Hemodynamic segmentation of brain perfusion images with delay and dispersion effects using an expectation-maximization algorithm.

Automatic identification of various perfusion compartments from dynamic susceptibility contrast magnetic resonance brain images can assist in clinical diagnosis and treatment of cerebrovascular diseases. The principle of segmentation methods was based on the clustering of bolus transit-time profiles to discern areas of different tissues. However, the cerebrovascular diseases may result in a delayed and dispersed local perfusion and therefore alter the hemodynamic signal profiles.

Blind source separation of hemodynamics from magnetic resonance perfusion brain images using independent factor analysis.

Perfusion magnetic resonance brain imaging induces temporal signal changes on brain tissues, manifesting distinct blood-supply patterns for the profound analysis of cerebral hemodynamics. We employed independent factor analysis to blindly separate such dynamic images into different maps, that is, artery, gray matter, white matter, vein and sinus, and choroid plexus, in conjunction with corresponding signal-time curves. The averaged signal-time curve on the segmented arterial area was further used to calculate the relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time (MTT).

Segmentation of diffusion-weighted brain images using expectation maximization algorithm initialized by hierarchical clustering.

Tissue segmentation based on diffusion-weighted images (DWI) provides complementary information of tissue contrast to the structural MRI for facilitating the tissue segmentation. In the previous literatures, DWI-based brain tissue segmentation was carried out using the parametric images, such as fractional anisotropy (FA) and apparent diffusion coefficient (ADC). However, the information of directions of neural fibers was very limited in the parametric images.

Brain MR perfusion image segmentation using independent component analysis and hierarchical clustering.

Extraction of various perfusion components from dynamic-susceptibility-contrast (DSC) MR brain images is critical for the analysis of brain perfusion. According to the variation of temporal signal on different brain tissues, one can segment whole brain area into distinct blood supply patterns which are vital for the profound analysis of cerebral hemodynamics. In this study, independent component analysis (ICA) is used to project the perfusion image data into independent components from which each elucidated tissue cluster can be automatically segment out by using the hierarchical clustering (HC).

Classification of hemodynamics from dynamic-susceptibility-contrast magnetic resonance (DSC-MR) brain images using noiseless independent factor analysis.

Dynamic-susceptibility-contrast (DSC) magnetic resonance imaging records signal changes on images when the injected contrast-agent particles pass through a human brain. The temporal signal changes on different brain tissues manifest distinct blood-supply patterns which are vital for the profound analysis of cerebral hemodynamics. Under the assumption of the spatial independence among these patterns, noiseless independent factor analysis (IFA) was first applied to decompose the DSC-MR data into different independent-factor images with corresponding signal-time curves.

Classification of spatiotemporal hemodynamics from brain perfusion MR images using expectation-maximization estimation with finite mixture of multivariate gaussian distributions.

The ability to cluster different perfusion compartments in the brain is critical for analyzing brain perfusion. This study presents a method based on a mixture of multivariate Gaussians (MoMG) and the expectation-maximization (EM) algorithm to dissect various perfusion compartments from dynamic susceptibility contrast (DSC) MR images so that each compartment comprises pixels of similar signal-time curves. This EM-based method provides an objective way to 1) delineate an area to serve as the in-plane arterial input function (AIF) of the feeding artery for adjacent tissues to better quantify the relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time (MTT); 2) demarcate regions with abnormal perfusion derangement to facilitate diagnosis; and 3) obtain parametric maps with supplementary information, such as temporal scenarios and recirculation of contrast agent.