Towards an accurate and robust method based on fuzzy logic principles for the reconstruction and quantification of large volumes from MR and CT images.

The authors have previously evaluated a new method of volume reconstruction and quantification from MR images, based on fuzzy logic (FL) principles. The technique is evaluated here for larger and more complex structures by investigating its accuracy and robustness using MR and CT images. Four large (50-71 cm(3)) and complex (e.g. mimicking a prostate) structures were created and imaged on MR and CT scanners, both with increasing slice thickness. Contours were delineated to generate 112 volumes. MR and CT images were processed using the FL method and a "classical" method of reconstruction on research software. In addition, the CT images were also processed on commercial virtual simulation software. Calculated volumes were compared with actual volumes. The mean +/- standard deviation of the relative variations in calculated target volume using the FL method was found to be 4.4%+/-2.8%, whereas with the "classical" method it was 23.7%+/-6% from axial MR images and 23.3%+/-9.8% from CT images. With the "classical" method, the relative variations in calculated volumes rise with increasing slice thickness, and the displayed volumes show deformations in the longitudinal direction. With the FL method, the volume calculation is not sensitive to the slice thickness and so the deformations are minimal. When used with MR images, our FL method of volume reconstruction is accurate and robust with respect to changes in slice thickness. For CT images, the results are encouraging but some work is still needed to improve the accuracy of the FL method.