JOURNAL OF MAGNETIC RESONANCE IMAGING 41:296–313 (2015)

Review Article

Principles of T2*-Weighted Dynamic Susceptibility Contrast MRI Technique in Brain Tumor Imaging Mark S. Shiroishi, MD,1* Gloria Castellazzi, PhD,2,3 Jerrold L. Boxerman, MD, PhD,4 Francesco D’Amore, MD,1,5 Marco Essig, MD, PhD,6 Thanh B. Nguyen, MD, FRCPC,7 James M. Provenzale, MD,8,9 David S. Enterline, MD,8 Nicoletta Anzalone, MD,10  €rfler, MD,11 Alex Arnd Do Rovira, MD,12 Max Wintermark, MD,13 and Meng Law, MD, MBBS, FRACR1 high-quality multicenter studies and ultimately help guide clinical care.

Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is used to track the first pass of an exogenous, paramagnetic, nondiffusible contrast agent through brain tissue, and has emerged as a powerful tool in the characterization of brain tumor hemodynamics. DSC-MRI parameters can be helpful in many aspects, including tumor grading, prediction of treatment response, likelihood of malignant transformation, discrimination between tumor recurrence and radiation necrosis, and differentiation between true early progression and pseudoprogression. This review aims to provide a conceptual overview of the underlying principles of DSC-MRI of the brain for clinical neuroradiologists, scientists, or students wishing to improve their understanding of the technical aspects, pitfalls, and controversies of DSC perfusion MRI of the brain. Future consensus on image acquisition parameters and postprocessing of DSC-MRI will most likely allow this technique to be evaluated and used in

Key Words: perfusion magnetic resonance imaging; gadolinium-based contrast agents; brain tumors; echo planar imaging; cerebral blood volume J. Magn. Reson. Imaging 2015;41:296–313. C 2014 Wiley Periodicals, Inc. V

1 Keck School of Medicine, University of Southern California, Los Angeles, California, USA. 2 Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy. 3 Brain Connectivity Center, IRCCS “C. Mondino Foundation,” Pavia, Italy. 4 Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA. 5 Department of Neuroradiology, IRCCS “C. Mondino Foundation,” University of Pavia, Pavia, Italy. 6 University of Manitoba’s Faculty of Medicine, Winnipeg, Manitoba, Canada. 7 Faculty of Medicine, Ottawa University, Ottawa, Ontario, Canada. 8 Duke University Medical Center, Durham, North Carolina, USA. 9 Emory University School of Medicine, Atlanta, Georgia, USA. 10 Scientific Institute H. S. Raffaele, Milan, Italy. 11 University of Erlangen-Nuremberg, Erlangen, Germany. 12 Vall d’Hebron University Hospital, Barcelona, Spain. 13 School of Medicine, University of Virginia, Charlottesville, Virginia, USA. The first three authors contributed equally to this work. *Address reprint requests to: M.S.S., Assistant Professor, Division of Neuroradiology, Department of Radiology, Keck School of Medicine, University of Southern California, 1520 San Pablo St., Lower Level Imaging L1600, Los Angeles, CA 90033. E-mail: [email protected] Received October 3, 2013; Accepted April 3, 2014. DOI 10.1002/jmri.24648 View this article online at wileyonlinelibrary.com. C 2014 Wiley Periodicals, Inc. V

Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is a technique that tracks the first pass of an exogenous, paramagnetic, nondiffusible contrast agent through a given tissue. The technique was first described by Villringer et al in 1988 (1). DSC-MRI uses rapid measurements of MRI signal change following the injection of a paramagnetic contrast agent with a high magnetic moment, typically a gadolinium chelate (gadolinium-based contrast agents or GBCA), which leads to a significant decrease in brain signal intensity on spin echo (SE) and gradient echo (GRE) echo planar imaging (EPI) images. This magnetic susceptibility effect results from local magnetic field gradients induced by intravascular compartmentalization of the contrast agent and it dominates the T1 relaxation enhancement due to direct interaction of intravascular protons with the coordination sphere of the gadolinium chelate. The signal loss resulting from the first passage of the contrast agent bolus on T2- or T2*-weighted images is used to evaluate the change in contrast agent concentration occurring in each voxel of the image. The application of a kinetic model based in general on the nondiffusible tracer theory, also called indicator dilution theory (2), allows estimation of quantitative maps of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) (3,4). However, although the contrast agent is compartmentalized within the vascular space in healthy brain during the first pass when the concentration of GBCA is the highest (1,5), its susceptibility effects are exerted

