Accuracy of 177Lu activity quantification in SPECT imaging: a phantom study
Bibliographic record
Abstract
The aim of the study is to assess accuracy of activity quantification of 177Lu studies performed according to recommendations provided by the committee on Medical Internal Radiation Dose (MIRD) pamphlets 23 and 26. The performances of two scatter correction and three segmentation methods were compared. Additionally, the accuracy of tomographic and planar methods for determination of the camera normalization factor (CNF) was evaluated. Eight phantoms containing inserts of different sizes and shapes placed in air, water, and radioactive background were scanned using a Siemens SymbiaT SPECT/CT camera. Planar and tomographic scans with 177Lu sources were used to measure CNF. Images were reconstructed with our SPEQToR software using resolution recovery, attenuation, and two scatter correction methods (analytical photon distribution interpolated (APDI) and triple energy window (TEW)). Segmentation was performed using a fixed threshold method for both air and cold water scans. For hot water experiments three segmentation methods were compared as folows: a 40% fixed threshold, segmentation based on CT images, and our iterative adaptive dual thresholding (IADT). Quantification error, defined as the percent difference between experimental and true activities, was evaluated. Quantification error for scans in air was better for TEW scatter correction (<6%) than for APDI (<11%). This trend was reversed for scans in water (<10% for APDI and <14% for TEW). For hot water, the best results (<18% for small objects and <5% for objects >100 ml) were obtained when APDI and IADT were used for scatter correction and segmentation, respectively. Additionally, we showed that planar acquisitions with scatter correction and tomographic scans provide similar CNF values. This is an important finding because planar acquisitions are easier to perform than tomographic scans. TEW and APDI resulted in similar quantification errors with APDI showing a small advantage for objects placed in medium with non-uniform density. Following the MIRD recommendations for data acquisition and reconstruction resulted in accurate activity quantification (errors <5% for large objects). However, techniques for better organ/tumor segmentation must still be developed.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".