The rate of water movement, or diffusion, within the brain and other tissues is believed to be the direct reflection of the local microstructure within the tissue. Anatomic magnetic resonance imaging (MRI) can be modified in a way that makes the images sensitive to the water diffusion within tissues [54]. DWI, which is currently the only MRI technique that provides information on water diffusion, involves the use of phase-defocusing and phase-refocusing gradients to enable the evaluation of the rate of microscopic water diffusion within tissues. This approach has been shown to be sensitive to changing microenvironment such as the increased cell density that typifies growing tumors [56–59]. The gold standard for brain tumor diagnosis is light microscopy analysis of histological tissue samples. Typically definitive diagnosis and differentiation between pathological and normal tissue are achieved by examination of cell-architecture parameters, such as cell arrangement, cell density, cell-size distribution, and nucleus-to-cytoplasm size ratio. MR diffusion imaging probes water molecular diffusion over distances that correspond to typical cell sizes, and this water diffusion is also impeded by membranes, i.e., structures that are an integral part of the cell architecture. With increasing cell density, the impeding effect of membranes is expected to increase. Thus, MR diffusion imaging provides an intriguing access to information that otherwise can only be obtained by invasive light microscopy [60].

DWI has been used to grade or differentiate brain tumors based on cellularity [61]. Because the majority of the translational movement of water occurs in the extracellular space, increased cellularity or swelling should affect the apparent diffusion coefficient (ADC), causing a drop in the values [62] (Fig. 19.8). Several studies have shown that the ADC correlates well with tumor cellularity at histological examination, and calculation of the ADC together with conventional MRI may aid the characterization of cerebral tumors [56, 59, 61]. Simply stated, the higher the cell density, the more boundaries or restriction the water experiences and the lower the apparent diffusion coefficient (ADC), a value that is estimated from the DWIs. In this regard, several reports in the literature have demonstrated an inverse correlation between ADC and cell density in brain tumors [56–59]. Glioma grading and typing are crucial for prognosis, but patients with tumors of the same histopathological characterization and receiving similar treatment may display diverse outcome because of differences in the proliferative potential of disease. Assessment of tumor proliferation has therefore been suggested as an important additional predictor of tumor behavior [63]. In addition, identification of the tumor “hot spot” is important for biopsy guidance. The proliferative activity of each tumor is measured by deriving the Ki-67 proliferation index from immunohistochemical staining of tumor specimens. Neoplasms are associated with increased cellular density, resulting in decreased signal intensity on ADC images. High tumor cellularity has been associated with increased proliferative activity, as assessed by the Ki-67 index [64]. Several studies have shown that lower ADCs suggest malignant glioma, whereas higher ADCs suggest low-grade astrocytomas findings reflecting more restricted diffusion with increasing cellularity [59–65] (Fig. 19.9). The median minimum ADC of the high-grade astrocytomas is significantly lower than that of the low-grade astrocytomas [66]. Higher ADC values in intracranial tumors are attributed to low tumor cellularity, necrosis, or cysts, and lower values are attributed to attenuated highly cellular tumors [67]. These higher ADC values in lower grade gliomas may reflect an increase in the water content of the interstitial spaces [66].

The commonly used b-factor is around 1000/s mm² for the diffusion-weighted images. With this b-factor and with the typical magnetic field gradient and echo time employed on clinical MR systems, the average water molecule has several tens of milliseconds to diffuse and may migrate up to 10 micrometers in a random direction and is therefore likely to be hindered by the cell membrane or intracellular membranes [60]. Unlike normal white and gray matter, which for a normal b-factor range exhibit very similar ADC values, diffusion values observed in different tumor types are dissimilar and cover a wide range. In general, brain tumor ADC values measured over a normal b-factor range, clearly exceed ADC values of normal gray and white matter. This difference in diffusion, however, can rarely be gainfully applied to determine the extent of tumor growth within normal tissue, since tumors are often surrounded by edematous tissue, where diffusion is also elevated and thus difficult to distinguish from diffusion values representative for tumor [60]. Very high diffusion values in peritumoral edema of high-grade gliomas may reflect the destruction of the extracellular matrix ultrastructure by malignant cell infiltration [68]. On the other hand, the elevated values of diffusion coefficients in a tumor may to some extent also be the result of edema that arises inside the tumor. The brain tumor edema is vasogenic, but may also be related to failure of membrane pump systems (cytotoxic) or to transependymal movement fluid from the ventricles.

