SU-E-I-25: Performance Evaluation of a Proposed CMOS-Based X-Ray Detector Using Linear Cascade Model Analysis

The need for high-resolution, dynamic x-ray imaging capability for neurovascular applications has put an ever increasing demand on x-ray detector technology. Present state-of-the-art detectors such as flat panels have limited resolution and noise performance. A linear cascade model analysis was used to estimate the theoretical performance for a proposed CMOS-based detector.

The proposed CMOS-based detector was assumed to have a 300-micron thick HL type CsI phosphor, 35-micron pixels, a variable gain light image intensifier (LU), and 400 electron readout noise. The proposed detector has a CMOS sensor coupled to an LII which views the output of the CsI phosphor. For the analysis the whole imaging chain was divided into individual stages characterized by one of the basic processes (stochastic/deterministic blurring, binomial selection, quantum gain, additive noise). Standard linear cascade modeling was used for the propagation of signal and noise through the stages and an RQA5 spectrum was assumed. The gain, blurring or transmission of different stages was either measured or taken from manufacturer's specifications. The theoretically calculated MTF and DQE for the proposed detector were compared with a high-resolution, high-sensitive Micro-Angio Fluoroscope (MAF), predecessor of the proposed detector.

Signal and noise for each of the 19 stages in the complete imaging chain were calculated and showed improved performance. For example, at 5 cycles/mm the MTF and DQE were 0.08 and 0.28, respectively, for the CMOS detector compared to 0.05 and 0.07 for the MAF detector.

The proposed detector will have improved MTF and DQE and slimmer physical dimension due to the elimination of the large fiber-optic taper used in the MAF. Once operational, the proposed CMOS detector will serve as a further improvement over standard flat panel detectors compared to the MAF which is already receiving a very positive reception by neuro-vascular interventionalists. (Support:NIH-Grant R01EB002873) NIH Grants R01- EB008425, R01-EB002873 and an equipment grant from Toshiba Medical Systems Corp.