Longitudinal diffusion tensor imaging in a rat brain glioma model

Integrative Magnetic Resonance Imaging and Metabolomic Characterization of a Glioblastoma Rat Model

Glioblastoma (GBM) stands as the most prevalent and lethal malignant brain tumor, characterized by its highly infiltrative nature. This study aimed to identify additional MRI and metabolomic biomarkers of GBM and its impact on healthy tissue using an advanced-stage C6 glioma rat model. Wistar rats underwent a stereotactic injection of C6 cells (GBM group, n = 10) or cell medium (sham group, n = 4). A multiparametric MRI, including anatomical T2W and T1W images, relaxometry maps (T2, T2*, and T1), the magnetization transfer ratio (MTR), and diffusion tensor imaging (DTI), was performed. Additionally, ex vivo magnetic resonance spectroscopy (MRS) HRMAS spectra were acquired. The MRI analysis revealed significant differences in the T2 maps, T1 maps, MTR, and mean diffusivity parameters between the GBM tumor and the rest of the studied regions, which were the contralateral areas of the GBM rats and both regions of the sham rats (the ipsilateral and contralateral). The ex vivo spectra revealed markers of neuronal loss, apoptosis, and higher glucose uptake by the tumor. Notably, the myo-inositol and phosphocholine levels were elevated in both the tumor and the contralateral regions of the GBM rats compared to the sham rats, suggesting the effects of the tumor on the healthy tissue. The MRI parameters related to inflammation, cellularity, and tissue integrity, along with MRS-detected metabolites, serve as potential biomarkers for the tumor evolution, treatment response, and impact on healthy tissue. These techniques can be potent tools for evaluating new drugs and treatment targets.

Anesthetic modulation of water diffusion: Insights from a diffusion tensor imaging study

Diffusion tensor imaging (DTI) in animal models are essential for translational neuroscience studies. A critical step in animal studies is the use of anesthetics. Understanding the influence of specific anesthesia regimes on DTI-derived parameters, such as fractional anisotropy (FA) and mean diffusivity (MD), is imperative when comparing results between animal studies using different anesthetics. Here, the quantification of FA and MD under different anesthetic regimes, alpha-chloralose and isoflurane, is discussed. We also used a range of b-values to determine whether the anesthetic effect was b-value dependent. The first group of rats (n = 6) was anesthetized with alpha-chloralose (80 mg/kg), whereas the second group of rats (n = 7) was anesthetized with isoflurane (1.5%). DTI was performed with b-values of 500, 1500, and 1500s/mm2, and the MD and FA were assessed individually. Anesthesia-specific differences in MD were apparent, as manifested by the higher estimated MD under isoflurane anesthesia than that under alpha-chloralose anesthesia (P < 0.001). MD values increased with decreasing b-value in all regions studied, and the degree of increase when rats were anesthetized with isoflurane was more pronounced than that associated with alpha-chloralose (P < 0.05). FA quantitation was also influenced by anesthesia regimens to varying extents, depending on the brain regions and b-values. In conclusion, both scanning parameters and the anesthesia regimens significantly impacted the quantification of DTI indices.

Reproducibility of diffusion tensor imaging-derived parameters: implications for the streptozotocin-induced type 1 diabetic rats

OBJECTIVE

Diffusion tensor imaging (DTI) is a useful approach for studying neuronal integrity in animals. However, the test-retest reproducibility of DTI techniques in animals has not been discussed. Therefore, the first part of this work was to systematically elucidate the reliability of DTI-derived parameters in an animal study. Subsequently, we applied the DTI approach to an animal model of diabetes in a longitudinal manner.

MATERIALS AND METHODS

In Study 1, nine rats underwent two DTI sessions using the same scanner and protocols, with a gap of 4 weeks. The reliability of the DTI-derived parameters was evaluated in terms of sessions and raters. In Study 2, nine rats received a single intraperitoneal injection of 70 mg/kg streptozotocin (STZ) to develop diabetes. Longitudinal DTI scans were used to assess brain alterations before and 4 weeks after STZ administration.

RESULTS

In the test-retest evaluation, the inter-scan coefficient of variation (CoV) ranged from 3.04 to 3.73% and 2.12-2.59% for fractional anisotropy (FA) and mean diffusivity (MD), respectively, in different brain regions, suggesting excellent reproducibility. Moreover, rater-dependence had minimal effects on FA and MD quantification, with all inter-rater CoV values less than 4%. Following the onset of diabetes, FA in striatum and cortex were noted to be significantly lower relative to the period where they had not developed diabetes (both P < 0.05). However, when compared to the control group, a significant change in FA caused by diabetes was detected only in the striatum (P < 0.05), but not in the cortex.

CONCLUSION

These results demonstrate good inter-rater and inter-scan reliability of DTI in animal studies, and the longitudinal setting has a beneficial effect on detecting small changes in the brain due to diseases.

White Matter Tracts and Diffuse Lower-Grade Gliomas: The Pivotal Role of Myelin Plasticity in the Tumor Pathogenesis, Infiltration Patterns, Functional Consequences and Therapeutic Management

For many decades, interactions between diffuse lower-grade glioma (LGG) and brain connectome were neglected. However, the neoplasm progression is intimately linked to its environment, especially the white matter (WM) tracts and their myelin status. First, while the etiopathogenesis of LGG is unclear, this tumor seems to appear during the adolescence, and it is mostly located within anterior and associative cerebral areas. Because these structures correspond to those which were myelinated later in the brain maturation process, WM myelination could play a role in the development of LGG. Second, WM fibers and the myelin characteristics also participate in LGG diffusion, since glioma cells migrate along the subcortical pathways, especially when exhibiting a demyelinated phenotype, which may result in a large invasion of the parenchyma. Third, such a migratory pattern can induce functional (neurological, cognitive and behavioral) disturbances, because myelinated WM tracts represent the main limitation of neuroplastic potential. These parameters are critical for tailoring an individualized therapeutic strategy, both (i) regarding the timing of active treatment(s) which must be proposed earlier, before a too wide glioma infiltration along the WM bundles, (ii) and regarding the anatomic extent of surgical resection and irradiation, which should take account of the subcortical connectivity. Therefore, the new science of connectomics must be integrated in LGG management, based upon an improved understanding of the interplay across glioma dissemination within WM and reactional neural networks reconfiguration, in order to optimize long-term oncological and functional outcomes. To this end, mechanisms of activity-dependent myelin plasticity should be better investigated.

