Optical surface information‐based respiratory phase‐sorting and motion‐incorporated reconstruction for SPECT imaging
Bibliographic record
Abstract
BACKGROUND: Respiratory motion during the single photon emission computed tomography (SPECT) acquisition can cause blurring artifacts in the reconstructed images, leading to inaccurate estimates for activity and absorbed doses. PURPOSE: To address the impact of respiratory motion, we utilized a new optical surface imaging (OSI) system to extract the respiratory signals for phase sorting and verified its effectiveness through simulation and patient data. Additionally, we implemented GPU-accelerated motion-incorporated reconstruction algorithms for the SPECT projections, integrating motion information to produce motion-free images from all acquired data. METHODS: We used the 4D XCAT Phantom to generate attenuation maps and activity images across different respiratory phases, with activity distributions based on patient images. SPECT projections were simulated using the SIMIND Monte Carlo program with Poisson noise. The OSI system was modeled by introducing Gaussian noise into the point clouds on the body surface within the attenuation map. The body surface images were registered across phases using a Gaussian mixture model combined with principal component analysis. The extracted respiratory signals were compared to the center-of-light (COL) approach, with or without filtering and kidney masking. The OSI method was further validated by comparing respiratory signals derived from a real patient using OSI to simultaneous cone-beam CT (CBCT) projections. Two motion-incorporated techniques, namely, 4D reconstruction (4D-Recon) and post-reconstruction registration and summation (post-Recon), were compared with non-motion-corrected images (non-MC) and single-phase gating (Gating). The quantitative evaluation of image quality utilized recovery coefficients (RC), contrast recovery coefficients (CRC), and uncertainty estimation. RESULTS: 0.001), it was less effective and more uncertain than the OSI method. Validation with patient data showed high consistency in breathing frequencies and phase alignment between CBCT-based and OSI-based signals. For reconstruction, both 4D-Recon and post-Recon significantly enhanced RC and CRC compared to non-MC, with less uncertainty than Gating. In addition, 4D-Recon outperformed post-Recon in certain aspects. CONCLUSIONS: Our novel respiratory signal extraction approach based on OSI demonstrated superior accuracy and reliability compared to a data-driven method. Applying motion-incorporated SPECT reconstruction using these accurate breathing signals has the potential to enhance image quality and improve absorbed dose quantification in radiopharmaceutical therapy. The relevant reconstruction algorithms are also made available for public use in the open-source library PyTomography.
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 | 0.001 |
| 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) | 0.001 | 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".