Contrast agent dose effects in cerebral dynamic susceptibility contrast magnetic resonance perfusion...

Contrast Agent Dose Effects in Cerebral Dynamic SusceptibilityContrast Magnetic Resonance Perfusion Imaging

Jeffry R. Alger, PhD1,2,3,4,*, Timothy J. Schaewe, DSc3, Tom C. Lai, BS1, Andrew J. Frew,PhD1, Paul M. Vespa, MD3,5, Maria Etchepare, RN5, David S. Liebeskind, MD3, Jeffrey L.Saver, MD3, and S. Chelsea Kidwell, MD3,61 Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School ofMedicine at the University of California Los Angeles (UCLA), UCLA, Los Angeles, California, USA2 Department of Radiological Sciences, David Geffen School of Medicine at UCLA, UCLA, LosAngeles, California, USA3 UCLA Stroke Center, Department of Neurology, David Geffen School of Medicine at UCLA, UCLA,Los Angeles, California, USA4 UCLA Brain Research Institute, David Geffen School of Medicine at UCLA, UCLA, Los Angeles,California, USA5 Neurosurgery Division, David Geffen School of Medicine at UCLA, UCLA, Los Angeles, California,USA6 Department of Neurology, Georgetown University, Washington, DC

AbstractPurpose—To study the contrast agent dose sensitivity of hemodynamic parameters derived frombrain dynamic susceptibility contrast MRI (DSC-MRI).

Materials and Methods—Sequential DSC-MRI (1.5T gradient-echo echo-planar imaging usingan echo time of 61–64 msec) was performed using contrast agent doses of 0.1 and 0.2 mmol/kgdelivered at a fixed rate of 5.0 mL/second in 12 normal subjects and 12 stroke patients.

Results—1) Arterial signal showed the expected doubling in relaxation response (ΔR2*) to dosedoubling. 2) The brain signal showed a less than doubled ΔR2* response to dose doubling. 3) The0.2 mmol/kg dose studies subtly under-estimated cerebral blood volume (CBV) and cerebral bloodflow (CBF) relative to the 0.1 mmol/kg studies. 4) In the range of low CBV and CBF, the 0.2 mmol/kg studies over-estimated the CBV and CBF compared with the 0.1 mmol/kg studies. 5) The 0.1mmol/kg studies reported larger ischemic volumes in stroke.

Conclusion—Subtle but statistically significant dose sensitivities were found. Therefore, it isadvisable to carefully control the contrast agent dose when DSC-MRI is used in clinical trials. Thestudy also suggests that a 0.1 mmol/kg dose is adequate for hemodynamic measurements.

Keywordsdynamic susceptibility contrast; magnetic resonance imaging; brain; perfusion; contrast agent; dose

*Address reprint requests to: J.R.A., Department of Neurology, David Geffen School of Medicine at UCLA, University of California,Los Angeles, Ahmanson-Lovelace Brain Mapping Center Room 163, 660 Charles E. Young Drive South, Los Angeles, CA 90095-7085.E-mail: [email protected].

NIH Public AccessAuthor ManuscriptJ Magn Reson Imaging. Author manuscript; available in PMC 2010 January 1.

Published in final edited form as:J Magn Reson Imaging. 2009 January ; 29(1): 52–64. doi:10.1002/jmri.21613.

NIH

-PA Author Manuscript

NIH


NIH


Dynamic susceptibility contrast MRI (DSC-MRI), which is sometimes referred to as perfusion-weighted imaging (PWI), provides a time-efficient means of imaging cerebral hemodynamicparameters by tracking contrast agent passage through brain tissue (1,2). DSC-MRI dataacquisition typically requires less than two minutes and, with appropriate postprocessing,provides images of relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF),and mean transit time (MTT) in addition to other qualitative measures of contrast transit, suchas time-to-peak (TTP) and Tmax (3), defined as the time at which the residue function derivedfrom deconvolution of a measured arterial and tissue passages reaches its maximum value.DSC imaging is routinely used in many centers for the assessment of brain perfusion in strokepatients. It is also being used to measure relevant biomarkers in several clinical trials of noveltherapies for stroke. A recently published consensus statement reviews the use of DSC-MRIin cerebral intraarterial thrombolysis trials and recommends that DSC-MRI be included infuture clinical trials (4).

DSC-MRI has limitations that prevent it from providing completely quantitative values ofcertain hemodynamic parameters. In particular, problems associated with delay and dispersionof the contrast bolus between the site of arterial sampling and the tissue under observation,which may be amplified by incomplete arterial occlusion in stroke patients, have been discussedin detail (5–11). Furthermore, even the technique’s developers have been critical of its abilityto accurately measure CBF (12), although they have repeatedly demonstrated meaningfulmeasures of CBV. After a series of studies establishing the extent to which microvascularmodels affected the interpretation of the results (13–16), the technique’s developersdemonstrated that accurate relative CBF images could be derived from DSCMRI. However,later work is unclear as to whether absolute CBV and CBF can be derived without using acalibration factor obtained from an alternate more quantitative technique. Some reports givethe impression that DSC-MRI provides absolute CBV and CBF measures (17–21), while othersemphasize the need for calibration (22,23). Recent progress with calibrating DSC-MRI throughlongitudinal relaxation measurements performed before and after DSC-MRI (23–25) isnoteworthy in demonstrating that an alternate quantitative imaging modality is not necessaryto calibrate the DSC-MRI data.

One issue of concern that has not yet received detailed study is the sensitivity of derived CBVand CBF measures to the contrast agent dose. In the absence of noise, CBV and CBF derivedfrom DSC-MRI are (hypothetically) expected to be insensitive to contrast agent dose. However,there are many practical concerns that may produce dose sensitivity. Whether the relaxationchange is a linear function of contrast agent concentration in both artery and tissue is a concern(1,26–31), and there is uncertainty in determining the values of scale factors used to relateinstantaneous concentration in artery and tissue from relaxation change (32). In the presenceof noise, the longer bolus characteristic of a higher dose may lead to error in the CBFdetermination. (Higher doses require longer bolus infusions because contrast agents arepackaged at a fixed concentration and it is customary to infuse at the maximal tolerable rate.)Dispersion effects may thus become more prominent for higher doses. A multitude of otherfactors may also come into play. Static field shifts are hypothesized to occur in the arteries(28,33) but not tissues during contrast passage and cause subtle time-dependent spatialmisregistration of the arterial signal when commonly used echo-planar imaging (EPI)techniques are employed. Higher doses may therefore result in greater spatial misregistrationof the arterial signal and thus be less accurate. It is a practical challenge to consistentlyadminister a particular dose on a per body mass basis. Indeed, it is common practice to merelygive a specific number of milliliters of contrast agent. This practice results in smaller patientsreceiving larger “per kilogram” doses, while larger patients receive smaller “per kilogram”doses. Stroke patients may also have heart disease or systemic arterial disease that results insuboptimal delivery of contrast agent to the parts of the brain that, are not ischemic, and hencesuch patients may receive a smaller “per kilogram” dose than otherwise normal subjects even

Alger et al.

J Magn Reson Imaging. Author manuscript; available in PMC 2010 January 1.

NIH


NIH


NIH


if the volume of contrast agent to be administered is accurately calculated based on the patient’sbody mass.

