Disclosed are systems, devices, and methods for determining pleura boundaries of a lung, an exemplary method comprising acquiring image data from an imaging device, generating a set of two-dimensional (2D) slice images based on the acquired image data, determining, by a processor, a seed voxel in a first slice image from the set of 2D slice images, applying, by the processor, a region growing process to the first slice image from the set of 2D slice images starting with the seed voxel using a threshold value, generating, by the processor, a set of binarized 2D slice images based on the region grown from the seed voxel, filtering out, by the processor, connected components of the lung in each slice image of the set of binarized 2D slice images, and identifying, by the processor, the pleural boundaries of the lung based on the set of binarized 2D slice images.

System and method for segmentation of lung

Determining diffusivity of multiple diffusion components within a tissue using diffusion magnetic resonance data representing a volume of the tissue. A plurality of candidate fibers having a direction is defined within the volume. A possibility coefficient is calculated by a processor for each candidate fiber of the plurality of candidate fibers based on the magnetic resonance data and the direction of the candidate fiber. The possibility coefficient represents a likelihood that the candidate fiber exists in the volume. Candidate fibers associated with a possibility coefficient greater than a threshold value are selected by the processor to create one or more probable fibers. For each probable fiber of the one or more probable fibers, an axial diffusivity indicating a diffusion of water in the direction of the probable fiber and a radial diffusivity indicating a diffusion of water perpendicular to the direction of the probable fiber are calculated by the processor. The diffusivity of isotropic diffusion component and the volume ratios of each fiber component and isotropic components are calculated.

Diagnosis of central nervous system white matter pathology using diffusion mri

In a method for determination of a diffusion-weighted image of an examination subject in a magnetic resonance system, a diffusion-weighted data set is acquired with magnetic diffusion gradients being activated; a diffusion-weighted image of the examination subject is calculated using this diffusion-weighted data set; dephasing or spoiler gradients are activated in order to reduce artifacts in the diffusion-weighted image due to additional signal echoes and the position and/or amplitude and/or polarity of the dephasing gradients is/are selected dependent on the diffusion gradients.

Magnetic resonance system and method for determining a diffusion-weighted image

A method of assessing fluid flow in a body is provided which includes phase contrast velocity encoded MRI scanning the body to obtain the velocity of fluids flowing in each of a plurality of volume elements (voxels) in three orthogonal directions; determining whether the flow in each voxel ( 1 ) is significant typically by checking if it has a value exceeding a threshold value selected from either or both of a noise level value and a minimum expected constant flow or flow-time profile; comparing each voxel with significant flow to each adjacent voxels ( 4, 6, 6 a, 8, 8 a ) in the same and adjacent parallel plains; registering a connection where an adjacent voxel has significant flow; and clustering and depicting connected voxels visually by computing isosurfaces.

Fluid flow assessment

An exemplary region growing method include at least the following steps: selecting a seed point of a current frame as an initial growing point of a region in the current frame; determining a background confidence value at a neighboring pixel around the seed point; and utilizing a processing unit for checking if the neighboring pixel is allowed to be included in the region according to at least the background confidence value.

Region growing method for depth map/color image

Diagnostic MR images of the lung are segmented to distinguish the airway tree from lung parenchyma. Diffusion weighted images of the lungs are acquired using hyperpolarized gas MRI and a segmentation image is produced from the apparent diffusion coefficient (ADC) image calculated from the diffusion weighted images. Voxels in the diagnostic MR images having an ADC above a selected amount are segmented as being in the airway tree.

Segmentation of the airway tree using hyperpolarized noble gases and diffusion weighted magnetic resonance imaging

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Segmentation of the airway tree using hyperpolarized noble gases and diffusion weighted magnetic resonance imaging