**9. References**


This method is able to indirectly image dissolved-phase xenon, but is limited to tissue in direct exchange with the air in the lungs. The gas exchange process could be similarly exploited for direct signal amplification of dissolved xenon with the remote detection technique, which extends the study area from lung to brain. Xenon gas can be extracted from the dissolved solutions and concentrated in the gas phase for detection. Furthermore, with the long longitudinal relaxation time of gas-phase xenon, extracted xenon gas from solution can be compressed or liquefied while preserving the encoded information. The xenon density in the liquid state is approximately four orders of magnitude higher than in aqueous solutions, which in principle could result in up to 10,000 times enhancement of spin

We have demonstrated the hyperpolarized xenon signal amplification by gas extraction (Hyper-SAGE) method (Zhou et al., 2009b) with enhanced NMR spectra and time-of-flight (TOF) images by using recently commercialized membrane technology for high-efficiency xenon dissolution (Baumer et.al, 2006). The Hyper-SAGE technique relies on physical amplification by exploiting a phase change and is completely distinct from chemical amplification. In combination with additional amplification techniques such as Hyper-CEST, this method promises to dramatically decrease the detection threshold of MRI and

Recent innovations in the production of highly polarized 129Xe and novel method of signal enhancement should make feasible the emergence of hyperpolarized 129Xe MRI as a viable adjunct method to conventional MRI for the study of brain function and disease. The high sensitivity of hyperpolarized noble gas signal and non-background noise in biological tissue offer xenon as an important and promising contrast agent to study the brain. Because the polarization of hyperpolarized xenon does not depend on the magnetic field strength, the technique for brain imaging could also be applied for use with low field portable MRI

This work was supported by the Chinese Academy of Sciences (the 100 talents program and KJCX2-EW-N06-04), Natural Science Foundation of China (11004228), and Innovation

Albert MS, et al. (1994). Biological magnetic resonance imaging using laser-polarized 129Xe.

Andrea & Angelo (2003), Hyperpolarised xenon in biology. *Nucl. Magn. Reson. Spectr.*, Vol.

Appelt S, et al. (2005). Mobile high-resolution xenon nuclear magnetic resonance spectroscopy in the Earth's magnetic field. *Phys Rev Lett,* Vol.94, pp.(197602). Baumer D, et al. (2006). NMR spectroscopy of laser-polarized 129Xe under continuous flow:

Betz E .(1972). Cerebral blood flow: Its measurement and regulation. *Physiol. Rev.*, Vol. 52:

A method to study aqueous solutions of biomolecules. *Angew Chem Int Ed*, Vol. 45,

has the potential to benefit molecular imaging applications and brain imaging.

density, thus allowing substantial signal amplification

devices (Appelt, 2007; Blümich, 2008; Paulsen, 2008).

**8. Acknowledgments** 

4, pp.(1–30).

pp.(7282–7284).

595–630

**9. References** 

Method Fund of China (2010IM030600).

*Nature*, Vol. 370, pp.(199–201).


**1. Introduction** 

must satisfy

image (Pham et al., 2000).

**8** 

**Segmentation of Brain MRI** 

(1)

Effective, precise and consistent brain cortical tissue segmentation from magnetic resonance (MR) images is one of the most prominent issues in many applications of medical image processing. These applications include surgical planning (Kikinis et al., 1996), surgery navigation (Grimson et al., 1997), multimodality image registration (Saeed, 1998), abnormality detection (Rusinek et al., 1991), multiple sclerosis lesion quantification (Udupa et al., 1997), brain tumour detection (Vaidyanathan et al., 1997), functional mapping (Roland et al., 1993), etc. Traditionally, the purpose of segmentation is to partition the image into non-overlapping, constituent regions (or called classes, clusters, subsets or sub-regions) that are homogeneous with respect to intensity and texture (Gonzalez & Woods, 1992). If the domain of the image is given by ߗ, then the segmentation problem is to determine the sets ܵ ؿ ߗ, whose union is the entire domain ߗ. Thus, the sets that make up a segmentation

1

where ܵ ת ܵ ൌ for ്݆݇, and each ܵ is connected. Ideally, a segmentation method is to find those sets that correspond to distinct anatomical structures or regions of interest in the

For brain MR image segmentation, some studies aim to identify the entire image into subregions such as white matter (WM), grey matter (GM), and cerebrospinal fluid spaces (CSF) of the brain (Lim & Pfefferbaum, 1989), whereas others aim to extract one specific structure, for instance, brain tumour (M.C. Clark et al., 1998), multiple sclerosis lesions (Mortazavi et al., 2011), or subcortical structures (Babalola et al., 2008). Due to varying complications in segmenting human cerebral cortex, the manual methods for brain tissues segmentation might easily lead to errors both in accuracy and reproducibility (operator bias), and are exceedingly time-consuming, we thus need fast, accurate and robust semi-automatic (i.e., supervised classification explicitly needs user interaction) or completely automatic (i.e., non-

supervised classification) techniques (Suri, Singh, et al., 2002b).

*K k k S* 

Rong Xu1, Limin Luo2 and Jun Ohya1

*1Waseda University, 2Southeast University,* 

> *1Japan, 2China*

