title: Heterogeneous Optimization Framework: Reproducible Preprocessing of Multi-Spectral Clinical MRI for Neuro-Oncology Imaging Research.
abstract: Neuroimaging research often relies on clinically acquired magnetic resonance imaging (MRI) datasets that can originate from multiple institutions. Such datasets are characterized by high heterogeneity of modalities and variability of sequence parameters. This heterogeneity complicates the automation of image processing tasks such as spatial co-registration and physiological or functional image analysis. Given this heterogeneity, conventional processing workflows developed for research purposes are not optimal for clinical data. In this work, we describe an approach called Heterogeneous Optimization Framework (HOF) for developing image analysis pipelines that can handle the high degree of clinical data non-uniformity. HOF provides a set of guidelines for configuration, algorithm development, deployment, interpretation of results and quality control for such pipelines. At each step, we illustrate the HOF approach using the implementation of an automated pipeline for Multimodal Glioma Analysis (MGA) as an example. The MGA pipeline computes tissue diffusion characteristics of diffusion tensor imaging (DTI) acquisitions, hemodynamic characteristics using a perfusion model of susceptibility contrast (DSC) MRI, and spatial cross-modal co-registration of available anatomical, physiological and derived patient images. Developing MGA within HOF enabled the processing of neuro-oncology MR imaging studies to be fully automated. MGA has been successfully used to analyze over 160 clinical tumor studies to date within several research projects. Introduction of the MGA pipeline improved image processing throughput and, most importantly, effectively produced co-registered datasets that were suitable for advanced analysis despite high heterogeneity in acquisition protocols.
authors.affiliation: Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA. mmilch@wustl.edu.
authors.forename: Mikhail
authors.initials: M
authors.lastname: Milchenko
authors.affiliation: Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: Abraham Z
authors.initials: AZ
authors.lastname: Snyder
authors.affiliation: Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: Pamela
authors.initials: P
authors.lastname: LaMontagne
authors.affiliation: Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: Joshua S
authors.initials: JS
authors.lastname: Shimony
authors.affiliation: Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: Tammie L
authors.initials: TL
authors.lastname: Benzinger
authors.affiliation: Department of Neurological Surgery, Swedish Medical Center, Seattle, WA, USA.
authors.forename: Sarah Jost
authors.initials: SJ
authors.lastname: Fouke
authors.affiliation: Neuroinformatics Research Group, Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Marcus
mesh_terms: D000465:Algorithms; D001271:Atlases as Topic; D001931:Brain Mapping / Q000379:methods*; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D064847:Multimodal Imaging; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted
keywords: DSC imaging; Data modeling; Diffusion imaging; Knowledge representation; MRI; Neuro-oncology imaging; Spatial co-registration