Design and construction of a brain phantom to simulate neonatal MR images

Kazemi, Kamran; Moghaddam, Hamid Abrishami; Grebe, Reinhard; Gondry-Jouet, Catherine; Wallois, Fabrice

doi:10.1016/j.compmedimag.2010.11.007

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…Huppi et al (1998) and Inder et al (2005) showed tissue class segmentation results of newborn infants using this method [59], [60]. Kazemi et al (2011) presented a neonatal brain phantom that consists of 9 different tissue types: skin, fat, muscle, skull, dura mater, gray matter, myelinated white matter, nonmyelinated white matter and cerebrospinal fluid [61].…”

Section: Discussionmentioning

confidence: 99%

Magnetic Resonance Imaging of the Newborn Brain: Automatic Segmentation of Brain Images into 50 Anatomical Regions

et al. 2013

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We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain.

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Section: Discussionmentioning

confidence: 99%

Magnetic Resonance Imaging of the Newborn Brain: Automatic Segmentation of Brain Images into 50 Anatomical Regions

et al. 2013

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“…21 Validation of MRI segmentation is difficult as the ground truth (the precise nature of the underlying anatomy) is not known although reliability and reproducibility of the FMRIB Software Library (FSL), statistical parametric mapping (SPM), and FreeSurfer have been benchmarked using human subjects. 22 Although digital phantoms are widespread for MRI, 20,23,24 they do not test the actual imaging chain as the scanner itself is simulated. Due to the difficulties in both creating and handling materials with realistic MR properties, no realistic full brain physical MRI phantoms are available.…”

Section: Introductionmentioning

confidence: 99%

Creation of an anthropomorphic CT head phantom for verification of image segmentation

Holmes¹,

Negus²,

Wiltshire³

et al. 2020

Medical Physics

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Purpose: Many methods are available to segment structural magnetic resonance (MR) images of the brain into different tissue types. These have generally been developed for research purposes but there is some clinical use in the diagnosis of neurodegenerative diseases such as dementia. The potential exists for computed tomography (CT) segmentation to be used in place of MRI segmentation, but this will require a method to verify the accuracy of CT processing, particularly if algorithms developed for MR are used, as MR has notably greater tissue contrast. Methods: To investigate these issues we have created a three-dimensional (3D) printed brain with realistic Hounsfield unit (HU) values based on tissue maps segmented directly from an individual T1 MRI scan of a normal subject. Several T1 MRI scans of normal subjects from the ADNI database were segmented using SPM12 and used to create stereolithography files of different tissues for 3D printing. The attenuation properties of several material blends were investigated, and three suitable formulations were used to print an object expected to have realistic geometry and attenuation properties. A skull was simulated by coating the object with plaster of Paris impregnated bandages. Using two CT scanners, the realism of the phantom was assessed by the measurement of HU values, SPM12 segmentation and comparison with the source data used to create the phantom. Results: Realistic relative HU values were measured although a subtraction of 60 was required to obtain equivalence with the expected values (gray matter 32.9-35.8 phantom, 29.9-34.2 literature). Segmentation of images acquired at different kVps/mAs showed excellent agreement with the source data (Dice Similarity Coefficient 0.79 for gray matter). The performance of two scanners with two segmentation methods was compared, with the scanners found to have similar performance and with one segmentation method clearly superior to the other. Conclusion: The ability to use 3D printing to create a realistic (in terms of geometry and attenuation properties) head phantom has been demonstrated and used in an initial assessment of CT segmentation accuracy using freely available software developed for MRI.

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“…13 Different neonatal probabilistic atlases and a phantom were proposed for neonates. [14][15][16][17][18] A spatiotemporal nonrigid atlas was also constructed for infants (neonates, 1-and 2-year-old) in which the neonates were considered as one group in the range of 38.7 to 46.4 weeks' gestational age (GA) at the time of image acquisition. 19 Nonetheless, due to the rapid rate of change in brain structure, in both size and shape, during the first few months of life, and due to the on-going cortical folding process, it necessitates close coverage of this age range with a time-resolving, dynamic atlas.…”

Section: Introductionmentioning

confidence: 99%

Temporal Resolvability Analysis of Macroscopic Morphological Development in Neonatal Cerebral Magnetic Resonance Images

et al. 2013

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Design and construction of a brain phantom to simulate neonatal MR images

Cited by 4 publications

References 37 publications

Magnetic Resonance Imaging of the Newborn Brain: Automatic Segmentation of Brain Images into 50 Anatomical Regions

Magnetic Resonance Imaging of the Newborn Brain: Automatic Segmentation of Brain Images into 50 Anatomical Regions

Creation of an anthropomorphic CT head phantom for verification of image segmentation

Temporal Resolvability Analysis of Macroscopic Morphological Development in Neonatal Cerebral Magnetic Resonance Images

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