Review

Real-Time Diffusion-Perfusion MismatchAnalysis in Acute Stroke

Matus Straka, PhD,1,2 Gregory W. Albers, MD,2,3 and Roland Bammer, PhD1*

Diffusion-perfusion mismatch can be used to identifyacute stroke patients that could benefit from reperfusiontherapies. Early assessment of the mismatch facilitatesnecessary diagnosis and treatment decisions in acutestroke. We developed the RApid processing of PerfusIonand Diffusion (RAPID) for unsupervised, fully automatedprocessing of perfusion and diffusion data for the purposeof expedited routine clinical assessment. The RAPIDsystem computes quantitative perfusion maps (cerebralblood volume, CBV; cerebral blood flow, CBF; meantransit time, MTT; and the time until the residue functionreaches its peak, Tmax) using deconvolution of tissue andarterial signals. Diffusion-weighted imaging/perfusion-weighted imaging (DWI/PWI) mismatch is automaticallydetermined using infarct core segmentation of ADC mapsand perfusion deficits segmented from Tmax maps. Theperformance of RAPID was evaluated on 63 acute strokecases, in which diffusion and perfusion lesion volumeswere outlined by both a human reader and the RAPIDsystem. The correlation of outlined lesion volumesobtained from both methods was r2 ¼ 0.99 for DWI andr2 ¼ 0.96 for PWI. For mismatch identification, RAPIDshowed 100% sensitivity and 91% specificity. The mis-match information is made available on the hospital’sPACS within 5–7 min. Results indicate that the auto-mated system is sufficiently accurate and fast enough tobe used for routine care as well as in clinical trials.

Key Words: perfusion; diffusion; mismatch; acute stroke;automated data processingJ. Magn. Reson. Imaging 2010;32:1024–1037.VC 2010 Wiley-Liss, Inc.

RECENT STUDIES SUGGEST that acute strokepatients with a significant mismatch between diffu-sion and perfusion lesion volumes on brain MRI arelikely to benefit from reperfusion therapies (1,2). Onthe other hand, those with little or no mismatch aswell as those with very large ischemic lesions may notbenefit or may even be harmed (e.g., can suffer brainhemorrhage or reperfusion injury) following earlyreperfusion. Therefore, accurate and reliable diffu-sion-perfusion mismatch maps available in real-timeis of great benefit to physicians who are treating acutestroke patients.

While perfusion and diffusion maps can be obtainedseparately with many software packages (3–5), thereare only a few tools available to compute diffusion-perfusion mismatch maps (6–8). Moreover, currentlyavailable mismatch software relies heavily on userinteraction, which is time-consuming and introducesoperator-dependence into the results. Such complexsoftware packages often require substantial trainingof stroke treatment team and operator errors are morethan likely to occur. Overall, this poses significant dis-advantages because immediate treatment of strokepatients is of utmost importance.

An ideal software package for routine diagnosticworkup of acute stroke would (i) be fully automaticand operator-independent, (ii) generate maps ofmotion-corrected perfusion and diffusion parameters,(iii) determine diffusion-perfusion mismatch, (iv) pro-vide maps and mismatch information no later than 5min after the MRI scan, (v) be seamlessly integratedinto existing clinical MR systems, (vi) output to a hos-pital’s Picture Archiving and Communication System(PACS) and other communication devices, such assmartphones, for immediate review by the stroketreatment team, and (vii) deliver reliable processingresults that are comparable to those generated byhuman observers.

In this study, we review common methods for diffu-sion-perfusion mismatch processing and present theRApid processing of PerfusIon and Diffusion (RAPID),which has been developed by our group during thelast 3 years and is currently used on approximately10 patients per day on a routine basis at our hospital.Specifically, we will introduce implementation details,compare results generated by human observers andthe automated system, and provide a discussion

1Department of Radiology, Stanford University, Stanford, California,USA.2Stanford Stroke Center, Stanford University Medical Center, Stanford,California, USA.3Department of Neurology and Neurological Sciences, StanfordUniversity, Stanford, California, USA.

Contract grant sponsor: NINDS; Contract grant number:R01NS39325; Contract grant sponsor: NIBIB; Contract grantnumber: R01EB2711; Contract grant sponsor: NIH; Contract grantnumber: 2R01 NS039325.

*Address reprint requests to: R.B., Department of Radiology, StanfordUniversity, 1201 Welch Road, Lucas Center, Stanford, CA,94305-5488. E-mail: rbammer@stanford.edu

Received March 31, 2010; Accepted August 2, 2010.