The complete absence of cell membranes in cystic lesions and the destruction of cell membranes in necrotic brain lesions create an environment where diffusion is virtually unhindered. Consequently, the diffusion constant is very high and easily distinguished from the diffusion constant of any other tissue, including tumor tissue. Large areas of necrosis can be well detected and delineated based on elevated ADC within the tumor lesion [69].

Diffusion imaging is also useful to document characteristic fluid volume changes in the intra- and extracellular compartments that occur during nonsurgical therapy of brain tumors. For gliomas, studies have shown that ADC values increase during successful cytotoxic therapy and decrease during tumor growth [70], suggesting ADC values can be used as a biomarker for brain cellularity. Tumor areas at initial stage of treatment response present transient cell swelling or ischemia with decreased diffusion values. Tissue experiencing successful response with cell lysis or apoptosis displays a characteristic increase of diffusion, meanwhile tumor areas resistant to therapy exhibit unaltered diffusion values [71]. It has also been demonstrated that changes observed during the first stage of therapy are an early indicator of the final treatment response [72]. The correct interpretation about an unresponsive to therapy tumor may facilitate a timely decision for protocol adjustment or alternative therapy.

Diffusion-weighted brain images with very high b-factors of 5000 s/mm² or higher, which clearly depict tissue structures above the noise threshold, can readily be obtained on clinical scanners. The residual signal at very high b-values is distinctly higher. Of course, compared to images acquired at b-factors of 1000 s/mm², there is an increased signal loss of up 90 % or more, which typically has to be compensated with a decrease in spatial resolution [60]. However, the acquisition of image data at multiple b-factors permits a more detailed analysis of the diffusion-related signal decay. Without diffusion weighting, tumor and edema cannot be distinguished and exhibit higher signal than normal white matter. With increasing diffusion weighting, the signal of the edematous tissue decreases more rapidly than the signal of the other tissue and approaches the signal level of normal white matter. Tumor tissue exhibits the slowest signal decay and shows the highest residual signal at very high b-factors. High b-diffusion-weighted imaging, although currently purely experimental, seems to provide a good differentiation in normal tissues and in different pathologies, in relation to the different diffusion signal decay observed in tissues. Serious limitations for clinical studies are currently the long scan times, low SNR, and lower resolution [60].

Diffusion tensor imaging (DTI) is an MRI-based technique that can demonstrate white matter anatomy by measuring the directional anisotropy of water. Diffusion MRI is the first noninvasive technique for measuring white matter fiber structure in vivo [73]. In DTI analysis, a tensor model is used to represent the orientation of white matter fibers, in relation to the preferential diffusion of water in brain tissue, which decreases perpendicular to the myelin sheaths and cell membranes of white matter axons. Computation of the diffusion tensor yields for each voxel three diffusion coefficients (diffusion eigenvalues) along three orthogonal principal direction (diffusion eigenvectors). In white matter (WM), the eigenvector associated with the largest eigenvalue (principal eigenvector) defines the tissue’s fiber tract axis [74]. In voxels where one white matter fiber population is predominant, the principal diffusion direction is aligned with the white matter fiber tract direction [75]. By following principal directions of diffusion, a process called tractography [76, 77] estimates the trajectories of white matter fiber tracts. These reconstructions may then be displayed in three dimensions providing a detailed map of the configuration of the tracts and their relationship to other structures. Increasingly color code maps are used for a combined and easy to interpret visualization of anisotropy and principal eigenvector direction [78].

