Constrained Source Space MR Spectroscopy: Multiple Voxels, No Gradient Readout
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Our goal was to develop a novel technique for measuring a small number of localized spectra simultaneously and in a time-efficient manner. MATERIALS AND METHODS: Using appropriate radiofrequency pulses, the magnetization from multiple voxels is excited simultaneously and then separated (reconstructed) by using the individual coil-sensitivity profiles from a multichannel receiver coil. Because no gradients are used for k-space encoding, constrained source space MR spectroscopy provides a time advantage over conventional spectroscopic imaging and an improved signal-to-noise ratio per square root of unit time over single-voxel spectroscopy applied at each successive location. In the present work, we considered prototype application of constrained source space MR spectroscopy for 2 voxels. RESULTS: Experimental data from healthy volunteers and simulation results showed that constrained source space MR spectroscopy is effective at extracting 2 independent spectra even in the challenging scenario of the voxels being closely spaced. Also, from 6 patients with various types of brain cancer we obtained 2-voxel constrained source space MR spectroscopy data, which showed spectra of clinical quality in half the time required to perform successive single-voxel MR spectroscopy. CONCLUSIONS: Constrained source space MR spectroscopy provides clinical quality spectra and could be used to probe multiple voxels simultaneously in combination with Hadamard encoding for further scan-time reductions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".