Index of content:
Volume 28, Issue 8, August 2001
- PH. D. THESES ABSTRACTS
A study on computer-aided diagnosis based on temporal subtraction of sequential chest radiographs (in Japanese)28(2001); http://dx.doi.org/10.1118/1.1388905View Description Hide Description
An automated digital image subtraction technique for use with pairs of temporally sequential chest radiographs has been developed to aid radiologists in the detection of interval changes. Automated image registration based on nonlinear geometric warping is performed prior to subtraction in order to deal with complicated radiographic misregistration. Processing includes global matching, to achieve rough registration between the entire lung fields in the two images, and local matching, based on a cross-correlation method, to determine local shift values for a number of small regions. A proper warping of is determined by fitting two-dimensional polynomials to the distributions of the shift values. One image is warped and then subtracted from the other. The resultant subtraction images were able to enhance the conspicuity of various types of interval changes. Improved global matching based on a weighted template matching method achieved robust registration even with photofluorographs taken in chest mass screening programs, which had previously presented us with a relatively large number of poor-registration images. The new method was applied to 129 pairs of chest mass screening images, and offered registration accuracy as good as manual global matching. An observer test using 114 cases including 57 lungcancer cases presented better sensitivity and specificity on average compared to conventional comparison readings. In addition, newly developed image processing that eliminates the rib edge artifacts in subtraction images was applied to 26 images having pathological interval changes; results showed the potential for application to automated schemes for the detection of interval change patterns. With its capacity to improve the diagnostic accuracy of chest radiographs, the chest temporal subtraction technique promises to become an important element of computer-aided diagnosis(CAD) systems.
28(2001); http://dx.doi.org/10.1118/1.1388906View Description Hide Description
Tomosynthetic image reconstruction allows for the production of a virtually infinite number of slices from a finite number of projection views of a subject. If the reconstructed image volume is viewed in toto, and the three-dimensional (3D) impulse response is accurately known, then it is possible to solve the inverse problem(deconvolution) using canonical image restoration methods (such as Wiener filtering or solution by conjugate gradient least squares iteration) by extension to three dimensions in either the spatial or the frequency domains. This dissertation presents modified direct and iterative restoration methods for solving the inverse tomosynthetic imaging problem in 3D. The significant blur artifact that is common to tomosynthetic reconstructions is deconvolved by solving for the entire 3D image at once. The 3D impulse response is computed analytically using a fiducial reference schema as realized in a robust, self-calibrating solution to generalized tomosynthesis. 3D modulation transfer function analysis is used to characterize the tomosynthetic resolution of the 3D reconstructions. The relevant clinical application of these methods is 3D imaging for brachytherapy source localization. Conventional localization schemes for brachytherapy implants using orthogonal or stereoscopic projection radiographs suffer from scaling distortions and poor visibility of implanted seeds, resulting in compromised source tracking (reported errors: 2–4 mm) and dosimetric inaccuracy. 3D image reconstruction (using a well-chosen projection sampling scheme) and restoration of a prostate brachytherapy phantom is used for testing. The approaches presented in this work localize source centroids with submillimeter error in two Cartesian dimensions and just over one millimeter error in the third.