Effect of dose on image quality in a detector-based dual-exposure, dual-energy system for chest radiography

Optimization of image acquisition techniques for dual-energy imaging of the chest

Experimental and theoretical studies were conducted to determine optimal acquisition techniques for a prototype dual-energy (DE) chest imaging system. Technique factors investigated included the selection of added x-ray filtration, kVp pair, and the allocation of dose between low- and high-energy projections, with total dose equal to or less than that of a conventional chest radiograph. Optima were computed to maximize lung nodule detectability as characterized by the signal-difference-to-noise ratio (SDNR) in DE chest images. Optimal beam filtration was determined by cascaded systems analysis of DE image SDNR for filter selections across the periodic table (Z(filter) = 1-92), demonstrating the importance of differential filtration between low- and high-kVp projections and suggesting optimal high-kVp filters in the range Z(filter) = 25-50. For example, added filtration of approximately 2.1 mm Cu, approximately 1.2 mm Zr, approximately 0.7 mm Mo, and approximately 0.6 mm Ag to the high-kVp beam provided optimal (and nearly equivalent) soft-tissue SDNR. Optimal kVp pair and dose allocation were investigated using a chest phantom presenting simulated lung nodules and ribs for thin, average, and thick body habitus. Low- and high-energy techniques ranged from 60-90 kVp and 120-150 kVp, respectively, with peak soft-tissue SDNR achieved at [60/120] kVp for all patient thicknesses and all levels of imaging dose. A strong dependence on the kVp of the low-energy projection was observed. Optimal allocation of dose between low- and high-energy projections was such that approximately 30% of the total dose was delivered by the low-kVp projection, exhibiting a fairly weak dependence on kVp pair and dose. The results have guided the implementation of a prototype DE imaging system for imaging trials in early-stage lung nodule detection and diagnosis.

Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

When lung nodules overlap with ribs or clavicles in chest radiographs, it can be difficult for radiologists as well as computer-aided diagnostic (CAD) schemes to detect these nodules. In this paper, we developed an image-processing technique for suppressing the contrast of ribs and clavicles in chest radiographs by means of a multiresolution massive training artificial neural network (MTANN). An MTANN is a highly nonlinear filter that can be trained by use of input chest radiographs and the corresponding "teaching" images. We employed "bone" images obtained by use of a dual-energy subtraction technique as the teaching images. For effective suppression of ribs having various spatial frequencies, we developed a multiresolution MTANN consisting of multiresolution decomposition/composition techniques and three MTANNs for three different-resolution images. After training with input chest radiographs and the corresponding dual-energy bone images, the multiresolution MTANN was able to provide "bone-image-like" images which were similar to the teaching bone images. By subtracting the bone-image-like images from the corresponding chest radiographs, we were able to produce "soft-tissue-image-like" images where ribs and clavicles were substantially suppressed. We used a validation test database consisting of 118 chest radiographs with pulmonary nodules and an independent test database consisting of 136 digitized screen-film chest radiographs with 136 solitary pulmonary nodules collected from 14 medical institutions in this study. When our technique was applied to nontraining chest radiographs, ribs and clavicles in the chest radiographs were suppressed substantially, while the visibility of nodules and lung vessels was maintained. Thus, our image-processing technique for rib suppression by means of a multiresolution MTANN would be potentially useful for radiologists as well as for CAD schemes in detection of lung nodules on chest radiographs.