Image Registration in Intensity- Modulated, Image-Guided and Stereotactic Body Radiation Therapy
Bibliographic record
Abstract
Many recent advances in the technology of radiotherapy have greatly increased the amount of image data that must be rapidly processed. With the increasing use of multimodality imaging for target definition in treatment planning, and daily image guidance in treatment delivery, the importance of image registration emerges as key to improving the radiotherapy planning and delivery process at every step. Both clinicians and nonclinicians are affected in their work efficiency. Image registration can improve the correspondence of information in multimodality imaging, allowing more information to be obtained for tumor and normal tissue definition. Image registration at treatment delivery can improve the accuracy of therapy by taking greater advantage of images available prior to treatment. Technical advances have enhanced the accuracy and efficiency of registration through several approaches to automation, and by beginning to address the tissue deformation that occurs during the planning and therapy period. When using an automated registration technique, the user must understand the components of the registration process and the accuracy and limitations of the algorithm involved. This review presents the fundamental components of image registration, compares the benefits and limitations of different algorithms, demonstrates methods of visualizing registration.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".