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Figure 1. Diagram explaining calculation of relative cerebral blood volume (rCBV), cerebral blood flow (CBF), and mean transit time (MTT) using dynamic contrast-enhanced T2-weighted technique. Signal-time course data for each voxel is converted to tracer tissue concentration-time course data using the well-characterized relationship between T2* signal intensity and tracer tissue concentration. Maps of rCBV are obtained by determining area below tracer concentration-time curve. Maps of relative CBF are obtained by determining height of ideal tissue concentration-time curve, or tissue response function. Maps of MTT are obtained by dividing area under tissue response function by its height. To obtain the tissue response function, arterial concentration-time curve, or arterial input function, must be deconvolved from measured tissue concentrationtime curve. This arterial input function may be derived directly from imaging data. EPI, echoplanar imaging. From Petrella JR, Provenzale JM. MR perfusion imaging of the brain: techniques and applications. AJR Am J Roentgenol 2000;175:207– 219. Reprinted with permission from the American Journal of Roentgenology.

beyond vessels walls. Figure 1 shows the steps of the quantification process of perfusion DSC-MRI: from the detection of the DSC-MRI signal intensity–time curves and estimation of concentration–time curves to the generation of the CBV, CBF, and MTT maps (6). Along with relaxivity-based T1-weighted dynamic contrast enhanced MRI (DCE-MRI), DSC-MRI has shown its potential to characterize brain tumor hemodynamics for applications such as determination of tumor grade (7–12), prediction of clinical response and malignant transformation (13,14) distinguishing between recurrent tumor and radiation necrosis (15–18), and differentiating true early progression from pseudoprogression (19–21). However, much of the data are derived from small patient sample sizes in single institutions with variability in technique. In general, relative cerebral blood volume (rCBV) is correlated with tumor grade, with higher values in high-grade gliomas compared to low-grade tumors (7– 12). In a study examining the use of DSC-MRI in glioma grading, Law et al (10) found that an rCBV ratio threshold of 1.75 resulted in 95.0% sensitivity, 57.5% specificity, 87.0% positive predictive value, and 79.3% negative predictive value to determine high-grade gliomas. A recent small study in 17 patients with histologically proven nonenhancing gliomas found that using maximum rCBV normalized to contralateral normal-appearing white matter, a threshold of 0.94

resulted in 90.9% sensitivity and 100% specificity to differentiate high-grade from low-grade gliomas (11). In a study of 35 patients with low-grade gliomas, Law et al found that tumors with a baseline rCBV ratio < 1.75 had a significantly longer time to progression than for those >1.75 (P < 0.005). The authors suggested that the results of the study imply that baseline rCBV may be a stronger predictor of patient outcome than initial histopathologic diagnosis (13). A study of 13 patients with low-grade gliomas by Danchaivijitr et al (14) found that for those tumors undergoing malignant transformation, a significant increase in rCBV could be detected 12 months before contrast enhancement can be seen on conventional T1weighted imaging. In addition, there was also significantly higher rates of change of rCBV between two successive examination timepoints in transformers compared to nontransformers. In a recent study of 59 newly diagnosed glioblastoma patients with new or enlarging contrastenhancing lesions following chemoradiation with temozolomide, Kong et al (19) reported that an rCBV ratio > 1.47 had an 81.5% sensitivity and 77.8% specificity to differentiate pseudoprogression from true early progression. Another recent study used changes in histogram shape (percent change of skewness and kurtosis) of normalized rCBV to differentiate true early progression from pseudoprogression in GBM patients with an area under the ROC curve of 0.934

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(95% confidence interval: 0.855–0.977), with 85.7% sensitivity and 89.2% specificity (20). A 2013 study of 19 patients compared use of ferumoxytol, an iron oxide nanoparticle that functions as a blood pool agent that is not prone to extravasate from a leaky blood–brain barrier (BBB), with gadoteridol for diagnosis of pseudoprogression with DSC-MRI (21). They concluded that rCBV determined with ferumoxytol or leakage-corrected rCBV with gadoteridol may allow diagnosis of pseudoprogression and is significantly associated with survival, while nonleakage-corrected gadoteridol measurements are not. The authors concluded that by acting as a blood pool agent, ferumoxytol offers a simplified and reliable method of rCBV measurement without leakage correction (see section on GBCA Leakage below). A small study in metastatic brain tumors treated with stereotactic radiosurgery by Mitsuya et al (15) demonstrated that an rCBV ratio of greater than 2.1 provided superior diagnostic accuracy with sensitivity and specificity for diagnosing recurrent tumor at 100% and 95.2%, respectively, while a study by Hoefnagels et al (16) reported an rCBV ratio of 2 to have 85% and 92% sensitivity and specificity, respectively. Among the many peculiar features of the central nervous system (CNS) is the presence of a BBB that, when intact, compartmentalizes GBCAs within the intravascular compartment and thus provides a unique physiological environment suitable for DSCMRI (22). The analysis of DSC-MRI data is based on the assumptions that the contrast agent remains inside the vascular lumen during its passage through the brain, that the BBB is intact, and that there are no recirculation effects, which, if not accounted for, systematically contribute to miscalculation of the hemodynamic parameters of interest (23–25). However, in those cases where the BBB has been disrupted, commonly seen in high-grade brain tumors, contrast enhancement is the result of increased tumor vascularity and leakage of GBCA into the extravascular extracellular compartment. Such extravasation of GBCA violates the assumption of tracer intravascularity and the application of the “indicator dilution theory” model yields inaccurate CBV, CBF, and MTT maps. In this case the CBV is underestimated if T1weighted effects induced by increased permeability of tumor vessels dominate (9,24), or overestimated if T2*-weighted effects dominate. These effects of contrast agents extravasation may be corrected to some extent by injecting a small dose of contrast agent prior to the acquisition of the first-pass DSC-MRI series (24).

