The Effect of Single-Scan and Scan-Pair Intensity Inhomogeneity Correction Methods on Repeatability of Voxel-Based Morphometry With Multiple Magnetic Resonance Scanners

Background: Scan acceleration techniques, such as parallel imaging, can reduce scan times, but reliability is essential to implement these techniques in neuroimaging. Objective: To evaluate the reproducibility of the longitudinal changes in brain morphology determined by longitudinal voxel-based morphometry (VBM) between non-accelerated and accelerated magnetic resonance images (MRI) in normal aging, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Methods: Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 2 database, comprising subjects who underwent non-accelerated and accelerated structural T1-weighted MRI at screening and at a 2-year follow-up on 3.0 T Philips scanners, we examined the reproducibility of longitudinal gray matter volume changes determined by longitudinal VBM processing between non-accelerated and accelerated imaging in 50 healthy elderly subjects, 54 MCI patients, and eight AD patients. Results: The intraclass correlation coefficient (ICC) maps differed among the three groups. The mean ICC was 0.72 overall (healthy elderly, 0.63; MCI, 0.75; AD, 0.63), and the ICC was good to excellent (0.6–1.0) for 81.4%of voxels (healthy elderly, 64.8%; MCI, 85.0%; AD, 65.0%). The differences in image quality (head motion) were not significant (Kruskal–Wallis test, p = 0.18) and the within-subject standard deviations of longitudinal gray matter volume changes were similar among the groups. Conclusion: The results indicate that the reproducibility of longitudinal gray matter volume changes determined by VBM between non-accelerated and accelerated MRI is good to excellent for many regions but may vary between diseases and regions.

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Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

BioMed Research International ◽

10.1155/2016/6727290 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10

Author(s):

Yunjie Chen ◽

Tianming Zhan ◽

Ji Zhang ◽

Hongyuan Wang

Keyword(s):

Magnetic Resonance ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Magnetic Resonance Images ◽

Intensity Inhomogeneity ◽

Mr Images ◽

Proposed Model ◽

Brain Mr Images ◽

The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

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Liver MRI segmentation with edge-preserved intensity inhomogeneity correction

Signal Image and Video Processing ◽

10.1007/s11760-017-1221-5 ◽

2017 ◽

Vol 12 (4) ◽

pp. 791-798 ◽

Cited By ~ 3

Author(s):

Hui Liu ◽

Pinpin Tang ◽

Dongmei Guo ◽

HaiXia Liu ◽

Yuanjie Zheng ◽

...

Keyword(s):

Intensity Inhomogeneity ◽

Liver Mri ◽

Mri Segmentation ◽

Inhomogeneity Correction

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A magnetic resonance imaging study of patients with Parkinson's disease with mild cognitive impairment and dementia using voxel-based morphometry

Journal of Neurology Neurosurgery & Psychiatry ◽

10.1136/jnnp.2006.093849 ◽

2006 ◽

Vol 78 (3) ◽

pp. 254-259 ◽

Cited By ~ 174

Author(s):

M. K Beyer ◽

C. C Janvin ◽

J. P Larsen ◽

D. Aarsland

Keyword(s):

Magnetic Resonance Imaging ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Magnetic Resonance ◽

Imaging Study ◽

Magnetic Resonance Imaging Study ◽

Voxel Based Morphometry ◽

Resonance Imaging

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Volume and surface coil simultaneous reception (VSSR) method for intensity inhomogeneity correction in MRI

Technology and Health Care ◽

10.3233/thc-213149 ◽

2021 ◽

pp. 1-12

Author(s):

Lin Wu ◽

Tian He ◽

Jie Yu ◽

Hang Liu ◽

Shuang Zhang ◽

...

Keyword(s):

Bias Field ◽

Correction Method ◽

Surface Coil ◽

Intensity Inhomogeneity ◽

Acquisition Time ◽

Surface Coils ◽

In Vivo Experiments ◽

Inhomogeneity Correction ◽

Volume Coil

BACKGROUND: Addressing intensity inhomogeneity is critical in magnetic resonance imaging (MRI) because associated errors can adversely affect post-processing and quantitative analysis of images (i.e., segmentation, registration, etc.), as well as the accuracy of clinical diagnosis. Although several prior methods have been proposed to eliminate or correct intensity inhomogeneity, some significant disadvantages have remained, including alteration of tissue contrast, poor reliability and robustness of algorithms, and prolonged acquisition time. OBJECTIVE: In this study, we propose an intensity inhomogeneity correction method based on volume and surface coils simultaneous reception (VSSR). METHODS: The VSSR method comprises of two major steps: 1) simultaneous image acquisition from both volume and surface coils and 2) denoising of volume coil images and polynomial surface fitting of bias field. Extensive in vivo experiments were performed considering various anatomical structures, acquisition sequences, imaging resolutions, and orientations. In terms of correction performance, the proposed VSSR method was comparatively evaluated against several popular methods, including multiplicative intrinsic component optimization and improved nonparametric nonuniform intensity normalization bias correction methods. RESULTS: Experimental results show that VSSR is more robust and reliable and does not require prolonged acquisition time with the volume coil. CONCLUSION: The VSSR may be considered suitable for general implementation.

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