A feasibility study to investigate the use of thin-plate splines to account for prostate deformation
Bibliographic record
Abstract
Image registration is an important step in the radiotherapy treatment planning process. It provides a method of fusing different types of diagnostic imaging information. One such application is to combine magnetic resonance spectroscopic images (MRSI) of the prostate with anatomical MRI and/or computed tomography images that are routinely used in the radiation treatment planning of prostate cancer. MRSI provides in vivo information related to the underlying metabolic activity of tissues, and can be related to the presence of cancer. However, the inflated endorectal coil required during MRS imaging poses a potential problem by deforming the prostate when it is filled with approximately 100 cm3 of air during image acquisition. This pushes the prostate superiorly/anteriorly, deforming the prostate and consequently the spectroscopic imaging data in a nonlinear manner. In this application, the coil-deformed MRS images are warped back to a non-deformed state, using a single data set. A nonlinear warping algorithm is presented to achieve this. Results indicate that the algorithm attains an accuracy of 97% (4 cm3 difference) when reproducing the total prostate volume compared to a Radiation Oncologist defined prostate volume. This difference is slightly smaller than the measured intra-operator variance of +/-1.5 cm3 (deflated coil) and the measured algorithm variance of +/-1.0 cm3. Additionally, intraprostatic nodules were used to assess the accuracy of the warping algorithm in regions inside the prostate. While choosing anatomical tie points along the external prostate surface, analysis of the nodules revealed the algorithm accuracy reduced to 63-93%.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".