Hadamard‐encoded dual‐voxel SPECIAL: Short‐TE MRS acquired in two brain regions simultaneously using Hadamard encoding
Bibliographic record
Abstract
PURPOSE: The spin-echo, full-intensity acquired localized (SPECIAL) sequence is a method for single-voxel, localized MRS in vivo with short TEs. In this study we modified the SPECIAL sequence to simultaneously record spectra from two volumes of interest. This new technique is called Hadamard-encoded dual-voxel SPECIAL (HD-SPECIAL). METHODS: The SPECIAL sequence consists of a spin echo localized to a column of tissue, preceded by a slice-selective inversion pulse in alternating scans to invert a section of the column. Full localization is achieved by subtraction of the inversion-on scans from the inversion-off scans. In HD-SPECIAL, the two-step inversion scheme is replaced by a four-step Hadamard-encoded scheme involving single-band and dual-band inversion pulses to select two regions of the spin-echo column. By appropriate Hadamard combination of the four acquired shots, spectra can be reconstructed from both desired regions. This approach does not rely on parallel imaging reconstruction. Using a 3T scanner, HD-SPECIAL localization is demonstrated both in phantoms and in the human brain in vivo, and the performance of HD-SPECIAL is assessed by comparing with the conventional SPECIAL sequence. RESULTS: Phantom and in vivo measurements show excellent agreement between measures from HD-SPECIAL and SPECIAL sequences. Relative to consecutive SPECIAL measurements from two regions, HD-SPECIAL reduces the total scan time 2-fold with minimal penalty in terms of spectral quality or SNR. CONCLUSION: The HD-SPECIAL sequence enables reliable acquisition of MR spectra simultaneously from two regions at 3 T, offering the potential to study interregional variations in metabolite concentrations.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".