The most useful application of the directional dependence of the diffusion-related signal decay lies not inside the main tumor lesion, but rather within the surrounding highly structured white matter.

In patients with brain tumors, DTI can demonstrate displacement, interruption, or infiltration of white matter tracts by the tumor [79–84]. The distinction of these situations is often not possible to organize. A useful tool to understand the various scenarios is the paper by Fields et al. [85] who described four basic categories of tumor growth-related white matter changes in relation to anisotropy maps and principal eigenvector direction:

A clear differentiation is not often possible because more than one of these characteristics can be present and it has to be considered the confounding effect of edema. Due to these and other pathological changes throughout the brain, the identification of known neuroanatomy is often not straightforward in tumor patient. For Golby et al. [86] in order to define relevant anatomy, it would be useful to identify those fibers that pass within a certain distance of the tumor or which run through the tumor, as well as those associated with particular, patient specific, cortical areas such as fMRI activation or magnetoencephalography (MEG) findings. This can provide a preoperative functional brain map defining both the critical cortical functional areas and the WM tracts leading to and from these areas.

If clinically it is critically important to distinguish between cases where the lesion infiltrates the white matter and cases where the fibers are displaced by the lesion, this also determines the extent of resectability of the lesion. DTI and fiber tractography can demonstrate the orientation and integrity of white matter fibers in vivo. The metrics commonly used include the mean diffusivity (MD) and fractional anisotropy (FA). MD represents a measure of the overall magnitude of diffusion, independent of tissue orientation, similar to the ADC. FA, which is a measure of the directionality of molecular motion, is thought to reflect white matter fiber integrity [87, 88]. Normal white matter appears brighter than tumors on FA image maps due to the directional movement along white matter structures in normal brain that are often disrupted or absent in tumors. FA in brain tumors has been found to be influenced by histological characteristic and correlates with cellularity, vascularity, cell density, neuronal and axonal structures, and fiber tracts [89–91]. The decrease in FA values within brain tumors can be explained as a loss of structural organization, and a strong correlation was observed between the FA and Ki-67 index, although a clear physiological basis linking FA values with the Ki-67 labeling index remains to be determined [92]. For this reason, the DTI has been used in the attempt at differentiating tumor-infiltrated edema from purely vasogenic edema. It has been demonstrated [62] as significantly higher MD in the peritumoral edema, which surrounds metastatic tumors when compared with that in the edema surrounding high-grade gliomas. Increases in the peritumoral MD for malignant gliomas are tempered by the infiltrative rather than expansive growth patterns with the presence of tumor cells in the peritumoral abnormality. At the same way, a reduced FA in the peritumoral region of malignant glioma reflects the presence of infiltrating tumor cells, which may destroy rather than simply displace the white matter tracts [87]. Nevertheless it has been observed [93] that FA values in the tumor marginal area, but not in tumor core, differed significantly between low-grade and high-grade gliomas. This could be consequence of more extensive fiber tract destruction and accordingly lower anisotropy in high-grade gliomas than in low-grade gliomas, whereas in the center of the lesions, the fiber destruction was assumed to be more or less complete, irrespective of tumor grade.

In a recent study [94], it has demonstrated that DTI tractography could be a useful tool in predicting surgical outcome in patients with gliomas located near eloquent areas (Fig. 19.11). By establishing the presence of intact, displaced, or infiltrated fascicles, it was possible to estimate the chance of performing a total resection. Unchanged reconstructed tracts showed normal anisotropy, location, and orientation, compared with homologous contralateral tracts. Displaced tracts had a normal or only slightly decreased anisotropy and showed abnormal location or trajectories when compared with those of contralateral unaffected hemisphere. Infiltrated tracts showed substantially decreased FA with abnormal hues on directional color maps, because infiltrating tumor disrupts the directional organization of fiber tracts causing altered color patterns on directional maps (Fig. 19.10).

