A new approach in patient motion correction for cardiac SPECT: A simulation study
Bibliographic record
Abstract
Patient motion artifacts created in cardiac SPECT imaging can lead to misinterpretation of the images, resulting in false diagnoses. This simulation study proposes a new technique for patient motion correction (MC), where we utilize a modified template projection/reconstruction (TPR) algorithm to perform a voxel-by-voxel correction to the original image. Using NCAT, we developed two female phantoms with large breasts containing a non-beating heart (heart: background = 5:1). Phantom 1 had a healthy heart, and phantom 2 had a heart with a small (10%) perfusion defect in the lateral wall (severity = 50%). The SimSET code was used to perform simulations for both phantoms modeling cardiac SPECT acquisitions with Tc-99m, 128 × 128 matrix, and 60 camera stops. In addition to two standard (no motion) acquisitions (ST) for each phantom, seven acquisitions with different degrees of phantom motion were created by manually shifting a selected number of projections in a given direction (motion ranged from 8 to 22 mm). MC images (MCI) were created using a modified TPR, where the projected template was adjusted to match the motion detected in the experimental projections by aligning the center of mass in each projection. All reconstructions were performed using OSEM with resolution recovery and attenuation correction. For all simulated movements, the MCI images exhibited improvements in both standard deviation (SD) and mean accuracy relative to the uncorrected experimental reconstructions (ER). On average, the accuracies calculated for ER, MCI and the ST reconstructions were 68%, 77%, and 76%, respectively. The average SD for ER, MCI and the ST reconstructions were 5.2, 4.0, and 4.0, respectively. Our proposed technique offers a voxel-by-voxel motion correction, which provides improved image accuracy and standard deviation of counts relative to the uncorrected images and, in many cases, the images created without patient motion.
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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 | 0.003 |
| 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 | 0.001 | 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".