Bibliographic record
Abstract
The flat-panel image detectors capture an X-ray image electronically, and enable a smooth clinical transition to digital radiography by replacing traditional film/cassette based system. They provide excellent X-ray images and have been commercialized for different X-ray imaging modalities. However, there still remain significant scientific challenges in these detectors associated with dark current and ghosting which constitute critical performance requirements for modalities such as digital fluoroscopy. This doctoral dissertation involves both experimental characterization and physics-based theoretical modelling of time and bias dependent dark current behaviour and X-ray induced change in sensitivity (ghosting) in X-ray imaging detectors. The theoretical investigations are based on the physics of the individual phenomenon and a systematic solution of physical equations in the photoconductor layer; (i) semiconductor continuity equations (ii) Poisson’s equation, and (iii) trapping rate equations. The theoretical model has been validated with the measured and published experimental results. \n \nThe developed dark current model has been applied to a-Se and poly-HgI2 based detectors (direct conversion detectors), and a-Si:H p-i-n photodiode (indirect conversion detectors). The validation of the model with the experimental results determines the physical mechanisms responsible for the dark current in X-ray imaging detectors. The dark current analysis also unveils the important material parameters such as trap center concentrations in the blocking layers, trap depths, and effective barrier heights for injecting carriers. The analysis is important for optimization of the dark current consistent with having good transport properties which can ultimately improve the dynamic range of the detector. \n \nThe physical mechanisms of sensitivity reduction (ghosting) and its recovery has been investigated by exposing a-Se detector at high dose and then monitoring the recovery process under (i) resting the samples (natural recovery), (ii) reversing the bias polarity and magnitude, and (iii) shining light. The continuous monitoring of the sensitivity as a function of exposure and time reveals the ghosting mechanisms in a-Se mammography detectors. This research finds a faster sensitivity recovery by reversing the bias during the natural recovery process. The sensitivity recovery mechanisms (e.g., recombination between trapped and oppositely charged free carrier, trapping of oppositely charged free carriers, or relaxation of trap centers) have been qualitatively investigated by validating the simulation results with the experimental data. The ghost removal mechanisms and techniques are important to improve the image quality which can ultimately lead to the reduction of the patient exposure consistent with better diagnosis for different X-ray imaging modalities.
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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 | 0.001 | 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".