SU‐GG‐BRC‐10: Shape Matters: Utilization of a Conformal Voxel Technique to Acquire Robust in Vivo Prostate MRSI at Short Echo Times
Bibliographic record
Abstract
Purpose: We seek to improve the quality of in vivo prostate MRSI data acquisition by utilizing an optimized conformal voxel technique coupled with a spatial‐spectral excitation PRESS pulse sequence for short echo time acquisitions. Method and Materials: All subjects were scanned on a GE 1.5T Signa MR scanner equipped with Echospeed gradients. A standard endorectal coil in combination with a torso phased‐array coil was used. The PRESS pulse sequence was modified to include the optimized conformal voxel MR spectroscopic imaging technique (CV‐MRS). This method uses up to twenty Very Selective Saturation (VSS) pulses, automatically positioned in three dimensions, to “conform” the excitation volume to the shape of the prostate, effectively nulling signal from periprostatic lipids. Subjects were scanned using both the standard PRESS and the optimized CV‐MRS techniques at long and short echo times (TE). In vivo prostate spectra were collected and processed using a modified version of LCModel. Results: We observed an average lipid reduction of 60±18% for 17 subjects over the entire prostate when using the optimized CV‐MRS technique as compared to standard MRSI techniques. In specific regions along the peripheral zone, we observed lipid reduction greater than 95%. The effect of reducing the lipid contamination has resulted in a ∼70% improvement in peak identification of key prostate metabolites, based on goodness‐of‐fit parameters. Furthermore, short TE acquisitions have resulted in a substantial increase in the citrate signal, full visualization of the citrate multiplet and other metabolites not seen at long echo times. Conclusion: In vivo implementation of this optimized MRSI technique has confirmed the reduction in peripheral lipid contamination, and improved the quality of spectra throughout the prostate. Furthermore, this is the first demonstration of short TE in vivo prostate MRSI acquisitions, which provides significant signal increase and reveal short TE metabolites to potentially improve prostate cancer detection.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".