Comparison of optimized magnetic resonance sequences for patient‐specific treatment planning in surface brachytherapy
Bibliographic record
Abstract
BACKGROUND: The clinical standard practice of surface brachytherapy (SB) planning has long been to use computed tomography (CT) imaging to visualize applicators for catheter reconstruction in the treatment planning process. Recent work in SB has suggested that magnetic resonance (MR)-guidance can be used in place of CT-guidance in SB planning to utilize the increased soft tissue contrast for visualization of diseased tissue. This soft tissue visualization can be used to verify the target depth for enhanced coverage of the clinical target volume. Two optimized MR sequences (pointwise encoded time reduction with radial acquisition (PETRA) and volumetric interpolated breath-hold examination (VIBE) obtaining Dixon in-phase (DIP) and Dixon opposed-phase (DOP)) have been shown to detect sufficient signal from the silicone-based applicators to perform accurate catheter reconstruction and produce SB treatment plans. PURPOSE: This study compares three in-house MR series optimized for applicator visualization to determine which is best-suited for SB planning based on tissue contrast and applicator visibility. This study then applies this series to produce MR-only SB treatment plans geometrically and dosimetrically comparable to those produced by CT-only for a phantom and eight patients. METHODS: An anthropomorphic phantom (True Phantom Solutions, Canada) with applicators (Elekta, Netherlands) on the foot and hand and eight patients undergoing SB for Dupuytren's Contracture/Palmar fascial fibromatosis were imaged by two optimized MR sequences: 1) PETRA and 2) VIBE obtaining DIP and DOP images. CT scans were acquired for verification. SB planning was performed in Oncentra Brachy (Elekta, Netherlands) treatment planning software using three MR series and CT. MR-based and CT-based plans were compared for geometric and dosimetric accuracy. Geometric accuracy was determined by registering CT-based to MR-based catheter digitizations and calculating distances between corresponding dwell positions. Patient MR images were compared using signal-to-noise ratios (SNR's) and contrast-to-noise ratios (CNR's) for various regions of interest (ROIs) including bone, fat, muscle, and applicator. The series with the greatest tissue contrast and applicator visualization was used to produce treatment plans. MR-based plans were compared to CT-based plans by point-based dose differences (DD's). The MR-based plan was rigidly registered to the CT-based plans, and the isodose volumes were segmented to V150, V125, V100, V95, V90, V80, and V65 and compared using the Dice similarity coefficient (DSC) and volumetric similarity (VS) metric. RESULTS: The distances between the CT-based and MR-based dwell positions were on average 1 mm. The DOP series displayed superior SNR's for all ROIs compared to PETRA and DIP. CNR's for DOP were equivalent to DIP and superior to PETRA. DD's were all below 5% between MR-based and CT-based plans. DSC's were above 0.9 for all segmentations associated with the phantoms and 0.8 for those associated with the patients. VS was above 0.98 for all segmentations across all subjects. CONCLUSIONS: The geometric accuracy of each MR sequence suggests that each can produce accurate treatment plans. The higher SNR's for DOP suggest DOP's suitability for SB, and DOP was utilized to create plans comparable to CT. This novel approach can result in more robust target coverage and potentially improve patient outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".