Diffusion tensor imaging derived metrics in high grade glioma and brain metastasis differentiation

Background: Pretreatment differentiation between glioblastoma and metastasis is a frequently encountered dilemma in neurosurgical practice. Distinction is required for precise planning of resection or radiotherapy, and also for defining further diagnostic procedures. Morphology and spectroscopy imaging features are not specific and frequently overlap. This limitation of magnetic resonance imaging and magnetic resonance spectroscopy was the reason to initiate this study. The aim of the present study was to determine whether the dataset of diffusion tensor imaging metrics contains information which may be used for the distinction between primary and secondary intra-axial neoplasms. Methods: Two diffusion tensor imaging parameters were measured in 81 patients with an expansive, ring-enhancing, intra-axial lesion on standard magnetic resonance imaging (1.5 T system). All tumors were histologically verified glioblastoma or secondary deposit. For qualitative analysis, two regions of interest were defined: intratumoral and immediate peritumoral region (locations 1 and 2, respectively). Fractional anisotropy and mean difusivity values of both groups were compared. Additional test was performed to determine if there was a significant difference in mean values between two locations. Results: A statistically significant difference was found in fractional anisotropy values among two locations, with decreasing values in the direction of neoplastic infiltration, although such difference was not observed in fractional anisotropy values in the group with secondary tumors. Mean difusivity values did not appear helpful in differentiation between these two entities. In both groups there was no significant difference in mean difusivity values, neither in intratumoral nor in peritumoral location. Conclusion: The results of our study justify associating the diffusion tensor imaging technique to conventional morphologic magnetic resonance imaging as an additional diagnostic tool for the distinction between primary and secondary intra-axial lesions. Quantitative analysis of diffusion tensor imaging metric, in particular measurement of fractional anisotropy in peritumoral edema facilitates accurate diagnosis.

Passive targeting of high-grade gliomas via the EPR effect: a closed path for metallic nanoparticles?

Passive tumor targeting via the enhanced permeability and retention (EPR) effect has long been considered the most effective mechanism for the accumulation of nanoparticles inside solid tumors. However, several studies have demonstrated that the EPR effect is largely dependent on the tumor type and location. Particularly complex is the situation in brain tumors, where the presence of the blood-brain tumor barrier (BBTB) adds an extra limiting factor in reaching the tumor interstitium. However, it remains unclear whether these restraints imposed by the BBTB prevent the EPR effect from acting as an efficient tumor targeting mechanism for metallic nanoparticles. In this work, we have studied the EPR effect of metallic magnetic nanoparticles (MMNPs) in a glioblastoma (GBM) model by parametric MRI. Our results showed that only MMNPs ≤50 nm could reach the tumor interstitium, whereas larger MMNPs were unable to cross the BBTB. Furthermore, even for MMNPs around 30-50 nm, the amount of them found within the tumor was scarce and restricted to the vicinity of large tumor vessels, indicating that the BBTB strongly limits the passive accumulation of metallic nanoparticles in brain tumors. Therefore, active targeting becomes the most reasonable strategy to target metallic nanoparticles to GBMs.

High-frequency electrical properties tomography at 9.4T as a novel contrast mechanism for brain tumors

PURPOSE

To establish high-frequency magnetic resonance electrical properties tomography (MREPT) as a novel contrast mechanism for the assessment of glioblastomas using a rat brain tumor model.

METHODS

Six F98 intracranial tumor bearing rats were imaged longitudinally 8, 11 and 14 days after tumor cell inoculation. Conductivity and mean diffusivity maps were generated using MREPT and Diffusion Tensor Imaging. These maps were co-registered with T2 -weighted images and volumes of interests (VOIs) were segmented from the normal brain, ventricles, edema, viable tumor, tumor rim, and tumor core regions. Longitudinal changes in conductivity and mean diffusivity (MD) values were compared in these regions. A correlation analysis was also performed between conductivity and mean diffusivity values.

RESULTS

The conductivity of ventricles, edematous area and tumor regions (tumor rim, viable tumor, tumor core) was significantly higher (P < .01) compared to the contralateral cortex. The conductivity of the tumor increased over time while MD from the tumor did not change. A marginal positive correlation was noted between conductivity and MD values for tumor rim and viable tumor, whereas this correlation was negative for the tumor core.

CONCLUSION

We demonstrate a novel contrast mechanism based on ionic concentration and mobility, which may aid in providing complementary information to water diffusion in probing the microenvironment of brain tumors.

Diffusion kurtosis imaging for characterizing tumor heterogeneity in an intracranial rat glioblastoma model

The utility of diffusion kurtosis imaging (DKI) for assessing intra-tumor heterogeneity was evaluated in a rat model of glioblastoma multiforme. Longitudinal MRI including T₂ -weighted and diffusion-weighted MRI (DWI) was performed on six female Fischer rats 8, 11 and 14 days after intracranial transplantation of F98 cells. T₂ -weighted images were used to measure the tumor volumes and DWI images were used to compute diffusion tensor imaging (DTI) and DWI based parametric maps including mean diffusivity (MD), mean kurtosis (MK), axial diffusivity (AD), axial kurtosis, radial diffusivity, radial kurtosis, fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA). Median values from the segmented normal contralateral cortex, tumor and edema from the diffusion parameters were compared at the three imaging time points to assess any changes in tumor heterogeneity over time. ex vivo DKI was also performed in a representative sample and compared with histology. Significant differences were observed between normal cortex, tumor and edema in both the DTI and DKI parameters. Notably, at the earliest time point MK and KFA were significantly different between normal cortex and tumor in comparison with MD or FA. Although a decreasing trend in MD, AD and FA values of the tumor were observed as the tumor grew, no significant changes in any of the DTI or DKI parameters were observed longitudinally. While DKI was equally sensitive to DTI in differentiating tumor from edema and normal brain, it was unable to detect longitudinal increases in intra-tumoral heterogeneity in the F98 model of glioblastoma multiforme.

