Sci—Fri AM: Imaging — 05: Cone‐beam computed tomography for breast biopsy analysis: Simulations
Bibliographic record
Abstract
PURPOSE: To determine the feasibility and potential utility of cone beam CT on breast biopsies. METHODS: CBCT simulations were done with a setup which emulates an MX-20 digital specimen radiography system (Faxitron X-Ray Corporation, Chicago, IL), albeit a DQE = 1 was assumed. The phantom consisted of a 5mm diameter cylinder with a length of 5mm, made up of a homogeneous 50:50 mixture of fibrous and fatty tissue. Small cancerous spheres (lesions) with diameters ranging from 5 to 500 μm were embedded. Projections were acquired at magnification 5 using 26 kV-0.3 mA-3.8 sec beams. Images were reconstructed using a varied number of projections (50, 100, 150, 300). Values of contrast and SNR were calculated between the lesions and nearby regions. Noise was estimated by generating 10 images for each task. Scatter was found to be negligible. RESULTS: The 50 μm spheres were only visible when at least 150 projections were used, but the 100 μm ones were visible regardless of the number. SNR increased with the number of projections, whereas contrast was insensitive. For ≥100 μm spheres contrast ranged from 0.26 on the outer radial edge to 0.28 nearer the center. The SNRs for 100 μm spheres were above 5 for reconstructions composed of ≥100 projections. Cancerous lesions within a fibrous biopsy may be detectable. CONCLUSIONS: The findings from this simulation study suggest that CBCT for breast biopsy characterization via the MX-20 system could be of potential use to see structures on the order of 100 μm.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".