Relative cerebral blood volume (rCBV) measured using dynamic susceptibility contrast MRI suffers from interpatient and interstudy variability for the same tissue type. Traditionally, when a more quantitative assessment of rCBV is required, as for comparison across studies and patients, the rCBV values are normalized to the rCBV in a reference region such as normal-appearing white matter. However, this technique of normalization is subjective and time consuming and introduces user-dependent variability. In this study, we demonstrate that a method called standardization, applied to rCBV maps, is an objective means of translating all rCBV values to a consistent scale. This approach reduces interpatient and interstudy variability for the same tissue type, thus enabling easy and accurate visual and quantitative comparison across studies. One caveat to this approach is that it is not appropriate for the evaluation of global changes in blood volume, since systematic differences are removed in the process of standardization.
rCBV; standardization; DSC; MRI; intrapatient comparisons
Diffusion-weighted MRI is an intrinsically low signal-to-noise ratio application due to the application of diffusion-weighting gradients and the consequent longer echo times. The signal-to-noise ratio worsens with increasing image resolution and diffusion imaging methods that use multiple and higher b-values. At low signal-to-noise ratios, standard magnitude reconstructed diffusion-weighted images are confounded by the existence of a rectified noise floor, producing poor estimates of diffusion metrics. Herein, we present a simple method of rectified noise floor suppression that involves phase correction of the real data. This approach was evaluated for diffusion-weighted imaging data, obtained from ethanol and water phantoms and the brain of a healthy volunteer. The parameter fits from monoexponential, biexponential, and stretched-exponential diffusion models were computed using phase-corrected real data and magnitude data. The results demonstrate that this newly developed simple approach of using phase-corrected real images acts to reduce or even suppress the confounding effects of a rectified noise floor, thereby producing more accurate estimates of diffusion parameters.
diffusion; DWI; MRI; stretched-exponential; phase correction; image reconstruction
To characterize the influence of perfusion on the measurement of diffusion changes over time when ADC is computed using standard two-point methods.
Materials and Methods
Functional diffusion maps (FDMs), which depict changes in diffusion over time, were compared to rCBV changes in patients with brain tumors. The FDMs were created by coregistering and subtracting ADC maps from two time points and categorizing voxels where ADC significantly increased (iADC), decreased (dADC), or did not change (ncADC). Traditional FDMs (tFDMs) were computed using b=0,1000 s/mm2. Flow-compensated FDMs (fcFDMs) were calculated using b=500,1000 s/mm2. Perfusion’s influence on FDMs was determined by evaluating changes in rCBV in areas where the ADC change significantly differed between the two FDMs.
The mean ΔrCBV in voxels that changed from iADC (dADC) on the tFDM to ncADC on the fcFDM was significantly greater (less) than zero. In addition, mean ΔrCBV in iADC (dADC) voxels on the tFDM was significantly higher (lower) than in iADC (dADC) voxels on the fcFDM.
The ability to accurately identify changes in diffusion on traditional FDMs is confounded in areas where perfusion and diffusion changes are co-localized. Flow-compensated FDMs, which use only non-zero b-values, should therefore be the standard approach.
Functional diffusion map; diffusion; perfusion; ADC; brain tumor; b-value
Despite the early promising results with the anti-angiogenic agent, bevacizumab, to prolong time to progression in patients with brain tumors, the optimal dose and drug combinations have not yet been defined. The purpose of this study was to characterize the bevacizumab dose–response relationship for brain tumors by measuring the contrast-agent enhanced tumor volumes and relative cerebral blood volume (rCBV) using dynamic susceptibility contrast (DSC) imaging. The studies, performed in the U87 brain tumor model using doses of bevacizumab ranging from 0 to 10 mg/kg, demonstrate that tumor growth and vascularity are inhibited at all doses used, compared to untreated controls. However, only the maximum dose showed a statistically significant difference in growth rate. Conversely tumor vascularity, as measured with rCBV, was inhibited equally well for all doses used with no clear indication that higher doses are more effective.
Glioblastoma; Bevacizumab; MRI; rCBV; Dose–response
To determine the potential of using a computer-aided detection method to intelligently distinguish peritumoral edema alone from peritumor edema consisting of tumor using a combination of high-resolution morphological and physiological magnetic resonance imaging (MRI) techniques available on most clinical MRI scanners.
