Cascaded systems analysis of photon counting detectors. Issue 10 (17th September 2014)

• Record Type: Journal Article
• Authors: Xu, J.; Zbijewski, W.; Gang, G.; Stayman, J. W.; Taguchi, K.; Lundqvist, M.; Fredenberg, E.; Carrino, J. A.; Siewerdsen, J. H.
• Is Part Of: Medical physics. Volume 41:Issue 10(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-09-17
• Subjects: diagnostic radiography -- image denoising -- image resolution -- medical image processing -- optical transfer function -- photon counting; Radiography -- Spatial resolution -- Noise; Photometry, e.g. photographic exposure meter -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image; photon counting detector -- cascaded systems analysis -- modulation transfer function -- noise‐power spectrum -- detective quantum efficiency -- image quality -- charge sharing model; Modulation transfer functions -- Photons -- Image sensors -- Image detection systems -- Medical image noise -- Silicon detectors -- Charge carriers -- Diffusion -- X‐ray detectors; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4894733
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : Photon counting detectors (PCDs) are an emerging technology with applications in spectral and low‐dose radiographic and tomographic imaging. This paper develops an analytical model of PCD imaging performance, including the system gain, modulation transfer function (MTF), noise‐power spectrum (NPS), and detective quantum efficiency (DQE). Methods: : A cascaded systems analysis model describing the propagation of quanta through the imaging chain was developed. The model was validated in comparison to the physical performance of a silicon‐strip PCD implemented on an experimental imaging bench. The signal response, MTF, and NPS were measured and compared to theory as a function of exposure conditions (70 kVp, 1–7 mA), detector threshold, and readout mode (i.e., the option for coincidence detection). The model sheds new light on the dependence of spatial resolution, charge sharing, and additive noise effects on threshold selection and was used to investigate the factors governing PCD performance, including the fundamental advantages and limitations of PCDs in comparison to energy‐integrating detectors (EIDs) in the linear regime for which pulse pileup can be ignored. Results: : The detector exhibited highly linear mean signal response across the system operating range and agreed well with theoretical prediction, as did the system MTF and NPS. The DQE analyzed as a function of kilovolt (peak), exposure, detector threshold, and readout mode revealed important considerations for system optimization. The model also demonstrated the important implications of false counts from both additive electronic noise and charge sharing and highlighted the system design and operational parameters that most affect detector performance in the presence of such factors: for example, increasing the detector threshold from 0 to 100 (arbitrary units of pulse height threshold roughly equivalent to 0.5 and 6 keV energy threshold, respectively), increased the f 50 (spatial‐frequency at which the MTF falls to a value of 0.50) by ∼30% with corresponding improvement in DQE. The range in exposure and additive noise for which PCDs yield intrinsically higher DQE was quantified, showing performance advantages under conditions of very low‐dose, high additive noise, and high fidelity rejection of coincident photons. Conclusions: : The model for PCD signal and noise performance agreed with measurements of detector signal, MTF, and NPS and provided a useful basis for understanding complex dependencies in PCD imaging performance and the potential advantages (and disadvantages) in comparison to EIDs as well as an important guide to task‐based optimization in developing new PCD imaging systems.