Impact of Pinhole Collimation on SPECT Image Quality Metrics, and Methods for Patient-Specific Assessment of Noise and Standardization of Imaging Protocols
Bibliographic record
Abstract
Dedicated cardiac pinhole SPECT camera designs offer improvements in overall sensitivity, thereby enabling the use of lower injected radiotracer activity and shorter imaging times than parallel-hole designs.The effect of these novel camera designs on image noise on a voxel-by voxel level has not previously been investigated.This work identifies position and orientation-dependent variability of spatial resolution in the fieldof-view (FOV) of pinhole cameras.It also identifies a 1.7-fold gradient in the magnitude of image noise across the length of the heart which leads to a 1.3-fold gradient in standard deviation values for a normal database for attenuation corrected images acquired with a commercially available cardiac pinhole camera.This pattern of noise varies with different patients and with different positioning of the heart within the FOV.Thus, to assist with clinical interpretation, a new 1-minute post-processing technique is developed to provide a patient-specific image of the noise distribution which may augment normal database information.Changes in attenuation result in varying levels of noise between patients of different body habitus administered the same radiotracer activity.A method for creating weight-based protocols is developed that standardizes the average noise in cardiac perfusion images by tailoring the radiotracer activity and acquisition time to the body mass of each patient.Methods developed in this thesis allow for more patientspecific imaging protocols, thereby standardizing the image noise level and providing physicians with more information about the noise and spatial-resolution distribution to aide in image interpretation.• I bootstrapped (all) and reconstructed (most, see above) images• I performed all calculations, evaluation, and statistical analysis using Matlab• I wrote articles and abstracts, and I presented oral and poster presentations meetings or conferences (oral or poster).
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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 | 0.018 | 0.071 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".