<title>Dual-energy computed radiography: improvements in processing</title>

Abstract

We have reported on a single-exposure dual-energy system based on computed radiography (CR) technology. In a clinical study conducted over a two year period, the dual-energy system proved to be highly successful in improving the detection (p=0.0005) and characterization (p=0.005) of pulmonary nodules when compared to conventional screen-film radiography. The basic components of our dual-energy detector system include source filtration with gadolinium to produce a bi-modal x-ray spectrum and a cassette containing four CR imaging plates. The front and back plates record the low-energy and high-energy images, respectively, and the middle two plates serve as an intermediate filter. Since our initial report, a number of improvements have been made to make the system more practical. An automatic registration algorithm based on image features has been developed to align the front and back image plates. There have been two improvements in scatter correction: a simple correction is now made to account for scatter within the multiplate detector; and a correction algorithm is applied to account for scatter variations between patients. An improved basis material decomposition (BMD) algorithm has been developed to facilitate automatic operation of the algorithm. Finally, two new noise suppression techniques are under investigation: one adjusts the noise filtering parameters depending on the strength of edge signals in the detected image in order to greatly reduce quantum mottle while minimizing the introduction of artifacts; a second routine uses knowledge of the region of valid low-energy and highenergy image data to suppress noise with minimal introduction of artifacts. This paper is a synthesis of recent work aimed at improving the performance of dual-energy CR conducted at three institutions:

Optimization of dual‐energy imaging systems using generalized NEQ and imaging task

“…The framework employed above provides a description of DE imaging performance accounting not only for quantum and electronic noise as factors for system optimization but also anatomical noise and imaging task. Other factors that warrant consideration are part of future work that incorporates scatter 26,[52][53][54][55] and noise reduction algorithms [56][57][58] in description of the DQE and NEQ. Such noise reduction algorithms include filtering of the high-energy imaging, correlated noise reduction, and noise clipping-outside the scope of the present analysis but certainly within the scope of this framework and an important consideration in assessing the full potential of DE imaging.…”

Quantitative evaluation of noise reduction strategies in dual-energy imaging

“…7,9,31 Several other methods were also introduced, focusing on improving the sharpness and noise texture, including noise forcing ͑NOF͒ and noise clipping ͑NOC͒ developed in our laboratory. 27,29 Three model-based algorithms are described below and were compared for noise reduction efficacy to a simple smoothing filter ͑SSF͒ consisting of a rotationally symmetric Gaussian filter to smooth the high-energy image. The Gaussian function was implemented in Matlab ͑Mathworks, Natick, MA͒ with a ROI size of 19 pixels and a FWHM of 4.71 pixels.…”

“…A regionally optimized nonadaptive form of correlated noise reduction, as described by Kalendar, 24 McCullough, 25 and Ergun. 26,29 was implemented. Kalendar demonstrated that noise in the reconstructed bone and tissue images was anticorrelated.…”

“…To combat the concomitant increased stochastic noise and regain the benefits of reducing anatomical noise, noise reduction strategies are often employed in dual-energy imaging. 7,8,11,[20][21][22][23][24][25][26][27][28][29] In this paper, we evaluate the performance of several noise reduction techniques by an experimental method that allows an accurate estimation of the original niose, or ''true noise,'' as well as the estimated and residual noise after noise reduction processing. This experimental methodology allows quantitative optimization of parameters pertinent to each algorithm.…”

Computer aided diagnosis in chest radiography

“…[40,214,74,45]), or on dual energy systems in chest radiography (e.g. [107,67,90]) is beyond the scope of this review as are studies that use psychophysics, e.g. to determine the optimum tube voltage [10] or to aid the detection of abnormalities by measuring visual dwell [106,144].…”