Improving the spectral resolution and spectral fitting of <sup>1</sup>H MRSI data from human calf muscle by the SPREAD technique
Bibliographic record
Abstract
Proton magnetic resonance spectroscopic imaging ((1) H MRSI) has been used for the in vivo measurement of intramyocellular lipids (IMCLs) in human calf muscle for almost two decades, but the low spectral resolution between extramyocellular lipids (EMCLs) and IMCLs, partially caused by the magnetic field inhomogeneity, has hindered the accuracy of spectral fitting. The purpose of this paper was to enhance the spectral resolution of (1) H MRSI data from human calf muscle using the SPREAD (spectral resolution amelioration by deconvolution) technique and to assess the influence of improved spectral resolution on the accuracy of spectral fitting and on in vivo measurement of IMCLs. We acquired MRI and (1) H MRSI data from calf muscles of three healthy volunteers. We reconstructed spectral lineshapes of the (1) H MRSI data based on field maps and used the lineshapes to deconvolve the measured MRS spectra, thereby eliminating the line broadening caused by field inhomogeneities and improving the spectral resolution of the (1) H MRSI data. We employed Monte Carlo (MC) simulations with 200 noise realizations to measure the variations of spectral fitting parameters and used an F-test to evaluate the significance of the differences of the variations between the spectra before SPREAD and after SPREAD. We also used Cramer-Rao lower bounds (CRLBs) to assess the improvements of spectral fitting after SPREAD. The use of SPREAD enhanced the separation between EMCL and IMCL peaks in (1) H MRSI spectra from human calf muscle. MC simulations and F-tests showed that the use of SPREAD significantly reduced the standard deviations of the estimated IMCL peak areas (p < 10(-8) ), and the CRLBs were strongly reduced (by ~37%).
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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 | 0.000 |
| 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".