SU‐GG‐I‐114: Detector Characterization of a New Flat‐Panel Imager: Prospects for Image Quality Improvement in Fluoroscopy and Cone‐Beam CT
Bibliographic record
Abstract
Purpose: To characterize the imaging performance of a new flat‐panel‐imager (FPI) with respect to metrics such as detector gain, linearity, lag, modulation transfer function (MTF), noise‐power spectrum (NPS), and detective quantum efficiency (DQE) and to evaluate its potential application in fluoroscopy and cone‐beam CT for radiation therapy guidance. Method and Materials: The detector examined was a Trixell (Pixium 4343RF) indirect‐ FPI with a 2881×2880 array of 0.143×0.143 mm 2 pixels, 43×43 cm 2 FOV and a 0.06 mm thick CsI:Tl x‐ray converter. The FPI was operated at a frame rate 3 fps. Gain, linearity and NPS were calculated using gain‐corrected flood images. MTF was measured using an edge‐spread function method. DQE was calculated from the measured MTF and NPS. Image lag was characterized as a function of incident exposure. NPS, MTF, DQE and lag were compared with a FPI design (Perkin Elmer RID1640) currently employed in image‐guided radiotherapy. Results: The dark current stabilizes after 30 minutes. The detector has high gain and linearity with R 2 ∼1. The 50% MTF was achieved at 1.51 and 0.91 lp/mm at 120 kVp for Trixel and Perkin Elmer (PE) FPI, respectively. The spatial resolution was limited by the focal spot size. The NPS(f) is found lower than the PE at 120 kVp for the same pixel saturation. The DQE is calculated 55 and 34% at 1.25 lp/mm for Trixel and PE, respectively. The first frame lag is 7 times lower than Perkin Elmer for the same pixel saturation at 120 kVp. Radiographic images of a head phantom show high contrast and spatial resolution. Conclusion: Imaging performance metrics (in particular, the high linearity, low lag, and high DQE(f)) suggest a significant improvement for the Trixell FPI and strongly support potential application of this detector for fluoroscopy and cone‐beam CT. Research sponsored by Elekta.
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) | 0.000 | 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".