This study assessed dose sensitivity by performing two DSC-MRI examinations in rapidsuccession using two contrast agent doses (0.1 mmol/kg and 0.2 mmol/kg) in stroke patientsand normal subjects. The objectives were to determine whether these two doses produceconsistent voxelwise and region-of-interest (ROI) CBV and CBF estimates, and to characterizethe dose sensitivity of various other key DSC-MRI measurements. Clinical trials that use DSC-MRI as well as routine clinical DSC-MRI imaging customarily use a maximum tolerableinjection rate of about 5 mL/second for all doses. This choice is necessary because injectinghigh doses using high rates is widely viewed as being unsafe. A goal of the present work wasto incorporate this practical aspect of dose change into the measurements. The 0.2 mmol/kgdose was injected at the same (maximum tolerable) rate as the 0.1 mmol/kg dose and thereforethe duration of the injection for the 0.2 mm/kg dose was twofold longer. Accordingly, bolusduration as well as total administered dose may be responsible for observed inconsistenciesbetween hemodynamic measurements derived from the two dose studies.

MATERIALS AND METHODSSubjects

Twelve normal adult subjects and 12 patients suffering from recent acute ischemiccerebrovascular events were studied after giving written informed consent using a protocolapproved by the institutional human subjects research review board. Stroke patients werestudied to obtain measures of contrast passage at two different doses in brain areas havingabnormal blood flow. Examinations were performed in ischemic stroke patients between 48and 96 hours after initial symptoms, and in patients having persistent ongoing ischemicsymptoms. Studies of acute stroke patients were not attempted because the time required toperform two sequential DSC-MRI studies could have complicated clinical management.Subacute ischemic stroke is characterized by persistent CBF abnormalities and thereforeprovides the opportunity to study dose effects in brain areas having abnormal blood flowwithout impacting clinical management. Normal subjects were studied to evaluate differencesbetween the two doses in brain areas having normal hemodynamics.

Image AcquisitionThe image acquisition procedure was designed to mimic procedures that are typically used instroke clinical trials. Dynamic gradient-echo EPI (GE-EPI) was used to acquire a time seriesof multislice susceptibility-weighted images. Two dynamic contrast passage imaging studieswere performed in each subject. One of these used a “single dose” (SD) of contrast agent (0.1mmol/kg) and the other used a “double dose” (DD) of contrast agent (0.2 mmol/kg). The twostudies were performed as closely in time as possible. Usually less than five minutes elapsedbetween the end of one DSC-MRI study and the beginning of the next. Subjects remained inthe scanner and were not repositioned between the two acquisitions. The order of the SD andDD studies was randomized. Contrast agent, was administered at the packaged concentration(0.5 mmol/mL) at an infusion rate of 5.0 mL/second, requiring that the DD infusion have twicethe duration of the SD infusion. The subject’s body mass was used to calculate the volume ofcontrast agent, to be infused to reach doses of 0.1 mmol/kg and 0.2 mmol/kg. A MedRadSpectris power injector was used for bolus infusion into an antecubital vein. The SD and DDinfusions had durations of approximately four and eight seconds, respectively. A 10–15-mLsaline infusion followed each contrast agent bolus using the same infusion rate. Four of thenormal subject studies used gadopentetate dimeglumine (Magnevist®; Berlex Laboratories,Wayne, NJ, USA) as a contrast agent. The remainder of the studies used gadodiamide(Omniscan; Nycomed, Inc., Princeton, NJ, USA). The use of two different, contrast agent

Alger et al.


NIH


NIH


NIH


preparations was the result of a change in the contrast agent vendor that occurred at ourinstitution during the course of the study. The two contrast agent preparations (gadopentetatedimeglumine and gadodiamide) contain the same number of gadolinium equivalents per unitvolume.

DSC-MRI studies were performed using two Siemens Medical Solutions (Erlangen, Germany)1.5T MRI scanners. Initial studies (N = 9) were acquired on a Siemens Vision and later studies(N = 15) used a Siemens Sonata. This transition resulted from the fact that the Vision scannerwas the only one available at the time of study initiation. The Sonata scanner became availableduring the study and was subsequently used because of improvements in image quality,gradient performance, and slice coverage. The Vision parameters were: GE-EPI, TR = 2000msec, TE = 64 msec, slice thickness = 7 mm, number of slices = 12, number of volume images= 40. Gadodiamide was used as a contrast agent for five of the studies performed with theSiemens Vision. Gadopentetate dimeglumine was used for the remainder (N = 4) of the studiesperformed with the Siemens Vision. The Sonata parameters were: GE-EPI, TR = 2060 msec,TE = 61 msec, slice thickness = 7 mm, number of slices = 18, number of volume images = 60.The chosen TE was the minimum value compatible with acquisition of 128 × 128 in-planeresolution using the available GE-EPI pulse sequences. Contrast agent infusion was initiatedduring the acquisition of the fifth volume in the time series, approximately 10 seconds afterthe start of image acquisition, Gadodiamide was used as a contrast agent for all (N = 15) studiesperformed with the Siemens Sonata. In summary, the following scanner-contrast agentcombinations were included in the study: Vision-Gadopentetate dimeglumine (N = 4), Vision-gadodiamide (N = 5), and Sonata-gadodiamide (N = 15).

Image ProcessingImage processing was based on techniques that have been previously summarized (1). The“instantaneous” local concentration of contrast agent, was inferred from the measured changein susceptibility driven spin dephasing:

[1]

where S(t) is the measured signal during contrast passage, and Sb is the “baseline” signalmeasured prior to contrast agent arrival. Instantaneous contrast agent concentration, C(t), wasassumed to be linearly related to the relaxation change through an arbitrary and generallyunknown scale factor, K. CBV was determined as the ratio of the time integrals of the tissuepassage and the arterial passage

[2]

where ρ is the apparent brain density (1.04 g/mL), HA (= 0.45) is the assumed hematocrit inlarge arterial vessels, and HT (= 0.25) is the assumed hematocrit in the smaller capillaries inthe tissue. A value of 0.1369 was used for the tissue to artery concentration scale factor ratio

because in an earlier study of a small group of stroke patients, this correction broughtabsolute DSC-MRI hemodynamic measures into agreement with those obtained by xenoncomputed tomography (CT) (34). Nonparametric singular value decomposition (SVD) was

Alger et al.


NIH


NIH


NIH


used deconvolve the arterial and tissue passages to determine the “residue” function (R(t)) ineach image voxel (35). Singular values were truncated at 0.15 to suppress artifacts in thedeconvolution. CBF was then determined as the maximum value attained by the residuefunction.

Software for image processing was written using Interactive Data Language (IDL; ITT VisualInformation Solutions, Boulder, CO, USA). The software performed the following operations:1) image reading, 2) time-series motion correction, 3) concentration vs. time calculation foreach voxel in the three-dimensional (3D) image space (Eq. [1]), 4) manual selection of arterialinput function (AIF), 5) CBV calculation for each voxel (Eq. [2]), 6) SVD deconvolution andCBF determination for each voxel, and 7) storage of the key ΔR2* parameters (maximum, timeintegral, and concluding steady-state values) in each voxel. The time integrals of arterialpassages, tissue passages, and residue functions used for CBV and CBF calculation werecomputed using the five-point Newton-Cotes integration formula implemented by theINT_TABULATED function of IDL. This function integrates a tabulated set of data over aclosed time interval representing the entire data array.

AIFs were chosen in the middle cerebral artery (MCA), The right MCA was consistently usedacross studies except in patients who had evidence of flow deficit in right frontal arterialterritories, in which cases the left MCA was used. An automated routine that selects voxelshaving sharp contrast passages with a large time integral was used to identify candidate AIFvoxels in the vicinity of the MCA. The concentration passage in each candidate voxel was thenmanually examined. Voxels showing large time integrals, but broad passages were eliminated.For each study at least 20 suitable voxels were identified. The signals in these arterial voxelswere averaged to lessen the sensitivity to noise. The arterial sampling technique is similar tothat, proposed by Carroll et al (36), but was developed independently.

