Mapping and characterizing endometrial implants by registering 2D transvaginal ultrasound to 3D pelvic magnetic resonance images. (October 2015)

• Record Type: Journal Article
• Authors: Yavariabdi, Amir; Bartoli, Adrien; Samir, Chafik; Artigues, Maxime; Canis, Michel
• Is Part Of: Computerized medical imaging and graphics. Volume 45(2015)
• Page Start: 11
• Page End: 25
• Publication Date: 2015-10
• Subjects: Endometriosis -- Iterative closest point -- Slice-to-volume registration -- Multi-modality -- Fusion -- Localization; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2015.07.007
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Highlights: We propose a deformable slice-to-volume registration method to map a 2D TVUS into a 3D MR volume. The method uses curve to surface correspondences through a variational one-step ICP. This fusion technique localizes endometriosis with respect to the patient's anatomy. Our method maps the TVUS planar image to a curved 2D surface in MR volume. One-step ICP shows less sensitivity to increment of deformation and slant. Abstract: We propose a new deformable slice-to-volume registration method to register a 2D Transvaginal Ultrasound (TVUS) to a 3D Magnetic Resonance (MR) volume. Our main goal is to find a cross-section of the MR volume such that the endometrial implants and their depth of infiltration can be mapped from TVUS to MR. The proposed TVUS-MR registration method uses contour to surface correspondences through a novel variational one-step deformable Iterative Closest Point (ICP) method. Specifically, we find a smooth deformation field while establishing point correspondences automatically. We demonstrate the accuracy of the proposed method by quantitative and qualitative tests on both semi-synthetic and clinical data. To generate semi-synthetic data sets, 3D surfaces are deformed with 4–40% degrees of deformation and then various intersection curves are obtained at 0–20° cutting angles. Results show an average mean square error of 5.7934 ± 0.4615 mm, average Hausdorff distance of 2.493 ± 0.14 mm, and average Dice similarity coefficient of 0.9750 ± 0.0030.