Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review

Abstract

Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy, the PVE is an important factor for accurate brain structure quantification. Partial volume estimation refers to a generalized segmentation task where the amount of each tissue type within each voxel is solved. This review aims to provide a systematic, tutorial-like overview and categorization of methods for partial volume estimation in brain MRI. The review concentrates on the statistically based approaches for partial volume estimation and also explains differences to other, similar image segmentation approaches.

Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting

“…for a weight vector x and noise term e. As formulated in Equation [1], the problem is ill-posed, and without adding any prior information, the solution may not be unique or may be too sensitive to small perturbations in the data. The solution x should be a sparse, or near sparse, vector, with larger weights corresponding to the entries of D that contribute most significantly to the mixed voxel signal.…”

“…; l N g is known a priori, the weight vector x can be found as the solution to a linear least-squares problem. Because the subset L is generally unknown, the problem needs to be modeled using the full dictionary as in Equation [1], where x is the vector of corresponding weights and e is complex zero-mean Gaussian noise. However, solving Equation [1] in the least-squares sense (i.e., by minimizing jjy À Dxjj 2 ; Þ will result in a dense solution, and it will be extremely difficult to pick out the few tissue types that have the most significant contributions to the voxel signal.…”

Blood correction reduces variability and gender differences in native myocardial T1 values at 1.5 T cardiovascular magnetic resonance – a derivation/validation approach

“…The T1 value of blood is higher than the T1 value of the myocardium, thus the T1 value of the myocardium is expected to be influenced by both the T1 value, and the amount of blood in the myocardium. The partial volume effect describes the concept that a single voxel may contain tissue of different characteristics, whereas the pixel value for that voxel will represent a combination of these contents [15]. Furthermore, the myocardial T1 values reflect a volume of tissue consisting of myocytes, extracellular connective tissue, nerves, blood vessels and the blood therein [16, 17].…”

Magnetic resonance imaging atlas of the cervical spine musculature

“…However, for accurate quantification of cervical muscle morphometry it is recommended that 3D MR sequences and measurement methods are employed in order to overcome partial volume effects associated with measures of cross‐sectional area. Partial volume effects occur because a single image voxel may contain several types of tissues due to the finite spatial resolution of the MR scanner (Tohka, ).…”