IDL software for quantitatively comparing derived parametric 3D volume images was alsowritten. This software performed a linear regression analysis of the SD vs. DD results forvarious parameters over the set of all voxels in the 3D image space.

Additional measurements were generated over selected gray matter (GM) and white matter(WM) subregions. The subregions were defined in the first slice superior to the ventricles inall subjects. WM was sampled in a single, roughly elliptical region between the mid-line andthe cortex on the SD CBV image. Multiple GM regions of irregular shape and approximatelyequal volume to the identified WM region were identified on the SD CBV image with specificattention to avoiding the vasculature. The GM/WM regions were chosen in the oppositehemisphere to the infarct for all stroke patients. The mean values of SD CBV, DD CBV, SDCBF, and DD CBF in the defined ROIs were then measured. Regression analysis comparingthe SD and DD results from these homogeneous tissue regions was performed.

Ischemic and hyperemic volumes were identified in the 3D CBV and CBF images of the strokepatients. Two different definitions of the ischemic territory were used: 1) voxels having CBVless than 1 mL/100 g, and 2) voxels having CBF below 10 mL/100 g/minute. Voxels havingCBV between 7 and 15 mL/100 g were labeled as hyperemic in an effort to identify tissues atthe infarct periphery that sometimes show vasodilation and high CBV. The total volumes ofischemic and hyperemic tissue were determined for each subject and dose by counting theidentified voxels and multiplying by the voxel volume.

Statistical analyses were performed using StatView software (SAS Institute, Cary, NC, USA).Numerical results reported in the text and the tables are expressed as mean ± standard deviation.Paired t-tests were used to test for significant differences between numerical measures for SDand DD studies, with P < 0.05 as the criterion for significance. One-way analysis of variance(ANOVA) followed by Fisher’s protected least significant difference (PLSD) test was used to

Alger et al.


NIH


NIH


NIH


assess the effect of the three agent-scanner combinations (Vision-Gadopentetate dimeglumine,Vision-gadodiamide, and Sonata-gadodiamide) on various numerical measures. A pairwisesignificance level of P < 0.05 was used to assert significant differences.

A Bland-Altman analysis (37) provides an alternative approach to the above describedregression analysis for identifying the effects of contrast agent dose on CBV and CBFmeasurements. In Bland-Altman analysis, the difference between a measured parameter (i.e.,CBV or CBF) assessed using DD and SD protocols is plotted against the mean of the sameparameter measured using these protocols (e.g., [DD CBF − SD CBF] vs. [DD CBF + SDCBF]/2.0). Agreement, between SD and DD measurements is indicated on the Bland-Altmanplots as regression lines having slope and intercept of zero. Disagreement is indicated by astatistically significant deviation from this ideal behavior. IDL software was written toconstruct Bland-Altman plots for CBV and CBF using the measurements from all voxels inthe 3D image space followed by a linear regression analysis to determine the regressionintercept and slope. This Bland-Altman analysis was performed for each of the 24 studies. TheROI measures of CBV and CBF for GM and WM were also subjected to a Bland-Altmananalysis. StatView software was used to construct the plots and perform the linear regressionanalyses for these data.

RESULTSSD vs. DD Arterial Passage

Figure 1 shows typical SD and DD arterial passages measured in the MCA in studies of anormal subject and a stroke patient. These examples illustrate that the DD study produced anarterial bolus with a larger peak ΔR2*, a larger concluding steady-state ΔR2*, and a largerΔR2* time integral but only a slightly wider bolus in comparison with the SD study. Key arterialpassage measures for SD and DD studies are as follows: The DD arterial maximum ΔR2* (40.5± 9.4 seconds−1) was significantly larger than the SD arterial maximum AR2* (28.7 ± 7.1seconds−1). The time integral of AR2* for the DD arterial passage (1011.4 ± 316.3seconds−1 second) was significantly larger than that measured in the SD studies (580.1 ± 226.5seconds−1 second). The concluding steady-state ΔR2* for the DD arterial passages (7.5 ± 2.6seconds−1) was significantly larger in comparison with that measured in the SD arterialpassages (4.0 ± 2.7 seconds−1). The full width at half maximum (FWHM) of the SD arterialpassage (9.0 ± 2.6 seconds) was significantly smaller in comparison with the FWHM of theDD arterial passage (10.5 ± 2.9 seconds).

The DD study involves the administration of twice as much contrast agent compared with theSD study. Hence, if the ΔR2* concentration relationship is indeed linear, one would expectthe ratio of the DD to SD ΔR2* time integrals to be 2.0. Similarly, at the conclusion of thepassage, the vascular system contains twice as much contrast agent in the DD study comparedwith the SD study, and it is therefore expected that, the ratio of the DD to SD ΔR2* steady-state values in artery should be equal to 2.0. Neither the DD to SD ΔR2* time integral rationor the DD to SD ΔR2* steady-state ratio were significantly different from 2.0. These findings,summarized in Table 1, empirically support the assertion of a linear relationship betweenΔR2* and concentration for artery.

What the DD to SD arterial maximum ΔR2* ratio should hypothetically be is less clear. A ratioof 1.0 would be expected based on the venous bolus infusion protocols that were used. (Thepeak venous concentrations were the same for both SD and DD). The observed ratio, however,was significantly greater than 1.0 (one group t-test, P < 0.05). The DD to SD arterial maximumΔR2* ratio was also significantly different from 2.0. The DD passage results in an arterialΔR2* maximum that is approximately 45% larger than that produced by the SD passage. It istempting to assert that the DD study showed some “saturation” of the signal response at the

Alger et al.


NIH


NIH


NIH


peak compared with the SD study. However, this assertion would be inappropriate because itis not known what the true peak concentrations should be for the particular infusion protocolthat was used. If saturation were occurring in this experiment, we would expect the DD andSD ΔR2* time integral ratio in Table 1 to be significantly less than 2.0, which is not the case(see above). The most likely explanation for this somewhat nonintuitive finding is the complex,cumulative dispersion effects of the cardiovascular system from the injection site to the MCA.

SD vs. DD Brain Tissue PassageFigure 2 presents examples of Images of the time integral of ΔR2* from a normal subject, anda stroke patient. As discussed above, the DD study Involves the administration of twice asmuch contrast agent compared with the SD study. Therefore, if the ΔR2* vs. concentrationrelationship is linear for brain tissue passage, it is expected that the ratio of the DD to SDΔR2* time integrals for the brain tissue should be equal to 2.0. The graphs shown in Fig. 2 arecorrelation plots. In these graphs, the ΔR2* time integral produced by the SD study is plottedon the horizontal axis against the ΔR2* time integral produced by the DD study on the verticalaxis for each voxel in the respective subjects’ brains. The solid lines in these graphs are thebest linear fits to the data. A slope of 2.0 as expected in the case of perfectly linear dose responseis shown as a dotted line. These correlation plots illustrate that for these two cases the measuredslopes are less than 2.0. Over all the subjects, the mean DD to SD correlation plot slope for theΔR2* time integral is approximately 1.56 and is significantly less than the expected value of2.0 (one group t-test, P < 0.05). Table 1 presents further analyses for brain tissue ΔR2*passages. The table reports linear correlation slopes (SD vs. DD) for the time integral,maximum and steady-state values of the ΔR2*. At the conclusion of the passage, the vascularsystem contains twice as much contrast in the DD study compared with the SD study, and itis therefore expected that the ratio of the DD to SD final steady-state ΔR2* values for braintissue should be equal to 2.0. The DD to SD correlation plot slope for steady-state ΔR2* Isapproximately 1.3 and is significantly less than the expected value of 2.0. As was the case forthe arterial passages, the DD to SD ratio of maximum ΔR2* reached in the brain passages wassignificantly different than 2.0 and also significantly different, from 1.0 (one group t-tests,P<0.05).

