title: Twenty new digital brain phantoms for creation of validation image data bases.
abstract: Simulations provide a way of generating data where ground truth is known, enabling quantitative testing of image processing methods. In this paper, we present the construction of 20 realistic digital brain phantoms that can be used to simulate medical imaging data. The phantoms are made from 20 normal adults to take into account intersubject anatomical variabilities. Each digital brain phantom was created by registering and averaging four T1, T2, and proton density (PD)-weighted magnetic resonance imaging (MRI) scans from each subject. A fuzzy minimum distance classification was used to classify voxel intensities from T1, T2, and PD average volumes into grey-matter, white matter, cerebro-spinal fluid, and fat. Automatically generated mask volumes were required to separate brain from nonbrain structures and ten fuzzy tissue volumes were created: grey matter, white matter, cerebro-spinal fluid, skull, marrow within the bone, dura, fat, tissue around the fat, muscles, and skin/muscles. A fuzzy vessel class was also obtained from the segmentation of the magnetic resonance angiography scan of the subject. These eleven fuzzy volumes that describe the spatial distribution of anatomical tissues define the digital phantom, where voxel intensity is proportional to the fraction of tissue within the voxel. These fuzzy volumes can be used to drive simulators for different modalities including MRI, PET, or SPECT. These phantoms were used to construct 20 simulated T1-weighted MR scans. To evaluate the realism of these simulations, we propose two approaches to compare them to real data acquired with the same acquisition parameters. The first approach consists of comparing the intensities within the segmented classes in both real and simulated data. In the second approach, a whole brain voxel-wise comparison between simulations and real T1-weighted data is performed. The first comparison underlines that segmented classes appear to properly represent the anatomy on average, and that inside these classes, the simulated and real intensity values are quite similar. The second comparison enables the study of the regional variations with no a priori class. The experiments demonstrate that these variations are small when real data are corrected for intensity nonuniformity.
authors.affiliation: Montreal Neurological Institute Brain Imaging Center, McGill University, Montreal, QC H3A 2B4, Canada.
authors.forename: Berengère
authors.initials: B
authors.lastname: Aubert-Broche
authors.forename: Mark
authors.initials: M
authors.lastname: Griffin
authors.forename: G Bruce
authors.initials: GB
authors.lastname: Pike
authors.forename: Alan C
authors.initials: AC
authors.lastname: Evans
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
mesh_terms: D000328:Adult; D001921:Brain / Q000033:anatomy & histology*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods* / Q000592:standards; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000592:standards; D008297:Male; D016347:Medical Records Systems, Computerized; D008953:Models, Anatomic; D008959:Models, Neurological; D019047:Phantoms, Imaging; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted