title: Population-based prediction of subject-specific prostate deformation for MR-to-ultrasound image registration.
abstract: Statistical shape models of soft-tissue organ motion provide a useful means of imposing physical constraints on the displacements allowed during non-rigid image registration, and can be especially useful when registering sparse and/or noisy image data. In this paper, we describe a method for generating a subject-specific statistical shape model that captures prostate deformation for a new subject given independent population data on organ shape and deformation obtained from magnetic resonance (MR) images and biomechanical modelling of tissue deformation due to transrectal ultrasound (TRUS) probe pressure. The characteristics of the models generated using this method are compared with corresponding models based on training data generated directly from subject-specific biomechanical simulations using a leave-one-out cross validation. The accuracy of registering MR and TRUS images of the prostate using the new prostate models was then estimated and compared with published results obtained in our earlier research. No statistically significant difference was found between the specificity and generalisation ability of prostate shape models generated using the two approaches. Furthermore, no statistically significant difference was found between the landmark-based target registration errors (TREs) following registration using different models, with a median (95th percentile) TRE of 2.40 (6.19) mm versus 2.42 (7.15) mm using models generated with the new method versus a model built directly from patient-specific biomechanical simulation data, respectively (N = 800; 8 patient datasets; 100 registrations per patient). We conclude that the proposed method provides a computationally efficient and clinically practical alternative to existing complex methods for modelling and predicting subject-specific prostate deformation, such as biomechanical simulations, for new subjects. The method may also prove useful for generating shape models for other organs, for example, where only limited shape training data from dynamic imaging is available.
authors.affiliation: Centre for Medical Image Computing, University College London, London, UK. Electronic address: yipeng.hu@ucl.ac.uk.
authors.forename: Yipeng
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Centre for Medical Image Computing, University College London, London, UK; Diagnostic Image Analysis group, Radboud University Medical Centre, Nijmegen, The Netherlands.
authors.forename: Eli
authors.initials: E
authors.lastname: Gibson
authors.affiliation: Division of Surgery and Interventional Science, University College London, London, UK.
authors.forename: Hashim Uddin
authors.initials: HU
authors.lastname: Ahmed
authors.affiliation: Division of Surgery and Interventional Science, University College London, London, UK.
authors.forename: Caroline M
authors.initials: CM
authors.lastname: Moore
authors.affiliation: Division of Surgery and Interventional Science, University College London, London, UK.
authors.forename: Mark
authors.initials: M
authors.lastname: Emberton
authors.affiliation: Centre for Medical Image Computing, University College London, London, UK.
authors.forename: Dean C
authors.initials: DC
authors.lastname: Barratt
mesh_terms: D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008953:Models, Anatomic; D064847:Multimodal Imaging / Q000379:methods; D066230:Patient-Specific Modeling; D010363:Pattern Recognition, Automated / Q000379:methods; D011467:Prostate / Q000033:anatomy & histology* / Q000000981:diagnostic imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014463:Ultrasonography / Q000379:methods*
keywords: Image registration; Kernel regression; Organ motion; Statistical shape modelling; Tissue deformation