DOI 10.1002/jmri.22338View this article online at wileyonlinelibrary.com.

JOURNAL OF MAGNETIC RESONANCE IMAGING 32:1024–1037 (2010)

VC 2010 Wiley-Liss, Inc. 1024

about our initial experience with the system in a rou-tine clinical setting.

METHODS

General Description

The analysis of a patient’s diffusion-perfusion mis-match with RAPID is based on diffusion-weightedimaging (DWI) and dynamic susceptibility contrast(DSC) perfusion-weighted imaging (PWI). Specifically,the system has been developed and validated for dif-fusion-weighted spin-echo echo-planar-imaging (EPI)sequences with and without parallel imaging and withboth conventional Stejskal-Tanner and twice-refo-cused diffusion preparation. Likewise, gradient-echoDSC-MRI EPI scans with and without parallel imagingare compatible with RAPID.

To allow easy and vendor-independent use of RAPID,the software has been packaged in a small form factorcomputer (6.5’’ � 6.5’’ � 10’’) installed within a hospitalnetwork. Serving as an independent DICOM node, thecomputer communicates directly with MR scanners inthe network using a standardized DICOM protocol. Thesystem works as shown on Figure 1. The processing onthe RAPID processor is fully automated. Specifically,RAPID computes quantitative diffusion and perfusion

parameter maps, segments lesions on these maps, andthen derives the mismatch ratio. The results are auto-matically sent to PACS, to the process-initiating MRscanner, and also to email clients and smartphonesupon configuration.

The system was written in C/Cþþ and leverages onparallel computing to accelerate data processing.Thus, in the current implementation, the processingspeed scales linearly with the number of central proc-essing units (CPUs) or CPU cores available. The sys-tem currently runs on a Linux operating system(openSuSE 11.2, supported by Novell, Inc., Waltham,MA) with an Intel Core i7 860S 2.53GHz CPU (con-taining 4 processing cores with hyperthreading whichthus allow 8 processing threads to run in parallel)using 4 GB of RAM. The RAPID processing box is con-nected to the hospital network by means of a gigabitEthernet link. DICOM compatibility is currently pro-vided by the dcmtk 3.5.4 library (Offis e.V., Olden-burg, Germany). The output image annotation sub-routines are currently implemented using MATLABscripts (MathWorks, Natick, MA) compiled into execut-able binaries.

The following sections provide a more detaileddescription of the processing steps (Fig. 2) that defineRAPID.

Figure 1. Basic scheme of the RAPID system workflow. After the diffusion and perfusion data are acquired, the scanner op-erator pushes the images over the network to a DICOM-compatible RAPID node. There, automatic processing is spawned andthe resulting mismatch assessment and computation of PWI parameter maps are performed. Results are then available tohospital PACS, scanner console, email and smartphones in approximately 5–7 min. Color figure can be viewed in the onlineissue, which is available at wileyonlinelibrary.com.

Real-Time DWI/PWI Mismatch (RAPID) 1025

Perfusion

In RAPID, brain regions with altered perfusion areidentified on Tmax maps. Here, the Tmax parameter is abolus-shape-independent estimate of time-delay forblood delivery between a main feeding artery (e.g.,middle cerebral artery) and tissue at a given spatiallocation, similar to time-to-peak (TTP) parameter.Although Tmax and TTP do not directly measure tissueperfusion, they correlate well with hypoperfusion(Fig. 3) based on multiple studies performed by sev-eral groups (9–17). It has also been proposed thatTmax can be used to predict final stroke outcome inpatients who do not achieve reperfusion (1,2).

RAPID calculates the quantitative perfusion param-eter maps from the acquired DSC-MRI T2*-weightedraw perfusion data. The PWI pipeline (Fig. 2) starts bycorrecting for patient motion using a two-dimensional(2D) in-plane rigid-body registration algorithm. The

next step involves correction of slice acquisition times.Typical DSC-MRI acquisitions use single-shot EPIwith interleaved slice acquisition, where individual sli-ces are acquired at different time points within thegiven repetition period. To ensure that signalsthroughout the whole acquired volume are interpretedequally, RAPID interpolates the signals using b-splinesand then resamples them to a common temporalframe-of-reference. This common time base is con-structed by taking the time point of the first acquiredslice, followed by regularly spaced intervals of the scanrepetition period. The b-spline interpolation is usedbecause it allows for resampling of both equidistantlyand nonequidistantly sampled PWI data. After timecorrection, the DSC-MRI signal values are convertedto estimates of relative changes in transverse relaxivity.For gradient-echo-based acquisitions this can beexpressed as:

Figure 2. Details of the RAPID pipeline used to calculate mismatch between DWI and PWI. The PWI processing componentconsists of motion-correction and adjustments for different acquisition times in multi-slice EPI scans, conversion of measuredMR signal to estimated changes of transverse relaxivity, automatic detection of AIF and VOF, correction for nonlinear effect ofgadolinium tracer in bulk blood and capillaries, deconvolution, and final computation of perfusion parameters. After the PWIprocessing, DWI Sb¼0 and Sb¼1000 images are spatially coregistered with the PWI data and later resliced to the PWI referenceframe. The acute stroke core is identified on ADC maps based on an absolute ADC threshold. Critical hypoperfusion is identi-fied based on Tmax prolongation beyond a prespecified threshold. Finally, DWI and PWI lesion volumes are determined. Themismatch ratio, as well as the difference in PWI and DWI lesion volumes (estimated penumbra), are computed and included inthe output summary. Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.

1026 Straka et al.

DR�2 tð Þ ¼ � 1

TElog

S tð ÞS0

� �; ½1�

where S(t) is the time-resolved MR signal, S0 is the av-erage of the prebolus (baseline) MR signal after thefirst few (typically 3) non–steady state time pointswere discarded, and TE is the echo time of the acqui-sition. Next, RAPID selects locations for both arterialinput (AIF) and venous output (VOF) functions in a

fully automatic, operator-free manner. RAPID identi-fies the height, h, arrival time, a, and width, w, of theDR2* signals at all spatial locations that representbrain tissue or vessels and computes their mean val-ues h, a, and w. Then, the AIF detection algorithmsearches for spatially clustered locations with signalsof above-average amplitude, below-average width, andearly bolus-arrival time to identify possible locationsfor AIF. Specifically, at a given spatial location, the‘‘fitness’’ c of a voxel containing a suitable AIF is deter-mined by the following cost function:

c ¼ k1ðh � hÞ þ k2ða � aÞ þ k3ðw �wÞ; ½2�

where the weighting coefficients k1 ¼ 1.0, k2 ¼ �3.5,and k3 ¼ �1.0 were optimized empirically by processing74 PWI datasets from the DEFUSE study (1), as well asother several hundred routine human brain PWI scans.The parameters were optimized for robust delivery ofAIF estimates in anatomically appropriate locations (i.e.,large arteries of anterior cerebral circulation) undervarying sequence parameters, slice prescription, acqui-sition noise, and bolus shape. The selection algorithmchooses final AIF locations in a region with the highestsum of the spatially clustered values of c. Specifically,we use a cluster of at least three adjacent locations for apixel size of 1.88 � 1.88 mm and six adjacent locationsfor a pixel size of 0.94 � 0.94 mm. The selection of theVOF uses a similar algorithm with the assumption of a3–12 s arteriovenous transit time. For AIF selection, thesearch is limited to the anterior circulation, whereas forVOF only posterior locations are allowed (i.e., sagittal ortransverse sinus). The algorithm currently prefers AIFand VOF locations inside large vessels. Selection of AIFoutside of the vessel leads to very complex partial vol-ume effects that can only be corrected with complex-val-ued data. Such corrections were not considered in thepresent implementation because the majority of currentDSC studies use magnitude MR data only.

Once the locations for both the AIF and the VOF areknown, the DR*2 values are converted to estimates oftracer concentration in tissue ct(t):

ctðtÞ ¼ x1DR�2ðtÞ ½3a�

and in large vessels (i.e., ca(t) for AIF and cv(t) for VOF)as suggested in (18):

ca;vðtÞ ¼�x2þ

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffix22þ4x3DR�

2ðtÞ

p2x3

for DR�2ðtÞ � 0

x2�ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffix22þ4x3DR�

2ðtÞ

p2x3

for DR�2ðtÞ < 0

8><>: : ½3b�

Values of x1, x2, and x3 are derived from (18), x1 ¼ 22.7ms.mM, x2 ¼ 7.6 � 10�3 (ms.mM)�1 and x3 ¼ 574 �10�6

(ms.mM2)�1 at 1.5 T and x1 ¼ 11.5 ms.mM, x2 ¼0.493�10�3 (ms.mM)�1 and x3 ¼ 2.62 � 10�3

(ms.mM2)�1 at 3.0T. Although theoretically DR2*< 0 is notpossible, the quadratic model for estimates of tracer con-centration in large vessels (18) does not consider noisepossibly present in the signals, and, therefore, to retainzero-mean noise properties, situations with DR2* � 0 andDR2* < 0 must be treated separately. It has been

Figure 3. Relation between Tmax, relative CBF (a) and rela-tive MTT (b) in acute stroke. The plots suggest that prolongeddelay of blood delivery correlates with reduced perfusion inacute stroke. Regions with mild delays in Tmax (�6 s) are typ-ically related to delayed flow in deep white matter and water-shed regions, or represent regions with benign oligemia,however, delays of 6–14 s are typically related to significantreduction in perfusion. Of note here is that the rCBF andrMTT parameters were obtained by circular, i.e., delay-inde-pendent deconvolution. The plots were obtained by analyzing63 baseline acute stroke cases from DEFUSE trial (1) andaveraging results across these datasets. Regions with Tmax <

4 s were used to determine mean value of normal (i.e.,100%) relative CBF and normal relative MTT. Regions withTmax around 0 s mostly represent arteries, hence the relativeCBF is above 100%. Regions with Tmax around 2 s representproximally fed tissue, whereas regions with Tmax around 4 sis tissue fed distally or watershed regions. Note that to exem-plify the relationship between rCBF, rMTT, and Tmax in acutestroke, absolute values defining 100% rCBF and rMTT arenot important. There is little to no tracer arriving in regionswith severely reduced perfusion and thus it is difficult tomeasure any of the PWI parameters accurately (CBF, MTT,Tmax). As a result, in regions with apparent Tmax > 14 s(shaded parts of the plots) the determined CBF and MTT aremostly a noise-induced random values.

Real-Time DWI/PWI Mismatch (RAPID) 1027

speculated that x1, x2, and x3 depend on hematocrit(18,20); however, for the current implementation ofRAPID, literature values for hematocrit levels wereassumed (18) and were held constant for all subjects. Ofcourse, a deviation in hematocrit level from the assumedvalue may introduce error into computed blood volumeand flow, but the extent of such error was notinvestigated.

The quantitative perfusion parameters (cerebralblood volume, CBV; cerebral blood flow, CBF; meantransit time, MTT; and the time until the residuefunction reaches its peak, Tmax) are obtained by appli-cation of a deconvolution method as outlined in litera-ture (3,20–25). Specifically, a circular deconvolutionis used which has been shown to provide estimates ofthe residue functions that are independent from anydelays between AIF and bolus arrival in the tissue(25–27). According to indicator-dilution theory, tem-porally resolved tracer concentrations observed in tis-sue ct(t) depend on tissue properties r(t) and the tracerconcentration time-course within the vessel that feedsthe voxel of interest, ca(t):

ctðtÞ ¼ caðtÞ � rðtÞ; ½4�

where the residue function r(t) ¼ 1 � h(t) representsamount of tracer that remains within tissue capillaries attime t following a hypothetic instantaneous bolus, andh(t) is a distribution function of transit times of the tracer(28). The ‘‘�’’ symbol denotes convolution. Of note here isthat r(t) allows one to compute bolus-shape independenthemodynamic parameters, and can be determined as:

rðtÞ ¼ ctðtÞ ��1 caðtÞ; ½5�

where ‘‘��1’’ denotes deconvolution operation. In prac-tice, deconvolution must be regularized (29) to stabi-lize the solution of r(t) and to produce physicallyplausible results particularly when the signals to bedeconvolved contain noise.

It can be shown that circular deconvolution in thetime domain (e.g., using singular value decomposition,SVD) and deconvolution executed in the frequency do-main are equivalent (25,27,30). RAPID implements thedeconvolution in the frequency domain, as follows:

rðtÞ ¼ 1

TRFT�1 gðf Þ Ctðf Þ

Caðf Þ� �

; ½6�

where

Ctðf Þ ¼ FT c0tðtÞ� � ½7a�

and

Caðf Þ ¼ FT c0aðtÞ� � ½7b�

and ct0(t) and ca

0(t) are the ct(t) and ca(t) signals zero-padded to twice their length to avoid time-aliasing(25). TR is the sampling rate, FT{} represents the Fou-rier transform, and f represents frequency, whileFT{}�1 is the inverse Fourier transform. Here, g(f) is aregularization operator:

g fð Þ ¼Ca ðf Þ2�N2

Ca ðf Þ2 if Caðf Þ > N and f 6¼ 0

1 if f ¼ 00 elsewhere

8><>: ;