Materials and Methods
This retrospective study consisted of patients with two types of primary brain tumors: meningiomas (n=7) and glioblastomas (n=11). Meningiomas are typically benign and have a clear delineation of tumor and edema. Glioblastomas are known to invade outside the contrast-enhancing area. Four classifiers of differing designs were trained using morphological, diffusion-weighted, and perfusion-weighted features derived from MRI to discriminate tumor and edema, tested on edematous regions surrounding tumors, and assessed for their ability to detect nonenhancing tumor invasion.
The four classifiers provided similar measures of accuracy when applied to the training and testing data. Each classifier was able to identify areas of non-enhancing tumor invasion supported with adjunct images or follow-up studies.
The combination of features derived from morphological and physiological imaging techniques contains the information necessary for computer-aided detection of tumor invasion and allows for the identification of tumor invasion not previously visualized on morphological, diffusion-weighted, and perfusion-weighted images and maps. Further validation of this approach requires obtaining spatially co-registered tissue samples in a study with a larger sample size.
MRI; computer-aided detection (CAD); brain tumor; perfusion; diffusion
Abnormal brain tumor vasculature has recently been highlighted by a dynamic susceptibility contrast (DSC) MRI processing technique. The technique uses independent component analysis (ICA) to separate arterial and venous perfusion. The overlap of the two, i.e. arterio-venous overlap or AVOL, preferentially occurs in brain tumors and predicts response to anti-angiogenic therapy. The effects of contrast agent leakage on the AVOL biomarker have yet to be established. DSC was acquired during two separate contrast boluses in ten patients undergoing clinical imaging for brain tumor diagnosis. Three components were modeled with ICA, which included the arterial and venous components. The percentage of each component as well as a third component were determined within contrast enhancing tumor and compared. AVOL within enhancing tumor was also compared between doses. The percentage of enhancing tumor classified as not arterial or venous and instead into a third component with contrast agent leakage apparent in the time-series was significantly greater for the first contrast dose compared to the second. The amount of AVOL detected within enhancing tumor was also significantly greater with the second dose compared to the first. Contrast leakage results in large signal variance classified as a separate component by the ICA algorithm. The use of a second dose mitigates the effect and allows measurement of AVOL within enhancement.
Perfusion MRI; Independent Component Analysis (ICA); Dynamic Susceptibility Contrast (DSC); Brain Tumor; Glioblastoma
The purpose of the current study was to develop a voxel-wise analytical solution to a glioma growth model using serial diffusion MRI. These cell invasion, motility, and proliferation level estimates (CIMPLE maps) provide quantitative estimates of microscopic tumor growth dynamics. After an analytical solution was found, noise simulations were performed to predict the effects that perturbations in apparent diffusion coefficient (ADC) values and the time between ADC map acquisitions would have on the accuracy of CIMPLE maps. CIMPLE maps were then created for 53 patients with gliomas with WHO grades of II–IV. MR spectroscopy estimates of the Cho/NAA ratio were compared to cell proliferation estimates in CIMPLE maps using Pearson’s correlation analysis. Median differences in cell proliferation and diffusion rates between WHO grades were compared. A strong correlation (R2 = 0.9714) and good spatial correspondence were observed between MR spectroscopy measurements of the Cho/NAA ratio and CIMPLE map cell proliferation rate estimates. Estimates of cell proliferation and diffusion rates appear to be significantly different between low (WHO II) and high-grade (WHO III–IV) gliomas. Cell diffusion rate (motility) estimates are highly dependent on the time interval between ADC map acquisitions, whereas cell proliferation rate estimates are additionally influenced by the level of noise present in ADC maps.
CIMPLE maps; diffusion MRI; glioma; invasion; proliferation; glioma
To present comprehensive examinations of the assumptions made in functional diffusion map (fDM) analyses and provide a biological basis for fDM classification.
Materials and Methods
Sixty-nine patients with gliomas were enrolled in this study. To determine the sensitivity of ADC to cellularity, cell density from stereotactic biopsy specimens was correlated with pre-operative ADC maps. For definition of ADC thresholds used for fDMs, the 95% confidence intervals (C.I.) for changes in voxel-wise ADC measurements in normal appearing tissue was analyzed. The sensitivity and specificity to progressing disease was examined using both radiographic and neurological criteria.
Results support the hypothesis that ADC is inversely proportional to cell density with a sensitivity of 1.01 × 10-7 [mm2/s]/[nuclei/mm2]. The 95% C.I. for white matter = 0.25×10-3mm2/s, gray matter = 0.31×10-3mm2/s, a mixture of white and gray matter = 0.40×10-3mm2/s, and a mixture of white matter, gray matter, and cerebrospinal fluid = 0.75×10-3mm2/s. Application of these measurements as ADC thresholds produce varying levels of sensitivity and specificity to disease progression, which were all significantly better than chance.
This study suggests fDMs are valid biomarkers for brain tumor cellularity.
Diffusion MRI; brain tumor; glioma; functional diffusion maps; fDM
Recently, we demonstrated that vessel geometry is a significant determinant of susceptibility-induced contrast in MRI. This is especially relevant for susceptibility-contrast enhanced MRI of tumors with their characteristically abnormal vessel morphology. In order to better understand the biophysics of this contrast mechanism, it is of interest to model how various factors, including microvessel morphology contribute to the measured MR signal, and was the primary motivation for developing a novel computer modeling approach called the Finite Perturber Method (FPM). The FPM circumvents the limitations of traditional fixed-geometry approaches, and enables us to study susceptibility-induced contrast arising from arbitrary microvascular morphologies in 3D, such as those typically observed with brain tumor angiogenesis. Here we describe this new modeling methodology and some of its applications. The excellent agreement of the FPM with theory and the extant susceptibility modeling data, coupled with its computational efficiency demonstrates its potential to transform our understanding of the factors that engender susceptibility contrast in MRI.
Dynamic susceptibility; contrast; arbitrary geometry; microvasculature; tumor angiogenesis; BOLD fMRI
Standard pre- and postcontrast (T1 + C) anatomical MR imaging is proving to be insufficient for accurately monitoring bevacizumab treatment response in recurrent glioblastoma (GBM). We present a novel imaging biomarker that detects abnormal tumor vasculature exhibiting both arterial and venous perfusion characteristics. We hypothesized that a decrease in the extent of this abnormal vasculature after bevacizumab treatment would predict treatment efficacy and overall survival.
Dynamic susceptibility contrast perfusion MRI was gathered in 43 patients with high-grade glioma. Independent component analysis separated vasculature into arterial and venous components. Voxels with perfusion characteristics of both arteries and veins (ie, arterio-venous overlap [AVOL]) were measured in patients with de novo untreated GBM and patients with recurrent high-grade glioma before and after bevacizumab treatment. Treated patients were separated on the basis of an increase or decrease in AVOL volume (+/−ΔAVOL), and overall survival following bevacizumab onset was then compared between +/−ΔAVOL groups.
AVOL in untreated GBM was significantly higher than in normal vasculature (P < .001). Kaplan-Meier survival curves revealed a greater median survival (348 days) in patients with GBM with a negative ΔAVOL after bevacizumab treatment than in patients with a positive change (197 days; hazard ratio, 2.51; P < .05). Analysis of patients with combined grade III and IV glioma showed similar results, with median survivals of 399 days and 153 days, respectively (hazard ratio, 2.71; P < .01). Changes in T1+C volume and ΔrCBV after treatment were not significantly different across +/−ΔAVOL groups, and ΔAVOL was not significantly correlated with ΔT1+C or ΔrCBV.
The independent component analysis dynamic susceptibility contrast–derived biomarker AVOL adds additional information for determining bevacizumab treatment efficacy.
angiogenesis; bevacizumab; brain tumor; DSC; glioblastoma; glioma; perfusion; ICA; independent component analysis; MRI
Frequently, bevacizumab is combined with chemotherapeutics such as irinotecan, motivated by studies showing improved clinical outcomes compared to historical controls. However, no systematic studies have been performed to determine if and how these drugs should be combined for optimal therapeutic response. The purpose of this study was to characterize the temporal combinations of bevacizumab and irinotecan by measuring the contrast-agent enhanced tumor volumes and relative cerebral blood volume (rCBV) using dynamic susceptibility contrast (DSC) imaging. The studies, performed in the U87 brain tumor model, show a vascular normalization window with bevacizumab monotherapy and are consistent with clinical indications of no additional benefit in the addition of irinotecan to bevacizumab therapy.
