title: MRI to CT Prostate Registration for Improved Targeting in Cancer External Beam Radiotherapy.
abstract: External radiotherapy is a major clinical treatment for localized prostate cancer. Currently, computed tomography (CT) is used to delineate the prostate and to plan the radiotherapy treatment. However, CT images suffer from a poor soft-tissue contrast and do not allow an accurate organ delineation. On the contrary, magnetic resonance imaging (MRI) provides rich details and high soft-tissue contrast, allowing tumor detection. Thus, the intraindividual propagation of MRI delineations toward the planning CT may improve tumor targeting. In this paper, we introduce a new method to propagate MRI prostate delineations to the planning CT. In the first step, a random forest classification is performed to coarsely detect the prostate in the CT images, yielding a prostate probability membership for each voxel and a prostate hard segmentation. Then, the registration is performed using a new similarity metric which maximizes the probability and the collinearity between the normals of the manual registration (MR) existing contour and the contour resulting from the CT classification. The first study on synthetic data was performed to analyze the influence of the metric parameters with different levels of noise. Then, the method was also evaluated on real MR-CT data using manual alignments and intraprostatic fiducial markers and compared to a classically used mutual information (MI) approach. The proposed metric outperformed MI by 7% in terms of Dice score coefficient, by 3.14 mm the Hausdorff distance, and 2.13 mm the markers position errors. Finally, the impact of registration uncertainties on the treatment planning was evaluated, demonstrating the potential advantage of the proposed approach in a clinical setup to define a precise target.
authors.forename: Frederic
authors.initials: F
authors.lastname: Commandeur
authors.forename: Antoine
authors.initials: A
authors.lastname: Simon
authors.forename: Romain
authors.initials: R
authors.lastname: Mathieu
authors.forename: Mohamed
authors.initials: M
authors.lastname: Nassef
authors.forename: Juan David Ospina
authors.initials: JDO
authors.lastname: Arango
authors.forename: Yan
authors.initials: Y
authors.lastname: Rolland
authors.forename: Pascal
authors.initials: P
authors.lastname: Haigron
authors.forename: Renaud
authors.initials: R
authors.lastname: de Crevoisier
authors.forename: Oscar
authors.initials: O
authors.lastname: Acosta
mesh_terms: D003663:Decision Trees; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010388:Pelvis / Q000000981:diagnostic imaging; D011467:Prostate / Q000000981:diagnostic imaging; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D011881:Radiotherapy, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*