Spatial domain image filtering in computed tomography: feasibility study in pulmonary embolism.

Abstract

Our objective was to evaluate the clinical feasibility of spatial domain filtering as an alternative to additional image reconstruction using different kernels in chest CT. Spatial domain filtering generates smooth images from sharp images and thus avoids the need for additional reconstructions when two sets of images are desired. Forty adult patients with clinical suspicion of pulmonary embolism were examined utilizing multi-slice CT (Somatom Volume Zoom, Siemens, Germany). Derived from thin collimated source images (100 mAs, collimation 4x1 mm, rotation time 0.5 s, table speed 7 mm/rotation), two sets of images [effective slice thickness (S(eff)) 5 mm, reconstruction increment (RI) 5 mm) were generated using lung (Siemens B50) and soft tissue (Siemens B30) kernels. Additionally, B50 images were filtered in the spatial domain, producing images largely equivalent to B30 images. Firstly, diagnostic accuracy was assessed on spatial domain filtered images regarding central, segmental, and subsegmental pulmonary embolism. In a second step, diagnostic accuracy was assessed for the initially reconstructed B30 images. The results were compared with thin axial slices from the same data set, which were considered as the gold standard in this respect (S(eff )1.25 mm, RI 0.8 mm; B30). Initially reconstructed B30 slices and secondary filtered images were rated for subjective image quality, using a five-point scale (1=excellent, 2=good, 3=moderate, 4=poor, 5=non-diagnostic). Finally, quantitative measurements were assessed using the region of interest (ROI) methodology. In 20 patients pulmonary embolism was proven. Five-millimeter images revealed 10 of 10 central emboli, 18 of 19 segmental thrombi, and 18 of 20 emboli on the subsegmental level. Pulmonary embolism was excluded in 18 of 20 subjects, and in 2 patients a false-positive result was obtained in subsegmental arteries. These findings were concordant for reconstructed and filtered images. Quantitative density measurements provided comparable Hounsfield units in this respect. Subjective gradings of image quality, based on soft tissue settings, were 1.30 (+/-0.61) for reconstructed slices vs 1.35 (+/-0.62) for filtered images (weighted kappa coefficient 0.6117; 95% confidence intervals 0.3298-0.8935). Spatial domain filtering has proved to be feasible. Compared with conventional soft tissue reconstructions for central, segmental, and subsegmental pulmonary embolism, no significant difference in the diagnostic value of spatial domain filtered images was found. Online modifications of image sharpness and pixel noise in real time leads to a considerable reduction of processing time and cost saving for storage of CT images. Despite different data processing methods, thin effective slice thicknesses and overlapping reconstruction increments are mandatory for detailed CT analysis of pulmonary embolism on the segmental and subsegmental level.