PubMed Health⌕ Search

Biomedical subjects

P Desmedt

Publications and source records attributed to P Desmedt.

4 recordsLinked to original sources

A comparison of similarity measures for use in 2-D-3-D medical image registration.

A comparison of six similarity measures for use in intensity-based two-dimensional-three-dimensional (2-D-3-D) image registration is presented. The accuracy of the similarity measures are compared to a "gold-standard" registration which has been accurately calculated using fiducial markers. The similarity measures are used to register a computed tomography (CT) scan of a spine phantom to a fluoroscopy image of the phantom. The registration is carried out within a region-of-interest in the fluoroscopy image which is user defined to contain a single vertebra. Many of the problems involved in this type of registration are caused by features which were not modeled by a phantom image alone. More realistic "gold-standard" data sets were simulated using the phantom image with clinical image features overlaid. Results show that the introduction of soft-tissue structures and interventional instruments into the phantom image can have a large effect on the performance of some similarity measures previously applied to 2-D-3-D image registration. Two measures were able to register accurately and robustly even when soft-tissue structures and interventional instruments were present as differences between the images. These measures were pattern intensity and gradient difference. Their registration accuracy, for all the rigid-body parameters except for the source to film translation, was within a root-mean-square (rms) error of 0.54 mm or degrees to the "gold-standard" values. No failures occurred while registering using these measures.

Algorithms↗

Voxel-based 2-D/3-D registration of fluoroscopy images and CT scans for image-guided surgery.

Registration of intraoperative fluoroscopy images with preoperative three-dimensional (3-D) CT images can be used for several purposes in image-guided surgery. On the one hand, it can be used to display the position of surgical instruments, which are being tracked by a localizer, in the preoperative CT scan. On the other hand, the registration result can be used to project preoperative planning information or important anatomical structures visible in the CT image onto the fluoroscopy image. For this registration task, a novel voxel-based method in combination with a new similarity measure (pattern intensity) has been developed. The basic concept of the method is explained at the example of two-dimensional (2-D)/3-D registration of a vertebra in an X-ray fluoroscopy image with a 3-D CT image. The registration method is described, and the results for a spine phantom are presented and discussed. Registration has been carried out repeatedly with different starting estimates to study the capture range. Information about registration accuracy has been obtained by comparing the registration results with a highly accurate "ground-truth" registration, which has been derived from fiducial markers attached to the phantom prior to imaging. In addition, registration results for different vertebrae have been compared. The results show that the rotation parameters and the shifts parallel to the projection plane can accurately be determined from a single projection. Because of the projection geometry, the accuracy of the height above the projection plane is significantly lower.

Fluoroscopy↗

Measured attenuation correction using the Maximum Likelihood algorithm.

Quantitative determination of local radioactivity concentration in positron emission tomography (PET) requires a good attenuation correction procedure to reconstruct the emission image. Using a similar Maximum Likelihood (ML) algorithm as for the reconstruction of the emission image, a method is proposed to reconstruct a transmission image, i.e. a map of absorption coefficients. This reconstructed transmission image is then used to calculate the attenuation correction factors needed for the ML reconstruction of the emission image. This approach takes automatically into account the convolution step in the acquisition process (caused by various smoothing factors, e.g. the detector width). This results in appreciable noise suppression without loss of resolution due to filtering, thus making the reconstructed images easier to interpret. A comparison is made with other estimates for the measured attenuation correction using phantom studies.

Algorithms↗

[Ankyloglossia].

Explore the source record for details and available documents.

Anti-Bacterial Agents↗