Experimental sepsis-associated encephalopathy is accompanied by altered cerebral blood perfusion and water diffusion and related to changes in cyclooxygenase-2 expression and glial cell morphology but not to blood-brain barrier breakdown

Sepsis-associated encephalopathy (SAE) refers to brain dysfunction, including delirium, occurs during severe infection and is associated with development of post-traumatic stress disorder. SAE has been proposed to be related to reduced cerebral blood flow (CBF), blood-brain barrier breakdown (BBB), white matter edema and disruption and glia cell activation, but their exact relationships remain to be determined. In the present work, we set out to study CBF using Arterial Spin Labeling (ASL) and grey and white matter structure with T2- and diffusion magnetic resonance imaging (dMRI) in rats with cecal ligation and puncture (CLP)-induced encephalopathy. Using immunohistochemistry, the distribution of the vasoactive prostaglandin-synthesizing enzyme cyclooxygenase-2 (COX-2), perivascular immunoglobulins G (IgG), aquaporin-4 (AQP4) and the morphology of glial cell were subsequently assessed in brains of the same animals. CLP induced deficits in the righting reflex and resulted in higher T2-weighted contrast intensities in the cortex, striatum and at the base of the brain, decreased blood perfusion distribution to the cortex and increased water diffusion parallel to the fibers of the corpus callosum compared to sham surgery. In addition, CLP reduced staining for microglia- and astrocytic-specific proteins in the corpus callosum, decreased neuronal COX-2 and AQP4 expression in the cortex while inducing perivascular COX-2 expression, but did not induce widespread perivascular IgG diffusion. In conclusion, our findings indicate that experimental SAE can occur in the absence of BBB breakdown and is accompanied by increased water diffusion anisotropy and altered glia cell morphology in brain white matter.

Comparison between MRI-derived ADC maps and 18FLT-PET in pre-operative glioblastoma

BACKGROUND AND PURPOSE

Among principal MRI sequences used for a better pre-therapeutic characterization of glioblastoma (GBM), DWI-derived ADC is expected to be a good parameter for the evaluation of cellularity, due to restricted water diffusivity. We aimed here to compare ADC maps to ¹⁸FLT-PET, a proliferation tracer, in GBM cases.

MATERIALS AND METHODS

Patients underwent ¹⁸FLT-PET, followed by multiparametric magnetic resonance imaging (MRI) just prior to surgery. We analysed in this study twenty GBM confirmed patients. The 5th percentile (5p) of the ADC values were thresholded to define the ADC_min ROI, while the 95th percentile (95p) of the SUV FLT values were used to define the FLT_max ROI. The statistical and spatial correlations between these two groups of ROIs were analyzed.

RESULTS

We did not observe any significant correlations between ADC_min and FLT_max cut-off values (R²=0.0285), neither between ADC_min and FLT_max ROIs (mean Dice=0.09±0.12). Mean ADC values in the FLT_max defined ROI were significantly higher than the values in the ADC_min ROI (P<0.001). Mean FLT values in the FLT_max ROI were significantly higher than the values in the ADC_min ROI (P<0.001).

CONCLUSIONS

When comparing ADC maps to ¹⁸FLT uptake, we did not observe significant anatomical overlap nor correlation, between the regions of low ADC and high FLT disabling to clearly link ADC values to cellular proliferation. The exact significance of ADC maps in GBM has yet to be elaborated.

Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI

BACKGROUND

Targeted localized biopsies and treatments for diffuse gliomas rely on accurate identification of tissue subregions, for which current MRI techniques lack specificity.

PURPOSE

To explore the complementary and competitive roles of a variety of conventional and quantitative MRI methods for distinguishing subregions of brain gliomas.

STUDY TYPE

Prospective.

POPULATION

Fifty-one tissue specimens were collected using image-guided localized biopsy surgery from 10 patients with newly diagnosed gliomas.

FIELD STRENGTH/SEQUENCE

Conventional and quantitative MR images consisting of pre- and postcontrast T1 w, T2 w, T2 -FLAIR, T2 -relaxometry, DWI, DTI, IVIM, and DSC-MRI were acquired preoperatively at 3T.

ASSESSMENT

Biopsy specimens were histopathologically attributed to glioma tissue subregion categories of active tumor (AT), infiltrative edema (IE), and normal tissue (NT) subregions. For each tissue sample, a feature vector comprising 15 MRI-based parameters was derived from preoperative images and assessed by a machine learning algorithm to determine the best multiparametric feature combination for characterizing the tissue subregions.

STATISTICAL TESTS

For discrimination of AT, IE, and NT subregions, a one-way analysis of variance (ANOVA) test and for pairwise tissue subregion differentiation, Tukey honest significant difference, and Games-Howell tests were applied (P < 0.05). Cross-validated feature selection and classification methods were implemented for identification of accurate multiparametric MRI parameter combination.

RESULTS

After exclusion of 17 tissue specimens, 34 samples (AT = 6, IE = 20, and NT = 8) were considered for analysis. Highest accuracies and statistically significant differences for discrimination of IE from NT and AT from NT were observed for diffusion-based parameters (AUCs >90%), and the perfusion-derived parameter as the most accurate feature in distinguishing IE from AT. A combination of "CBV, MD, T2 _ISO, FLAIR" parameters showed high diagnostic performance for identification of the three subregions (AUC ∼90%).

DATA CONCLUSION

Integration of a few quantitative along with conventional MRI parameters may provide a potential multiparametric imaging biomarker for predicting the histopathologically proven glioma tissue subregions.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;48:938-950.

MRI analysis to map interstitial flow in the brain tumor microenvironment

Glioblastoma (GBM), a highly aggressive form of brain tumor, is a disease marked by extensive invasion into the surrounding brain. Interstitial fluid flow (IFF), or the movement of fluid within the spaces between cells, has been linked to increased invasion of GBM cells. Better characterization of IFF could elucidate underlying mechanisms driving this invasion in vivo. Here, we develop a technique to noninvasively measure interstitial flow velocities in the glioma microenvironment of mice using dynamic contrast-enhanced magnetic resonance imaging (MRI), a common clinical technique. Using our in vitro model as a phantom "tumor" system and in silico models of velocity vector fields, we show we can measure average velocities and accurately reconstruct velocity directions. With our combined MR and analysis method, we show that velocity magnitudes are similar across four human GBM cell line xenograft models and the direction of fluid flow is heterogeneous within and around the tumors, and not always in the outward direction. These values were not linked to the tumor size. Finally, we compare our flow velocity magnitudes and the direction of flow to a classical marker of vessel leakage and bulk fluid drainage, Evans blue. With these data, we validate its use as a marker of high and low IFF rates and IFF in the outward direction from the tumor border in implanted glioma models. These methods show, for the first time, the nature of interstitial fluid flow in models of glioma using a technique that is translatable to clinical and preclinical models currently using contrast-enhanced MRI.

