title: Prostate multimodality image registration based on B-splines and quadrature local energy.
abstract: OBJECTIVE
  Needle biopsy of the prostate is guided by Transrectal Ultrasound (TRUS) imaging. The TRUS images do not provide proper spatial localization of malignant tissues due to the poor sensitivity of TRUS to visualize early malignancy. Magnetic Resonance Imaging (MRI) has been shown to be sensitive for the detection of early stage malignancy, and therefore, a novel 2D deformable registration method that overlays pre-biopsy MRI onto TRUS images has been proposed.
  
  
  METHODS
  The registration method involves B-spline deformations with Normalized Mutual Information (NMI) as the similarity measure computed from the texture images obtained from the amplitude responses of the directional quadrature filter pairs. Registration accuracy of the proposed method is evaluated by computing the Dice Similarity coefficient (DSC) and 95% Hausdorff Distance (HD) values for 20 patients prostate mid-gland slices and Target Registration Error (TRE) for 18 patients only where homologous structures are visible in both the TRUS and transformed MR images.
  
  
  RESULTS
  The proposed method and B-splines using NMI computed from intensities provide average TRE values of 2.64 ± 1.37 and 4.43 ± 2.77 mm respectively. Our method shows statistically significant improvement in TRE when compared with B-spline using NMI computed from intensities with Student's t test p = 0.02. The proposed method shows 1.18 times improvement over thin-plate splines registration with average TRE of 3.11 ± 2.18 mm. The mean DSC and the mean 95% HD values obtained with the proposed method of B-spline with NMI computed from texture are 0.943 ± 0.039 and 4.75 ± 2.40 mm respectively.
  
  
  CONCLUSIONS
  The texture energy computed from the quadrature filter pairs provides better registration accuracy for multimodal images than raw intensities. Low TRE values of the proposed registration method add to the feasibility of it being used during TRUS-guided biopsy.
authors.affiliation: Computer Vision and Robotics Group, Universitat de Girona, Girona, Spain. jmitra@eia.udg.edu
authors.forename: Jhimli
authors.initials: J
authors.lastname: Mitra
authors.forename: Robert
authors.initials: R
authors.lastname: Martí
authors.forename: Arnau
authors.initials: A
authors.lastname: Oliver
authors.forename: Xavier
authors.initials: X
authors.lastname: Lladó
authors.forename: Soumya
authors.initials: S
authors.lastname: Ghose
authors.forename: Joan C
authors.initials: JC
authors.lastname: Vilanova
authors.forename: Fabrice
authors.initials: F
authors.lastname: Meriaudeau
mesh_terms: D000465:Algorithms; D001707:Biopsy, Needle; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D011467:Prostate / Q000000981:diagnostic imaging / Q000473:pathology; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014463:Ultrasonography