1
|
Bilgel M, Carass A, Resnick SM, Wong DF, Prince JL. Deformation field correction for spatial normalization of PET images. Neuroimage 2015; 119:152-63. [PMID: 26142272 DOI: 10.1016/j.neuroimage.2015.06.063] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Revised: 06/19/2015] [Accepted: 06/23/2015] [Indexed: 11/17/2022] Open
Abstract
Spatial normalization of positron emission tomography (PET) images is essential for population studies, yet the current state of the art in PET-to-PET registration is limited to the application of conventional deformable registration methods that were developed for structural images. A method is presented for the spatial normalization of PET images that improves their anatomical alignment over the state of the art. The approach works by correcting the deformable registration result using a model that is learned from training data having both PET and structural images. In particular, viewing the structural registration of training data as ground truth, correction factors are learned by using a generalized ridge regression at each voxel given the PET intensities and voxel locations in a population-based PET template. The trained model can then be used to obtain more accurate registration of PET images to the PET template without the use of a structural image. A cross validation evaluation on 79 subjects shows that the proposed method yields more accurate alignment of the PET images compared to deformable PET-to-PET registration as revealed by 1) a visual examination of the deformed images, 2) a smaller error in the deformation fields, and 3) a greater overlap of the deformed anatomical labels with ground truth segmentations.
Collapse
Affiliation(s)
- Murat Bilgel
- Image Analysis and Communications Laboratory, Johns Hopkins University School of Engineering, Baltimore, MD, USA; Dept. of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
| | - Aaron Carass
- Image Analysis and Communications Laboratory, Johns Hopkins University School of Engineering, Baltimore, MD, USA; Dept. of Electrical and Computer Engineering, Johns Hopkins University School of Engineering, Baltimore, MD, USA; Dept. of Computer Science, Johns Hopkins University School of Engineering, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Dean F Wong
- Dept. of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jerry L Prince
- Image Analysis and Communications Laboratory, Johns Hopkins University School of Engineering, Baltimore, MD, USA; Dept. of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Dept. of Electrical and Computer Engineering, Johns Hopkins University School of Engineering, Baltimore, MD, USA; Dept. of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| |
Collapse
|
2
|
Temporal Trajectory and Progression Score Estimation from Voxelwise Longitudinal Imaging Measures: Application to Amyloid Imaging. INFORMATION PROCESSING IN MEDICAL IMAGING : PROCEEDINGS OF THE ... CONFERENCE 2015; 24. [PMID: 26221692 PMCID: PMC4591058 DOI: 10.1007/978-3-319-19992-4_33] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Abstract. Cortical β-amyloid deposition begins in Alzheimer's disease (AD) years before the onset of any clinical symptoms. It is therefore important to determine the temporal trajectories of amyloid deposition in these earliest stages in order to better understand their associations with progression to AD. A method for estimating the temporal trajectories of voxelwise amyloid as measured using longitudinal positron emission tomography (PET) imaging is presented. The method involves the estimation of a score for each subject visit based on the PET data that reflects their amyloid progression. This amyloid progression score allows subjects with similar progressions to be aligned and analyzed together. The estimation of the progression scores and the amyloid trajectory parameters are performed using an expectation-maximization algorithm. The correlations among the voxel measures of amyloid are modeled to reflect the spatial nature of PET images. Simulation results show that model parameters are captured well at a variety of noise and spatial correlation levels. The method is applied to longitudinal amyloid imaging data considering each cerebral hemisphere separately. The results are consistent across the hemispheres and agree with a global index of brain amyloid known as mean cortical DVR. Unlike mean cortical DVR, which depends on a priori defined regions, the progression score extracted by the method is data-driven and does not make assumptions about regional longitudinal changes. Compared to regressing on age at each voxel, the longitudinal trajectory slopes estimated using the proposed method show better localized longitudinal changes.
Collapse
|