DIFFUSION-KURTOSIS IMAGING IN ASSESMENT OF BRAIN MICROSTRUCTURE. HEALTHY VOLUNTEERS MEASURMENTS

Aim:discover quantitative and qualitative variance of diffusion parameters in white and gray matter of healthyvolunteers brain. Discover correlation between diffusion and kurtosis parameters, find out if there is correlation between the parameters and aging microstructural changes.Materials and methods.14 healthy volunteers were investigated (9 men, 5 women; age from 21 to 55 years, mean 34). The volunteers were classified into two groups by age: 7 subjects who younger 35 (6 men and 1 woman, mean age 25) and 7 subjects who older 35 years (3 men and 4 women, mean age 44). We used 3.0 Tesla MRI (3.0T SignaHDxt, General Electric, USA) with 8 channel head coil, gradient strength 50 mT/m, slew rate 150 T/m/s. Diffusion imaging was based on echo planar “spin echo” sequence (SE EPI), TR = 10000 ms, TEmin = 102 ms, FOV = 240 mm, voxel size 3 × 3 × 3 mm3, 60 non-coplanar diffusion directions and three b-values: 0, 1000, 2500 s/mm2. Acquisition time of diffusion kurtosis imaging was 22 minutes. We excluded extracerebral areas on diffusion and kurtosis parametric maps using semi-automatic approach. After that, brain images were transformed to MNI152 space using affine method. Masks of 9 anatomical structures were applied to the transformed images. Diffusion and kurtosis values were measured in these structures.Results.Fractional anisotropy (FA) changed from 0.06 in lateral occipital cortex to 0.25 in cerebral white matter, kurtosis anisotropy (KA) changed from 0.03 to 0.14 in the same cerebral structures. Axial (AK), radial (RK) and mean kurtosis (MK) were minimal in superior frontal gyrus and maximal in cerebral white matter. AK changed from 0.55 to 0.72, RK changed from 0.62 to 1.05, MK from 0.59 to 0.88. Axial(AxEAD) and radial extra axonal water diffusion (RadEAD) were minimal in putamen and maximal in superior frontal gyrus. AxEAD was changing from 1.38 • 10–3 to 2.57 • 10–3, RadEAD from 1.03 • 10–3 to 2.34 • 10–3. Axonal water fraction (AWF) had minimal value 0,18 in superior frontal gyrus and maximal value 0.29 in cerebral white matter. Tortuosity (TORT) changed from 1.06 in lateral occipital cortex to 1.43 in cerebral white matter. There was significant difference between age groups in AWF, RK, RadEAD in putamen and in KA in superior temporal gyrus. Maximal correlation with age was in MK in superior temporal gyrus, anterior division. It was equal to 0.562.Conclusions:Diffusion kurtosis imaging is highly sensitive method of brain tissue microstructure assessment, which detects age-related changes.

A combined diffusion tensor imaging and Ki-67 labeling index study for evaluating the extent of tumor infiltration using the F98 rat glioma model

Diffusion tensor imaging (DTI) has been proven to be a sophisticated and useful tool for the delineation of tumors. In the present study, we investigated the predictive role of DTI compared to other magnetic resonance imaging (MRI) techniques in combination with Ki-67 labeling index in defining tumor cell infiltration in the peritumoral regions of F98 glioma-bearing rats. A total of 29 tumor-bearing Fischer rats underwent T2-weighted imaging, contrast-enhanced T1-weighted imaging, and DTI of their brain using a 7.0-T MRI scanner. The fractional anisotropy (FA) ratios were correlated to the Ki-67 labeling index using the Spearman correlation analysis. A receiver operating characteristic curve (ROC) analysis was established to evaluate parameters with sensitivity and specificity in order to identify the threshold values for predicting tumor infiltration. Significant correlations were observed between the FA ratios and Ki-67 labeling index (r = - 0.865, p < 0.001). The ROC analysis demonstrated that the apparent diffusion coefficient (ADC) and FA ratios could predict 50% of the proliferating cells in the regions of interest (ROI), with a sensitivity of 88.1 and 81.3%, and a specificity of 86.2 and 90.2%, respectively (p < 0.001). Meanwhile, the two ratios could also predict 10% of the proliferating cells in the ROI, with a sensitivity of 82.5 and 94.9%, and a specificity of 100 and 88.9%, respectively (p < 0.001). The present study demonstrated that the FA ratios are closely correlated with the Ki-67 labeling index. Furthermore, both ADC and FA ratios, derived from DTI, were useful for quantitatively predicting the Ki-67 labeling of glioma cells.

Diffusion Tensor Imaging (DTI)

Diffusion Tensor Imaging is an MRI technique that allows in vivo noninvasive measurement of the translational motion of water, providing information about its anisotropy (or lack of it) in different tissues. DTI has been commonly used to quantitatively measure the integrity of tissues which may be compromised by neurological disease, such as white matter tracks of the brain, which normally impart significant anisotropy to water motion in healthy brains. However, this anisotropic effect is diminished when axonal or neuronal damage is present. This chapter describes a standard protocol for DTI data acquisition in preclinical studies.

Diffusion Kurtosis Imaging in the Assessment of Peritumoral Brain Edema in Glioblastomas and Brain Metastases

Aim: to explore the opportunities of application of diffusionkurtosis imaging (DKI) for assessment and estimation of diffusion scalar metrics in different locations of peritumoral edema for extra- and intracerebral tumors and in contralateral normal tissue.

Materials and methods. 38 patients with supratentorial brain tumors were investigated: 24 (63%) patients with primarily revealed glioblastomas (GB) and 14 (37%) patients with solitary cancer brain metastasis (MTS). MRI was performed on 3.0 T MR-scanner (Signa HDxt, General Electric, USA) with the standard protocols for brain tumor and additional protocol for DKI. The standard protocol for brain tumor included: T1-, T2-weighted images, T2-FLAIR, DWI,  T1 with contrast enhancement. Diffusion kurtosis MRI based on SE  EPI with TR = 10000 ms, TE = 102 ms, FOV = 240 mm, isotropic voxel size 3 × 3 × 3 mm3, 60 noncoplanar diffusion directions. We  used three b-values: 0, 1000 and 2500 s/mm2. Аcquisition time was 22 min. Total acquisition time was near 40 min. This study was approved by Ethical committee of Burdenko National Scientific  and Practical Center for Neurosurgery. Parametric maps were  constructed for the following diffusion coefficients: mean (MK),  transverse / radial (RK), longitudinal / axial (AK) kurtozis; medium  (MD), transverse / radial (RD) and longitudinal / axial (AD) diffusion; fractional anisotropy (FA) and a bi-exponential diffusion model  coefficients: axonal water fractions (AWF), axial (AxEAD) and radial  (RadEAD) extra-axonal water diffusion and the water molecules  trajectory tortuosity index (TORT). Normative quantitative indicators  were obtained for the six regions of the peritumoral zone as they  moved away from the tumor (region 2) to the edema periphery  (regions 4–5), as well as in the normal brain on the contralateral  hemisphere (C/L) (zone 7). A comparative analysis of these  indicators was conducted for cases with GB and MTS. DKI scalar metrics were estimated using Explore DTI (http://www.exploredti.com/).