½8�

where N is an estimate of the noise level of the inputsignal (23). Because of the nonlinear character of thenoise in concentration-related DSC-MRI signals andits dependence on shape and amplitude of the bolus(31), it is difficult to estimate N accurately. Therefore,we applied an approximation approach as proposed in(3,25). Specifically, we define:

N ¼ pr

2:max Ca fð Þk k; ½9�

where pr is a regularization parameter. The meaningand value of pr is identical to the regularization pa-rameter proposed previously (3,25). Based on previ-ous works and empirical evidence a value of pr ¼ 0.15is used. Such regularized deconvolution delivers per-fusion estimates that are equivalent to those obtainedwith block-circulant SVD (25) at 15% regularizationlevel. It is known that selection of the optimal regula-rization threshold should be governed by noise levelsin the DSC-MRI signals, but existing optimizationapproaches are time consuming and computationallyexpensive and would prolong delivery of the mismatchanalysis results. The intention was also to keepdeconvolution algorithm in RAPID as similar as possi-ble to algorithms used in the DEFUSE study (1) toensure consistency of computed Tmax values. Applica-tion of Wiener-filter-style regularization (Eq. [8])results in a smoother low-pass filtering than the typi-cally applied sharp box-car-shaped regularization fil-ters (3,25) and thus mitigates oscillatory artifacts inthe reconstructed residue function. By formulatingthe deconvolution problem in the frequency domain,the residue function can be reconstructed with an ar-bitrary temporal resolution by means of zero-padding(i.e., sinc-interpolation). This allows better estimationof the amplitude and peak position of r(t), and, there-fore, improves estimates of CBF and Tmax. Formulat-ing the deconvolution problem using the Fouriertransform also simplifies the application of the Wie-ner-filter-style regularization. Here, the f ¼ 0 compo-nent (i.e., the term representing area under theresidue curve) must be treated differently in the regu-larization process from those with f = 0 (Eq. [8]), toensure that the CBV computations (which are a scal-ing factor for the CBF), are unaffected byregularization. Typical SVD-based deconvolutionapproaches do not allow direct identification of thisterm, what could contribute to CBF underestimationwhen combined with Tikhonov regularization (32),which, in this case, is equivalent to Wiener filteringimplemented in RAPID.

Next, quantitative perfusion parameters are com-puted using the following expressions:

CBV ¼ 100 � kAV

r

ð1�HSV Þð1�HLV Þ

RctðtÞdtRcaðtÞdt ; ½ml=100 g� ½10�

1028 Straka et al.

CBF ¼ 100 � 60 � kAV

r

ð1�HSV Þð1�HLV Þ max rðtÞð Þ½ �;

½ml=100 g=min� ½11�

MTT ¼ 60 � CBVCBF

; ½s� ½12�

and

Tmax ¼ argmax rðtÞ½ �t

: ½s� ½13�

Eqs. [10–13] use infinite integration bounds underthe assumption that the analyzed signals: (i) are con-tinuous, (ii) are temporally infinite, (iii) accurately rep-resent concentration of the tracer at each time point,and (iv) have a white noise figure. However, real DSC-MRI measurements are finite and discrete, so the infi-nite property must be approximated by treating thesignals as a single period of an infinite periodic func-tion. The actual implementation of the integrationoperations is discussed later. Here, max[r(t)] deliversthe maximum value of the residue function r(t), andthe function arg max[r(t)] yields the earliest time t atwhich this maximum value is detected. Furthermore,HSV and HLV represent a correction for different levelsof hematocrit in large vessels and capillaries due tothe Fahreus-Lindquist effect (33,34). Here values ofHSV ¼ 0.25, HLV ¼ 0.45 and r ¼ 1.04 g/mL (density ofthe brain) were used as suggested previously (24).Lastly, kAV is a term to correct for partial volumeeffects (PVE) affecting the AIF:

kAV ¼RcaðtÞdtRcvðtÞdt ; ½14�

where it is expected that cv(t) is a signal from a vessellarge enough not to be subject to PVE. Essentially, kAV

is equal to the ratio of areas under AIF and VOF curves(Fig. 4). The use of kAV simply assumes that any PVE inthe AIF can be represented by linear scaling. In reality,however, the PVE is very complex and can distortthe AIF shape (35). Such shape distortions can affectthe computed perfusion parameters. To mitigate theeffects of AIF or VOF shape distortions due to high con-trast concentration/signal loss during bolus passage,more advanced DSC-MRI acquisition methods such asPERMEATE (36) have been proposed.

