A geometric flow-based approach for diffusion tensor image segmentation

Diffusion tensor magnetic resonance imaging (DT-MRI, shortened as DTI) produces, from a set of diffusion-weighted magnetic resonance images, tensor-valued images where each voxel is assigned a 3×3 symmetric, positive-definite matrix. This tensor is simply the covariance matrix of a local Gaussian process with zero mean, modelling the average motion of water molecules. We propose a three-dimensional geometric flow-based model to segment the main core of cerebral white matter fibre tracts from DTI. The segmentation is carried out with a front propagation algorithm. The front is a three-dimensional surface that evolves along its normal direction with speed that is proportional to a linear combination of two measures: a similarity measure and a consistency measure. The similarity measure computes the similarity of the diffusion tensors at a voxel and its neighbouring voxels along the normal to the front; the consistency measure is able to speed up the propagation at locations where the evolving front is more consistent with the diffusion tensor field, to remove noise effect to some extent, and thus to improve results. We validate the proposed model and compare it with some other methods using synthetic and human brain DTI data; experimental results indicate that the proposed model improves the accuracy and efficiency in segmentation.

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Diffusion Tensor Imaging with Three-dimensional Fiber Tractography of Traumatic Axonal Shearing Injury: An Imaging Correlate for the Posterior Callosal “Disconnection” Syndrome: Case Report

Neurosurgery ◽

10.1227/01.neu.0000144846.00569.3a ◽

2005 ◽

Vol 56 (1) ◽

pp. E195-E201 ◽

Cited By ~ 57

Author(s):

Tuong H. Le ◽

Pratik Mukherjee ◽

Roland G. Henry ◽

Jeffrey I. Berman ◽

Marcus Ware ◽

...

Keyword(s):

Diffusion Coefficient ◽

Diffusion Tensor Imaging ◽

White Matter ◽

Magnetic Resonance ◽

Diffusion Tensor ◽

Three Dimensional ◽

Magnetic Resonance Images ◽

Fiber Tractography ◽

Disconnection Syndrome ◽

Apparent Diffusion

Abstract OBJECTIVE: To demonstrate that magnetic resonance diffusion tensor imaging (DTI) with three-dimensional (3-D) fiber tractography can visualize traumatic axonal shearing injury that results in posterior callosal disconnection syndrome. METHODS: A 22-year-old man underwent serial magnetic resonance imaging 3 days and 12 weeks after blunt head injury. The magnetic resonance images included whole-brain DTI acquired with a single-shot spin echo echoplanar sequence. 3-D DTI fiber tractography of the splenium of the corpus callosum was performed. Quantitative DTI parameters, including apparent diffusion coefficient and fractional anisotropy, from the site of splenial injury were compared with those of a normal adult male volunteer. RESULTS: Conventional magnetic resonance images revealed findings of diffuse axonal injury, including a lesion at the midline of the splenium of the corpus callosum. DTI performed 3 days posttrauma revealed that the splenial lesion had reduced apparent diffusion coefficient and fractional anisotropy, reflecting a large decrease in the magnitude of diffusion parallel to the white matter fibers, which had partially recovered as revealed by follow-up DTI 12 weeks postinjury. 3-D tractography revealed an interruption of the white matter fibers in the posteroinferior aspect of the splenium that correlated with the patient's left hemialexia, a functional deficit caused by disconnection of the right visual cortex from the language centers of the dominant left hemisphere. CONCLUSION: DTI with 3-D fiber tractography can visualize acute axonal shearing injury, which may have prognostic value for the cognitive and neurological sequelae of traumatic brain injury.

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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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The Safety of Operating on Breasts With a History of Prior Reduction Mammoplasty: Dynamic Magnetic Resonance Imaging Analysis of Angiogenesis

Aesthetic Surgery Journal ◽

10.1093/asj/sjab318 ◽

2021 ◽

Author(s):

Joseph Kyu-hyung Park ◽

Seokwon Park ◽

Chan Yeong Heo ◽

Jae Hoon Jeong ◽

Bola Yun ◽

...

Keyword(s):

Magnetic Resonance ◽

Three Dimensional ◽

Venous Drainage ◽

Reduction Mammoplasty ◽

Magnetic Resonance Images ◽

Areolar Complex ◽

Internal Mammary ◽

Initial Procedure ◽

Nipple Sparing Mastectomy ◽

Nipple Sparing

Abstract Background Vascularity of the nipple-areolar complex (NAC) is altered after reduction mammoplasty, which increases complications risks after repeat reduction or nipple-sparing mastectomy. Objectives To evaluate angiogenesis of the NAC via serial analysis of breast magnetic resonance images (MRIs). Methods Breast MRIs after reduction mammoplasty were analyzed for 35 patients (39 breasts) using three-dimensional reconstructions of maximal intensity projection images. All veins terminating at the NAC were classified as internal mammary, anterior intercostal, or lateral thoracic in origin. The vein with the largest diameter was considered the dominant vein. Images were classified based on the time since reduction: <6 months, 6-12 months, 12-24 months, >2 years. Results The average number of veins increased over time: 1.17 (<6 months), 1.56 (6–12 months), 1.64 (12–24 months), 1.73 (>2 years). Within 6 months, the pedicle was the only vein. Veins from other sources began to appear at 6–12 months. In most patients, at least two veins were available after 1 year. After 1 year, the internal mammary vein was the most common dominant vein regardless of the pedicle used. Conclusions In the initial 6 months after reduction mammoplasty, the pedicle is the only source of venous drainage; however, additional sources are available after 1 year. The internal thoracic vein was the dominant in most patients. Thus, repeat reduction mammoplasty or nipple-sparing mastectomy should be performed ≥1 year following the initial procedure. After 1 year, the superior or superomedial pedicle may represent the safest option when the previous pedicle is unknown.

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