IMAGING METHODS DSC-MRI Acquisition Sequences The passage of the paramagnetic GBCA through the cerebral microvasculature causes inhomogeneities of the local magnetic fields around blood vessels, thereby accelerating the proton dephasing in the surrounding tissue. A fast acquisition multislice imaging technique such as EPI is generally performed using

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either T2*-weighted GRE or T2-weighted SE pulse sequences. Since the bolus transit time lasts only a few seconds, EPI is able to characterize the transient MR signal drop (of 10 seconds) with sufficient temporal resolution. In particular, single-shot EPI is the most widely used sequence to perform DSC-MRI acquisitions, facilitating whole-brain coverage with reasonable signal-to-noise ratios (SNRs) (26). Both T2*-weighted GRE EPI and T2-weighted SE-EPI provide sensitive measurements of perfusion hemodynamics, although the sensitivity of the perfusion technique to capillary versus macrovascular blood flow depends crucially on the features of the chosen DSC-MRI pulse sequence. Specifically, SE sequences remove the dephasing generated by the static field inhomogeneities using an additional refocusing pulse. Therefore, SE sequences are sensitive to changes in T2. GRE acquisitions, by comparison, do not refocus static magnetic field inhomogeneities and are therefore sensitive to changes in T2* (27–29). Both computer modeling (27,28) and in vitro (30) and in vivo (27) experiments have demonstrated that, whereas SE DSC-MRI sensitivity peaks for capillary-sized vessels, GRE DSC-MRI sensitivity plateaus over a broad range of vessel sizes. As a result, SE DSC-MRI acquisitions are more sensitive to the microvasculature (vessels with diameter smaller than 8 mm assuming a B0 field strength of 1.5 T (27)), whereas GRE DSC-MRI images are sensitive to a broader range of vessel sizes, with greater sensitivity to macrovessels. Because microvascular density is used as a marker for tumor angiogenesis, it has been proposed that SE DSC-MRI images are superior compared to GRE DSC-MRI for brain tumor studies. However, other studies (8) have determined that GRE DSC-MRI, with its ability to more accurately quantify the contrast concentration within the vessels and tissue, and its sensitivity to enlarged, morphologically abnormal vessels that are the hallmark of tumor angiogenesis, provides better results than SE DSC-MRI when assessing glioma grade (12,31,32). For stroke imaging, by comparison, the microvascular sensitivity of SE CBF and CBV maps may be desirable in principle for detecting subtle hemodynamic changes on the capillary level that may be obscured by the large-vessel sensitivity of GE maps. Dominant large-vessel sensitivity for GE may also prevent the detection of subtle perfusion defects in the setting of capillary shunting. From susceptibility contrast principles, a change in GRE relaxivity (DR2*) exceeds the change in SE relaxivity (DR2) for all vessel sizes (27) and therefore GRE DSC-MRI has inherently higher SNR and sensitivity than SE DSC-MRI, providing either greater signal changes with equal contrast agent doses, or equal signal changes with reduced contrast agent dose. DR2* is also linear with respect to contrast agent concentration over a broader range of vessel sizes than DR2 (27) and less sensitive to changes in proton diffusion rate (30), making GRE DSC-MRI inherently more accurate than SE DSC-MRI from a tracer kinetic perspective. Furthermore, GRE-EPI sequences require shorter TE than SE-EPI and therefore allow more rapid acquisition of the DSC-MRI images. However, GRE DSC-MRI