glioblastoma; bevacizumab; MRI; irinotecan
Diffusion-weighted imaging (DWI) is a powerful MRI method, which probes abnormalities of tissue structure by detecting microscopic changes in water mobility at a cellular level beyond what is available with other imaging techniques. Accordingly, DWI has the potential to identify pathology before gross anatomic changes are evident on standard anatomical brain images. These features of tissue characterization and earlier detection are what make DWI particularly appealing for the evaluation of gliomas and the newer therapies where standard anatomical imaging is proving insufficient. This article focuses on the basic principles and applications of DWI, and its derived parameter, the apparent diffusion coefficient, for the purposes of diagnosis and evaluation of glioma, especially in the context of monitoring response to therapy.
Diffusion imaging has shown promise as a predictive and prognostic biomarker in glioma. We assessed the ability of graded functional diffusion maps (fDMs) and apparent diffusion coefficient (ADC) characteristics to predict overall survival (OS) in recurrent glioblastoma multiforme (GBM) patients treated with bevacizumab. Seventy-seven patients with recurrent GBMs were retrospectively examined. MRI scans were obtained before and approximately 6 weeks after treatment with bevacizumab. Graded fDMs were created by registering datasets to each patient's pretreatment scan and then performing voxel-wise subtraction between post- and pretreatment ADC maps. Voxels were categorized according to the degree of change in ADC within pretreatment fluid-attenuated inversion recovery (FLAIR) and contrast-enhancing regions of interest (ROIs). We found that the volume of tissue showing decreased ADC within both FLAIR and contrast-enhancing regions stratified OS (log-rank, P < .05). fDMs applied to contrast-enhancing ROIs more accurately predicted OS compared with fDMs applied to FLAIR ROIs. Graded fDMs (showing voxels with decreased ADC between 0.25 and 0.4 µm2/ms) were more predictive of OS than traditional (single threshold) fDMs, and the predictive ability of graded fDMs could be enhanced even further by adding the ADC characteristics from the fDM-classified voxels to the analysis (log-rank, P < .001). These results demonstrate that spatially resolved diffusion-based tumor metrics are a powerful imaging biomarker of survival in patients with recurrent GBM treated with bevacizumab.
diffusion MRI; biomarker; fDM; functional diffusion map; GBM; glioblastoma
Anti-angiogenic agents targeting brain tumor neovasculature may increase progression-free survival in patients with recurrent malignant gliomas. However, when these patients do recur it is not always apparent as an increase in enhancing tumor volume on MRI, which has been the standard of practice for following patients with brain tumors. Therefore alternative methods are needed to evaluate patients treated with these novel therapies. Furthermore, a method that can also provide useful information for the evaluation of conventional therapies would provide an important advantage for general applicability. Diffusion-weighted magnetic resonance imaging (DWI) has the potential to serve as a valuable biomarker for these purposes. In the current study, we explore the prognostic ability of functional diffusion maps (fDMs), which examine voxel-wise changes in the apparent diffusion coefficient (ADC) over time, applied to regions of fluid-attenuated inversion recovery (FLAIR) abnormalities in patients with malignant glioma, treated with either anti-angiogenic or cytotoxic therapies. Results indicate that the rate of change in fDMs is an early predictor of tumor progression, time to progression and overall survival for both treatments, suggesting the application of fDMs in FLAIR abnormal regions may be a significant advance in brain tumor biomarker technology.
Functional diffusion map (fDM); Diffusion MRI; Glioma; Bevacizumab; Chemotherapy; Angiogenesis; Brain tumor
This study demonstrates that a dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) perfusion parameter may indicate vascular abnormality in a brain tumor model and reflects an effect of dexamethasone treatment. In addition, X-ray computed tomography (CT) measurements of vascular tortuosity and tissue markers of vascular morphology were performed to investigate the underpinnings of tumor response to dexamethasone.
