Template-based B₁ inhomogeneity correction in 3T MRI brain studies

Relaxation time of brain tissue in the elderly assessed by synthetic MRI

BACKGROUND

Synthetic MRI (SyMRI) is a quantitative technique that allows measurements of T1 and T2 relaxation times (RTs). Brain RT evolution across lifespan is well described for the younger population. The aim was to study RTs of brain parenchyma in a healthy geriatric population in order to define the normal value of structures in this group population. Normal values for geriatric population could help find biomarker for age-related brain disease.

MATERIALS AND METHODS

Fifty-four normal-functioning individuals (22 females, 32 males) with mean age of 83 years (range 56-98) underwent SyMRI. RT values in manually defined ROIs (centrum semiovale, middle cerebellar peduncles, thalamus, and insular cortex) and in segmented whole-brain components (brain parenchyma, gray matter, white matter, myelin, CSF, and stromal structures) were extracted from the SyMRI segmentation software. Patients' results were combined into the group age. Main ROI-based and whole-brain results were compared for the all dataset and for age group results as well.

RESULTS

For white matter, RTs between ROI-based analyses and whole-brain results for T2 and for T1 were statistically different and a trend of increasing T1 in centrum semiovale and cerebellar peduncle was observed. For gray matter, thalamic T1 was statistically different from insular T1. A difference was also found between left and right insula (p < .0001). T1 RTs of ROI-based and whole-brain-based analyses were statistically different (p < .0001). No significant difference in T1 and T2 was found between age groups on ROI-based analysis, but T1 in centrum semiovale and thalamus increased with age. No statistical difference between age groups was found for the various segmented volumes except for myelin between 65-74 years of age and the 95-105 years of age groups (p = .038).

CONCLUSIONS

SyMRI is a new tool that allows faster imaging and permits to obtain quantitative T1 and T2. By defining RT values of different brain components of normal-functioning elderly individuals, this technique may be used as a biomarker for clinical disorders like dementia.

Characterization of B1 + field variation in brain at 3 T using 385 healthy individuals across the lifespan

PURPOSE

The transmit field B 1 + at 3 T in brain affects the spatial uniformity and contrast of most image acquisitions. Here, B 1 + spatial variation in brain at 3 T is characterized in a large healthy population.

METHODS

Bloch-Siegert B 1 + maps were acquired at 3 T from 385 healthy subjects aged 5-90 years on a single MRI system. After transforming all B 1 + maps to a standard brain atlas space, region-of-interest analysis was performed, and intersubject voxel-wise coefficient of variation was calculated across the whole brain. The B 1 + variability due to age and brain size was studied separately in males and females, along with B 1 + variability due to nonideal transmit calibration.

RESULTS

The voxel-based mean coefficient of variation was 4.0% across all subjects, and the difference in B 1 + between central (left thalamus) and outer regions (left frontal gray matter) was 24.2% ± 2.3%. The least intersubject variability occurred in central regions, whereas regions toward brain edges increased markedly in variation. The B 1 + variability with age was mostly attributed to lifespan changes in CSF volume (which alters brain conductivity) and head orientation. Larger brain size correlated with more B 1 + inhomogeneity (p < .001). Varying head position and anatomy resulted in an inaccurate transmit calibration.

CONCLUSION

In standard atlas space, intersubject B 1 + variability at 3 T was relatively small in a large population aged 5-90 years. The B 1 + varied with age-related changes of CSF volume and head orientation, as well as differences in brain size and transmit calibration.

The Use of Large-Particle Aerosol Exposure to Nipah Virus to Mimic Human Neurological Disease Manifestations in the African Green Monkey

Nipah virus (NiV) is an emerging virus associated with outbreaks of acute respiratory disease and encephalitis. To develop a neurological model for NiV infection, we exposed 6 adult African green monkeys to a large-particle (approximately 12 μm) aerosol containing NiV (Malaysian isolate). Brain magnetic resonance images were obtained at baseline, every 3 days after exposure for 2 weeks, and then weekly until week 8 after exposure. Four of six animals showed abnormalities reminiscent of human disease in brain magnetic resonance images. Abnormalities ranged from cytotoxic edema to vasogenic edema. The majority of lesions were small infarcts, and a few showed inflammatory or encephalitic changes. Resolution or decreased size in some lesions resembled findings reported in patients with NiV infection. Histological lesions in the brain included multifocal areas of encephalomalacia, corresponding to known ischemic foci. In other regions of the brain there was evidence of vasculitis, with perivascular infiltrates of inflammatory cells and rare intravascular fibrin thrombi. This animal model will help us better understand the acute neurological features of NiV infection and develop therapeutic approaches for managing disease caused by NiV infection.

Rough Sets and Stomped Normal Distribution for Simultaneous Segmentation and Bias Field Correction in Brain MR Images

The segmentation of brain MR images into different tissue classes is an important task for automatic image analysis technique, particularly due to the presence of intensity inhomogeneity artifact in MR images. In this regard, this paper presents a novel approach for simultaneous segmentation and bias field correction in brain MR images. It integrates judiciously the concept of rough sets and the merit of a novel probability distribution, called stomped normal (SN) distribution. The intensity distribution of a tissue class is represented by SN distribution, where each tissue class consists of a crisp lower approximation and a probabilistic boundary region. The intensity distribution of brain MR image is modeled as a mixture of finite number of SN distributions and one uniform distribution. The proposed method incorporates both the expectation-maximization and hidden Markov random field frameworks to provide an accurate and robust segmentation. The performance of the proposed approach, along with a comparison with related methods, is demonstrated on a set of synthetic and real brain MR images for different bias fields and noise levels.

Rough sets for bias field correction in MR images using contraharmonic mean and quantitative index

One of the challenging tasks for magnetic resonance (MR) image analysis is to remove the intensity inhomogeneity artifact present in MR images, which often degrades the performance of an automatic image analysis technique. In this regard, the paper presents a novel approach for bias field correction in MR images. It judiciously integrates the merits of rough sets and contraharmonic mean. While the contraharmonic mean is used in low-pass averaging filter to estimate the bias field in multiplicative model, the concept of lower approximation and boundary region of rough sets deals with vagueness and incompleteness in filter structure definition. A theoretical analysis is presented to justify the use of both rough sets and contraharmonic mean for bias field estimation. The integration enables the algorithm to estimate optimum or near optimum bias field. Some new quantitative indexes are introduced to measure intensity inhomogeneity artifact present in a MR image. The performance of the proposed approach, along with a comparison with other approaches, is demonstrated on both simulated and real MR images for different bias fields and noise levels.