Sci‐YIS Fri ‐ 06: High spatial resolution magnetic resonance spectroscopic imaging of the brain at 3T for external beam radiation therapy planning
Bibliographic record
Abstract
Recent research has shown that Magnetic Resonance Spectroscopic Imaging (MRSI) has good prospects for cancer detection and staging. Recent advancements in hardware in the form of RF coils and gradient sets have resulted in a good outlook for MRSI to be used in detection, localization, and classification of the lesions. Work done by other groups has shown that merging molecular imaging information from MRSI with anatomic scans used for treatment planning proved beneficial in delineating the tumor volume. MRSI involves defining an MR image volume which can be divided into a matrix of 3‐D sub‐volumes. Spectroscopic data is then acquired for all these sub‐volumes simultaneously. This leads to the quantification and localization of different levels of metabolites such as Choline, Creatine and N‐ acetylaspartate that are highly useful in detecting and possibly staging lesions. We focused on performing MRSI on the brain using a head transmit/receive (T/R) coil. Utilizing a reduced 2‐Dimensional Point Resolved Spectroscopy (2‐D PRESS) Turbo Spectroscopic Imaging (TSI) sequence to improve the spectral resolution of different metabolite peaks and the spatial resolution of the spectroscopic scan, good spectral/spatial information can be acquired revealing more accurate bio‐chemical imaging. Thus, an MRSI study could be easily used for improved treatment planning. Using 3T MRI we have improved the spatial resolution of the MRSI scans to 5×5×10mm3/voxel, meanwhile maintaining a good signal to noise ratio. This would increase the spatial relevance of the spectra acquired in the scan and in turn provide more information in the treatment planning phase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.015 |
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".