Application of a Fourier Shift Preprocessing Stage to Improve the Resolution of Resting State fMRI Images
Bibliographic record
Abstract
Multiple sclerosis (MS) is a disease common in many northern-climate countries with Canada having 28% higher MS numbers on a population basis than second place Denmark. Optical Neuritis (ON) is known to affect the properties of the visual pathways in the brain, is often a precursor to MS, and has been suggested as a system model for MS pathology. We have investigated possible resting state functional magnetic resonance imaging (rs-fMRI) markers to track ON recovery or progression to MS. To obtain the necessary rs-fMRI temporal resolution requires discrete Fourier transform (DFT) reconstruction applied to 2D truncated (finite length) frequency domain MRI data sets followed by a 2D DFT-based correlation analysis across a time sequence of images to identify image regions that are connected through the brain's optical pathways. Another DFT-based transfer function determination identifies pathways impacted by ON; permitting differentiation between normal volunteers and ON patients. Windowing or low-pass filtering is required to remove ringing distortions from these five DFT application stages, but leads to lower fMRI spatial resolution and an undesirable loss in ON marker accuracy. Recently we have theoretically identified a Fourier shift manipulation (FSM) preprocessing stage that avoids the unnecessary loss of resolution that occurs with the use of global windowing during DFT application. We have previously demonstrated how applying FSM to data improves 1D DFT-based analysis under certain experimental MR-relevant situations. In this paper we extend the FSM approach to demonstrate an improvement in the 2D resolution of rs-fMRI images generated from truncated MRI k-space data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".