Fast bound pool fraction imaging of the in vivo rat brain: Association with myelin content and validation in the C6 glioma model

Abstract

Cross-relaxation imaging (CRI) is a quantitative magnetic resonance technique that measures the kinetic parameters of magnetization transfer between protons bound to water and protons bound to macromolecules. In this study, in vivo, four-parameter CRI of normal rat brains (N = 5) at 3.0 T was first directly compared to histology. The bound pool fraction, f, was strongly associated with myelin density (Pearson's r = 0.99, p < 0.001). The correlation persisted in separate analyses of gray matter (GM; r = 0.89, p = 0.046) and white matter (WM; r = 0.97, p = 0.029). Subsequently, a new time-efficient approach for solely capturing the whole-brain parametric map of f was proposed, validated with histology, and used to estimate myelin density. Since the described approach for the rapid acquisition of f applied constraints to other CRI parameters, a theoretical analysis of error was performed. Estimates of f in normal and pathologic tissue were expected to have < 10% error. A comparison of values for f obtained from the traditional four-parameter fit of CRI data versus the proposed rapid acquisition of f was within this expected margin for in vivo rat brain gliomas (N = 4; mean ± SE; 3.9 ± 0.2% vs. 4.0 ± 0.2%, respectively). In both whole-brain f maps and myelin density maps, replacement of normal GM and WM by proliferating and invading tumor cells could be readily identified. The rapid, whole-brain acquisition of the bound pool fraction may provide a reliable method for detection of glioma invasion in both GM and WM during animal and human imaging.

Introduction

Quantitative magnetization transfer (qMT) extracts the parametric indices that govern magnetization exchange in tissues. In the two-pool model of magnetization transfer (Edzes and Samulski, 1978, Grad et al., 1991, Morrison and Henkelman, 1995), magnetization exchange occurs between protons bound to water (i.e. the free pool) and protons bound to macromolecules (i.e. the bound pool). Mapping of the various tissue-specific parameters that govern magnetization transfer has been accomplished by a variety of techniques (Cercignani et al., 2005, Gloor et al., 2008, Gochberg and Gore, 2003, Helms and Piringer, 2005, Ramani et al., 2002, Ropele et al., 2003, Sled and Pike, 2001, Tozer et al., 2003, Yarnykh, 2002, Yarnykh and Yuan, 2004) that make use of various pulse sequences, sampling schemes and reconstruction algorithms.

A key parameter of qMT captured by all approaches is the molar fraction of protons bound to macromolecules (i.e. bound pool fraction), f. The acquisition of f has been shown to be obtainable with reasonable accuracy regardless of methodology (Portnoy and Stanisz, 2007). In the brain, f has been associated with white matter (WM) fiber tracts (Yarnykh and Yuan, 2004). However, in contrast to quantitative diffusion imaging techniques, f provides data that is not associated with directional coherence of fibers (Underhill et al., 2009). In multiple sclerosis (MS), in vivo alterations to the bound pool fraction have been identified in WM (Davies et al., 2004, Sled and Pike, 2001, Tozer et al., 2003, Yarnykh, 2002). The decrease in bound pool fraction from within MS lesions has been attributed to a localized reduction in the rich macromolecular content of myelin (Fralix et al., 1991, Koenig, 1991, Wolff and Balaban, 1989) associated with demyelination. These in vivo observations have been supported by ex vivo human studies and in vivo animal models. Schmierer et al. (2007) imaged post-mortem ex vivo brains afflicted with MS and found significant differences in the bound pool fraction when measured from histologically identified normal-appearing WM, remyelinated WM lesions, and demyelinated WM lesions. Rausch et al. has produced demyelinating lesions in the rat brain by inducing an experimental autoimmune encephalitis (Rausch et al., 2009). A decrease in the bound pool fraction from regions of interest (ROIs) within demyelinated lesions compared to ROIs from unaffected WM within the same animal and corresponding WM from healthy animals was reported (Rausch et al., 2009). Samsonov et al. (2006, 2010) demonstrated a dramatic and uniform decrease of the bound pool fraction in demyelinated WM using the mutant canine model. While the evidence indicates an association between myelin content and the bound pool fraction, the data (Rausch et al., 2009, Samsonov et al., 2006, Samsonov et al., 2010, Schmierer et al., 2007) were derived from the assessment of abnormal tissues. As such, the dominant underlying tissue property that governs bound pool fraction variations in normal brain tissues remains ambiguous since factors such as inflammation, edema, or genetically-determined structural myelin disorganization may confound observations based on post-mortem diseased brain or animal models.