Results. Anatomic MRI (T1 without/with contrast enhancement) for  all cases with GB and MTS visualized a contrast enhancement tumor.  The peritumoral edema, spreading mainly over the brain white  matter, was well visualized on T2-FLAIR. Diffusion kurtosis  coefficients decreased in the near peritumoral edema (regions 2–3)  and a gradually increased to the edema periphery (regions 5–6). In Region 2, MK in both GB and MTS groups were MKGB(2) = 0.637 ±  0.140 and MKMTS(2) = 0.550 ± 0.046; RK in this region were  RKGB(2) = 0.690 ± 0.154 and RKMTS (2) = 0.584 ± 0.051.  Differences both MK and RK coefficients in patients with GB and MTS of region 2 were significant (p < 0.001). There were no differences in AK values for GB and MTS in region 2 (p > 0.05), but in regions 3  and 4 differences were observed (p < 0.01). The minimum value of  AK in the central edema (regions 3–4) was AKMTS(3–4) = 0.433 ± 0.063 in patients with MTS. The values of MK and RK on the  contralateral side in patients with MTS were significantly higher than  in the GB group (p < 0.02); MKC/LMTC = 0.954 ± 0.140, RKC/LMTC  = 1.257 ± 0.308 and MKC/LGB = 0.829 ± 0.146, RKc/LGB = 0.989  ± 0.282. There was no significant difference for contralateral AK between the groups.

Conclusions. We found that DKI scalar metrics are the sensitive  tumor biomarkers. It allows us to perform a robust differentiation  between the infiltrating GB tumor and purely vasogenic edema of  МТS. The obtained results will allow further differential diagnosis of  extra- and intracerebral tumors and can be used to plan surgical /  radiosurgical treatment for brain tumors.

Analysis of DTI-Derived Tensor Metrics in Differential Diagnosis between Low-grade and High-grade Gliomas

Purpose: It is critical and difficult to accurately discriminate between high- and low-grade gliomas preoperatively. This study aimed to ascertain the role of several scalar measures in distinguishing high-grade from low-grade gliomas, especially the axial diffusivity (AD), radial diffusivity (RD), planar tensor (Cp), spherical tensor (Cs), and linear tensor (Cl) derived from diffusion tensor imaging (DTI). Materials and Methods: Fifty-three patients with pathologically confirmed brain gliomas (21 low-grade and 32 high-grade) were included. Contrast-enhanced T1-weighted images and DTI were performed in all patients. The AD, RD, Cp, Cs, and Cl values in the tumor zone, peritumoral edema zone, white matter (WM) adjacent to edema and contralateral normal-appearing white matter (NAWM) were calculated. The DTI parameters and tumor grades were statistically analyzed, and receiver operating characteristic (ROC) curve analysis was also performed. Results: The DTI metrics in the affected hemisphere showed significant differences from those in the NAWM, except for the AD values in the tumor zone and the RD values in WM adjacent to edema in the low-grade groups, as well as the Cp values in WM adjacent to edema in the high-grade groups. AD in the tumor zone as well as Cs and Cl in WM adjacent to edema revealed significant differences between the low- and high-grade gliomas. The areas under the curve (Az) of all three metrics were greater than 0.5 in distinguishing low-grade from high-grade gliomas by ROC curve analysis, and the best DTI metric was Cs in WM adjacent to edema (Az: 0.692). Conclusion: AD in the tumor zone as well as Cs and Cl in WM adjacent to edema will provide additional information to better classify gliomas and can be used as non-invasive reliable biomarkers in glioma grading.

Early detection of human glioma sphere xenografts in mouse brain using diffusion MRI at 14.1 T

Glioma models have provided important insights into human brain cancers. Among the investigative tools, MRI has allowed their characterization and diagnosis. In this study, we investigated whether diffusion MRI might be a useful technique for early detection and characterization of slow-growing and diffuse infiltrative gliomas, such as the proposed new models, LN-2669GS and LN-2540GS glioma sphere xenografts. Tumours grown in these models are not visible in conventional T₂ -weighted or contrast-enhanced T₁ -weighted MRI at 14.1 T. Diffusion-weighted imaging and diffusion tensor imaging protocols were optimized for contrast by exploring long diffusion times sensitive for probing the microstructural alterations induced in the normal brain by the slow infiltration of glioma sphere cells. Compared with T₂ -weighted images, tumours were properly identified in their early stage of growth using diffusion MRI, and confirmed by localized proton MR spectroscopy as well as immunohistochemistry. The first evidence of tumour presence was revealed for both glioma sphere xenograft models three months after tumour implantation, while no necrosis, oedema or haemorrhage were detected either by MRI or by histology. Moreover, different values of diffusion indices, such as mean diffusivity and fractional anisotropy, were obtained in tumours grown from LN-2669GS and LN-2540GS glioma sphere lines. These observations highlighted diverse tumour microstructures for both xenograft models, which were reflected in histology. This study demonstrates the ability of diffusion MRI techniques to identify and investigate early stages of slow-growing, invasive tumours in the mouse brain, thus providing a potential imaging biomarker for early detection of tumours in humans.

Microscopic DTI accurately identifies early glioma cell migration: correlation with multimodal imaging in a new glioma stem cell model

Monitoring glioma cell infiltration in the brain is critical for diagnosis and therapy. Using a new glioma Glio6 mouse model derived from human stem cells we show how diffusion tensor imaging (DTI) may predict glioma cell migration/invasion. In vivo multiparametric MRI was performed at one, two and three months of Glio6 glioma growth (Glio6 (n = 6), sham (n = 3)). This longitudinal study reveals the existence of a time window to study glioma cell/migration/invasion selectively. Indeed, at two months only Glio6 cell invasion was detected, while tumor mass formation, edema, blood-brain barrier leakage and tumor angiogenesis were detected later, at three months. To robustly confirm the potential of DTI for detecting glioma cell migration/invasion, a microscopic 3D-DTI (80 μm isotropic spatial resolution) technique was developed and applied to fixed mouse brains (Glio6 (n = 6), sham (n = 3)). DTI changes were predominant in the corpus callosum (CC), a known path of cell migration. Fractional anisotropy (FA) and perpendicular diffusivity (D_⊥ ) changes derived from ex vivo microscopic 3D-DTI were significant at two months of tumor growth. In the caudate putamen an FA increase of +38% (p < 0.001) was observed, while in the CC a - 28% decrease in FA (p < 0.005) and a + 95% increase in D_⊥ (p < 0.005) were observed. In the CC, DTI changes and fluorescent Glio6 cell density obtained by two-photon microscopy in the same brains were correlated (p < 0.001, r = 0.69), validating FA and D_⊥ as early quantitative biomarkers to detect glioma cell migration/invasion. The origin of DTI changes was assessed by electron microscopy of the same tract, showing axon bundle disorganization. During the first two months, Glio6 cells display a migratory phenotype without being associated with the constitution of a brain tumor mass. This offers a unique opportunity to apply microscopic 3D-DTI and to validate DTI parameters FA and D_⊥ as biomarkers for glioma cell invasion.

Multiparametric magnetic resonance in the assessment of the gender differences in a high-grade glioma rat model

Background

Glioblastoma, the most frequent and aggressive of all astrocytomas, presents a clear predominance in male humans, but the assessment of sexual differences in its tumourigenesis and growth has received little attention so far. In this study, we aim to identify gender-dependent surrogate markers in an animal model of this cancer by means of magnetic resonance (MR) imaging and biochemical and behavioural studies.

Methods

A high-grade glioma model developed in male and female rats was used. Multiparametric magnetic resonance images and localized spectra were acquired. The MR parameters linked to tumoural features were quantified. Motor and metabolic activity was also assessed. Postmortem analyses were carried out to measure indicators of malignancy, tumoural metabolism and viability of the blood-brain barrier (BBB).

Results

Statistically significant differences dependent on the animal sex were found in the study of pathological indicators like oedema, inflammation, cellularity and microvasculature. Results suggest higher cell proliferative rate, inflammation and vasogenic oedema and or necrosis in glioma-bearing male rats. Haemodynamic parameters measured indicated a major disruption of the BBB, postmortem confirmed, in this sex. Metabolomic and energetic metabolism activity data are in agreement with a major malignancy and aggressiveness of this cancer model on males.

Conclusions

Gender differences should be taken into account in preclinical studies of glioblastoma models, in the characterization of the tumoural behaviour and consequently in the development and validation of new therapeutic approaches. MR imaging and spectroscopy allow to non-invasively monitor this sexual dimorphism in the diagnosis and prognosis of brain cancer.

Gradient of apparent diffusion coefficient values in peritumoral edema helps in differentiation of glioblastoma from solitary metastatic lesions

OBJECTIVE

Glioblastoma and solitary metastatic lesions can be difficult to differentiate with conventional MRI. The use of diffusion-weighted MRI to better characterize peritumoral edema has been explored for this purpose, but the results have been conflicting. The purpose of this study was to test the hypothesis that the gradient of apparent diffusion coefficient (ADC) values in peritumoral edema--that is, the difference in ADC values from the region closest to the enhancing tumor and the one closest to the normal-appearing white matter--may be a marker for differentiating glioblastoma from a metastatic lesion.

MATERIALS AND METHODS

Forty patients, 20 with glioblastoma and 20 with a solitary metastatic lesion, underwent diffusion-weighted brain MRI before surgical resection. The ADC values were retrospectively collected in the peritumoral edema in three positions: near, an intermediate distance from, and far from the core enhancing tumor (G1, G2, and G3). The ADC gradient in the peritumoral edema was calculated as the subtractions ADCG3 - ADCG1, ADCG3 - ADCG2, and ADCG2 - ADCG1. The ADC values in the enhancing tumor, peritumoral edema, ipsilateral normal-appearing white matter, contralateral healthy white matter, and CSF were also collected.

RESULTS

A gradient of ADC values was found in the peritumoral edema of glioblastoma. The ADC values increased from the region close to the enhancing tumor (1.36 ± 0.24 × 10(-3) mm(2)/s) to the area near the normal-appearing white matter (1.57 ± 0.34 × 10(-3) mm(2)/s). In metastatic lesions, however, those values were nearly homogeneous (p = 0.04).

CONCLUSION

The ADC gradient in peritumoral edema appears to be a promising tool for differentiating glioblastoma from a metastatic lesion.

Definition of K(trans) and FA thresholds for better assessment of experimental glioma using high-field MRI: a feasibility study

PURPOSE

To define K(trans) and fractional anisotropy (FA) thresholds in correlation to histology for improved magnetic resonance imaging (MRI) tumor assessment in an animal model of brain glioma.

METHODS

Twelve rats underwent 4.7 T MRI at day 10 after tumor implantation. Anatomical scans (T2, T1 at 8 min after double dose contrast application) as well as dynamic contrast-enhanced (DCE) imaging with calculation of K(trans) and diffusion tensor imaging (DTI) with calculation of FA were performed. T2- and T1-derived tumor volumes were calculated and thresholds for K(trans) and FA were defined for best MRI tumor assessment correlated to histology.

RESULTS

Tumor volumes were 159 ± 14 mm(3) (histology), 126 ± 26 mm(3) (T1 with contrast, r=0.76), and 153 ± 12 mm(3) (T2, r=0.84), respectively. K(trans)- and FA-derived tumor volumes were 160 ± 16 mm(3) (for K(trans ≥ 0.04 min(-1), r=0.94), and 159 ± 14 mm(3) (for FA £0.14, r=0.96), respectively.

CONCLUSIONS

DCE-MRI and DTI with calculation of K(trans) and FA maps allow very precise brain glioma assessment comparable to histology if established thresholds for the given tumor model are used.

pH sensing and regulation in cancer

Cells maintain intracellular pH (pH_i) within a narrow range (7.1–7.2) by controlling membrane proton pumps and transporters whose activity is set by intra-cytoplasmic pH sensors. These sensors have the ability to recognize and induce cellular responses to maintain the pH_i, often at the expense of acidifying the extracellular pH. In turn, extracellular acidification impacts cells via specific acid-sensing ion channels (ASICs) and proton-sensing G-protein coupled receptors (GPCRs). In this review, we will discuss some of the major players in proton sensing at the plasma membrane and their downstream consequences in cancer cells and how these pH-mediated changes affect processes such as migration and metastasis. The complex mechanisms by which they transduce acid pH signals to the cytoplasm and nucleus are not well understood. However, there is evidence that expression of proton-sensing GPCRs such as GPR4, TDAG8, and OGR1 can regulate aspects of tumorigenesis and invasion, including cofilin and talin regulated actin (de-)polymerization. Major mechanisms for maintenance of pH_i homeostasis include monocarboxylate, bicarbonate, and proton transporters. Notably, there is little evidence suggesting a link between their activities and those of the extracellular H⁺-sensors, suggesting a mechanistic disconnect between intra- and extracellular pH. Understanding the mechanisms of pH sensing and regulation may lead to novel and informed therapeutic strategies that can target acidosis, a common physical hallmark of solid tumors.

