Theoretical comparison of the detective quantum efficiency of halide lead perovskite, cesium iodide and selenium x-ray imaging detectors
Bibliographic record
Abstract
Halide lead perovskites have been proposed for direct-conversion x-ray imaging because of their high stopping power, high charge mobility and high bulk resistivity. We modeled the detective quantum efficiency (DQE) of methylammonium lead iodide (MAPbI<sub>3</sub>) and compared with that of amorphous selenium (a-Se) and columnar cesium iodide (CsI). For CsI, we calculated the DQE for RQA-5, RQA- 7 and RQA-9 x-ray spectra for 200 µm detectors elements; for a-Se we calculated the DQE for MMA 28 x-ray spectrum for 75 µm elements. Our DQE model included the quantum efficiency, x-ray fluorescence, fluorescence reabsorption, charge conversion, collection of secondary quanta (i.e. charges or optical photons), charge diffusion in MAPbI<sub>3</sub>, optical blur in CsI, noise aliasing, and electronic noise. The model DQE of CsI was compared with published data; the model photoelectric noise power spectrum of lead was compared with published Monte Carlo data; there was excellent agreement. For fluoroscopic applications, the theoretical DQE of MAPbI<sub>3</sub> was approximately equal to that of CsI at exposures of 10µR per image, but was ~40% lower than CsI at an exposure of 0.1µR per image. This result is due to the relatively high levels of electronic noise present in prototype MAPbI3 systems. For chest radiography applications, the theoretical DQE of MAPbI<sub>3</sub> was 25% greater than that of CsI at typical exposure levels (i.e. 0.04mR 3mR at the detector). For mammography, the theoretical DQE of MAPbI<sub>3</sub> was ~5% greater than that of a-Se across all spatial frequencies and all exposures between 0.6mR 250mR. These results suggest that halide lead perovskites may provide superior dose efficiency than CsI-based systems in chest radiography applications, but may offer little to no improvement in mammographic or fluoroscopic applications.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".