SD vs. DD CBF (Whole Brain)Figure 3 shows representative CBF images calculated from SD and DD studies of selectedsubjects. The CBF images that result, from the SD and DD studies are qualitatively similar.They show approximately the same absolute CBF values and have similar contrast betweenGM and WM. The whole-brain average CBFs determined from the DD studies were notsignificantly different from those determined from the SD studies. However, there are subtledifferences between the DD and SD CBF images that become apparent, when more detailedquantitative analyses are performed. The mean slope of the CBF correlation plots shown to theright of the images in Fig. 3 is 0.79 ± 0.24 (mean ± standard deviation) and is significantly lessthan 1.0 (one group t-test, P < 0.05). This finding, included in the summary of statisticalmeasurements in Table 2, indicates that, on average, the DD passage underestimates the CBFby about. 21% compared with the SD passage. Interestingly, the correlation plots uniformlyshow positive intercepts, indicating that the DD study produced a slightly higher CBF measurecompared with the SD study at low CBF values, while the opposite was true at higher CBFvalues. The agreement between the SD and DD studies is closest in the intermediate CBF rangeof 50–100 mL/100 g/minute.

SD vs. DD CBV (Whole Brain)Table 2 also presents a summary of statistical measurements of derived CBV values equivalent,to those discussed in the previous paragraph related to CBF. The DD and SD passages produced

Alger et al.


NIH


NIH


NIH


mean whole-brain CBV averages that did not significantly differ. However, the DD studyappears to underestimate the CBV over the entire measured range of CBV by about 20%compared with the SD study. The DD to SD CBV correlation plots showed positive intercepts,indicating that the DD studies tended to give higher CBV measures in the low CBV rangecompared with the SD studies, with the opposite being true in the high CBV range. Thecorrelation plots showed that. SD and DD CBV measurements were consistent in the 3–7 mL/100 g range.

SD vs. DD CBV and CBF in Homogeneous GM and WM RegionsAdditional regression analyses were performed using the CBV and CBF values extracted fromhomogeneous GM and WM regions for each subject. Figure 4 provides a representativeillustration of GM and WM ROI identification. This analysis was performed in order to avoidthe possible confounding influence of vasculature and CSF in the whole-brain results discussedin the previous sections. The homogeneous tissue region results are presented in Table 2. TheGM/WM results are not separated into normal and stroke subpopulations as the regions werechosen in the opposite hemisphere to the infarct for all stroke patients. The general trendsobserved for whole brain persist for the GM/WM regions, with no significant differencebetween SD and DD CBV and CBF measurements, mean correlation slopes less than 1.0, andpositive intercept values. The most significant difference in the results is that the meanregression slope and intercept values for the GM and WM regions are closer to 1.0 and 0.0,respectively. This indicates that the dose dependence observed in the whole-brain analysis ismore pronounced in highly vascular regions, possibly due to saturation effects.

Constrast Agent Preparations and ScannersOne-way ANOVA followed by Fisher’s PLSD test was used to assess the effect of the threeagent-scanner combinations (Vision-Gadopentetate dimeglumine, Vision-gadodiamide, andSonata-gadodiamide) on each of the 26 numerical measurements reported in the “All” columnsof Tables 1 and 2. Only one of these 26 analyses showed a significant agent-scanner effect atthe P < 0.05 level, and even this result was only marginally significant. A formal correctionfor multiple comparisons would eliminate even this one finding of significance. These findingsindicate that the variance in the measured values reported in Tables 1 and 2 is largely the resultof study-to-study variation as opposed to systematic differences attributable to agent-scannercombinations. This result justifies pooling all results into a single analysis. This does not meanthat differences attributable to scanner or contrast agent preparation are nonexistent. It onlymeans there is not sufficient power in these data to detect differences. Any agent/scanner-attributable differences are overwhelmed by study-to-study variation resulting from some otherunknown cause.

SD vs. DD for the Delineation of Ischemic TissueThe differences between the hemodynamic images obtained with SD and DD passages haveimplications for the identification and quantification of ischemic tissue. Table 3 illustrates thatthe SD and DD studies reported significantly different volumes of ischemic tissue in the strokepatients. The DD studies reported about 50% smaller ischemic volumes (identified as regionshaving CBF less than 10 mL/100 g/minute) compared with the SD studies, and the differencewas statistically significant. However, the volume of hyperemic tissue measured by the SDand DD studies did not differ (Table 3). A second evaluation, which identified CBF values lessthan 15 mL/100 g/minute as ischemic, also demonstrated that the DD studies result insignificantly smaller estimated volumes of ischemic tissue.

Alger et al.


NIH


NIH


NIH


Bland-Altman AnalysesFigure 5 provides Bland-Altman plots derived from the ROI measurements of CBV and CBFof GM and WM. The slope and intercept of the CBV regression are not significantly differentfrom zero, indicating there is agreement between DD and SD measures of CBV. The CBF plotshows a slight downward slope, indicating a tendency of DD protocols to underestimate CBFrelative to SD protocols at higher CBF values, but this is not statistically significant at the P <0.05 level. The plots also identify specific agent-scanner combinations to further illustrate thatvariance in measured GM and WM CBF and CBV is not attributable to scanner or agent choice.Separate Bland-Altman regression analyses were performed for each agent-scannercombination. No statistically significant nonzero slopes or intercepts were identified for anyof the agent-scanner combinations.

Plots showing the distribution of the Bland-Altman slopes and intercepts derived from analysisall voxels in the 3D image space for each study are provided in Fig. 6. These plots show positiveand generally significant nonzero Bland-Altman intercepts for CBV and CBF. They also shownegative Bland-Altman slopes for CBV and CBF that are not (or only marginally) statisticallysignificant. Nevertheless, these data are consistent with a subtle tendency of DD proceduresto underestimate CBF relative to SD procedures in the high CBF range. The plots also illustratethe absence of significant differences due to agent-scanner combinations in the various Bland-Altman slopes and intercepts.

In Fig. 7 are plotted the ROI CBF and CBV distributions for SD and DD protocol as a functionof the order of the SD and DD injections (i.e., SD 1 or SD 2) used for each study. ANOVAevaluations showed only two of the eight pairwise comparisons (i.e., SD 1 vs. SD 2) in whichthere was a statistically significant effect of injection order on the measured values. MeasuredSD CBV was significantly reduced when a DD study had been completed a few minutes earlier,and measured SD CBF was significantly reduced when a DD study had been completed a fewminutes earlier. These results indicate that the presence of contrast agent administered as a DDas few minutes prior to an SD will lead to a slight, underestimation of CBV and CBF in WM.Otherwise, the plots and the ANOVA analyses illustrate that the effects of previous SD or DDcontrast agent administration on CBF and CBV assessment are much smaller than the study-to-study variation in these measures.

Results summaryThe summary of the principal results is as follows: 1) The arterial passages show the expecteddoubled response in ΔR2* to doubling the contrast agent dose. 2) The brain passages show aless than doubled ΔR2* response to doubling the contrast agent dose. 3) The DD arterialpassages have a larger peak ΔR2* in comparison with the SD arterial passages. 4) The FWHMof the primary DD arterial passage through the MCA was only 16% larger than the SD arterialpassage. 5) The DD studies underestimate the brain tissue CBV and CBF relative to the SDstudies. 6) In the range of low CBV and CBF, the DD studies overestimate the CBV and CBFcompared with the SD studies, and this causes the SD studies to report significantly greaterischemic volumes compared with the DD studies. 7) Neither the use of different contrast agentpreparations and scanners nor the dose order had a substantially significant influence on thevarious measured values.

