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Published on: June 23, 2018
A forward bias method for lag correction of an a-Si flat panel detector
Jared Starman1, Carlo Tognina, Larry Partain
1Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA. jared.starman@gmail.com
A new hardware method significantly reduces detector lag in amorphous silicon flat panel x-ray detectors. This technique minimizes ghosting in images and shading artifacts in cone-beam computed tomography (CBCT) reconstructions.
Area of Science:
- Medical Imaging Physics
- Digital Radiography
- Cone-Beam Computed Tomography
Background:
- Amorphous silicon (a-Si) flat panel (FP) x-ray detectors can suffer from detector lag, causing ghosting and radar artifacts in images.
- Detector lag is primarily attributed to defect states (traps) within the a-Si layer.
- Existing software correction methods may rely on assumptions like system linearity, which may not always hold true.
Purpose of the Study:
- To investigate a novel hardware-based method for reducing detector lag in a-Si FP x-ray detectors.
- To evaluate the effectiveness of this hardware method in mitigating shading artifacts in CBCT reconstructions.
- To examine the feasibility of a partially hardware-based solution for lag reduction.
Main Methods:
- A minor modification to the FP detector was implemented, introducing a forward bias operation step.
- This forward bias step is applied between readout and data collection for pulsed irradiation, filling defect states with charge.
- Measurements included residual lag contrast, detector step response, signal-to-noise ratio (SNR), modulation transfer function (MTF), and detective quantum efficiency (DQE).
- CBCT data were acquired using pelvic and head phantoms with both standard and forward bias modes.
Main Results:
- The forward bias method reduced residual lag signals by 70%-88% in lag frames 2 and 100.
- A slight decrease in collected signal and a minor increase in noise were observed, with small impacts on SNR, MTF, and DQE.
- The hardware successfully reduced radar artifacts in CBCT reconstructions by 48%-81% for both phantom types.
Conclusions:
- The forward bias hardware method effectively reduces detector lag ghosts in projection data and radar artifacts in CBCT.
- The current method's improvements are limited to the a-Si photodiode response.
- Future hybrid approaches may address the limitations of this hardware-based solution.
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