One cohort of Fisher 344 rats (N = 13), inoculated intracerebrally with 9L gliosarcoma cells, was treated with dexamethasone (i.p. 3 mg/kg/day) for five consecutive days, and another cohort (N = 11) was treated with equal volume of saline. Longitudinal DSC-MRI studies were performed at the first (baseline), third and fifth day of treatments. Relative cerebral blood volume (rCBV) was significantly reduced on the third day of dexamethasone treatment (0.65±.13) as compared to the fifth day during treatment (1.26±.19, p<0.05). In saline treated rats, relative CBV gradually increased during treatment (0.89±.13, 1.00±.21, 1.13±.23) with no significant difference on the third day of treatment (p>0.05). In separate serial studies, microfocal X-ray CT of ex vivo brain specimens (N = 9) and immunohistochemistry for endothelial cell marker anti-CD31 (N = 8) were performed. Vascular morphology of ex vivo rat brains from micro-CT analysis showed hypervascular characteristics in tumors, and both vessel density (41.32±2.34 branches/mm3, p<0.001) and vessel tortuosity (p<0.05) were significantly reduced in tumors of rats treated with dexamethasone compared to saline (74.29±3.51 branches/mm3). The vascular architecture of rat brain tissue was examined with anti-CD31 antibody, and dexamethasone treated tumor regions showed reduced vessel area (16.45±1.36 µm2) as compared to saline treated tumor regions (30.83±4.31 µm2, p<0.001) and non-tumor regions (22.80±1.11 µm2, p<0.01).
Increased vascular density and tortuosity are culprit to abnormal perfusion, which is transiently reduced during dexamethasone treatment.
Diffusion-weighted magnetic resonance imaging (DWI) is a sensitive imaging biomarker for tumor cellularity. Functional diffusion maps (fDMs), which examine voxel-by-voxel changes in the apparent diffusion coefficient (ADC) calculated from serial DWIs, have previously been applied to regions of contrast-enhancement; however, application of fDMs to non-enhancing brain tumors has not been pursued. In this case study we demonstrate the utility of applying fDMs to regions of abnormal FLAIR signal intensity in a patient diagnosed with gliomatosis cerebri: a relatively rare, infiltrative, non-enhancing brain tumor. The absolute volume of hyper-cellularity extracted from fDMs was useful in tracking tumor growth, which correlated in time with a progressive decline in neurological status despite no change in traditional magnetic resonance images. Results of this study demonstrate the value of fDMs, applied to regions of FLAIR abnormal signal intensity, for localizing regions of hypercellularity and for monitoring overall tumor status.
Gliomatosis cerebri; Functional diffusion maps; Diffusion MRI; Non-enhancing glioma; Cancer biomarkers
Depending on dose, dexamethasone has been shown to inhibit or stimulate growth of rat 9L gliosarcoma and decrease the expression of vascular endothelial growth factor (VEGF), an important mediator of tumor-associated angiogenesis. We demonstrate, by constructing relative cerebral blood volume (rCBV) maps with MRI, that dexamethasone also decreases total blood volume while increasing microvascular blood volume in Fischer rats bearing intracranial 9L gliosarcoma. Animals were inoculated with 1 x 10(5) 9L gliosarcoma tumor cells. On days 10-14 after tumor cell inoculation, animals were intra-peritoneally injected with dexamethasone (3 mg/kg) over 5 days. MRI-derived gradient echo (GE) and spin-echo (SE) rCBV maps were created to demonstrate total vasculature (GE) and microvasculature (SE). After MRI studies were performed, the rat's vasculature was perfused with a latex compound. Total vessel volume and diameters were assessed by microscopy. Dexamethasone decreased the tumor-enhancing area of postcontrast T1-weighted images (P < 0.0001) and total tumor volume(P = 0.0085). In addition, there was a greater than 50% decrease in GE rCBV (total vasculature) (P = 0.007) as well as a significant decrease in total fractional blood volume, as validated by histology (P = 0.0007). Conversely, there was an increase in SE rCBV signal (microvasculature) in animals treated with dexamethasone (P = 0.05), which was consistent with microscopy (P < 0.0001). These data demonstrate that (1) dexamethasone selectively treats tumor vasculature, suggesting a vessel-size selective effect and (2) MRI-derived rCBV is a noninvasive technique that can be used to evaluate changes in blood volume and vascular morphology.