DISCUSSIONIn recent, years DSC-MRI has been used extensively for the assessment of stroke. The majorityof users tend to rely on images such as TTP, Tmax, or MTT that depict the temporal aspectsrelated either to contrast agent arrival or passage. A motivating factor for the present study wasthe desire to make better use of the CBV and CBF images that can also be obtained from DSC-

Alger et al.


NIH


NIH


NIH


MRI. The primary goal was to study the influence of contrast agent dose when contrast agentis delivered in the customary manner (i.e., at the maximum tolerable rate for all doses).

Prior to this study it was not known whether a substantial error in CBV or CBF might resultfrom a particular choice of the contrast agent dose. In theory, CBV images should not showdose dependence. This is because the analysis methodology “scales” the tissue measures ofcontrast agent concentration against those measured in artery and then integrates over time.Time integration should eliminate any influence a longer arterial bolus characteristic of higherdose may have on derived CBV. This approach is applicable only if the ΔR2* response is linearin concentration for both artery and brain tissue. In addition, scale factors for artery and brainmust be identical or known with accuracy. If there are deviations from these ideal conditions,the CBV images that are derived from DSC-MRI may acquire contrast agent dose dependence.Sensitivity issues may also come into play for tissues that have relatively small blood volumes(i.e., WM) and experience relatively small contrast agent delivery. For such tissues, higherdoses may result in improved sensitivity and accuracy. At the other end of the spectrum, tissueswith high blood volumes may experience so much contrast agent delivery that the ΔR2*response saturates to the point at which the measurement becomes insensitive. CBF is derivedby deconvolution of arterial and tissue passages, and one would expect the deconvolutionprocess to eliminate any influence a longer arterial bolus characteristic of a high dose mighthave on CBF determination. However, this is true only for noise-free measurements. Whennoise is present, bolus length may have an influence on CBF determination. Furthermore, allthe potential influences that were discussed above with reference to dose effects on CBV mayalso influence CBF determination. Because the interaction between all these possibilities wastoo complex to model, an empirical approach to establishing whether dose influences CBVand CBF determination was chosen.

This research systematically studied CBV and CBF images derived from two DSC-MRI studiesperformed with different administered contrast agent doses. Doses of 0.1 mmol/kg (SD) and0.2 mmol/kg (DD) were used because most existing studies use one of these two doses andbecause both doses are clinically practical. The research was performed using what are nowthe most commonly employed DSC-MRI techniques (1.5 T multislice GE-EPI with fixedmaximal rate injections). There have been previous studies of DSC-MRI dose dependence, butthese did not use techniques or contrast agents that are in current clinical use. While Manka etal (38) studied dose sensitivity using three contrast agent doses, they used a susceptibility-weighted imaging procedure that differs from the EPI technique that is now used in the majorityof centers, and furthermore their studies were performed at 3 Tesla (T) rather than the morecommonly used 1.5 T. Moreover, Manka et al (38) did not specifically evaluate CBF or CBVimages. Similarly, earlier studies of DSC-MRI contrast agent dose sensitivity (26,27) wereperformed at 1.0 T or used a different pulse sequence or contrast agent than are typically nowused for stroke assessment.

The arterial ΔR2* measures in the present work (Fig. 1) agree with the modeling studies ofHeiland et al (39), which predict that increasing the length of the injection at a fixed rate andconcentration leads to a higher peak concentration in the brain, as was found in the presentwork. Moreover, there is rough quantitative agreement between the bolus widths measured inthe present study and those predicted by Heiland et al. For instance, Fig. 2 of Heiland et al’s(39) paper predicts that a DD whole-brain passage resulting from a 5 mL/second injection willhave an FWHM of about 14 seconds. The mean FWHM for DD arterial passage measured inthe present study was 10.5 ± 2.9 seconds (mean ± standard deviation). The present observationstogether with Heiland et al’s (39) results confirm that bolus dispersion during transit betweenthe arm vein and the brain artery is a dominating factor that adds significantly to the width ofthe injected bolus.

Alger et al.


NIH


NIH


NIH


The present study shows that neither the CBV nor the CBF images derived from a DD studyare fully consistent with those obtained from an SD study, although the discrepancy is relativelysubtle. On average, over the entire range of CBF and CBV, regression analysis shows that DDstudies underestimate CBV and CBF by only about 20% relative to SD studies for both strokepatients and normal subjects. At very low CBV and CBF, the DD studies slightly overestimateCBV and CBF compared with SD studies. This latter effect causes DD studies to report smallervolumes of ischemic tissues in stroke patients in comparison with SD studies. Bland-Altmananalysis confirms the regression analysis findings but is not as statistically powerful.

Although the contrast agent bolus delivery to the antecubital vein was 200% longer for the DDstudies in comparison with the SD studies, the bolus widths measured in the MCA were only16% larger for the DD studies relative to the SD studies. This small difference in the measuredarterial bolus width between the SD and DD studies may be responsible for the subtle dosedependencies of derived CBF found in this study. It is not possible to determine whether thisis the case without being able to control the arterial bolus widths more precisely. Faster injectionof the DD could be considered, but is probably unsafe. Alternatively, diluting the contrast agentused for the SD study and lengthening the duration of the SD injection could be considered,but this may be viewed as a suboptimal way of performing the SD measurement.

It seems unlikely that the 16% wider arterial bolus characteristic of the DD studies isresponsible for the observed dose dependence of CBV because the CBV determination isgenerally insensitive to the length of the arterial bolus.

The dose dependencies of the derived CBV and CBF measures found in this study are subtle,but they are statistically significant and may well have an influence on the results of clinicaltrials that use DSC-MRI. Therefore, it is advisable for clinical trials to prospectively prescribea dose of contrast agent relative to body mass.

It must be emphasized that the CBV and CBF dose dependencies reported here applyspecifically to imaging techniques and parameters used in this study. The results are notnecessarily generalizable to other dosing regimens, different TE values, or different fieldstrengths.

It is furthermore important to emphasize that the CBV and CBF images obtained at either doseare incorrect in an absolute sense. The application of an empirical artery/tissue scale factor thatwas derived from comparisons between DSC-MRI perfusion studies and xenon CT in a fewsubjects was necessary to bring CBV and CBF measures obtained at either dose into the rangeof expected values. Without this empirical scale factor, the CBV and CBF images obtained ateither dose are more than sevenfold too high to be consistent with known physiology. In thisregard, the DD passages tend to give CBF and CBV values that are modestly more consistentwith the true values than do the SD passages because DD studies tend to give CBF and CBVvalues that are about 20% lower than SD studies.

There are three possible explanations for the failure of the DD to SD ΔR2* correlation slopesfor brain tissue (Fig. 2 and Table 1) to reach the expected values of 2.0. The first explanationis that the relationship between ΔR2* and contrast agent concentration in the capillaries ofbrain tissue is not linear. It is not possible to fit a linear function (Eq. [1]) to these observations,suggesting that an alternative nonlinear formalism should be considered. The report by Kjolbyet al (30) suggests that this explanation is unlikely. These authors developed a comprehensivetheoretical model that demonstrates that the ΔR2* response of brain tissue is most probably asimple linear function of contrast agent concentration when GE-EPI is used to detect signalchange. The second explanation is that the raw signal becomes so heavily saturated during theDD passage (but not the SD passage) that, it is overwhelmed by noise and the true ΔR2* cannot be measured with sufficient precision. Examination of the present, data suggests that, this

Alger et al.