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images are more prone to magnetic susceptibility artifacts due to partial volume effects in the vicinity of large vessels (26,33) and present greater image distortion and susceptibility artifact arising from the calvarium, skull base, paranasal sinuses, hematomas, or resection cavities (9,27,31,32,34–36). Therefore, GRE DSC-MRI may be desirable when using lower relaxivity contrast agents (or low contrast agent dose for glomerular filtration rate [GFR] issues) and at lower field strengths where susceptibility artifacts are comparably small and a “boost” in relative signal drop is desired, and SE may have advantages with higher relaxivity contrast agents and at higher field strengths where SNR and potential susceptibility artifacts are inherently greater. Despite its limitations, to date GRE-EPI is more commonly used in the routine clinical practice to perform perfusion DSC-MRI. An alternative technique to acquire DSC-MRI images is the asymmetric spin-echo (ASE) sequence (27). ASE is a hybrid multiecho pulse sequence that includes alternating SE and GRE data collections during the same first-pass circulation of GBCA through the brain. Combined SE-EPI and GRE-EPI sequences (37) provide the advantages of a high microvascular specificity of SE-EPI along with the ability to measure the arterial contrast concentration with GRE-EPI, which is required to determine the arterial input function (26). An effective “vessel size index” can also be approximated using the ratio of the GRE and SE relaxation rates. The ASE sequence contains mixed GRE and SE contrast and the derived CBV maps therefore have blended vessel size sensitivity. Recently, other sequences that combine SE- and GRE-EPI (SAGE EPI) have been used to acquire DSCMRI images. SAGE sequences allow the acquisition of DSC-MRI images using simultaneous measurements of GRE-EPI and SE-EPI (38), combining the advantages of higher sensitivity of GRE DSC-MRI to the contrast agent passage with the better selectivity of SE DSC-MRI measurements to the microvasculature (38). Furthermore, most modern scanners are equipped with multiple receive coils, and so parallel imaging can be used to improve the spatial coverage and temporal resolution of DSC-MRI acquisitions (39). Either 2D or 3D sequences can be used, although 2D sequences are more commonly employed (40,41). While 3D sequences allow increased coverage, they may require decreased spatial resolution or increased TR (40). Also, a smoothing of the time changes during the first pass may occur with 3D sequences because the time of acquisition is not well defined compared with 2D multislice methods. Therefore, if the temporal resolution is on the order of several seconds, this can result in incorrect characterization of the passage of the GBCA bolus. The typical acquisition times for T2* DSC-MRI acquisition are on the order of 45–60 seconds. Because MTT (ie, the time for the contrast material bolus to pass through the tissue of interest) is on the order of a few seconds, a temporal resolution (TR) of 1–2 seconds would be adequate for baseline acquisition and evaluation of the first pass with allowance for whole brain coverage with GRE (34,42,43).

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However, because the number of baseline acquisitions significantly impacts CBV map SNR (44), there are advantages to beginning image acquisition at least 30–50 timepoints before contrast injection via power injector. Postprocessing leakage correction algorithms and percent signal recovery (PSR) analysis use postbolus “tail” signal intensities, necessitating acquisition of sufficient postbolus timepoints. Therefore, some protocols recommend acquiring 120 total timepoints (45). In stroke, baseline acquisitions can be traded for tail acquisitions to avoid potential truncation artifacts from slow or delayed flow, but there are arguments based on SNR of derived perfusion parameters for acquiring at least 30–50 baseline timepoints. Field Strength Either 1.5T or 3T field strength can be used for DSCMRI. 3T allows for higher SNR and less GBCA dose compared with 1.5T; however, greater magnetic susceptibility artifacts may be encountered at this field strength. Contrast agent dose, pulse sequence, and acquisition parameters may depend on whether imaging is performed at 1.5T or 3T. Contrast Agents: Dose, Injection Techniques, and Special Recommendations Contrast agents in DSC-MRI are paramagnetic tracers in the form of GBCAs used off-label for brain perfusion imaging because they do not have specific U.S. Food and Drug Administration (FDA) approval for this purpose. These agents are low molecular weight (500 Da) organic molecules that chelate the lanthanide rare earth metal gadolinium. They are very effective at producing longitudinal relaxation (or T1 shortening) which results in relative T1 hyperintensity on conventional postcontrast T1-weighted images (46). As stated before, as long as the BBB is intact, they remain in the intravascular space and effectively function as blood pool agents (during the first pass through the brain). Under normal circumstances, the only T1-contrast enhancement (“positive enhancement”) that can be seen is typically in macrovessels. On the other hand, DSC-MRI exploits the strong T2 or T2* susceptibility effect (“negative enhancement”) of the compartmentalized intravascular paramagnetic GBCAs. In cases where the BBB is interrupted or nonexistent (such as in the neurohypophysis and in extracranial organs), the GBCAs will extravasate into the extravascular extracellular space (see section on GBCA Leakage for further discussion). The choice of administered contrast agent dose may be impacted by the field strength of the scanner as well as whether SE or GRE sequences are used (47). For GRE, 0.1 mmol/kg can be used at both 1.5 and 3T. In general, GRE allows one to tradeoff SNR for GBCA dose (ie, greater signal drop at the same dose compared to SE, or same signal drop with a lower dose). In general, the larger the dose of GBCA, the higher the SNR of DSC-MRI parameter maps, as long as the TE is concordantly decreased to an appropriate degree (44). However, should signal drop be too great,

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Table 1 Imaging Parameters for DSC-MRI Optimized for a 1.5T B0 Static Magnetic Field Sequence Mathematical model Model Parameters Absolute quantitation Image acquisition time Postprocessing time Flip angle TR/temporal resolution TE FOV Matrix Slice thickness Number of slices Interslice gap Number of repeated images at each slice Volume coverage Time of injection Amount of GBCA GBCA injection rate Amount of saline flush IV catheter gauge

