A method for assessing voxel correspondence in longitudinal tumor imaginga)
Bibliographic record
Abstract
PURPOSE: Tumor characterization employing a voxel-wise analysis of image signal facilitates the determination of the spatial distribution of tumor attributes, and when employed for therapy response assessment offers the promise of greater sensitivity to change than conventional approaches. However, the accuracy of a voxel-wise analysis of change is limited by local registration uncertainties that can disrupt the spatiotemporal correspondence between assessed voxels. We present a method for assessing voxel correspondence strength using a multiresolution local histogram-based measure of image structure similarity. When employed in a longitudinal tumor imaging context, a voxel similarity measure must be robust to intensity variations that can arise from the image acquisition, treatment effects, or changes in underlying disease processes. Consequently, the local histogram-based similarity measure is evaluated for sensitivity to structural change and robustness to intensity variation and is compared against normalized mutual information and normalized cross-correlation. METHODS: T1-weighted (T1W) magnetic resonance (MR) images of glioblastoma acquired as part of a longitudinal response assessment study are first rigidly registered, and then similarity between spatially corresponding voxels is evaluated using multiresolution local histograms. Region-based and nonuniform intensity changes of varying magnitude as well as deformations to image structure are applied individually and in combination to the test images. Statistical analysis is used to test for interaction effects between the applied perturbations and the value of the local histogram similarity function. Pair-wise voxel similarity maps are computed for pairs of longitudinal clinical MR image volumes and compared with observed patterns of change on conventional imaging. RESULTS: The simulations demonstrated that the local histogram measure was robust to intensity modulations applied to increasing region sizes and exhibited a strong negative correlation with the magnitude of local deformation. No statistically significant interaction effects were observed upon the value of the local histogram similarity function when deformation was applied in conjunction with a nonuniform intensity change. Pair-wise voxel similarity maps were consistent with image change observed on T1W MR imaging and revealed patterns of change not apparent in conventional image sequences. CONCLUSIONS: A measure of local histogram image structure similarity can be used to assess the strength of voxel to voxel correspondences independently of intensity nonuniformities. The metric can provide a local estimate of the limits of achievable correspondence underlying the registration and voxel-wise comparison of signal in longitudinal imaging used for assessing tumor response to treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".