Proton magnetic resonance spectroscopy in the brain: Report of AAPM MR Task Group #9
Bibliographic record
Abstract
AAPM Magnetic Resonance Task Group #9 on proton magnetic resonance spectroscopy (MRS) in the brain was formed to provide a reference document for acquiring and processing proton (1H) MRS acquired from brain tissue. MRS is becoming a common adjunct to magnetic resonance imaging (MRI), especially for the differential diagnosis of tumors in the brain. Even though MR imaging is an offshoot of MR spectroscopy, clinical medical physicists familiar with MRI may not be familiar with many of the common practical issues regarding MRS. Numerous research laboratories perform in vivo MRS on other magnetic nuclei, such as 31P, 13C, and 19F. However, most commercial MR scanners are generally only capable of spectroscopy using the signals from protons. Therefore this paper is of limited scope, giving an overview of technical issues that are important to clinical proton MRS, discussing some common clinical MRS problems, and suggesting how they might be resolved. Some fundamental issues covered in this paper are common to many forms of magnetic resonance spectroscopy and are written as an introduction for the reader to these methods. These topics include shimming, eddy currents, spatial localization, solvent saturation, and post-processing methods. The document also provides an extensive review of the literature to guide the practicing medical physicist to resources that may be useful for dealing with issues not covered in the current article.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".