SU‐C‐206‐01: Impact of Charge Sharing Effect On Sub‐Pitch Resolution for CZT‐Based Photon Counting CT Systems
Bibliographic record
Abstract
Purposes: Photon counting CT is a new imaging technology that can provide tissue composition information such as calcium/iodine content quantification. Cadmium zinc telluride CZT is considered a good candidate the photon counting CT due to its relatively high atomic number and band gap. One potential challenge is the degradation of both spatial and energy resolution as the fine electrode pitch is deployed (<50 µm). We investigated the extent of charge sharing effect as functions of gap width, bias voltage and depth‐of‐interaction (DOI). Methods: The initial electron cloud size and diffusion process were modeled analytically. The valid range of charge sharing effect refers to the range over which both signals of adjacent electrodes are above the triggering threshold (10% of the amplitude of 60keV X‐ray photons). The intensity ratios of output in three regions (I1/I2/I3: left pixel, gap area and right pixel) were calculated. With Gaussian white noises modeled (a SNR of 5 based upon the preliminary experiments), the sub‐pitch resolution as a function of the spatial position in‐between two pixels was studied. Results: The valid range of charge sharing increases linearly with depth‐of‐interaction (DOI) but decreases with gap width and bias voltage. For a 1.5mm thickness CZT detector (pitch: 50µm, bias: 400 V), the range increase from ∼90µm up to ∼110µm. Such an increase can be attributed to a longer travel distance and the associated electron cloud broadening. The achievable sub‐pitch resolution is in the range of ∼10–30µm. Conclusion: The preliminary results demonstrate that sub‐pixel spatial resolution can be achieved using the ratio of amplitudes of two neighboring pixels. Such ratio may also be used to correct charge loss and help improve energy resolution of a CZT detector. The impact of characteristic X‐rays hitting adjacent pixels (i.e., multiple interaction) on charge sharing is currently being investigated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".