Robust dynamic susceptibility contrast MR perfusion using 4D nonlinear noise filters
Bibliographic record
Abstract
PURPOSE: To investigate if 4D (simultaneous space and time) nonlinear filtering techniques can produce more robust cerebral blood flow (CBF) estimates by reducing noise in acquired dynamic susceptibility contrast (DSC) MR perfusion data. MATERIALS AND METHODS: A digital anthropomorphic brain perfusion phantom was constructed to analyze filter performance by: 1) deriving anthropomorphic tissue volume fractions from a human subject and 2) simulating DSC-MR perfusion signals for voxels with mixed tissue for various signal-to-noise ratios (SNRs). DSC-MR data for 11 acute ischemic stroke patients were also acquired at 3T. CBF maps cross-calibrated so that normal white matter CBF was 22 mL/minute/100 g were produced from DSC-MR data without filtering and from 4D-Gaussian and 4D-bilateral noise-filtered DSC-MR data. RESULTS: The nonlinear 4D-bilateral filter yielded the lowest CBF root-mean square error (RMSE) in the phantom experiments with noise (average RMSE across all tissues regions for no filtering, 4D-Gaussian, and 4D-bilateral was 5.3 mL/minute/100 g, 6.2 mL/minute/100 g, and 4.0 mL/minute/100 g, respectively) and had the best image quality in both the phantom and patient data. CONCLUSION: Nonlinear 4D noise filters are better suited to the 4D nature of DSC-MR data. Linear spatial filters are not appropriate and can produce larger CBF errors than without filtering.
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 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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".