Utility of diffusion tensor imaging in evaluation of the peritumoral region in patients with primary and metastatic brain tumors

In the brain, diffusion tensor imaging is a useful tool for defining white matter anatomy, planning a surgical approach to space-occupying lesions, and characterizing tumors, including distinguishing primary tumors from metastases. Recent studies have attempted, with varying success, to use DTI to define the extent of tumor microinfiltration beyond the apparent borders on T2-weighted imaging. In the present review, we discuss the current state of research on the utility of DTI for evaluating the peritumoral region of brain tumors.

Quantitative in vivo characterization of intracellular and extracellular pH profiles in heterogeneous tumors: a novel method enabling multiparametric pH analysis

Acid production and transport are currently being studied to identify new targets for efficient cancer treatment, as subpopulations of tumor cells frequently escape conventional therapy owing to their particularly acidic tumor microenvironment. Heterogeneity in intracellular and extracellular tumor pH (pHi, pHe) has been reported, but none of the methods currently available for measuring tissue pH provides quantitative parameters characterizing pH distribution profiles in tissues. To this intent, we present here a multiparametric, noninvasive approach based on in vivo (31)P nuclear magnetic resonance (NMR) spectroscopy and its application to mouse tumor xenografts. First, localized (31)P NMR spectrum signals of pHi and pHe reporter molecules [inorganic phosphate (Pi) and 3-aminopropylphosphonate (3-APP), respectively] were transformed into pH curves using established algorithms. Although Pi is an endogenous compound, 3-APP had to be injected intraperitoneally. Then, we developed algorithms for the calculation of six to eight quantitative pH parameters from the digital points of each pH curve obtained. For this purpose, each pH distribution profile was approximated as a histogram, and intensities were corrected for the nonlinearity between chemical-shift and pH.

A novel transgenic rat model for spinocerebellar ataxia type 17 recapitulates neuropathological changes and supplies in vivo imaging biomarkers

Spinocerebellar ataxia 17 (SCA17) is an autosomal-dominant, late-onset neurodegenerative disorder caused by an expanded polyglutamine (polyQ) repeat in the TATA-box-binding protein (TBP). To further investigate this devastating disease, we sought to create a first transgenic rat model for SCA17 that carries a full human cDNA fragment of the TBP gene with 64 CAA/CAG repeats (TBPQ64). In line with previous observations in mouse models for SCA17, TBPQ64 rats show a severe neurological phenotype including ataxia, impairment of postural reflexes, and hyperactivity in early stages followed by reduced activity, loss of body weight, and early death. Neuropathologically, the severe phenotype of SCA17 rats was associated with neuronal loss, particularly in the cerebellum. Degeneration of Purkinje, basket, and stellate cells, changes in the morphology of the dendrites, nuclear TBP-positive immunoreactivity, and axonal torpedos were readily found by light and electron microscopy. While some of these changes are well recapitulated in existing mouse models for SCA17, we provide evidence that some crucial characteristics of SCA17 are better mirrored in TBPQ64 rats. Thus, this SCA17 model represents a valuable tool to pursue experimentation and therapeutic approaches that may be difficult or impossible to perform with SCA17 transgenic mice. We show for the first time positron emission tomography (PET) and diffusion tensor imaging (DTI) data of a SCA animal model that replicate recent PET studies in human SCA17 patients. Our results also confirm that DTI are potentially useful correlates of neuropathological changes in TBPQ64 rats and raise hope that DTI imaging could provide a biomarker for SCA17 patients.

Diffusion tensor magnetic resonance imaging of rat glioma models: a correlation study of MR imaging and histology

INTRODUCTION

Diffusion tensor magnetic resonance (MR) imaging (DTI) can be used to characterize the microstructures of ordered biological tissues. This study was designed to assess histological features of gliomas and surrounding brain tissues in rats using DTI.

METHODS

Three types of tumors, a 9L gliosarcoma (n = 8), a F98 glioma (n = 5), and a human glioblastoma xenograft (GBM22; n = 8) were incubated in rat brains and underwent conventional MRI and DTI scanning using a 4.7-T animal MRI system. Fractional anisotropy (FA), isotropic apparent diffusion coefficient, parallel diffusivity (λ//), and perpendicular diffusivity (λ⊥), as well as histological features within several regions of interest were analyzed.

RESULTS

All tumor masses consisted of low-FA central zones (tumor center) and high-FA peripheral regions (tumor rim). Histological examination revealed the existence of highly coherent tumor organizations (circular for 9L and F98 or radial for GBM22) in the tumor rims. There were higher apparent diffusion coefficient, λ⊥, and λ// in the peritumoral edema compared to the contralateral gray matter. There were significantly lower FA and higher λ⊥ in the ipsilateral white matter than in the contralateral white matter for the GBM22 tumor, whereas there were no differences for the 9L and F98 tumors. Histologic examination showed GBM22 tumor infiltration into the ipsilateral damaged white matter.

CONCLUSIONS

Quantitative analysis of DTI indices provides useful information for assessing tumor microstructure and tumor cell invasion into the adjacent gray matter and white matter.

Correlation of MRI-derived apparent diffusion coefficients in newly diagnosed gliomas with [18F]-fluoro-L-dopa PET: what are we really measuring with minimum ADC?

BACKGROUND AND PURPOSE

There is significant interest in whether diffusion-weighted MR imaging indices, such as the minimum apparent diffusion coefficient, may be useful clinically for preoperative tumor grading and treatment planning. To help establish the pathologic correlate of minimum ADC, we undertook a study investigating the relationship between minimum ADC and maximum FDOPA PET uptake in patients with newly diagnosed glioblastoma multiforme.

MATERIALS AND METHODS

MR imaging and FDOPA PET data were acquired preoperatively from 15 patients who were subsequently diagnosed with high-grade brain tumor (WHO grade III or IV) by histopathologic analysis. ADC and SUVR normalized FDOPA PET maps were registered to the corresponding CE MR imaging. Regions of minimum ADC within the FDOPA-defined tumor volume were anatomically correlated with areas of maximum FDOPA SUVR uptake.