GRE-EPI (2D multislice) Meier-Zierler CBV, CBF, MTT, permeability Not in daily practice For a typical TR of 1500 msec, this corresponds to 40–120 timepoints < 1 min using modern commercially available software 60–70 degrees 1500 msec (range 1000–1500 msec) 35–45 msec at 1.5T and 25–30 msec at 3T 20 x 20 cm (range 20 x 20 to 24 x 24 cm) 128 x 128 (range 64 x 64 to 256 x 256) 5 mm (range 3 to 5 mm) 11 (range 5–20) 1 mm or interleaved (range 0 to 10 mm) 40–120 total timepoints, with emphasis towards 120 Whole brain 30–50 timepoints after imaging begins 0.1 mmol/kg Generally 5 ml/s, consider 2.5 ml/s for gadobutrol 25 cc (range 20 – 30 cc) at same rate of GBCA 20 gauge (range 22 to 14 gauge)

Based partly on Wintermark M, Stroke 2005 (180) and Calamante F, PNMRS 2013 (86).

as with an excessive dose of contrast agent or exceedingly large TE and T2*-weighting, the DSC-MRI signal may saturate, preventing accurate concentration-time estimation. Clinical experience by many investigators has suggested that for a standard 0.1 mmol/kg dose of GBCA, an injection rate of 5 cc/s yields reasonable signal drops during first pass. Injection rates are limited by physical constraints of the power injector and IV gauge. Table 1 presents suggested imaging parameters for DSC-MRI. For a “typical” 180-lb man (80 kg), a 0.1 mmol/kg (0.2 mL/kg) dose of contrast would be 8 mmol (16 cc). So at an injection rate of 5 cc/s, the patient would get 5/16 of 8 mmol Gd, or about 2.5 mmol Gd per second. Given this, typical injection rates correspond to a Gd delivery of 2–3 mmol/s, and this tends to give reasonable signal drops and CBV map SNR. Assuming no leakage from the vasculature, bolus injection, rather than slow infusion, of GBCAs is needed because of their ability to deliver a relatively high concentration of contrast agent, which is necessary to produce sufficient intra- to extravascular magnetic susceptibility differences for perfusion imaging. A narrow bolus is necessary to measure CBF; however, this feature is less necessary for CBV imaging (47), which can also be measured using steady-state techniques. For DSC-MRI, CBV map SNR is theoretically maximized for a given bolus dose with as short a bolus duration as possible (44), assuming adequate sampling (sufficiently short TR). Achieving a short

bolus duration within the brain is a challenging task because passage through the cardiopulmonary system will lead to bolus dispersion. Therefore, injection boluses lasting 3 seconds will typically disperse by the time it reaches the brain, lasting 5–10 seconds or more depending on cardiac output and other factors. Furthermore, about 20% of injected contrast agent is already present in the extravascular-extracellular space during the first passage through the lungs. Injection of higher dosages, up to 0.3 mmol/kg of a standard 0.5 mmol/mL GBCA, have been used to ensure maximal signal intensity decrease during DSC-MRI. However, such a dose would require large injection volumes at a high rate and, therefore, is generally avoided (22,47–51). Another approach involves the use of highly concentrated chelates, eg, 1.0 mmol/mL gadobutrol (Gd-BT-DO3A, Gadovist, Bayer HealthCare, Berlin, Germany) which through its double gadolinium concentration is able not only to produce a more compact bolus of contrast agent (47), but also its higher relaxivity compared with other nonprotein-binding gadolinium chelates (52) yields better contrast enhancement. Furthermore, the double concentration of this agent reduces the bolus volume, which is a great advantage because short bolus geometry allows particularly accurate determination of the peak for the arterial input function, which is of pivotal importance for quantitative perfusion data. Perfusion images produced by 1.0 mmol/mL gadobutrol were found to be superior to those produced by 0.5 mmol/mL gadobutrol in terms of superior contrast in rCBV and MTT maps (53). A further comparison between 10 mL of 1 mmol/mL gadobutrol and 20 mL of 0.5 mmol/mL gadopentetate dimeglumine (GdDTPA, Magnevist, Bayer, Berlin, Germany) at 1.5 T found no difference in maximal signal change (54). Giesel et al (55) recently reported on an intraindividual comparison between 10 mL of 0.5 mmol/mL gadopentetate dimeglumine versus 5 mL of 1 mmol/mL gadobutrol for DSC-MRI at 3T and found a significant difference in maximal signal change in gray and white matter for gadobutrol compared to gadopentetate dimeglumine, with improved definition of tumor boundaries in five out of six cases (Fig. 2). No absolute differences were reported in this study, however, they did report that the ratio of maximal signal change in gray matter to white matter was 13.7–36.5% (median 24.6%) higher for gadobutrol compared to gadopentetate dimeglumine. A power injector is typically used to ensure consistent bolus administration, and given TR constraints and IV limitations on power injector rates, an injection rate of 5 ml/s is usually chosen (44,53,56). For high-relaxivity contrast agents or high-concentration formulations where reduced bolus doses are used (ie, gadobutrol), the injection rate may be reduced (eg, 2.5–3 mL/s) to maintain similar perfusion bolus profiles. Reduced contrast agent doses may lead to shorter bolus profiles and better determination of peak AIF. An injection rate of less than 2.5 mL/s can lead to increased bolus dispersion and underestimation of the arterial input function; however, increasing the rate up to 10 mL/s does not have substantial

