Feasibility of SPECT SUV to quantify 123I-MIBG cardiac uptake in heart failure patients
Bibliographic record
Abstract
228 Objectives SPECT images are typically reconstructed in relative units of counts/voxel. By applying attenuation and scatter correction and using a calibration scan to determine the system absolute sensitivity, it is possible to convert SPECT image values into units of MBq/cm3. These values can in-turn be normalized with respect to injected dose and patient body mass to generate standardized uptake values (SUV) similar to those obtained with PET. The objective of this study was to obtain SPECT SUV values for uptake of 123I-MIBG in the heart and compare this to the heart-to-mediastinum (H/M) ratio in heart failure patients and normal controls. Methods Planar and SPECT/CT data from a total of 13 subjects including 5 heart failure patients (non-ischemic cardiomyopathy and LV ejection fraction Results There was good correlation between the measured H/M ratio on planar and SPECT images (R=0.53, p Conclusions This study demonstrates the feasibility of using SPECT SUV measurements to assess cardiac uptake in heart failure patients. The SUV measurements showed significant correlation (r=0.53) with the planar H/M ratio and had a correlation coefficient similar to that between SPECT H/M and planar H/M. $$graphic_38D6EA70-725E-42C9-9385-D125B3CA2C15$$
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".