Other commonly derived DTI parameters are axial (AD) and radial (RD) diffusivity that represent diffusion properties along the axial and radial directions, respectively. Server et al. [95] have investigated the value of AD coefficient and RD coefficient in grading tumor. They have demonstrated that ADC, RD, and AD are useful DTI metrics for the differentiation between low-grade and high-grade gliomas with a diagnostic accuracy of more than 90 % and can be used as noninvasive reliable biomarkers in the grading of gliomas. There was a significant difference in the minimum ADC values between low-grade and high-grade gliomas. The AD coefficient and RD coefficient values were significantly higher in low-grade gliomas compared with high-grade gliomas, according to Yuan et al. [96]. In their study it was revealed a good inverse correlation between ADC, RD, AD and WHO grade II–IV astrocytic tumors. Findings related to AD and RD within tumor groups may be an indirect manifestation of the combined effects of axonal damage, demyelination, and tumor mass within the cranial compartment. ADC describes microscopic water movement parallel to axonal tracts and has been associated with axonal damage [97]. In a recent study [98], the association of RD with both demyelination and axonal damage was reported. Inano et al. [99] have developed a new method using multiple DTI-based parameters for voxel-based clustered images in a supervised manner that can be used to visually grade gliomas. By algorithms applied to DTI data, they can noninvasively predict the grade of gliomas with accuracy and may perform regional grading of glioma, which is useful for targeting biopsy, because gliomas are heterogeneous tumors. In this way the regional grading of the tumor can preoperatively be predicted, and it’s possible to establish which region must be resected, including peritumoral edematous lesions. We know that LGGs develop into HGGs and >10 % of gliomas differentiate into more malignant grades [100] but we cannot know when tumor grade progresses. By using regional grading based with the Inano et al. methods, the authors can clarify when LGGs progress into HGGs during follow-up and provide an appropriate adjuvant treatment at the optimum time.

Most diffusion tensor imaging studies that measure the directional dependence of diffusion employ b-factors up to 1000 s/mm². Diffusion imaging at higher b-factors increases the dynamic range of directionally dependent signal variations, which can be gainfully applied to detect fibers crossing at high angular resolution. Typically, a prohibitive large number of around 250 directions or more are required to achieve a good level of confidence in measuring fiber orientations within crossings [101]. Scan time, at the moment, exceeds 1 h and volume coverage is limited. The potential for diagnostic imaging in brain tumors and improved detection of crossing nerve fiber tracts are interesting. However, further technical development is required to reduce the excessive scan times and limitation in spatial resolution.

More recently, diffusion tensor MR imaging has been used to noninvasively detect genetic characteristics of gliomas, especially the IDH1 R132H mutation [102]. High fractional anisotropic (FA) values and low apparent diffusion coefficient (ADC) values were shown in a wild-type IDH1 group when compared with a mutation group in grade II and III gliomas. The authors found that, although FA and ADC values can detect IDH1 mutation, the ratio of minimal ADC is the best measurement for this proposal.

Diffusion-weighted imaging is considered very sensitive for early pathological changes of a lesion and can potentially be useful in evaluating the feature of gliomas. To overcome a limitation of DWI, in which perfusion can substantially confound diffusion measurement because of the incoherent motion of the blood, a new MR technique, intravoxel incoherent motion (IVIM) based on DWI, has been proposed in differentiating the histologic grade among human gliomas [103, 104]. The major advantages of IVIM MR imaging are as follows: it allows the simultaneous acquisition of diffusion and perfusion parameters.

