3D phase contrast MRI: Partial volume correction for robust blood flow quantification in small intracranial vessels
Bibliographic record
Abstract
PURPOSE: Recent advances in 3D-PCMRI (phase contrast MRI) sequences allow for measuring the complex hemodynamics in cerebral arteries. However, the small size of these vessels vs spatial resolution can lead to non-negligible partial volume artifacts, which must be taken into account when computing blood flow rates. For this purpose, we combined the velocity information provided by 3D-PCMRI with vessel geometry measured with 3DTOF (time of flight MRI) or 3DRA (3D rotational angiography) to correct the partial volume effects in flow rate assessments. METHODS: The proposed methodology was first tested in vitro on cylindrical and patient specific vessels subject to fully controlled pulsatile flows. Both 2D- and 3D-PCMRI measurements using various spatial resolutions ranging from 20 to 1.3 voxels per vessel diameter were analyzed and compared with flowmeter baseline. Second, 3DTOF, 2D- and 3D-PCMRI measurements were performed in vivo on 35 patients harboring internal carotid artery (ICA) aneurysms indicated for endovascular treatments requiring 3DRA imaging. RESULTS: The in vitro 2D- and 3D-PCMRI mean flow rates assessed with partial volume correction showed very low sensitivity to the acquisition resolution above ≈2 voxels per vessel diameter while uncorrected flow rates deviated critically when decreasing the spatial resolution. 3D-PCMRI flow rates measured in vivo in ICA agreed very well with 2D-PCMRI data and a good flow conservation was observed at the C7 bifurcation. Globally, partial volume correction led to 10-15% lower flow rates than uncorrected values as those reported in most of the published studies on intracranial flows. CONCLUSION: Partial volume correction may improve the accuracy of PCMRI flow rate measurements especially in small vessels such as intracranial arteries. Magn Reson Med 79:129-140, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".