NIH


NIH


NIH


is likely the case. The maximum ΔR2* reached in the GM during the DD passage can be about40 seconds−1. This corresponds to a signal attenuation of approximately 10-fold relative tobaseline, and these attenuated signal levels are comparable to the representative noise levels.A third, somewhat more subtle explanation is related to the fact that a signal phase shift isexpected to occur during the contrast agent, passage. This phase shift will result in a subtletime-dependent spatial misregistration of the signal as the contrast agent passes. Because thesignal moves in the image space as the contrast agent passes, the total measured response maybe less than is expected and the true ΔR2* may be underreported in the DD passage relativeto the SD passage. Defining which of the three explanations is most likely will require furtherstudy.

The present data are in partial agreement with the ΔR2* modeling findings of Kjolby et al(30). The present finding of possible ΔR2* saturation in brain but not in artery during the DDpassage is consistent with Kjolby et al’s assertion that the ΔR2* response is stronger in braintissue than in arterial blood. On the other hand, Kjolby et al’s (30) study and a related study(28) assert that the arterial ΔR2* response is a quadratic function of contrast agent dose. Thepresent results are not consistent with this latter assertion because a twofold increase in contrastpassage through artery most definitely did not produce a fourfold ΔR2* response. Thisdiscrepancy probably has to do with the how the arterial signal is measured. The theory andexperiment that underlie the assertion of a quadratic relationship between ΔR2* response andconcentration are relevant to bulk blood in the carotid arteries and do not necessarily reflectthe environment in the vicinity of the smaller MCAs that were used for arterial sampling in thepresent study.

The influence of dose on time variables (e.g., MTT and Tmax) was not formally studied in thiswork in an effort to maintain focus and control the length of the presentation. However, thepresent results support hypotheses for future studies. MTT is usually calculated as the CBV/CBF ratio. The subtle underestimation of DD CBF (relative to SD CBF) found in the presentstudy together with the weaker CBV dose dependence suggests the CBV/CBF ratio (which isusually inferred to be MTT) will be subtly lower for DD procedures. Tmax is primarily ameasure of delay in the bolus peak in the brain tissue relative to that in the MCA. The differencebetween the arterial bolus FWHM for the SD and DD studies was much less than the Tmax thatis typically seen in acute stroke, and therefore differences in Tmax between SD and DD studiesare expected to be very subtle.

The overall picture that emerges from the present study is that when DDs are used at a maximumtolerable injection rate, there is some attenuation of the ΔR2* response in brain tissue(particularly GM) but not in artery, and this is likely responsible for the subtle dose sensitivitiesof the CBV and CBF images that have been discussed.

In conclusion, some subtle but statistically significant DSC-MRI dose sensitivities were foundthat may have an influence on clinical trial results. While normal GM and WM measures ofCBV and CBF were largely consistent, between SD and DD studies, evaluation of ischemiclesions and whole-brain correlation measurements suggested there are inconsistencies betweenSD and DD studies in these contexts. Therefore, it is advisable to carefully control the dose ofcontrast agent when DSC-MRI is used in clinical trials. With regard to the specific choice ofcontrast agent dosage, this work suggests that GM regions may show small to moderate signalsaturation at the peak of a DD passage when GE-EPI 1.5 T acquisitions with TEs in the 61–64 msec range are used. The possibility of loss of accuracy due to signal saturation at highdose, together with the higher cost and safety concerns associated with DD studies, argues forthe use of SD protocols in clinical trials. Moreover, the relatively minor differences betweenSD and DD CBV and CBF measures indicate that a dose of 0.1 mmol/kg is as adequate as adose of 0.2 mmol/kg for perfusion measurements.

Alger et al.


NIH


NIH


NIH


AcknowledgmentsThis work was supported by the National Institute of Biomedical Imaging and Bioengineering (EB002087), theNational Institute of Neurological Disorders and Stroke (NS39498 and NS46018), and the National Center for ResearchResources (RR12169, RR13642, and RR00865), components of the National Institutes of Health (NIH); its contentsare solely the responsibility of the authors and do not necessarily represent, the official views of the NIH or the Institutesand Center that provided financial support. Authors affiliated with the Ahmanson-Lovelace Brain Mapping Centeracknowledge the generous support of the Brain Mapping Medical Research Organization, Brain Mapping SupportFoundation, Pierson-Lovelace Foundation, Ahmanson Foundation, William M. and Linda R. Dietel PhilanthropicFund at the Northern Piedmont Community Foundation, Tamkin Foundation, Jennifer Jones-Simon Foundation,Capital Group Companies Charitable Foundation, Robson Family, and Northstar Fund.

Contract grant sponsor: National Institute of Biomedical Imaging and Bioengineering Contract grant number:EB002087; Contract grant sponsor: National Institute of Neurological Disorders and Stroke; Contract grant numbers:NS39498; NS46018; Contract grant sponsor: National Center for Research Resources; Contract grantnumbers:RR12169; RR13642; RR00865.

References1. Ostergaard L. Cerebral perfusion imaging by bolus tracking. Top Magn Reson Imaging 2004;15:3–9.

[PubMed: 15057169]2. Keston P, Murray AD, Jackson A. Cerebral perfusion imaging using contrast-enhanced MRI. Clin

Radiol 2003;58:505–513. [PubMed: 12834633]3. Shih LC, Saver JL, Alger JR, et al. Perfusion-weighted magnetic resonance imaging thresholds

identifying core, irreversibly infarcted tissue. Stroke 2003;34:1425–1430. [PubMed: 12738899]4. Higashida RT, Furlan AJ, Roberts H, et al. Trial design and reporting standards for intra-arterial cerebral

thrombolysis for acute ischemic stroke. Stroke 2003;34:e109–e137. [PubMed: 12869717]5. Ibaraki M, Shimosegawa E, Toyoshima H, Takahashi K, Miura S, Kanno I. Tracer delay correction of

cerebral blood flow with dynamic susceptibility contrast-enhanced MRI. J Cereb Blood Flow Metab2005;25:378–390. [PubMed: 15674238]

6. Willats L, Connelly A, Calamante F. Improved deconvolution of perfusion MRI data in the presenceof bolus delay and dispersion. Magn Reson Med 2006;56:146–156. [PubMed: 16767744]

7. Calamante F, Willats L, Gadian DG, Connelly A. Bolus delay and dispersion in perfusion MRI:implications for tissue predictor models in stroke. Magn Reson Med 2006;55:1180–1185. [PubMed:16598717]

8. Calamante F, Gadian DG, Connelly A. Delay and dispersion effects in dynamic susceptibility contrastMRI: simulations using singular value decomposition. Magn Reson Med 2000;44:466–473. [PubMed:10975900]

9. Ibaraki M, Shimosegawa E, Toyoshima H, et al. Effect of regional tracer delay on CBF in healthysubjects measured with dynamic susceptibility contrast-enhanced MRI: comparison with 15O-PET.Magn Reson Med Sci 2005;4:27–34. [PubMed: 16127251]

10. Salluzzi M, Frayne R, Smith MR. Is correction necessary when clinically determining quantitativecerebral perfusion parameters from multi-slice dynamic susceptibility contrast MR studies? PhysMed Biol 2006;51:407–424. [PubMed: 16394347]

11. Wu O, Ostergaard L, Koroshetz WJ, et al. Effects of tracer arrival time on flow estimates in MRperfusion-weighted imaging. Magn Reson Med 2003;50:856–864. [PubMed: 14523973]

12. Weisskoff RM, Chesler D, Boxerman JL, Rosen BR. Pitfalls in MR measurement of tissue bloodflow with intravascular tracers: which mean transit time? Magn Reson Med 1993;29:553–558.[PubMed: 8464373]

13. Boxerman JL, Hamberg LM, Rosen BR, Weisskoff RM. MR contrast due to intravascular magneticsusceptibility perturbations. Magn Reson Med 1995;34:555–566. [PubMed: 8524024]

14. Boxerman JL, Rosen BR, Weisskoff RM. Signal-to-noise analysis of cerebral blood volume mapsfrom dynamic NMR imaging studies. J Magn Reson Imaging 1997;7:528–537. [PubMed: 9170038]

15. Ostergaard L, Sorensen AG, Kwong KK, Weisskoff RM, Gyldensted C, Rosen BR. High resolutionmeasurement of cerebral blood flow using intravascular tracer bolus passages. Part II: Experimentalcomparison and preliminary results. Magn Reson Med 1996;36:726–736. [PubMed: 8916023]

Alger et al.