The IVIM model generated parametric images for apparent diffusion coefficient ADC, slow diffusion coefficient D (or D slow), fast diffusion coefficient D* (or D fast), and fractional perfusion-related volume f. Hu et al. [104] assess the relationship between ADC, D, D*, and f, based on IVIM models and the glioma grades, and determine the cutoff values of suggested parameters for differentiating high- from low-grade gliomas. They evaluated whether the IVIM MRI could be used preoperatively to grade gliomas. There were significant differences in parameters ADC, D, D*, and f between low- and high-grade gliomas. The ADC and D are correlates with cellularity and nuclear atypia [105, 106], and their values were significantly lower in high-grade gliomas compared with low-grade gliomas, according to Iima et al. [107]. The degree of neovascularization is critical in assessing tumor grade and malignancy [108]. Malignant gliomas are characterized by increased angiogenesis, which is a marker of the histological grading system [109]. The parameter D* from the IVIM model is influenced by microvessel density within the tumor, which is associated with an average length of capillaries and blood flow velocities [110]. Several study have suggested that the relative cerebral blood volume (rCBV) value is correlated with the grade and vascularity in gliomas [111, 112], low-grade gliomas show no increase in tumor rCBV, whereas high-grade gliomas demonstrate high rCBV that in some cases even extends outside the contrast-enhancing portions of the tumor [113]. The parameter f represents the fraction of the fast diffusion component and is affected by abundance of capillaries (Fig. 19.12). In the Hu et al. [104] study, the diagnostic efficacy of IVIM parameters in differentiating glioma grades was shown.

Also for Bisas et al. [103], D, D*, and f in the high-grade gliomas demonstrated significant differences compared to the healthy white matter. D* and f showed a significant difference between low- and high-grade gliomas. D tended to be slightly lower in the WHO grade II compared to WHO grade III–IV tumors. f and D* demonstrated higher coefficients of variation than the ADC and D in tumor.

In conclusion the IVIM-fitted post-processing of DWI signal decay in human gliomas could show significantly different values of fractional perfusion-related volume and fast diffusion coefficient between low- and high-grade tumors, which might enable a noninvasive WHO grading in vivo.

Perfusion is physiologically defined as the steady-state delivery of blood to an element of tissue. The term “perfusion” is also used to emphasize contact with the tissue or, in other words, capillary blood flow [114].

There are two major approaches to measure cerebral perfusion with MRI. The first is application of an exogenous, intravascular, nondiffusible contrast agent, usually a gadolinium-based contrast agent that emphasizes either the susceptibility effects of gadolinium-based contrast agents on the signal echo, namely, first-pass dynamic susceptibility contrast-enhanced (DSC) MR perfusion or the relaxivity effects of gadolinium-based contrast agents on the signal echo, namely, dynamic contrast-enhanced (DCE) MR perfusion. The second is application of an endogenous contrast agent using magnetically labeled arterial blood water as a diffusible flow tracer in arterial spin labeling (ASL) MR perfusion.

Dynamic susceptibility contrast-enhanced MR perfusion (DSC MR perfusion) is a technique in which the first pass of a bolus of gadolinium-based contrast agent through brain tissue is monitored by a series of T2-or T2*-weighted MR images. The susceptibility effect of the paramagnetic contrast agent leads to a signal loss in the intensity-time curve. Using the principles of the indicator dilution theory, the signal information can then be converted into a contrast medium concentration-time curve on a pixel-by-pixel basis. From these data, parametric maps of cerebral blood volume (CBV) and flow (CBF) can be obtained. Regional CBF and CBV values can be measured by region of interest analysis (Fig. 19.13e, f).

Dynamic contrast-enhanced MR perfusion (DCE MR perfusion) is based on the acquisition of serial T1-weighted images before, during, and after administration of extracellular low-molecular-weight MR contrast media, such as gadolinium-based contrast agent. The resulting signal intensity-time curve reflects a composite of tissue perfusion, vessel permeability, and extravascular-extracellular space [115]. DCE MR perfusion imaging depicts the wash-in, plateau, and washout contrast kinetics of the tissue, providing insight into nature of the bulk tissue properties at the microvascular level. With pharmacokinetic modeling of DCE MR perfusion data, several metrics are commonly derived: the transfer constant (k^trans), the fractional volume of the extravascular-extracellular space (v _e), the rate constant (k_ep), and the fractional volume of the plasma (v _p).