In DSC-MRI, the theoretical continuous time-domainsignals ct(t), ca(t), r(t) are approximated by discrete-timesignals ct(k), ca(k), r(k) sampled at time points tk ¼ k �TRfor k ¼ 0 .. n � 1, where n is number of scan repetitions.

In RAPID, the continuous integration y ¼ R1�1

xðtÞdt is

approximated by applying circular trapezoidal integra-tion rule. That is, y evaluates to:

y ¼ TR

�Xn�2

k¼0

x kð Þ þ x k þ 1ð Þ2

� �" #þ x 0ð Þ þ x n � 1ð Þ

2

� ( ):

When modeling the brain perfusion as a linear systemand assuming that the blood–brain barrier is intact, it isexpected that tracer recirculation will be observed in allca(t), cv(t), and ct(t) signals. Consequently, using decon-volution and assuming infinitely long scans, the com-puted estimates of r(t) should not be affected by recircu-lation of the tracer. However, real DSC-MRImeasurements are finite and, therefore, delays betweenarterial, tissue and venous curves will introduce anerror into shape and amplitude of the estimated r(t), dueto nonidentical lengths of recirculation parts of thecurves. A possible solution would be to fit gamma-vari-ate curves to the measured time-concentration signalsand thus remove the effect of recirculation, but suchapproach is time-consuming and might not besufficiently reliable and accurate. Recirculation can,therefore, have an effect on the accuracy of computedparameters in RAPID. However, with sufficiently longscans, i.e., with scans that sample tracer concentrationsufficiently long after the bolus first pass and until asteady concentration of tracer is achieved, the recircula-tion-induced errors are expected to be smaller thanother errors, e.g., those arising from fresh blood inflow(time-of-flight effect) and T1-shortening effects, etc.that distort the shape of tracer-concentration curves inlarge vessels. Nevertheless, the extent of error attribut-able to recirculation and delays was not furtherinvestigated.

To alleviate the coarse quantization of Tmax valueswhich are typically confined to multiples of the sam-pling rate TR, we zero-pad the regularized R(f) toobtain a sinc-interpolated estimate of the residuefunction r(t) with a virtual temporal resolution of1.0 s. Note that Tmax has a significantly reduced de-pendence on cardiac output and the bolus adminis-tration protocol compared with TTP.

Although MR contrast agents have been used forangiographic and perfusion measurements for almost20 years and are routinely used, it should be noted

Figure 4. Arterial input (AIF) and venous output (VOF) func-tions in DSC-MRI. Ideally, the AIF represents the shape oftracer bolus entering the brain. However, the AIF might bescaled due to partial volume effect (PVE). This introduces anunknown confounding factor leading to overestimation of CBVand CBF values. The area under the VOF curve can used tocorrect for this PVE using Eq. [14]. Note that the peak level ofVOF tracer concentration is ‘‘clipped’’ (44) at around 8 mM(usual for acquisition at 1.5T with TE ¼ 40 ms).

Real-Time DWI/PWI Mismatch (RAPID) 1029

that the DSC perfusion acquisition and postprocess-ing methods presented here rely on off-label use ofMR contrast agents.