NIH


NIH


NIH


16. Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR. High resolution measurementof cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach andstatistical analysis. Magn Reson Med 1996;36:715–725. [PubMed: 8916022]

17. Grandin CB, Bol A, Smith AM, Michel C, Cosnard G. Absolute CBF and CBV measurements byMRI bolus tracking before and after acetazolamide challenge: repeatability and comparison with PETin humans. Neuroimage 2005;26:525–535. [PubMed: 15907309]

18. Smith AM, Grandin CB, Duprez T, Mataigne F, Cosnard G. Whole brain quantitative CBF, CBV,and MTT measurements using MRI bolus tracking: implementation and application to data acquiredfrom hyperacute stroke patients. J Magn Reson Imaging 2000;12:400–410. [PubMed: 10992307]

19. Smith AM, Grandin CB, Duprez T, Mataigne F, Cosnard G. Whole brain quantitative CBF and CBVmeasurements using MRI bolus tracking: comparison of methodologies. Magn Reson Med2000;43:559–564. [PubMed: 10748431]

20. Lin W, Celik A, Derdeyn C, et al. Quantitative measurements of cerebral blood flow in patients withunilateral carotid artery occlusion: a PET and MR study. J Magn Reson Imaging 2001;14:659–667.[PubMed: 11747021]

21. Helenius J, Perkio J, Soinne L, et al. Cerebral hemodynamics in a healthy population measured bydynamic susceptibility contrast MR imaging. Acta Radiol 2003;44:538–546. [PubMed: 14510762]

22. Ostergaard L, Johannsen P, Host-Poulsen P, et al. Cerebral blood flow measurements by magneticresonance imaging bolus tracking: comparison with [(15)O]H2O positron emission tomography inhumans. J Cereb Blood Flow Metab 1998;18:935–940. [PubMed: 9740096]

23. Sakaie KE, Shin W, Curtin KR, McCarthy RM, Cashen TA, Carroll TJ. Method for improving theaccuracy of quantitative cerebral perfusion imaging. J Magn Reson Imaging 2005;21:512–519.[PubMed: 15834910]

24. Shin W, Cashen TA, Horowitz SW, Sawlani R, Carroll TJ. Quantitative CBV measurement fromstatic T1 changes in tissue and correction for intravascular water exchange. Magn Reson Med2006;56:138–145. [PubMed: 16767742]

25. Shin W, Horowitz S, Ragin A, Chen Y, Walker M, Carroll TJ. Quantitative cerebral perfusion usingdynamic susceptibility contrast MRI: evaluation of reproducibility and age- and gender-dependencewith fully automatic image postprocessing algorithm. Magn Reson Med 2007;58:1232–1241.[PubMed: 17969025]

26. Erb G, Benner T, Heiland S, Reith W, Sartor K, Forsting M. [Studies on contrast medium dosage inperfusion-weighted MR imaging]. Rofo 1997;167:599–604. [PubMed: 9465955][German]

27. Benner T, Reimer P, Erb G, et al. Cerebral MR perfusion imaging: first clinical application of a 1 Mgadolinium chelate (Gadovist 1.0) in a double-blinded randomized dose-finding study. J Magn ResonImaging 2000;12:371–380. [PubMed: 10992303]

28. Conturo TE, Akbudak E, Kotys MS, et al. Arterial input functions for dynamic susceptibility contrastMRI: requirements and signal options. J Magn Reson Imaging 2005;22:697–703. [PubMed:16261571]

29. Kiselev VG. Transverse relaxation effect of MRI contrast agents: a crucial issue for quantitativemeasurements of cerebral perfusion. J Magn Reson Imaging 2005;22:693–696. [PubMed: 16261568]

30. Kjolby BF, Ostergaard L, Kiselev VG. Theoretical model of intravascular paramagnetic tracers effecton tissue relaxation. Magn Reson Med 2006;56:187–197. [PubMed: 16724299]

31. Lia TQ, Guang CZ, Ostergaard L, Hindmarsh T, Moseley ME. Quantification of cerebral blood flowby bolus tracking and artery spin tagging methods. Magn Reson Imaging 2000;18:503–512.[PubMed: 10913711]

32. Newman GC, Hospod FE, Patlak CS, et al. Experimental estimates of the constants relating signalchange to contrast concentration for cerebral blood volume by T2* MRI. J Cereb Blood Flow Metab2006;26:760–770. [PubMed: 16319833]

33. van Osch MJ, Vonken EJ, Bakker CJ, Viergever MA. Correcting partial volume artifacts of the arterialinput function in quantitative cerebral perfusion MRI. Magn Reson Med 2001;45:477–485. [PubMed:11241707]

34. Kidwell, CS.; Leary, M.; Lai, TC., et al. Calibration of contrast bolus passage magnetic resonanceperfusion imaging with xenon computed tomography in human stroke. Proceedings of the 21st

Alger et al.


NIH


NIH


NIH


International Symposium on Cerebral Blood Flow, Metabolism and Function; Calgary, Canada.2003.

35. Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR. High resolution measurementof cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach andstatistical analysis. Magn Reson Med 1996;36:715–725. [PubMed: 8916022]

36. Carroll TJ, Rowley HA, Haughton VM. Automatic calculation of the arterial input function forcerebral perfusion imaging with MR imaging. Radiology 2003;227:593–600. [PubMed: 12663823]

37. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinicalmeasurement. Lancet 1986;1:307–310. [PubMed: 2868172]

38. Manka C, Traber F, Gieseke J, Schild HH, Kuhl CK. Three-dimensional dynamic susceptibility-weighted perfusion MR imaging at 3.0 T: feasibility and contrast agent dose. Radiology2005;234:869–877. [PubMed: 15665227]

39. Heiland S, Reith W, Forsting M, Sartor K. How do concentration and dosage of the contrast agentaffect the signal change in perfusion-weighted magnetic resonance imaging? A computer simulationMagn Reson Imaging 2001;19:813–820.

Alger et al.


NIH


NIH


NIH


Figure 1.Representative arterial sampling (top row). The highlighted voxels (arrows) overlaid on thegrayscale image obtained at the time of maximal signal attenuation show the typical arterysampling that was used in this study. The bottom row shows representative arterial ΔR2* time-course data for SD and DD passages in a normal subject and a stroke patient. These ΔR2* time-course data illustrate that the SD and DD passages produced boluses in the MCA with nearlyidentical widths.

Alger et al.