Arterial spin-labeling MR perfusion (ASL MR perfusion) is a method that uses magnetically labeled blood as an endogenous tracer. There are two main types of ASL technique: continuous ASL and pulsed ASL [116, 117]. In continuous ASL, there is a prolonged radio frequency pulse that continuously labels arterial blood water below the imaging slab until a steady state of tissue magnetization is reached [118]. In pulsed ASL, a short radio frequency pulse is used to label a thick slab of arterial blood at a single point in time, and imaging is performed after a period of time to allow distribution in the tissue of interest [119]. A new technique, called “pseudocontinuous ASL,” represents a compromise between pulsed ASL and continuous ASL. This technique may provide improved balance between labeling efficiency and signal-to-noise ratio (SNR) compared with conventional ASL methods [120]. This method is considered completely noninvasive, and it allows the determination of absolute quantitative values of CBF, in contrast with DSC MR perfusion (Fig. 19.13c, d). It is also insensitive to permeability.

In general DSC and DCE MR perfusion achieve a substantially higher SNR that allows imaging at a higher temporal and spatial resolution, and DSC MR perfusion allows the visualization and quantification of the whole brain in less than a minute of acquisition time. ASL has a limited SNR and much longer scanning time.

Clinically, established methods for quantitative perfusion measurement usually include normalization of the mean or maximum perfusion values in the tumor region in relation to mean values in normal appearing white matter. Normalized rCBV measurements are considered to be the clinical standard [121]. However, white matter is often affected by treatment and/or edema or may invaded by diffuse tumor growth. In these cases the cerebellum could be used as a reference region [122].

One of the characteristics of brain tumors, irrespective of grade, is to produce a mass effect, which compresses the microcirculation in the normal tissue around the lesion and alters the permeability properties of vessels, including those that feed the tumor. In addition, high-grade gliomas also generate new blood vessels, which have very abnormal physiologic properties, including the peculiarity of allowing fluid from the intravascular compartment to seep through them. This occur indiscriminately within and around the tumor [60].

Hypoxic tumors tend to grow and metastasize faster than well-oxygenated tumors, and to be more resistant to radio- and chemotherapy [123]. In high-grade gliomas, the extent of tumor hypoxia is correlated with time to progression and overall survival [124]. Tumor secures their supply of oxygen and other nutrients by stimulating tissue angiogenesis, and the extent of vessel formation is closely related to tumor development and is in indirect marker of high malignancy and aggressiveness. The effects of vascular endothelial growth factor/vascular permeability factor (VEGF/VPF) and other growth factors on vascular perfusion and permeability have studied since Folkman J. (1971) first described the association of tumoral growth with angiogenesis [125]. Perfusion and permeability magnetic resonance (MR) imaging can now measure parameters such as CBV e K^trans, which can be directly correlated with these histopathologic changes as well as molecular markers such as VEGF [126, 127]. rCBV have been shown to correlate reliably with tumor grade and histologic findings of increased tumor vascularity [128, 131]. The degree of vascular proliferation, or angiogenesis, is one of the most important histologic criteria for determination of the degree of malignancy of a glioma. Vascular networks are not only the principal route for delivery of oxygen and nutrients to the neoplastic cells but also serve as paths for tumor infiltration along perivascular spaces. The cerebral capillary endothelium, site of blood-brain barrier, is frequently destroyed by malignant tumor cells. A hypermeable blood barrier associated with or without immature angiogenic vessels allows for contrast agent enhancement, extravasation, and, hence, measurement of vascular permeability [132]. These pathophysiologic changes have been shown to provide good correlation between tumor biology and rCBV, CBF, CBV K^trans, and V_p measurement. Due to an increase in CBV from microvascular density, as well as many collateral and tortuous vessels, from angiogenesis, it is felt that the mean transit time (MTT) should be prolonged. However, because of the immense heterogeneity of the tumor microvasculature in some regions, MTT may also decrease due to increased CBF, particularly at the tumor margins, where there is rapid shunting of blood flow [133]. There have been numerous publications demonstrating a good correlation between the value of rCBV and tumor biology. Comparison of rCBV measurements between LGG and HGG has demonstrated LGG to have maximal rCBV values of between 1.11 and 2.14 and HGG to have maximal rCBV values of between 3.54 and 7.32 [67, 129, 130, 134]. A larger study demonstrated LGG to have rCBV values of 2.14 and HGG to have rCBV values of 5.18 [129]. DSC MR imaging increases the sensitivity and predictive value in predicting glioma grade compared with conventional contrast-enhancement MR imaging.