Diffusion

Diffusion-weighted MRI has been demonstrated toprovide a reliable surrogate measure for the infarctcore. In particular, the isotropic diffusion-weightedimages (b ¼ 1000 images) and the diffusion tensortrace maps (the isotropic apparent diffusion coeffi-cient � ADC) have high sensitivity for detecting acuteischemic brain lesions with excellent contrast (37).The b ¼ 1000 images (Sb¼1000) are typically preferredby human observers but cannot be used for compu-terized segmentation. This is because signal hyperin-tensities in these images can be due to both acuteinfarct and imaging artifacts, such as susceptibilitypile-up, T2-shine-through, and coil sensitivity fieldvariation. Geometric distortions, etc. can be alleviatedby using parallel imaging (SENSE, GRAPPA), howeversuch advanced imaging approaches are not availableon every clinical scanner. Moreover, a large number ofclinical scans use multi-channel head or head-neckcoil arrays. The sum-of-squares reconstruction typi-cally obtained from these coils tend to have muchhigher signal variation due to the underlying coil sen-sitivity changes than the bias fields seen on dataobtained with quadrature transmit-receive head coilsthat were most frequently used until a few years ago.The variation of the coil sensitivity (bias field) couldbe estimated and corrected for by a previous calibra-tion scan, but is often neglected. To mitigate segmen-tation errors arising from the image artifacts, RAPIDuses ADC maps to detect acute stroke lesions,because the bias fields on the Sb¼1000 and Sb¼0 arethe same and cancel each other. To compute the ADCmaps that are insensitive to patient motion, the DWIdata are spatially coregistered with the PWI images(Fig. 2). The prebolus PWI image is used as the staticreference image because the PWI images typicallyhave the lowest resolution and consequently are themost susceptible to detrimental resampling artifacts.Here, the Sb¼0 images are aligned with the time-aver-age of the prebolus (baseline) PWI images. The Sb¼1000

images are then aligned with the Sb¼0 data that werealready transformed to the PWI reference frame. Then,the realigned Sb¼1000 and Sb¼0 data are resliced intothe PWI coordinate space and ADC values are com-puted as:

ADC ¼ �1

bln

Sb¼1000

Sb¼0

� �; ½mm2=s� ½15�

where b is the diffusion weighting factor and Sb¼0

is the signal value when diffusion gradients areturned off.

Lesion Segmentation and Mismatch Computation

Mismatch in RAPID is defined as the ratio betweenthe volume of critically hypoperfused tissue (identifiedon Tmax maps) and the volume of the infarct core

(identified on ADC maps). The infarct core is tissuethat is deemed irreversibly damaged and which can-not be rescued by reperfusion. The ‘‘critically hypo-perfused region’’ refers to tissue that is likely to havecritically lowered perfusion, and includes both sal-vageable (if perfusion is restored early enough) and al-ready irreversibly damaged tissue.

To compute the mismatch between DWI and PWIlesions, the DWI data are first spatially coregisteredwith the PWI study (Fig. 2) as previously described.The DWI/PWI coregistration also improves robustnessof the automated brain tissue segmentation by allow-ing reliable automated identification of ventriclesbased on DWI. The ventricles are often difficult to seg-ment from the raw PWI data alone. An example of acoregistered dataset is given in Figure 5.

To exclude nonbrain regions from further segmenta-tion, RAPID forms a brain tissue mask where ADCand prebolus PWI data simultaneously demonstratevalid values. Specifically, ADC values are constrainedto lie between 100 � 10�6 mm2/s and 2100 � 10�6

mm2/s, whereas for PWI the prebolus signal intensityvalues must be above 50% of the mean value of theprebolus PWI data obtained by averaging through allvoxels in the dataset. As a result, regions representingair, cerebrospinal fluid (CSF) and imaging artifacts(e.g., EPI half field-of-view ghosts) are excluded fromfurther analysis. The masking also excludes locationsnot covered in both the DWI and the PWI sequencesfrom further analysis.

Ultimately, the stroke core is identified from hypoin-tense regions in ADC maps using an absolute ADCthreshold (ADCth). The ADC threshold currently usedto identify core is 600 � 10�6 mm2/s. This thresholdwas identified with help of a receiver-operator-curve(ROC) analysis that included 32 datasets from theDEFUSE trial (1). Here, we compared prediction ofirreversibly damaged and reversibly injured tissue onbaseline ADC maps with stroke outcome outlined on30-day follow-up FLAIR imaging. The analysis wasbased on an assumption that an optimal ADC thresh-old for stroke core identification should deliver nofalse positives outside of the 30-day lesion mask(ignoring contribution from noise), while it shouldmaximize the number of true positives inside the latefollow-up lesion. The optimal ADC threshold was cho-sen as the one that maximized the accuracy of strokecore identification, i.e., the one that showed maximaldeviation from the line of no discrimination (Fig. 6) inthe ROC plot. The accuracy of the segmentation wasrather insensitive to selection of the segmentationthreshold ADCth in range from 560 � 10�6 mm2/s to640 � 10�6 mm2/s. ADCth below 560�10�6 mm2/sproved to be insufficiently sensitive, whereas an ADCth

above 640�10�6 mm2/s yielded results not specificenough to separate the stroke core from reversibleDWI lesions, noise and ADC variations in normal tis-sue. Details of this analysis will be the subject of aseparate publication, but the selected value of ADCth

