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Record W2070865704 · doi:10.1055/s-2007-972132

Imaging cerebrovascular reactivity using BOLD MRI at 1.5T and 3.0T: comparison of spiral and EPI combined with parallel imaging

2007· article· de· W2070865704 on OpenAlexaff
Andrea Kassner, Julien Poublanc, Adrian P. Crawley

Bibliographic record

VenueRöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · 2007
Typearticle
Languagede
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpiral (railway)Grey matterSingle shotNeuroimagingMagnetic resonance imagingPsychologyMedicineNeuroscienceRadiologyPhysicsWhite matterMathematicsOptics

Abstract

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Purpose: Combining inhaled CO 2 manipulation with BOLD MRI is a promising method for assessing regional differences in cerebrovascular reactivity (CVR) which is a measurement of the brains autoregulatory capacity. Since the entire grey matter in the brain responds to the CO 2 stimulus, CVR measurements provide a way to evaluate different BOLD acquisition schemes with respect to signal drop-out and distortion. Specifically we compared spiral to a single-shot EPI technique combined with parallel imaging in normal healthy subjects. Methods: Five healthy male (age range 25–42 years) volunteers were imaged on a 1.5T and 3.0T GE Signa MR system on 2 separate days using a rebreathing circuit as described previously [1]. Each subject was placed inside the scanner and the rebreathing device was applied. At each field strength, scanning was performed using a standard single-shot BOLD protocol with a spiral read out (TE=40ms, TR=2100ms, FA=85°, FOV=20mm) for run 1 and an EPI technique combined with parallel imaging (TE=40ms, TR=2100ms, FA=85°, FOV=20mm, ASSET factor=2) for run 2. Scanning duration for each run was 8 minutes for an acquisition of 230 volumes. Each volume contained 28 slices and spatial resolution of the BOLD data was approx 3×3mm with a slice thickness of 4.5mm for both techniques. In addition, high resolution T1 weighted images were acquired for co-registration purposes. Changes in p ET CO 2 were achieved by controlling the subjects inspired gases with the aid of a nose clip, a mouthpiece, the rebreathing circuit, and a gas sequencer. To ensure that end-tidal gases are representative of lung gas concentrations, subjects were instructed to breathe deeply during the test. The test itself consisted of eight cycles of hypercapnia (45sec at ˜ 50mmHg) interspersed with eight cycles of hypocapnia (45sec at ˜ 30mmHg) all of which was regulated by an automated sequencer. Hypercapnia was induced by administering a gas mixture of 8% CO 2 /92% O 2 at 14L/min for 15s and maintained at plateau for a subsequent 30s by reducing gas flow to 1.5–2 L/min of O 2 . During the plateau phase, the decreased inflow of fresh gases resulted in rebreathing of previously exhaled gases contained in the expiratory reservoir tube. Intervals of low CO 2 were achieved by supplying subjects with 15sec of high flow (16–18 L/min) of 100% O 2 and maintained by O 2 flow at a rate of 12–14 L/min. Partial pressures of end-tidal CO 2 (p ET CO 2 ) and O 2 (p ET O 2 ) were monitored continuously using a commercially available capnograph and recorded digitally at a sampling rate of 60Hz/channel. After completion of the measurements, the collected p ET CO 2 data was reduced to one measure of p ET CO 2 per breath and correlation analysis with the BOLD data was performed. Prior to this, the BOLD data was co-registered to compensate for motion artifacts. Signal of the whole brain was used as a reference to determine the shift needed to bring the CO 2 and the MR data sets in phase. Once in phase, CVR maps were calculated on a pixel by pixel basis from the slope of the regression of the percentage change of MR signal on the p ET CO 2 . This provides a measure of reactivity expressed in units of % Δ MR signal/mmHg p ET CO 2 . Signal drop-out was measured as the sum of all the pixels within a mask automatically generated by AFNI [2]. Differences in reactivity, residual noise and signal drop-out between the 2 acquisition schemes were assessed using a paired student's t-test. Results: For 1.5T, there was no significant difference between spiral and EPI for any of the measures (% signal changes, residuals and signal drop-out) (p>0.74). For 3T, differences between the techniques were only significant for signal drop-out (p<0.043). All results are summarized in table 1. Figure 1 shows an example of more signal drop-out using spiral at 3T. Conclusion: Our results show no significant difference in sensitivity and systematic error between techniques. While signal-drop out was not significantly different at 1.5T, differences were more pronounced at 3T. In this study we used a conventional spiral-out technique. The more recently developed spiral in-out technique [3], however, has promise to markedly reduce signal drop-out. We will compare this method with single-shot parallel EPI in a future study. References: 1. Vesely et al. MRM 2002, 2. AFNI – http://afni.nimh.nih.gov/afni , 3. Preston et al. NeuroImage 2004 Table 1:

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.320
Teacher spread0.294 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
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".

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Citations0
Published2007
Admission routes1
Has abstractyes

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