Clinical Impact of Nonuniform CT-Based Attenuation Correction in Brain Perfusion SPECT/CT Using 99mTc-ECD
Bibliographic record
Abstract
INTRODUCTION: Brain perfusion SPECT is commonly used to evaluate patients with cognitive impairments. Physical limits such as attenuation compromise image quality do not allow the most accurate depiction of radionuclide distribution, and thus, application of attenuation correction (AC) has been recommended. Some reports have demonstrated discordances between the uniform (UAC) and nonuniform CT-based correction (NUAC) procedures. The aim was to study the impact of these discordances on visual interpretation and their concordance with clinical symptoms. METHODS: Twelve patients presenting cognitive impairments were included. Brain perfusion SPECT images were reconstructed using 2 AC methods. Qualitative image assessment was performed as uptake analysis in 21 predefined cortical ROIs for each patient. Interpretation of perfusion patterns was based on a 2-score uptake scale (normal and reduced/pathologic). Variation of uptake scores in UAC- versus NUAC-processed images and their concordance with clinical symptoms were studied. RESULTS: Normal image patterns generated by UAC and NUAC methods were found in 226 (90%) of 252 and in 201 (80%) of 252 ROIs, respectively. No difference between UAC and NUAC methods was found in posterior brain areas. However, differences were recorded in 51 (20%) of 252 ROIs, and this discordance was located in the anterior areas (frontal and temporal lobes), and evaluation changed from normal to pathological patterns using NUAC method. Two years later, patients showing frontal hypoperfusion on NUAC brain SPECT images expressed clinical frontal lobe dysfunctions. CONCLUSIONS: Discordances between UAC- and NUAC-processed images impact visual analysis of brain perfusion SPECT images. The NUAC-processed images show a good concordance with clinical symptoms, suggesting that it is an accurate method to correct attenuation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".