Quantitative 177 Lu SPECT (QSPECT) imaging using a commercially available SPECT/CT system
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: The combination of single photon emission computed tomography (SPECT) and computer tomography (CT) that incorporates iterative reconstruction algorithms with attenuation and scatter correction should facilitate accurate non-invasive quantitative imaging. Quantitative SPECT (QSPECT) may improve diagnostic ability and could be useful for many applications including dosimetry assessment. Using (177)Lu, we developed a QSPECT method using a commercially available SPECT/CT system. METHODS: Serial SPECT of (177)Lu sources (89-12,400 MBq) were acquired with multiple contiguous energy windows along with a co-registered CT, and were reconstructed using an iterative algorithm with attenuation and scatter correction. Camera sensitivity (based on reconstructed SPECT count rate) and dead-time (based on wide-energy spectrum count rate) were resolved by non-linear curve fit. Utilizing these parameters, a SPECT dataset can be converted to a QSPECT dataset allowing quantitation in Becquerels per cubic centimetre or standardized uptake value (SUV). Validation QSPECT/CT studies were performed on a (177)Lu cylindrical phantom (7 studies) and on 5 patients (6 studies) who were administered a therapeutic dose of [(177)Lu]octreotate. RESULTS: The QSPECT sensitivity was 1.08 x 10(-5) ± 0.02 x 10(-5) s(-1) Bq(-1). The paralyzing dead-time constant was 0.78 ± 0.03 µs. The measured total activity with QSPECT deviated from the calibrated activity by 5.6 ± 1.9% and 2.6 ± 1.8%, respectively, in phantom and patients. Dead-time count loss up to 11.7% was observed in patient studies. CONCLUSION: QSPECT has high accuracy both in our phantom model and in clinical practice following [(177)Lu]octreotate therapy. This has the potential to yield more accurate dosimetry estimates than planar imaging and facilitate therapeutic response assessment. Validating this method with other radionuclides could open the way for many other research and clinical applications.
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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) | 0.001 | 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) | 0.001 | 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 it