Sci-Fri AM(1): Imaging-09: Accurate Image Reconstructions for a Delineation of SPECT-Based Biological Target Volumes: Physical Phantom Evaluation
Bibliographic record
Abstract
In SPECT-guided treatment planning, the determination of gross target volume is supplemented by a generation of a functional map reflecting the metabolic activity of the tumor and surrounding tissue, namely, biological target volume (BTV). However, the reconstruction of SPECT images represents a non-trivial task and can be accurate only when physics phenomena accompanying the propagation and detection of photons are exactly modeled. We investigate the influence of different SPECT reconstruction techniques on the accuracy of the BTV delineation using phantom experiments with a clinical hybrid SPECT/CT camera. Three 33ml containers imitating tumors were placed at different locations inside a 7000ml thorax phantom imitating a human torso. For image reconstructions, we utilized the widely available iterative ordered-subsets expectation maximization (OSEM) method with different combinations of corrections for attenuation, scatter, and resolution loss. Using optimal thresholds, SPECT-based volumes having size, which is very close to 33ml, were delineated and compared with a CT-based one, which we considered as a “gold standard”. Our visual analysis shows that for all reconstructions the central part of the container is included in the BTV, but the determined boundaries vary. Moreover, the more irregular the contour is, the more challenging is the accurate delineation. Specifically, attenuation correction and resolution recovery should be considered as important factors contributing to the noticeable improvements. According to our quantitative analysis, the incorporation of these effects improved accuracy of BTV delineation by about 25%. Processing of patient data also displays the visible impact of reconstruction methods on the delineated BTV.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".