title: Microstructural models for diffusion MRI in breast cancer and surrounding stroma: an ex vivo study.
abstract: The diffusion signal in breast tissue has primarily been modelled using apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion tensor (DT) models, which may be too simplistic to describe the underlying tissue microstructure. Formalin-fixed breast cancer samples were scanned using a wide range of gradient strengths, durations, separations and orientations. A variety of one- and two-compartment models were tested to determine which best described the data. Models with restricted diffusion components and anisotropy were selected in most cancerous regions and there were no regions in which conventional ADC or DT models were selected. Maps of ADC generally related to cellularity on histology, but maps of parameters from more complex models suggest that both overall cell volume fraction and individual cell size can contribute to the diffusion signal, affecting the specificity of ADC to the tissue microstructure. The areas of coherence in diffusion anisotropy images were small, approximately 1 mm, but the orientation corresponded to stromal orientation patterns on histology.
authors.affiliation: University College London, Centre for Medical Image Computing, London, UK.
authors.forename: Colleen
authors.initials: C
authors.lastname: Bailey
authors.affiliation: University College London, Centre for Advanced Biomedical Imaging, London, UK.
authors.forename: Bernard
authors.initials: B
authors.lastname: Siow
authors.affiliation: University College London, Centre for Medical Image Computing, London, UK.
authors.forename: Eleftheria
authors.initials: E
authors.lastname: Panagiotaki
authors.affiliation: University College London, Centre for Medical Image Computing, London, UK.
authors.forename: John H
authors.initials: JH
authors.lastname: Hipwell
authors.affiliation: University College London, Centre for Medical Image Computing, London, UK.
authors.forename: Thomy
authors.initials: T
authors.lastname: Mertzanidou
authors.affiliation: King's College London, Guy's Hospital, Breast Research, Pathology, London, UK.
authors.forename: Julie
authors.initials: J
authors.lastname: Owen
authors.affiliation: King's College London, Guy's Hospital, Breast Research, Pathology, London, UK.
authors.forename: Patrycja
authors.initials: P
authors.lastname: Gazinska
authors.affiliation: King's College London, Guy's Hospital, Breast Research, Pathology, London, UK.
authors.forename: Sarah E
authors.initials: SE
authors.lastname: Pinder
authors.affiliation: University College London, Centre for Medical Image Computing, London, UK.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Alexander
authors.affiliation: University College London, Centre for Medical Image Computing, London, UK.
authors.forename: David J
authors.initials: DJ
authors.lastname: Hawkes
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D003198:Computer Simulation; D003238:Connective Tissue / Q000000981:diagnostic imaging* / Q000473:pathology*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014407:Tumor Cells, Cultured
keywords: DTI; MRI; anisotropy; breast cancer; diffusion; ex vivo; restriction