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Figure 2. A high-grade glioma demonstrating hyperperfusion, clearly demarked from the surrounding normal tissue. Within this tumor, four "hot spot" areas were identified on the gadobutrol-based maps, two of which can be seen of the corresponding Gd-DTPA maps. a: Intra-axial, T1-weighted image. b: T2-weighted image. c: T1-weighted postcontrast image with GdDTPA. d: Maximum concentration color map for perfusion-weighted image with Gd-DTPA. e: Signal intensity-time curve for whole tumor with Gd-DTPA (max. signal drop 421.59, FWHM 13.82). f: T1-weighted postcontrast image with gadobutrol. g: Maximum concentration color map for perfusion-weighted image with gadobutrol. h: Signal intensity-time curve for whole tumor with gadobutrol (max. signal drop 446.98, FWHM 15.14). Reproduced from Giesel FL, Mehndiratta A, Risse F, et al. Intraindividual comparison between gadopentetate dimeglumine and gadobutrol for magnetic resonance perfusion in normal C Informa UK Ltd. brain and intracranial tumors at 3 Tesla. Acta Radiol 2009;50:521–530. Reprinted with permission from V (Informa Healthcare, Taylor & Francis AS).

added benefit with regard to bolus shape (56). In addition, injection rates higher than 5 mL/s through smaller gauge intravenous access ports can result in significant errors (47). To reduce the risk of substantial backflow into the jugular vein, the injection should optimally be given in the right arm followed by a volume of at least 25 mL saline flush at the same administered rate as the contrast agent rate to push the bolus toward the heart and to assure bolus coherence (57). Although at the inception of DSC-MRI a dose of up to 0.3 mmol/kg of body weight was recommended, current DSC-MRI protocols typically prescribe a dose of 0.1 mmol/kg (42). Bolus injection of the GBCA should commence after 30–50 timepoints once the DSC MR perfusion sequence is launched. Given recent concerns about nephrogenic systemic fibrosis (NSF), a 0.1 mmol/kg dose of GBCA is generally recommended, particularly in patients diagnosed with severe acute or chronic kidney failure (GFR 50 kDa), which remain within the vasculature for a prolonged period of time, are a potential solution to the contrast agent leakage issue in DSC-MRI. However, there are currently no agents that have been approved for routine clinical use. Blood pool iron oxide contrast agents have recently been examined as an alternative to GBCAs in clinical DSC-MRI of brain tumor patients (116,121). Because of their relatively larger molecular size compared with gadolinium chelates, these ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles have the potential to overcome limitations imposed by GBCA leakage through a damaged BBB in DSC-MRI (64,121). Other blood pool agents in which a paramagnetic chelate is bound to a macromolecule such as albumin, polysaccharide, or polylysine have been examined (122–125). A potential safety concern regarding these agents revolves around their very slow clearance rate (126).

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present in the signal and the gamma-variate fitting approach fails (129) Calibration A calibration is needed to rescale absolute values of CBF to a range of expected normal values because the proportionality constants are not exactly known and so the concentration of contrast agent is not measured in absolute units (4). Different calibration techniques have been adopted, including assumption of various proportionality constants (78,98,128,131), selecting a scaling factor to produce CBF values in white matter that are equivalent to 22 mL/100 g/min (132), and using a common conversion factor derived from another technique, such as 15O-H2O PET CBF measurements (133). Other approaches make use of a complementary technique for each given patient to derive a patient-specific correction factor. Some examples include using a ratio of the area of each patient’s venous output function (VOF) to the mean VOF from normal volunteers (134) or combining DSC-MRI with arterial spin labeling (ASL) or steady-state CBV from T1-weighted images (“bookend” method) (135–137). Artifacts Susceptibility artifact resulting from bone-air-brain interfaces, bone, melanin, metal, and blood products can confound rCBV measurements (42,138,139). This is less of an issue with SE compared with GRE. A possible solution is to decrease the slice thickness, at a cost of lower SNR. Incorporation of parallel imaging techniques will also decrease susceptibility artifacts and scan time to allow for increased volume coverage and SNR (139).

