Imaging cerebrovascular reactivity using BOLD MRI at 1.5T and 3.0T: comparison of spiral and EPI combined with parallel imaging
Bibliographic record
Abstract
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:
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".