Automatic conformal prescription of very selective saturation bands for <i>in vivo</i><sup>1</sup>H‐MRSI of the prostate
Bibliographic record
Abstract
An important step in the implementation of three-dimensional in vivo proton magnetic resonance spectroscopic imaging ((1)H-MRSI) of the prostate is the placement of spatial saturation pulses around the region of interest (ROI) for the removal of unwanted contaminating signals from peripheral tissue. The present study demonstrates the use of a technique called conformal voxel magnetic resonance spectroscopy (CV-MRS). This method automates the placement, orientation, timing and flip angle of very selective saturation (VSS) pulses around an irregularly-shaped, user-defined ROI. The method employs a user adjustable number of automatically positioned VSS pulses (20 used in the present study) which null the signal from periprostatic lipids while closely conforming the shape of the excitation voxel to the shape of the prostate. A standard endorectal coil in combination with a torso-phased array coil was used for all in vivo prostate studies. Three-dimensional in vivo prostate (1)H-MRSI data were obtained using the proposed semi-automated CV-MRS technique, and compared with a standard point resolved spectroscopy (PRESS) technique at TE = 130 ms using manual placement of saturation pulses. The in vivo prostate (1)H-MRSI data collected from 12 healthy subjects using the CV-MRS method showed significantly reduced lipid contamination throughout the prostate, and reduced baseline distortions. On average there was a 50 ± 17% (range 12% - 68%) reduction in lipids throughout the prostate. A voxel-by-voxel benchmark test of over 850 voxels showed that there were 63% more peaks fitted using the LCModel when using a Cramer-Rao Lower Bound (CRLB) cut-off of 40% when using the optimized conformal voxel technique in comparison to the manual placement approach. The evaluation of this CV-MRS technique has demonstrated the potential for easy automation of the graphical prescription of saturation bands for use in (1)H-MRSI.
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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 | 0.000 | 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".