Recirculation Following the first pass of GBCAs through the voxel, the concentration of contrast agent would ideally be zero. However, recirculation occurs as the GBCA flows throughout the body and a second peak occurs (57). This second peak is lower and wider than the first due to bolus dispersion, and by the third recirculation, a small constant baseline elevation in contrast concentration is seen because the contrast agent is now well mixed in the blood volume. Errors in CBV measurement can occur because of the presence of recirculation during the later part of bolus (69,100,127,128). Before the calculation of perfusion metrics, the fitting of a gamma-variate function to the concentration–time curve has been used to remove the effect of recirculation; however, this may result in errors from underestimation of CBV (2,69). Other proposed methods to deal with recirculation include independent component analysis (ICA) (129,130). Assuming that the concentration–time curves are a linear superposition of MR signal changes arising from contributions such as arteries, veins, tissue, recirculation, and noise, it has been demonstrated that the ICA method can be used to minimize effects of recirculation in DSC-MRI experiments. Moreover, ICA has also demonstrated the capability of minimizing the effects of recirculation when an overlap between the first-pass and recirculation is

THE FUTURE AND CHALLENGES Evidence of Clinical Impact Acute stroke and brain tumors are the primary focus of most DSC-MRI studies. While there appears to be much potential in the use of metrics derived from DSC-MRI, there is still a lack of high-quality data to justify its use in directing patient management (42,103,140). Therefore, DSC-MRI remains largely an interesting research tool without proven clinical benefit. Recently, a single-center prospective study of glioma patients was published where 59 consecutive patients with gliomas were evaluated by three neuroradiologists in consensus, first using conventional MRI sequences and then with incorporation of qualitative analysis of perfusion imaging (which included both DSC and ASL perfusion MRI techniques) (141). Utilizing a neuro-oncology tumor board format, with the development of hypothetical treatment plans for each patient, it was concluded that the addition of perfusion imaging appeared to have a significant effect on the neuroradiologists’ and clinicians’ confidence in tumor status as well as on clinical decisionmaking. More confirmatory studies such as this are needed to provide evidence of a substantial clinical benefit provided by perfusion MRI.

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Technical Standardization A major hurdle toward clinical validation of DSC-MRI is the lack of technical standardization in acquisition and postprocessing methods (41,113,142,143). Similar concerns affect other functional MRI techniques such as diffusion imaging of the brain (144). A major limitation of MRI is that image signal intensities lack a standard and quantifiable method of interpretation (145). Unlike Hounsfield units in CT scanning, absolute signal intensity values in MRI lack a fixed meaning and may appear different because of various scanner-dependent issues, even in the case of the same protocol for the same body part of the same patient on the same MRI scanner (146). Therefore, preset windows cannot be used to display MR images and so window settings must often be adjusted for each examination, with obvious implications for image quantification and segmentation (147–149). Recently, a two-step postprocessing method has been applied to standardize rCBV maps produced from DSC-MRI, where the signal intensity scale is standardized so that for a given body region and MRI protocol, similar signal intensities will have similar meaning with regard to tissue (145,150). This method may allow easier and more accurate qualitative and quantitative comparison of rCBV across studies, particularly in light of difficulties and controversies surrounding determination of the AIF. Reproducibility Given the necessity of repeat administrations of a GBCA, there are relatively few studies examining the reproducibility of DSC-MRI (98,151–154) compared with those studies examining the relatively newer technique of ASL MRI, where repeat injections of GBCAs are not needed (155–166). As such, more reproducibility studies examining DSC-MRI are needed (43). In 1995, Levin et al (98) found that a repeat bolus of GBCA given 10 minutes to 2 hours after an initial bolus resulted in artifactually elevated measures of rCBV using a SE-EPI technique in 22 normal subjects. In 2005, Henry et al (154) examined reproducibility of relative CBV maps in eight healthy volunteers using a SE-EPI method with 0.05 mmol/kg GBCA dosage and found good reproducibility. Carroll et al (153) compared reproducibility of absolute quantification of DSC-MRI with 15O-H2O PET and found good reproducibility for PET but not for DSC-MRI that was attributed to variations in the choice of AIF. Jackson et al (167) found that measurement of rCBV in consecutive studies of 11 glioma patients was able to reliably measure differences in excess of 15% in between group studies and 25% in individual patients. However, there was less reproducibility in determining vascular tortuosity by measuring relative recirculation. A method that combined bookend scanning (which quantifies CBF and CBV by calibrating rCBF and rCBV parametric images based on steadystate T1 changes in blood pool and brain tissue) and an automated postprocessing algorithm without user input found that improved reproducibility of

