Nonlinear filters applied on computerized axial tomography: Theory and phantom images

Abstract

New nonlinear image processing techniques, in particular smoothing based on the understanding of the image, may create computerized tomography (CT) images of good quality using less radiation. Such techniques may be applied before the reconstruction and particularly after it. Current CT scanners use strong linear low-pass filters applied to the CT projections, reducing noise but also deteriorating the resolution of the image. The method in this study was to apply a weak low-pass filter on the projections, to perform the reconstruction, and only then to apply a nonlinear filter on the image. Various kinds of nonlinear filters were investigated based on the fact that the image is approximately piecewise constant. The filters were applied with many values of several parameters and the effects on the spatial resolution and the noise reduction were evaluated. The signal-to-noise ratio of a high-contrast phantom image processed were compared with the nonlinear filter, with the SNR of the phantom images obtained with the built-in CT linear filters in two scanning modes, the normal and the ultra high resolution modes. It was found that the nonlinear filters improve the SNR of the image, compared to the built-in filters, about three times for the normal mode and twice for the UHR scanning mode. The most successful filter on low-contrast phantom image was applied and it also seems to lead to promising results. These results seem to show that applying nonlinear filters on CT images might lead to better image quality than using the current linear filters.(ABSTRACT TRUNCATED AT 250 WORDS)

Improvement of image quality of multislice spiral CT scans of the head and neck region using a raw data-based multidimensional adaptive filtering (MAF) technique

“…MAF is particularly suitable for such images, because image sharpness, and thus spatial discrimination, requires that only a small proportion of the raw data points is changed through smoothing. In phantom examinations and in the first patient examinations, the MAF yielded promising results with noise reduction of up to 60%, especially in the shoulder region, with no detectable loss of image sharpness [16]. This suggests the need to apply this method to a larger number of patient raw data sets in the region of the thoracic inlet.…”

“…In the region of the thoracic inlet, the attenuation due to lateral projection is particularly high if the upper arm is in the scan region as is the case with computer tomographic examinations of the head and neck region. Pixel noise is not homogeneously distributed throughout the image, but is highest in the field between the humeral heads [16].…”

“…Using axial reconstructions with 180°MFI, we determined visually the transverse plane with the greatest noise artifacts (in all cases at the level of the head of the humerus) and the plane at the level of the carotid bifurcation as a reference for the fewest noise artifacts. Since 180°MAF modifies only image regions with high attenuation, we used four ROIs in visually homogeneous structures that are strongly affected by pixel noise (at the level of the head of the humerus, similar to Kachelrieß et al [16]). These were the subscapularis muscle, the deltoid muscle, the supraclavicular fat tissue and the spinal canal.…”

“…Kachelrieß et al reported on a multidimensional adaptive filtering (MAF), which uses the information from the raw data in all spatial planes, thus both in the detector plane, the projection plane and along the long axis of the patient. This increases its ability to suppress noise and, at the same time, minimizes the possible loss in image sharpness [16].…”

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

“…In particular, anisotropic diffusion filtering has been successfully applied for preprocessing of ultrasound, CT, and low-dose CT images [76][77][78]. Similarly, median filtering has been employed, both in spatial and sinogram domains, to reduce or eliminate metal artifacts and for filtering low-dose CT images [79,80]. In this dissertation we, therefore, focus on these two filtering techniques.…”