The signal-to-noise properties of active matrix, flat-panel imagers (AMFPIs) limit the imaging performance of this x-ray imaging technology under conditions of low dose per image frame. This limitation can affect cone-beam computed tomography (CBCT) procedures where an AMFPI is used to acquire hundreds of image frames to form a single volumetric data set. An approach for overcoming this limitation is to replace the energy-integrating pixel circuits of AMFPI arrays with photon counting pixel circuits which examine the energy of each x-ray interaction and count those events whose signals exceed user-defined energy thresholds. A promising material for fabricating the circuits of such photon-counting detectors (PCDs) is polycrystalline silicon (poly-Si)-a semiconductor that facilitates economic manufacture of large area, monolithic arrays of the size presently provided by AMFPIs as well as provides good radiation damage resistance. In this paper, results are reported from a theoretical investigation of the potential for poly-Si PCDs to satisfy the count rate needs, while maintaining good energy resolution, of two CBCT applications-CBCT used for breast imaging and kilo-voltage CBCT used for providing localization information in image guided radiotherapy (referred to as BCT and kV-CBCT, respectively). The study focused on the performance of the critical first component of a PCD pixel circuit, the amplifier, under conditions relevant to the two applications. The study determined that, compared to the average input fluxes associated with BCT and kV-CBCT, a promising amplifier design employing poly-Si thin-film transistors can provide count rates two and four times in excess of those levels, respectively, assuming a dead time loss of 10%. In addition, calculational estimates based on foreseeable poly-Si circuit densities suggest that it should be possible to include sufficient circuitry to support 2 and 3 energy thresholds per pixel, respectively. Finally, prospects for further improvements are discussed.
Cone-Beam Computed Tomography Humans