Improved Arterial Input Function For Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using Phase and T1 Measurements
Bibliographic record
Abstract
Perfusion is a bodily function that describes the transport of blood and nutrients to an organ.If characteristics of the blood flow to an organ can be measured, clinically useful quantitative data about the organ can be obtained.This is particularly useful in the diagnosis of tumours as their rapid and poorly-formed capillary network is unique compared to healthy tissue.Tumour perfusion information can be obtained by performing Dynamic Contrast-Enhanced (DCE)-MRI.Using DCE-MRI, blood flow into a tumour from a nearby artery can be computed by measuring the concentration of an injected contrast agent as a function of time.This measurement, known as the arterial input function (AIF), can be computed based on the change of either the intensity or the phase of the MR signal from the blood, due to the presence of a contrast agent.While both methods can be used to acquire the AIF, the change in phase is preferred due to its superior accuracy.Even so, the method conventionally used to obtain the phase-derived AIF has deficiencies that lead to an AIF intensity overestimation.The goal of this thesis was to determine a better method to obtain AIF data for patients with brain tumours by using a combination of MR signal phase and accurate T 1 relaxation measurements.This was done by first characterizing and deriving equations for phase measurement errors and simulating these effects on the AIF using realistic, clinical parameters.T 1 measurement methodology was developed and validated in staticiii water phantoms and applied to a flowing-water phantom system on which phase AIF data were also acquired.The novel AIF method was tested on this system before it was applied clinically to patients diagnosed with high-grade gliomas.The differences between the quantitative perfusion parameters from the new method and previous AIF methods were compared.The novel AIF measurement method presented in this thesis was designed to be less prone to experimental error than current clinical methods.Theoretical predictions, computed simulations, experimental work with MRI test objects, and clinical results all show that the new method for measuring the AIF is significantly superior to procedures currently used clinically.From his help with study development, to spending many long days at the scanner, to our love and passion for GCSC, Dr. Cron will never stop being a friend and mentor to me.Last but not least, I would like to thank those close to me, especially my wife Laura.This thesis could not be possible without your continuous love and support, especially when I needed it the most.I'd also like to thank my siblings and parents who helped to develop my love of science from a young age.They have supported me endlessly and have always been my biggest cheerleaders not only for this work, but for my entire life.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".