While the major field of application of qMT methods has been historically associated with demyelinating disorders (Davies et al., 2004, Rausch et al., 2009, Schmierer et al., 2007, Sled and Pike, 2001, Tozer et al., 2003, Yarnykh, 2002), another area of considerable interest is related to brain tumors, especially those of glial origin. The devastating mortality associated with gliomas, particularly high-grade gliomas (5-year survival < 3%) (Laws et al., 2003), has been attributed to tumor cell invasion of normal parenchyma, particularly WM (Giese and Westphal, 2001). Understanding glioma cell invasion is a key aspect necessary to improve survival, which has remained unchanged for more than 30 years (Polin et al., 2005, Scanlon and Taylor, 1979). Accordingly, animal models have been developed to characterize and study in vivo glioma cell invasion (Barth, 1998). Development of a technology that allows non-invasive detection of glioma spread across normal brain tissues would not only enrich animal studies, but may also improve treatment planning and prognostic guidance for clinical decision making (Giese et al., 2003). qMT methods have a promising potential for this purpose due to a high sensitivity of cross-relaxation parameters to WM organization (Underhill et al., 2009, Yarnykh and Yuan, 2004). Current knowledge about cross-relaxation in brain tumors and associated effects on surrounding tissues is very limited. The distinctions in the two-pool model parameters between glioma and normal brain tissues were first demonstrated using a well-established C6 rat glioma model based on continuous wave Z-spectroscopic data acquisition (Quesson et al., 1997). Parametric maps of the bound pool fraction and cross-relaxation rate constant in the single-case human observation (Yarnykh, 2002) identified clear contrast between the glioma tissue, surrounding edema, and normal brain. Recently, Garica et al. (2010) and Tozer et al. (2007) reported preliminary data identifying alterations in qMT parametric maps associated with low-grade and high-grade gliomas, respectively. In the former study, parametric maps appeared heterogenous compared to the relatively homogenous appearance of corresponding contrast-enhanced T1-weighted and T2-weighted images suggesting qMT may provide additional information for diagnostic tumor characterization (Garica et al., 2010). Additionally, the bound pool fraction appeared to differentiate better in gliomas between tumor and perifocal edema compared to other qMT parameters (Garica et al., 2010). These preliminary findings warrant further investigation of CRI in gliomas, and particularly, a detailed study of histological correlations using a well-established animal model.

From a technical standpoint, qMT remains a rather challenging experimental approach due to time-consuming multi-point data acquisition and sophisticated reconstruction algorithms utilizing voxel-based fit of several parameters (from two to five, depending on the model parameterization) (Cercignani et al., 2005, Gloor et al., 2008, Gochberg and Gore, 2003, Helms and Piringer, 2005, Ramani et al., 2002, Ropele et al., 2003, Sled and Pike, 2001, Tozer et al., 2003, Yarnykh, 2002, Yarnykh and Yuan, 2004). This circumstance to a large extent restricts clinical qMT applications. Several studies have been focused on the improvement of time efficiency of qMT techniques (Gloor et al., 2008, Ropele et al., 2003, Yarnykh and Yuan, 2004). Collectively, these approaches are aimed to reduce the parameter space of the two-pool MT model by focusing on the parameters which are assumed to be most clinically informative, while introducing certain model simplifications to avoid estimation of other parameters. In the cross-relaxation imaging (CRI) method (Yarnykh and Yuan, 2004), a reduction of the number of adjustable parameters to two (bound pool fraction, f, and forward cross-relaxation rate constant, k, defined for magnetization transfer from free to bound pool) in conjunction with constraining the remaining parameters (transverse relaxation times of the free and bound pool, T₂^F and T₂^B) has been shown to allow the use of four data points with variable off-resonance saturation, and therefore, provide considerable total scan time shortening as compared to multi-point Z-spectroscopic techniques. Ropele et al. (2003) proposed an inversion-based pulse sequence and a simplified theoretical model for determination of the bound pool fraction from two images. Recently, Gloor et al. (2008) introduced the technique based on the on-resonance MT effect induced by the fast SSFP sequence with multi-point data acquisition for reconstruction of the two MT parameters (bound pool fraction and cross-relaxation rate constant). The above techniques still suffer from a relatively long scan time if used for whole-brain imaging (at least 25–30 min for human brain studies) due to the need of either several data points with 3D acquisition (Gloor et al., 2008, Yarnykh and Yuan, 2004) or sequential 2D acquisition with a long repetition time (Ropele et al., 2003). To further improve time efficiency of qMT, a modified CRI technique for rapid acquisition of f maps has been recently proposed in human brain imaging at 3.0 T (Underhill et al., 2010b, Yarnykh et al., 2010). This method applies average-brain constraints for the parameter T₂^B and the product of the longitudinal relaxation rate and transverse relaxation time of the free pool, R₁^FT₂^F, similar to the earlier approach (Underhill et al., 2009, Yarnykh and Yuan, 2004), and additionally utilizes a constrained value for the parameter combination k(1 − f) / f, which physically corresponds to the inverse cross-relaxation rate constant describing magnetization transfer from bound to free pool. This approach (Underhill et al., 2010b, Yarnykh et al., 2010) is particularly fast, since only two off-resonance saturation data points are required, and all images are acquired using a spoiled gradient-echo sequence with a short repetition time. However, the accuracy of this method has not been studied in detail, and its further validation is needed.

In accordance with the above, we hypothesized that the bound pool fraction would be the most robust parameter for relating qMT to histology in the brain. In this study, we first aimed to determine associations between the parameters of the two-pool model (f, k, T₂^F, and T₂^B) and key histological characteristics (myelin density, axonal content, and cellularity) in the normal rat brain in vivo at 3.0 T. Second, we sought to identify histopathological correlations of cross-relaxation parameters in the C6 rat glioma model. Then, we aimed to validate with histology a recent time-efficient methodology for single-parameter bound pool fraction mapping in both normal and pathologic brain tissues. Lastly, this single-parameter bound pool fraction mapping method was compared to the magnetization transfer ratio (MTR) mapping, a widely used simple semi-quantitative magnetization transfer technique that describes the relative change in image contrast after a single off-resonance RF saturation pulse (Dousset et al., 1992). MTR has been previously used to study diseases associated with alterations to WM, such as MS (Dousset et al., 1992, Loevner et al., 1995), metastatic lesions (Boorstein et al., 1994), and progressive multifocal leukoencephalopathy (Dousset et al., 1997).