In clinical practice, 95–100 % sensitivity has been reported for differentiating HGGs from LGGs using threshold of 1.75 and 1.5 for rCBV, respectively [129, 135].

The role of VEGF, also known as VPF, as a mediator of tumor growth and angiogenesis has also resulted in several investigators demonstrating a good correlation between vascular permeability and glioma grade. It seems clear, however, in part due to the heterogeneity of glioma vasculature that the regions of increased rCBV are spatially heterogeneous and different to areas of increased permeability [136, 137].

Previous studies have demonstrated no correlation between rCBV and low- versus high-grade gliomas [138] and no statistical difference in rCBV between grade I and II, grade II and III, and grade III and IV gliomas [139]. The disparity of these findings suggests that careful attention must be paid to the rCBV computation process, and one point of differing methodology involves contamination of derived concentration-time data by contrast agent extravasation [140]. When confined to intravascular space, paramagnetic contrast agents (Gd-DTPA) produce signal intensity loss in the extravascular space on T2-weighted scan, and susceptibility contrast-based rCBV maps are computed by integrating the resulting transverse relaxivity changes that occur over a dynamic first-pass injection. However, because Gd-DTPA is also an effective enhancer, the susceptibility-contrast signal intensity loss can be masked by signal intensity increase in region where T1 effects are significant. This occurs in enhancing tumors, where Gd-DTPA extravasates into the interstitial space of lesions with significant blood-brain barrier breakdown. In such instances, rCBV will be underestimated, which may affect tumor grade prediction (Fig. 19.14). Boxerman et al. [140] use a linear fitting to estimate the T1 contamination due to agent extravasation and, by removing the leakage term, allow generation of both corrected rCBV maps and first-order estimate of vascular permeability. In this way they show that rCBV corrected for contrast agent leakage correlates significantly with histopathologic tumor grade, whereas uncorrected rCBV does not (Fig. 19.15).

Another problem is the kind of sequence to use. GE-EPI acquisition is sensitive to vessels of all size, as opposed to SE acquisition, which has peak sensitivity to capillary-sized microvessels [141]. Although early reports documented significant correlation between SE rCBV and tumor grade [134], this has been refuted subsequently by Donahue et al. [142] and Schmainda et al. [143], who demonstrated that GE- but not SE-derived rCBV maps significantly correlate with tumor grade. This would seem to underscore the fact that the neovascularity characteristic of aggressive, high-grade tumors often consists of disorganized, large-scale microvessels that do not have feature typical of capillaries and would therefore be best interrogated with GE acquisition possessing wide-range microvascular sensitivity that can distinguish tumor angiogenesis from normal capillary beds. For these reasons, GE acquisition should be used for tumor perfusion studies because GE-based rCBV will be a statistically significant predictor of tumor grade [140]. Of course, GE acquisition are also sensitive to macrovessels, which must carefully excluded from the regions of interest used to compute rCBV and estimate tumor grade.