Poster — Thur Eve — 02: Evaluation of Cross‐Talk for Dual‐Isotope Myocardial Perfusion Imaging Using a New Dedicated Cardiac CZT SPECT Camera
Bibliographic record
Abstract
Myocardial perfusion imaging (MPI) with single‐photon emission computed tomography (SPECT) is an important tool in the clinical management of heart disease. Simultaneous dual‐istope imaging offers a means to greatly reduce the time required for this test, but is limited by interference between the signals of the two isotopes. Newly developed dedicated cardiac SPECT cameras based on CZT detectors may reduce the interference between isotopes due to improved energy resolution. Our objective is to measure in clinical patients the magnitude of cross‐talk expected for simultaneous perfusion imaging with Tl‐201 and Tc‐99m‐tetrofosmin on a new CZT‐based multi‐pinhole dedicated cardiac SPECT camera. We retrospectively examined 25 matched pairs of Tl‐201 and Tc‐99m‐tetrofosmin patients. Reprocessing the listmode data, we determined the cross‐talk fraction for typical energy windows as well as for a Tc‐99m energy window that was reduced from 20% to 12%. Two protocols were considered: Tl‐rest/Tc‐stress and Tc‐rest / Tl‐stress. Cross‐talk into the Tl window was 74% and 36% respectively for the two protocols. Cross‐talk into the 10% Tc‐99m window was 2.4% and 11% respectively. The cross‐talk into the Tc‐99m window was reduced by 25% using a +/−6% window. Cross‐talk between Tc‐99m‐tetrofosmin and Tl‐201 has been assessed for the new dedicated CZT‐based cardiac SPECT cameras and the improved energy resolution of the CZT detectors decreases cross‐talk interference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".