Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines
International Journal of Computer Vision
The Softassign Procrustes Matching Algorithm
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Ultrasound image guided patient setup for prostate cancer conformal radiotherapy
Pattern Recognition Letters
MRI-ultrasound registration for targeted prostate biopsy
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Closed-loop control in fused MR-TRUS image-guided prostate biopsy
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
MRI-based finite element simulation on radiofrequency ablation of thyroid cancer
Computer Methods and Programs in Biomedicine
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Purpose: To guide ultrasound-driven prostate photodynamic therapy using information from MRI-based treatment planning. Methods: Robust points matching (RPM) and thin plate splines (TPS) are used to solve correspondences and to map optimally positioned landmarks from MR images to transrectal ultrasound (TRUS) images. The algorithm uses a reduced number of anatomical markers that are initialized on the images. Results: Both phantom and patient data were used to evaluate precision and robustness of the method. Mean registration error (+/-standard deviation) was of 2.18+/-0.25mm and 1.55+/-0.31mm for patient prostate and urethra, respectively. Repeated tests with different markers initialization conditions showed that the quality of registration was neither influenced by the number of markers nor to the human observer. Conclusion: This method allows for satisfyingly accurate and robust non rigid registration of MRI and TRUS and provides practitioners with substantial help in mapping treatment planning from pre-operative MRI to interventional TRUS.