Cone-beam CT statistical reconstruction with a model for fluence modulation and electronic readout noise

Purpose: Cone-beam CT (CBCT) systems with a flat-panel detector (FPD) have advanced in a variety of specialty diagnostic imaging scenarios, with fluence modulation and multiple-gain detectors playing important roles in extending dynamic range and improving image quality. We present a penalized weighted least-squares (PWLS) reconstruction approach with a noise model that includes the effects of fluence modulation and electronic readout noise, and we show preliminary results that tests the concept with a CBCT head scanner prototype. Methods: Statistical weights in PWLS were modified using a realistic noise model for the FPD that considers factors such as system blur and spatially varying electronic noise in multiple-gain readout detectors (PWLSe). A spatially varying gain term was then introduced in the calculation of statistical weights to account for the change in quantum noise due to fluence modulation (e.g. bowtie filter) (PWLS∗). The methods were tested in phantom experiments involving an elliptical phantom specially designed to stress dual-gain readout, and a water phantom and an anthropomorphic head phantom to quantify improvements in noise-resolution characteristics for the new PWLS methods (PWLS𝑒 and PWLS∗, and combined PWLS∗e). The proposed methods were further tested using a high-quality, low-dose CBCT head scanner prototype in a clinical study involving patients with head injury. Results: Preliminary results show that the PWLSe method demonstrated superior noise-resolution tradeoffs compared to conventional PWLS, with variance reduced by ~15-25% at matched resolution of 0.65 mm edge-spread-function (ESF) width. Clinical studies confirmed these findings, with variance reduced by ~15% in peripheral regions of the head without loss in spatial resolution, improving visual image quality in detection of peridural hemorrhage. A bowtie filter and polyenergetic gain correction improved image uniformity, and early results demonstrated that the proposed PWLS∗ method showed a ~40% reduction in variance compared to conventional PWLS when used with a bowtie filter. Conclusion: A more accurate noise model incorporated in PWLS statistical weights to account for fluence modulation and electronic readout noise reduces image noise and improves soft-tissue imaging performance in CBCT for clinical applications requiring a high degree of contrast resolution.