov, Ravi S. Menon, Kâmil Uludaǧ, Ali R. Khan

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity Health NetworkWestern University
Fundersnot available
KeywordsReproducibilityContrast (vision)Nuclear magnetic resonanceMaterials scienceNuclear medicineComputer scienceMedicinePhysicsMathematicsArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

Abstract Most neuroanatomical studies are based on MR images, whose intensity profiles are not solely determined by the tissue’s longitudinal relaxation times (T 1 ) but also affected by varying non-T 1 contributions, hampering data reproducibility. In contrast, quantitative imaging using the MP2RAGE sequence, for example, allows direct characterization of the brain based on the tissue property of interest. Combined with 7 Tesla (7T) MRI, this offers unique opportunities to obtain robust high-resolution brain data characterized by a high reproducibility, sensitivity and specificity. However, specific MP2RAGE parameters choices – e.g., to emphasize intracortical myelin-dependent contrast variations – can substantially impact image quality and cortical analyses through remnants of B 1 + -related intensity variations, as illustrated in our previous work. To follow up on this: we (1) validate this protocol effect using a dataset acquired with a particularly B 1 + insensitive set of MP2RAGE parameters combined with parallel transmission excitation; and (2) extend our analyses to evaluate the effects on hippocampal and subcortical morphometry. The latter remained unexplored initially but will provide important insights related to generalizability and reproducibility of neurodegenerative research using 7T MRI. We confirm that B 1 + inhomogeneities have a considerably variable effect on cortical T 1 and thickness estimates, as well as on hippocampal and subcortical morphometry depending on MP2RAGE setup. While T 1 differed substantially across datasets initially, we show inter-site T 1 comparability improves after correcting for the spatially varying B 1 + field using a separately acquired Sa2RAGE B 1 + map. Finally, as for cortical thickness, removal of B 1 + residuals affects hippocampal and subcortical volumetry and boundary definitions, particularly near structures characterized by strong intensity changes (e.g. cerebral spinal fluid and arteries). Taken together, we show that the choice of MP2RAGE parameters can impact T 1 comparability across sites and present evidence that hippocampal and subcortical segmentation results are modulated by B 1 + inhomogeneities. This calls for careful (1) consideration of sequence parameters when setting acquisition protocols; as well as (2) interpretation of results focused on neuroanatomical changes due to disease. Highlights Previously observed effects of B 1 + inhomogeneities on cortical T 1 and thickness depend strongly on MP2RAGE parameters Inter-site comparability of cortical T 1 and thickness greatly improves after removal of B 1 + residuals Post-hoc MP2RAGE B 1 + correction affects hippocampal (and subcortical) size and shape analyses Neuroradiological research would benefit from careful examination of imaging protocols and their impact on results, especially when B 1 + maps are not acquired

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.239
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designBench or experimental
DomainReproducibility
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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