Digital Processing Of Conventional Tomograms

Abstract Conventional x-ray tomograms are limited by the presence of defocussed contributions from over and underlying structures in the final image. Edges are relatively indistinct and image contrast low when compared to conventional radiographs of the same object. Assuming that the x-ray tomograph is linear and spatially invariant with a circularly symmetric blurring function, it is possible to compensate for the presence of defocus blur to a large extent. Conventional x-ray tomograms of a composite phantom (Littleton) were used to determine the system characteristics. Circularly and quadrilaterally symmetric blurring functions computed from the phantom images were used to calculate the blurred contribution of over and underlying structures. Processed images were obtained by subtracting the defocussed components of adjacent tomoplanes from the tomographic image obtained at the level of interest. This processing was performed using a PDP-11 minicomputer by discrete fixed point convolution operations with up to 19 by 19 operators applied to a 512 x 512 image matrix. The results demonstrate a substantial improvement in subjective image quality.IntroductionConventional x-ray tomography represents an important diagnostic tool in diagnostic radiology and non-destructive testing, but its usefulness is limited by the relative unsharpness and lack of contrast in the images. Tomographic images are degraded to a variable degree by the presence of blurred contributions of over and underlying structures.The problem of removing defocussed components from section images obtained in conventional tomography, nuclear medicine and optical microscopy has been studied many times in the past. Complex photographic(1,2) and analog electronic(3) methods have been demonstrated. Spatial filtering of tomoplane images by optical(4) and digital means(5) have been implemented. Electronic harmonization methods, usually associated with applications in conventional radiographic imaging(6) have been applied to medical tomograms(7,8).Methods for removal of defocus blur in longitudinal emission tomography were reviewed by Budinger(9) and Tomitani(lO). The iterative methods of Schmidlin(ll,12,13,14) and Tomitani(lO) and direct approach applied by Myers(15,16) are well known. Extensive work has been done with positron cameras with opposed planar detectors, beyond the scope of this report.In the development of a computerized optical microscopy system, Weinstein and Castleman(17) showed that combining local smoothing and differential operators(18) could be used to remove residual defocus blur from thick tissue sections. Shantz(19) applied this method to the studjf of neuronal structure in the retina, and we described a similar application in longitudinal emission tomography(20). In a recent book(21), Castleman has elegantly amplified the description of this method for application to 3-dimensional imaging systems which are linear and spatially invariant.The advantages of local image operators for the removal of defocus off-tomoplane blur are considerable when processing medical imagery because of the practical computational considerations. Iterative methods are impractical for moderate resolution (512 x 512) imagery when multiple tomoplanes (5 or more) are required. Local image operators, such as finite extent convolution kernels, can be used to model tomographic blur and to effect some degree of image enhancement, such as high pass filtering. These local operators may be readily designed and implemented, and under certain circumstances, may be implemented very efficiently using fixed point computations.The approach to the removal of tomographic blur that we have chosen is implemented as a post-processing step on single images or sets of equally spaced images. These techniques could be implemented as an additional step in the digital formation of conventional tomograms, tomosynthesis, with similar results.