RESULTS

Minimal anatomic overlap was found between regions exhibiting minimum ADC (a putative marker of tumor cellularity) and maximum FDOPA SUVR uptake (a marker of tumor infiltration and proliferation). FDOPA SUVR measures for tumoral regions exhibiting minimum ADC (1.36±0.22) were significantly reduced compared with those with maximum FDOPA uptake (2.45±0.88, P=.0001).

CONCLUSIONS

There was a poor correlation between minimum ADC and the most viable/aggressive component of high-grade gliomas. This study suggests that other factors, such as tissue compression and ischemia, may be contributing to restricted diffusion in GBM. Caution should be exercised in the clinical use of minimum ADC as a marker of tumor grade and the use of this index for guiding tumor biopsies preoperatively.

Boron neutron capture therapy (BNCT) in Finland: technological and physical prospects after 20 years of experiences

Boron Neutron Capture Therapy (BNCT) is a binary radiotherapy method developed to treat patients with certain malignant tumours. To date, over 300 treatments have been carried out at the Finnish BNCT facility in various on-going and past clinical trials. In this technical review, we discuss our research work in the field of medical physics to form the groundwork for the Finnish BNCT patient treatments, as well as the possibilities to further develop and optimize the method in the future. Accordingly, the following aspects are described: neutron sources, beam dosimetry, treatment planning, boron imaging and determination, and finally the possibilities to detect the efficacy and effects of BNCT on patients.

Evaluation of radiation necrosis and malignant glioma in rat models using diffusion tensor MR imaging

Standard MRI cannot distinguish between radiation necrosis and tumor progression; however, this distinction is critical in the assessment of tumor response to therapy. In this study, one delayed radiation necrosis model (dose, 40 Gy; radiation field, 10 × 10 mm(2); n = 13) and two orthotopic glioma models in rats (9L gliosarcoma, n =8; human glioma xenografts, n = 5) were compared using multiple diffusion tensor imaging (DTI) indices. A visible isotropic apparent diffusion coefficient (ADC) pattern was observed in the lesion due to radiation necrosis, which consisted of a hypointense central zone and a hyperintense peripheral zone. There were significantly lower ADC, parallel diffusivity, and perpendicular diffusivity in the necrotic central zone than in the peripheral zone (all P < 0.001). When radiation-induced necrosis was compared with viable tumor, radiation necrosis had significantly lower ADC than 9L gliosarcoma and human glioma xenografts (both P < 0.01) in the central zone, and significantly lower fractional anisotropy than 9L gliosarcoma (P = 0.005) and human glioma xenografts (P = 0.012) in the peripheral zone. Histological analysis revealed parenchymal coagulative necrosis in the central zone, and damaged vessels and reactive astrogliosis in the peripheral zone. These data suggest that qualitative and quantitative analysis of the DTI maps can provide useful information by which to distinguish between radiation necrosis and viable glioma.

Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging

BACKGROUND AND PURPOSE

Glioblastomas, brain metastases, and PCLs may have similar enhancement patterns on MR imaging, making the differential diagnosis difficult or even impossible. The purpose of this study was to determine whether a combination of DTI and DSC can assist in the differentiation of glioblastomas, solitary brain metastases, and PCLs.

MATERIALS AND METHODS

Twenty-six glioblastomas, 25 brain metastases, and 16 PCLs were retrospectively identified. DTI metrics, including FA, ADC, CL, CP, CS, and rCBV were measured from the enhancing, immediate peritumoral and distant peritumoral regions. A 2-level decision tree was designed, and a multivariate logistic regression analysis was used at each level to determine the best model for classification.

RESULTS

From the enhancing region, significantly elevated FA, CL, and CP and decreased CS values were observed in glioblastomas compared with brain metastases and PCLs (P < .001), whereas ADC, rCBV, and rCBV(max) values of glioblastomas were significantly higher than those of PCLs (P < .01). The best model to distinguish glioblastomas from nonglioblastomas consisted of ADC, CS (or FA) from the enhancing region, and rCBV from the immediate peritumoral region, resulting in AUC = 0.938. The best predictor to differentiate PCLs from brain metastases comprised ADC from the enhancing region and CP from the immediate peritumoral region with AUC = 0.909.

CONCLUSIONS

The combination of DTI metrics and rCBV measurement can help in the differentiation of glioblastomas from brain metastases and PCLs.

Diffusion tensor echo planar imaging using surface coil transceiver with a semiadiabatic RF pulse sequence at 14.1T

Diffusion magnetic resonance studies of the brain are typically performed using volume coils. Although in human brain this leads to a near optimal filling factor, studies of rodent brain must contend with the fact that only a fraction of the head volume can be ascribed to the brain. The use of surface coil as transceiver increases Signal-to-Noise Ratio (SNR), reduces radiofrequency power requirements and opens the possibility of parallel transmit schemes, likely to allow efficient acquisition schemes, of critical importance for reducing the long scan times implicated in diffusion tensor imaging. This study demonstrates the implementation of a semiadiabatic echo planar imaging sequence (echo time=40 ms, four interleaves) at 14.1T using a quadrature surface coil as transceiver. It resulted in artifact free images with excellent SNR throughout the brain. Diffusion tensor derived parameters obtained within the rat brain were in excellent agreement with reported values.

Diffusion imaging for therapy response assessment of brain tumor

Advanced imaging provides insight into biophysical, physiologic, metabolic, or functional properties of tissues. Because water mobility is sensitive to cellular homeostasis, cellular density, and microstructural organization, it is considered a valuable tool in the advanced imaging arsenal. This article summarizes diffusion imaging concepts and highlights clinical applications of diffusion MR imaging for oncologic imaging. Diffusion tensor imaging and its derivative maps of diffusion anisotropy allow assessment of tumor compression or destruction of adjacent normal tissue anisotropy and may aid to assess tumor infiltration and aid presurgical planning.

Advanced MRI: translation from animal to human in brain tumor research

Advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion MRI, and perfusion MRI, allow for a diverse range of multidimensional information regarding brain tumor physiology to be obtained in addition to the traditional anatomic images. Although it is well documented that MRI of rodent brain tumor models plays an important role in the basic research and drug discovery process of new brain tumor therapies, the role that animal models have played in translating these methodologies is rarely discussed in such articles. Even in consensus reports outlining the pathway to validation of these techniques, the use of animal models is given scant regard. This is despite that the use of rodent cancer models to test advanced MRI techniques predates and was integral to the development of clinical MRI. This article highlights just how integral preclinical imaging is to the discovery, development, and validation of advanced MRI techniques for imaging brain neoplasms.