Automatic brain segmentation using fractional signal modeling of a multiple flip angle, spoiled gradient-recalled echo acquisition

Partial volume mapping using magnetic resonance fingerprinting

Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that maps multiple tissue properties through pseudorandom signal excitation and dictionary-based reconstruction. The aim of this study is to estimate and validate partial volumes from MRF signal evolutions (PV-MRF), and to characterize possible sources of error. Partial volume model inversion (pseudoinverse) and dictionary-matching approaches to calculate brain tissue fractions (cerebrospinal fluid, gray matter, white matter) were compared in a numerical phantom and seven healthy subjects scanned at 3 T. Results were validated by comparison with ground truth in simulations and ROI analysis in vivo. Simulations investigated tissue fraction errors arising from noise, undersampling artifacts, and model errors. An expanded partial volume model was investigated in a brain tumor patient. PV-MRF with dictionary matching is robust to noise, and estimated tissue fractions are sensitive to model errors. A 6% error in pure tissue T₁ resulted in average absolute tissue fraction error of 4% or less. A partial volume model within these accuracy limits could be semi-automatically constructed in vivo using k-means clustering of MRF-mapped relaxation times. Dictionary-based PV-MRF robustly identifies pure white matter, gray matter and cerebrospinal fluid, and partial volumes in subcortical structures. PV-MRF could also estimate partial volumes of solid tumor and peritumoral edema. We conclude that PV-MRF can attribute subtle changes in relaxation times to altered tissue composition, allowing for quantification of specific tissues which occupy a fraction of a voxel.

Towards Reinforced Brain Tumor Segmentation on MRI Images Based on Temperature Changes on Pathologic Area

Brain tumor segmentation is the process of separating the tumor from normal brain tissues; in clinical routine, it provides useful information for diagnosis and treatment planning. However, it is still a challenging task due to the irregular form and confusing boundaries of tumors. Tumor cells thermally represent a heat source; their temperature is high compared to normal brain cells. The main aim of the present paper is to demonstrate that thermal information of brain tumors can be used to reduce false positive and false negative results of segmentation performed in MRI images. Pennes bioheat equation was solved numerically using the finite difference method to simulate the temperature distribution in the brain; Gaussian noises of ±2% were added to the simulated temperatures. Canny edge detector was used to detect tumor contours from the calculated thermal map, as the calculated temperature showed a large gradient in tumor contours. The proposed method is compared to Chan–Vese based level set segmentation method applied to T1 contrast-enhanced and Flair MRI images of brains containing tumors with ground truth. The method is tested in four different phantom patients by considering different tumor volumes and locations and 50 synthetic patients taken from BRATS 2012 and BRATS 2013. The obtained results in all patients showed significant improvement using the proposed method compared to segmentation by level set method with an average of 0.8% of the tumor area and 2.48% of healthy tissue was differentiated using thermal images only. We conclude that tumor contours delineation based on tumor temperature changes can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostic.

Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting

PURPOSE

To estimate multiple components within a single voxel in magnetic resonance fingerprinting when the number and types of tissues comprising the voxel are not known a priori.

THEORY

Multiple tissue components within a single voxel are potentially separable with magnetic resonance fingerprinting as a result of differences in signal evolutions of each component. The Bayesian framework for inverse problems provides a natural and flexible setting for solving this problem when the tissue composition per voxel is unknown. Assuming that only a few entries from the dictionary contribute to a mixed signal, sparsity-promoting priors can be placed upon the solution.

METHODS

An iterative algorithm is applied to compute the maximum a posteriori estimator of the posterior probability density to determine the magnetic resonance fingerprinting dictionary entries that contribute most significantly to mixed or pure voxels.

RESULTS

Simulation results show that the algorithm is robust in finding the component tissues of mixed voxels. Preliminary in vivo data confirm this result, and show good agreement in voxels containing pure tissue.

CONCLUSIONS

The Bayesian framework and algorithm shown provide accurate solutions for the partial-volume problem in magnetic resonance fingerprinting. The flexibility of the method will allow further study into different priors and hyperpriors that can be applied in the model. Magn Reson Med 80:159-170, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

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

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.