NIH


NIH


NIH


Figure 2.Comparison of ΔR2* time integral images from a representative normal subject and a strokepatient derived from SD and DD contrast passages. Only one slice of 18 acquired slices isdisplayed in each case. To the right of each image comparison is a correlation plot, showingthe relationship between the ΔR2* time integral images derived from SD and DD contrastpassages for each voxel within the brain in all 18 slices. The solid lines in the graphs are thebest-fit linear regressions to all the data. The dotted lines show the hypothetical regression lines(slope = 2, intercept = 0) for the ideal situation in which twice the contrast passage is detectedin the DD passage compared with the SD passage.

Alger et al.


NIH


NIH


NIH


Figure 3.Comparison of CBF images from a representative normal subject and a representative strokepatient derived from SD and DD contrast passages. Only one slice of 18 acquired slices isdisplayed in each case. To the right of each image comparison is a correlation plot showingthe relationship between the CBF values derived from SD and DD contrast passages for eachvoxel within the brain in the 18 slices, The solid lines in the graphs are the best-fit linearregressions to all the data. The dotted lines show the hypothetical regression lines (slope = 1,intercept = 0) for the ideal situation in which CBV values derived from the SD and DD contrastpassages are identical.

Alger et al.


NIH


NIH


NIH


Figure 4.Representative illustration of WM and GM ROI identification. The regions were identifiedusing the SD CBV image shown in grayscale.

Alger et al.


NIH


NIH


NIH


Figure 5.Bland-Altman regression plots for the ROI CBV (upper panel) and CBF (lower panel)measurements. Two points (one GM measurement and one WM measurement) are plotted foreach study. WM readings are clustered on the left of each plot. GM readings are clustered tothe right. The contrast agent, preparation and the scanner used for each study are identified bythe symbols shown in the legend. Regression lines determined from all the points are plottedas solid lines. The 95% confidence intervals for the regression lines are shown by dotted curves.

Alger et al.


NIH


NIH


NIH


Figure 6.Box plots showing the distribution of the Bland-Altman regression intercepts and slopes forCBV and CBF derived from Bland-Altman regression analysis of all voxels in the 3D imagedata. Ideal agreement between SD and DD measures is indicated by zero intercept and slope(dotted) line on the plots. The distributions for the various contrast agent-scanner combinationsare plotted separately.

Alger et al.


NIH


NIH


NIH


Figure 7.Box plots ROI CBV and CBF measures in GM and WM segregated according to whether theSD study was performed first (SD 1) or second (SD 2). Comparison of each pair (e.g., SD GMCBF: SD 1 vs. SD GM CBF: SD 2) provides a means of determining whether dose order hadan effect on the SD and DD CBF and CBV measurements; * indicates a statistically significant(P < 0.05) pairwise mean difference.

Alger et al.


NIH


NIH


NIH


NIH


NIH


NIH


Alger et al.

Table 1ΔR2* Measurements†

Normal (N = 12) Stroke (N = 12) All (N = 24)

Arterial DD/SD ΔR2* time integral ratio 1.94 ± 0.68 1.86 ± 0.65 1.90 ± 0.65

Arterial DD/SD ΔR2* steady state ratio 2.48 ± 1.36 2.38 ± 1.48 2.43 ± 1.39

Arterial DD/SD ΔR2* maximum ratio 1.54 ± 0.41a,b,c 1.35 ± 0.20a,b,c 1.45 ± 0.33a,b,c

Whole brain DD vs. SD ΔR2* time integralcorrelation slope

1.54 ± 0.20a, 1.59 ± 0.25a, 1.56 ± 0.22a,

Whole brain DD vs. SD ΔR2* steady statecorrelation slope

1.38 ± 0.29a,d 1.18 ± 0.42a,d 1.28 ± 0.37a,d

Whole brain DD vs. SD ΔR2* maximumcorrelation slope

1.34 ± 0.13b,e 1.34 ± 0.22b,e 1.34 ± 0.18b,e

†Values are means ± standard deviation.

aSignificantly different from 2.0 (one-group t-test, P < 0.05).

bSignificantly different from 1.0 (one-group t-test, P < 0.05).

cSignificantly different from steady state (paired t-test, P < 0.05).

dSignificantly different from time integral.

eNot significantly different from steady state.


NIH


NIH


NIH


Alger et al.

Table 2Dose Dependence of Derived CBV and CBF*

Normal Stroke All

SD whole brain mean CBV (mL/100 g) 3.0 ± 0.8 3.3 ± 0.9 3.1 ± 0.8

DD whole brain mean CBV (mL/100 g) 3.1 ± 0.5 3.6 ± 0.7 3.4 ± 0.7

SD gray matter CBV (mL/100 g) 3.9 ± 0.6

DD gray matter CBV (mL/100 g) 4.0 ± 0.9

SD white matter CBV (mL/100 g) 1.1 ± 0.3

DD white matter CBV (mL/100 g) 1.2 ± 0.3

SD whole brain mean CBF (mL/100 g minute) 44.1 ± 15.0 44.7 ± 17.2 44.4 ± 15.7

DD whole brain mean CBF (mL/100 g minute) 45.0 ± 6.9 46.4 ± 12.2 45.7 ± 12.3

SD gray matter CBF (mL/100 g minute) 61.2 ± 16.1

DD gray matter CBF (mL/100 g minute) 59.3 ± 12.9

SD white matter CBF (mL/100 g minute) 13.7 ± 5.5

DD white matter CBF (mL/100 g minute) 15.8 ± 3.7a

Whole brain DD vs. SD CBV correlation slope 0.80 ± 0.28b 0.84 ± 0.27b 0.82 ± 0.27b

Whole brain DD vs. SD CBV correlation intercept (mL/100 g) 0.85 ± 0.29c 1.07 ± 0.41c 0.96 ± 0.36c

Gray and white matter DD vs. SD CBV correlation slope 0.96 ± 0.23

Gray and white matter DD vs. SD CBV correlation intercept (mL/100 g)

0.22 ± 0.25c

Whole brain DD vs. SD CBF correlation slope 0.79 ± 0.23b 0.78 ± 0.25b 0.79 ± 0.24b

Whole brain DD vs. SD CBF correlation intercept (mL/100 g/minute)

12.5 ± 4.2c 13.4 ± 4.4c 13.0 ± 4.2c

Gray and white matter DD vs. SD CBF correlation slope 0.90 ± 0.25

Gray and white matter DD vs. SD CBF correlation intercept (mL/100 g)

5.27 ± 3.38c

*Values are means ± standard deviation.

aSignificantly different from single dose (paired t-test, P < 0.05).

bSignificantly different from 1.0 (P < 0.05).

cSignificantly different from 0.0 (P < 0.05).


NIH


NIH


NIH


Alger et al.

Table 3Volume Measurements from CBV and CBF Images of Stroke Patients (N = 12)*

SD DD P (paired t-test)

Brain volume (cm3) with CBV < 1.0 mL/100 g 104.8 ± 63.5 53.1 ± 37.0 0.01

Brain volume (cm3) with CBV between 7.0 and 15mL/100 g

79.9 ± 55.9 89.9 ± 64.8 ns

Brain volume (cm3) with CBF < 10 mL/100 g/minute 64.1 ± 51.9 32.2 ± 22.6 0.01

Brain volume (cm3) with CBF < 15 mL/100 g/minute 145.5 ± 104.0 77.9 ± 48.2 <0.01

*Values are means ± standard deviation.

ns = not significant.


Contrast agent dose effects in cerebral dynamic susceptibility contrast magnetic resonance perfusion...

Documents

Transcript of Contrast agent dose effects in cerebral dynamic susceptibility contrast magnetic resonance perfusion...