SU‐E‐I‐92: Is Photon Starvation Preventing Metal Artifact Reduction Algorithm From Working in KVCT?
Bibliographic record
Abstract
Purpose: High density/high atomic number metallic objects create shading and streaking metal artifacts in the CT image that can cause inaccurate delineation of anatomical structures or inaccurate radiation dose calculation. A modified iterative maximum‐likelihood polychromatic algorithm for CT (mIMPACT) that models the energy response of detectors, photon interaction processes and beam polychromaticity has successfully reduced metal artifacts in MVCT. Our extension of mIMPACT in kVCT did not significantly reduce metal artifacts for high density metal like steel. We hypothesize that photon starvation may result in the measured data in a commercial kVCT imaging beam. Methods: We measured attenuation of a range of steel plate thicknesses, sandwiched between two 12cm thick solid water blocks, using a Phillips Big Bore CTTM scanner in scout acquisition mode with 120kVp and 200mAs. The transmitted signal (y) was normalized to the air scan signal (y 0 ) to get attenuation [i.e., ln(y/y 0 )] data for a detector. Results: Below steel plate thickness of 13.4mm, the variations in measured attenuation as a function of view number are characterized by a quantum noise and show increased attenuation with metal thickness. On or above this thickness the attenuation shows discrete levels in addition to the quantum noise. Some views have saturated attenuation value. The histograms of the measured attenuation for up to 36.7mm of steel show this trend. The detector signal is so small that the quantization levels in the analog to digital (A‐to‐D) converter are visible, a clear indication of photon starvation. Conclusion: Photons reaching the kVCT detector after passing through a thick metal plate are either so low in number that the signal measured has large quantum noise, or are completely absorbed inside the plate creating photon starvation. This is un‐interpretable by the mIMPACT algorithm and cannot reduce metal artifacts in kVCT for certain realistic thicknesses of steel hip implants. Moti Raj Paudel is supported by the Vanier Canada Graduate Scholarship, the Endowed Graduate Scholarship in Oncology and the Dissertation Fellowship at the University of Alberta. The authors acknowledge the CIHR operating grant number MOP 53254.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".