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quantitative CBF estimates could be achieved through automation in postprocessing (43,136,152). ROI Analysis Current methods in brain tumor imaging rely largely on accurate placement of user-defined ROIs around a portion or the entire lesion (97). Care should be taken to avoid large extra- and intratumoral vessels when placing ROIs (168). While the most optimal method of ROI placement is not known, the use of multiple ROIs to determine the highest rCBV was shown in one study to have clinically acceptable reproducibility among three independent neuroradiologists (94). This type of “hot spot” analysis has the advantage that it is easy to perform; however, it can be prone to an excessive level of data reduction (97). In addition, other disadvantages include inter- and intraobserver variability, difficulty in precise replication in longitudinal studies, and placement error in lesions with complex boundaries (169,170). Heterogeneous lesions, such as those often seen in neuro-oncologic imaging, can pose a particular challenge as high and low values in an ROI can cancel each other out. As a result, various modes of analysis such as histogram-based analysis (171–174) have been proposed as a potential alternative to conventional ROI analysis. Histogram analysis can give insight into the heterogeneity of the tissue of interest; however, spatial specificity is lost (175). Parametric response mapping is a sophisticated method of analysis where rCBV, rCBF, or the apparent diffusion coefficient images are coregistered over serial examinations and compared on a voxel-wise basis, before and after treatment (172,173,176). Early results show promise; however, the technical demands of coregistration of image voxels can be challenging because neoplasms may move in nonlinear ways during the course of, and following, treatment or if the tumor size is small relative to the resolution of the voxel size (144). Higher Field Strengths The last decade has seen increased use of MRI scanners with field strengths of 3T and higher for brain imaging. A prime advantage of higher field strengths include an increase in SNR that could be seen with increasing field strength as well as T2* relaxation times that could result in greater effectiveness of a given dose of GBCA for DSC-MRI (177). However, increasing susceptibility artifacts in EPI sequences which could result in blurring and image distortions are a concern with increasing field strength. Manka et al (178) found that the increase in dynamic SNR with DSC-MRI at 3T can allow for the use of half the GBCA dose as that at 1.5T. In addition, they found that the use of a principle of echo-shifting with a train of observations (PRESTO) sequence could reduce susceptibility artifacts in DSCMRI at 3T. Another study of DSC-MRI at 3T found that the use of a multichannel coil array and partially parallel acquisition could result in even greater SNR and decreased susceptibility artifacts (179). In conclusion, this review has focused on technical principles relevant to T2*-weighted dynamic

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susceptibility contrast MRI in brain tumor imaging. Current techniques as well as novel methods and potential pitfalls in image acquisition and data analysis have been presented. Optimization of these techniques is ongoing and will advance the use of DSC-MRI for research and clinical applications in neurooncology. ACKNOWLEDGMENTS Editorial assistance was provided by PAREXEL International, and this assistance was funded by Bayer HealthCare. We also greatly appreciate Drs. Jan Endrikat and Ulrike Bergmann from Bayer HealthCare for their assistance with this project. POTENTIAL CONFLICTS OF INTEREST Mark S. Shiroishi: Grant Support: Supported in part by the GE Healthcare/RSNA Research Scholar Grant, Zumberge Research Grant, Southern California Clinical and Translational Science Institute (CTSI) Pilot Grant (NIH CTSA grant 5 UL1 RR031986-02). Paid consultant for Bayer HealthCare. Marco Essig: Serves on scientific advisory boards for Bayer HealthCare and Medical Imaging Heidelberg, serves as a consultant for Olea Medical, has received speaker honoraria from Bayer HealthCare and Bracco, and has received research support from Bayer HealthCare. Thanh B. Nguyen: Has received grant support from Brain Tumor Foundation of Canada. Paid consultant for Bayer HealthCare. James M. Provenzale: Has received research funding from GE Healthcare; Scientific Advisory Board member for Bayer HealthCare; Consultant for Millennium Pharmaceuticals, Amgen, and Biomedical Systems; Data Safety Management Board for Theradex, Inc. Author and stockholder, Amirsys, Inc. David S. Enterline: Bayer HealthCare, Y&R Inc, Bracco Diagnostics, and DFine Inc; and research support from Guerbet Group. Nicoletta Anzalone: Has received speaker honoraria and serves as a consultant  for Bayer HealthCare. Alex Rovira: Serves on scientific advisory boards for NeuroTEC, Bayer HealthCare and BTG International Ltd, has received speaker honoraria from Bayer HealthCare, Stendhal America, SanofiAventis, Bracco, Merck-Serono, Teva Pharmaceutical Industries Ltd, and Biogen Idec, received research support from Bayer HealthCare, and serves as a consultant for Novartis. Max Wintermark: Received grants from Philips Healthcare and GE Healthcare. Meng Law: Serves on scientific advisory boards for Bayer HealthCare Toshiba Medical, has received speaker honoraria from Siemens Medical Solutions, iCAD Inc, Bayer HealthCare and Bracco, Prism Clinical Imaging and has received research support from the NIH, Bayer HealthCare. Francesco D’Amore, Glo€ rfler: No ria Castellazzi, Jerrold L. Boxerman, Arnd Do disclosures reported. REFERENCES 1. Villringer A, Rosen BR, Belliveau JW, et al. Dynamic imaging with lanthanide chelates in normal brain: contrast due to

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