JADA: A graphical user interface for comprehensive internal dose assessment in nuclear medicine
Bibliographic record
Abstract
PURPOSE: The main objective of this work was to design a comprehensive dosimetry package that would keep all aspects of internal dose calculation within the framework of a single software environment and that would be applicable for a variety of dose calculation approaches. METHODS: Our MATLAB-based graphical user interface (GUI) can be used for processing data obtained using pure planar, pure SPECT, or hybrid planar/SPECT imaging. Time-activity data for source regions are obtained using a set of tools that allow the user to reconstruct SPECT images, load images, coregister a series of planar images, and to perform two-dimensional and three-dimensional image segmentation. Curve fits are applied to the acquired time-activity data to construct time-activity curves, which are then integrated to obtain time-integrated activity coefficients. Subsequently, dose estimates are made using one of three methods. RESULTS: The organ level dose calculation subGUI calculates mean organ doses that are equivalent to dose assessment performed by OLINDA/EXM. Voxelized dose calculation options, which include the voxel S value approach and Monte Carlo simulation using the EGSnrc user code DOSXYZnrc, are available within the process 3D image data subGUI. CONCLUSIONS: The developed internal dosimetry software package provides an assortment of tools for every step in the dose calculation process, eliminating the need for manual data transfer between programs. This saves times and minimizes user errors, while offering a versatility that can be used to efficiently perform patient-specific internal dose calculations in a variety of clinical situations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.028 |
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".