Cerebral blood flow measurement using intra-arterial bolus injection of 2H|2O monitored with magnetic resonance spectroscopy
Bibliographic record
Abstract
This thesis reports the development of a MR-based, non-invasive cerebral blood flow (CBF) measurement method. The technique is a 3 step process: (1) Introduce a certain amount of tracer into the brain; (2) Monitor the kinetics of the tracer in the brain; (3) Fit a theoretical model to the monitored result to measure CBF. Measurements were performed on Sprague-Dawley rats pre-treated with Atropine$\\rm\\sp{TM},$ anaesthetized with Na-Pentobarbital. Animal preparation was a 3 step process: setting up mechanisms to monitor rat physiology; placement of the injection catheter; placement of the MR surface coil. Once prepared the animal was inserted into a Bruker Biospec 7/21 MR scanner for $\\sp2$H spectral acquisition. A series of 360 free induction decays (FID) were taken. The $\\sp2$H labeled water indicator was injected as an instantaneous bolus at the beginning of the 15$\\rm\\sp{th}$ FID. The serial data acquisition procedure was repeated to yield 5 sets of data per rat. The FID in the series was used to generate360 $\\sp2$H spectra. The peak heights of the spectra represent the relative amount of indicator in the brain at a particular time. The theoretical model fit to the washin and washout data was modified from the Stimulus-Response theorem. The model enabled measurement of the fast CBF (fCBF) and average CBF (aCBF) using the same indicator washin and washout curve. The validity of the CBF measurement method developed was tested, by examining its ability to track changes in CBF with changes in PaCO$\\sb2.$ Experiments were conducted in which PaCO$\\sb2$ levels were purposefully altered to hypocarbia and hypercarbia by adjusting ventilation rate and volume. The technique was able to track the anticipated changes in CBF with changes in PaCO$\\sb2.$ The reproducibility of the CBF measurement method developed was evaluated by repetitive CBF measurements on the same animal at the same PaCO$\\sb2$ level. The method was qualitatively reproducible. The CBF results obtained with the method developed were compared with values published in the literature using other techniques. The developed quantitative CBF method yielded an average CBF. 37% lower than the average of all values appearing in the literature (115 ml/100g/min). It is proposed that CBF underestimation is due to indicator not being freely diffusible and due to indicator recirculation. Two methods were employed to eliminate the CBF underestimation due to the indicator not being freely diffusible. The first was the extraction coefficient (E) method. E was the ratio of indicator extracted by the brain extravascular space to the total amount injected. The obtained E was related to PS to assess the degree of CBF underestimation. The second method employed deuterated alcohols as the indicators. Deuterated water and deuterated alcohol were simultaneously introduced into the brain. The more freely diffusible alcohol indicator resulted in a CBF that was larger. A convolution analysis was employed to account for CBF underestimation due to indicator recirculation. This technique utilized in input function obtained from the derivative of the washout curve and a point spread function from injecting an instantaneous bolus downstream of the brain while monitoring at the brain. This technique was successful in delineating and removing indicator recirculation from the CBF measurements. It was found that the diffusibility and recirculation corrected CBF values agree with a gold standard from the literature. (Abstract shortened by UMI.)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".