Influence of Depth of Interaction on Spatial Resolution and Image Quality for the HRRT
Bibliographic record
Abstract
The high resolution research tomograph (HRRT) is an octagonal design PET camera with 119,808 crystals arranged in a dual layer to determine the depth of interaction (DOI) and compensate for the parallax effect. The DOI discrimination is based on the identification of the layer in which the gamma interaction occurred using pulse shape discrimination. However the observed fractional crystal efficiency is count rate dependent, thus affecting the accuracy of the pulse shape discrimination. In this study we investigated the impact of the mismatch between the emission and the normalization scan count rate on image uniformity using phantom data when DOI correction was applied and when it was switched off. Count rate mismatch was found to manifest itself in form of streaking artifacts and high frequency non-uniformities with a star shape pattern in Fourier space. It was found to be enhanced when DOI correction was applied. In realistic scanning conditions assessed with non-human primate data the effect of count rate mismatch was found to be nearly negligible with DOI correction present or absent. Since DOI corrected data proved to be more sensitive to an emission/normalization count rate mismatch, the impact of DOI on resolution and biological measure obtained in realistic scanning conditions was further evaluated. With DOI determination, spatial resolution was improved by up to 7% in the outer part of the FoV where it was measured to be 2.9 /spl plusmn/ 0.2 mm (SPAN 3) and 3.3 /spl plusmn/ 0.2 mm (SPAN 9) and the biological parameters (binding potentials) extracted from the non-human primate study were improved by up to 5%. In summary this study shows a greater sensitivity to emission/normalization count rate mismatch in phantom studies when DOI correction is present. However much less sensitivity is observed in realistic data, while the resolution uniformity advantage due to DOI determination is still noticeable, not only in resolution measurement but also in the accuracy of the biological measures extracted from realistic scanning protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".