title: Application-driven MRI: joint reconstruction and segmentation from undersampled MRI data.
abstract: Medical image segmentation has traditionally been regarded as a separate process from image acquisition and reconstruction, even though its performance directly depends on the quality and characteristics of these first stages of the imaging pipeline. Adopting an integrated acquisition-reconstruction-segmentation process can provide a more efficient and accurate solution. In this paper we propose a joint segmentation and reconstruction algorithm for undersampled magnetic resonance data. Merging a reconstructive patch-based sparse modelling and a discriminative Gaussian mixture modelling can produce images with enhanced edge information ultimately improving their segmentation.
authors.forename: Jose
authors.initials: J
authors.lastname: Caballero
authors.forename: Wenjia
authors.initials: W
authors.lastname: Bai
authors.forename: Anthony N
authors.initials: AN
authors.lastname: Price
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Hajnal
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D006352:Heart Ventricles / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted