Small field-of-view cardiac SPECT can be implemented on hybrid SPECT/CT platforms where data acquisition and reconstruction are guided by CT
Bibliographic record
Abstract
INTRODUCTION: Image truncation in nuclear medicine is a common problem that can lead to artifacts in reconstructed images. We evaluate a modified single-photon emission computed tomography/computed tomography (SPECT/CT) acquisition and reconstruction method for truncated SPECT, which is guided by nontruncated CT. The method nearly eliminates truncation errors, and is ideal for cardiac imaging. We demonstrate its application on phantom and clinical cardiac SPECT/CT scans. METHODS: Tc-MIBI (2-methoxy isobutyl isonitrile) SPECT/CT scans were acquired on 14 patients, and on an anthropomorphic cardiac chest phantom. The original 34 x 34 cm field-of-view (FOV) projections were truncated to simulate a small 16 x 16 cm FOV acquisition. Data were reconstructed in three ways: (i) nontruncated and standard reconstruction (NTOSEM), which was our gold standard; (ii) truncated and standard reconstruction (TOSEM); and (iii) truncated and a modified reconstruction (TMOSEM). TMOSEM and TOSEM were both compared with NTOSEM by comparing relative count ratios in the heart, looking at the change in perfusion defect size, and comparing pixel correlation coefficients. RESULTS: Compared with NTOSEM, the use of TOSEM for small FOV clinical imaging incurred an average count ratio error greater than 100%, and decreased the calculated defect size by 17.13%. For TMOSEM, the average count ratio error was only 8.9%, and the defect size was only decreased by 0.19% compared with NTOSEM. When we plotted TOSEM against NTOSEM a correlation coefficient of 0.734 was calculated, and when we plotted TMOSEM against NTOSEM a correlation coefficient of 0.996 was measured. Comparing NTOSEM with TOSEM in the phantom study produced an average count ratio error greater than 100%. TMOSEM produced an error of 4.3% compared with NTOSEM. CONCLUSION: Projection truncation due to small FOV cameras in cardiac SPECT/CT can lead to significant errors. TMOSEM guided by nontruncated CT reconstruction shows promise in reducing these errors.
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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 | 0.001 | 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".