¼ 600 � 10�6 mm2/s is in excellent agreement withvalues available in literature (38,39). The wide plateauon the plot is beneficial as it ensures correctness ofsegmentation results even under slightly changing

1030 Straka et al.

Figure 5. Example of spatially coregistered DWI and PWI datasets. From left to right: Sb¼1000 (‘‘isotropic’’ diffusion), Sb¼0 (dif-fusion-weighting gradients turned off ¼ T2-weighted data), apparent diffusion coefficient (ADC), cerebral blood volume (CBV),cerebral blood flow (CBF), mean transit time (MTT), and Tmax. The Sb¼1000 and ADC images are used to identify the strokecore, whereas abnormalities on Tmax and MTT maps indicate hypoperfused tissue. The CBF maps reflect hypoperfusion aswell (bottom row), but differences between CBF in gray and white matter and the smaller dynamic range render these mapsless conspicuous than maps of temporal perfusion parameters.

Figure 6.

Figure 6. Accuracy of stroke core identification on baselineADC versus ADC threshold for the RAPID algorithm. Identifi-cation of stroke core lesions in RAPID requires: (i) threshold-ing of the ADC values and (ii) removal of remaining specklenoise by means of morphological opening operation. Thisplot depicts the accuracy of stroke core lesion identificationafter thresholding and morphologic opening as evaluated byROC analysis. Specifically, the value on vertical axis is thedeparture of the ROC curve from line of no discrimination(i.e., random guess) for a given ADC threshold. The wide pla-teau between 560 and 640 � 10�6 mm2/s is beneficial, as itensures robustness and accuracy of lesion segmentationeven if there are slight changes in imaging parameters suchas applied b-value or intersubject variation of normal ADC.

Figure 7. Automatic assessment of DWI/PWI mismatch with RAPID in an acute stroke patient. (Left side) Sb¼1000 imagesoverlaid with stroke core identified using ADC threshold. (Right side) Tmax maps with green overlay for regions with abnormalflow (Tmax > 6 s). (Bottom) DWI lesion volume, PWI lesion volume, their ratio, and absolute difference is presented. This entirepanel is presented automatically to the clinical readers by means of PACS, scanner console, email, and smartphones to facili-tate mismatch-based patient triage.

imaging and physiologic parameters that would affectcomputed ADCs. To make the segmentation robust inpresence of noise and imaging artifacts, small objectswith the volume below 1 mL were removed from thelesion mask after thresholding the ADC maps.

To segment the abnormal area on PWI, a thresholdis applied to identify voxels with Tmax > 6 s. As dem-onstrated in Figure 3 and as current experience indi-cates (14,15,17), Tmax > 6 s most accurately estimatescritical hypoperfusion, which is beyond what isnormally seen due to collateral flow, stenotic feedingvessels or delayed tracer arrival in watershed regions.Based on a virtual sampling rate of 1 s, a Tmax >6 s is, therefore, used for identifying regions withabnormal flow. For final PWI, lesion outlining smallobjects with the volume below 3 mL were removed.The noise conditions and imaging artifacts in DSC-MRI are in general worse than in DWI and thusremoving small, noise-related objects requires higherthresholds. A typical example of a mismatch assess-ment in an acute stroke patient is shown on Figure 7.

Evaluation of RAPID

The goal in the development of RAPID was to auto-matically perform a time-critical assessment of poten-tially salvageable tissue in acute stroke patients.These data allow the physician to avoid potentiallyharmful reperfusion therapies in stroke patients witheither a malignant profile or little salvageable tissue(1), without the need for a skilled operator to performPWI and DWI analysis and quantification. Therefore,the system’s performance and reliability in predictingmismatch was evaluated and compared with the anal-ysis of the same data by an experienced stroke neu-rologist or neuroradiologist.

For testing and evaluation purposes, data from theDEFUSE multi-center (7 sites) trial (1) were analyzed.In this trial, single-shot GRE-EPI DSC-MRI perfusionMRI data were acquired at 1.5T whole-body scanners.Depending on make and model, the scan parameterswere as follows: TR ¼ 1.4–2.0 s, TE ¼ 40–60 ms, ma-trix 1282, field-of-view 24 cm, 12–16 slices (5–7 mmthick separated by gaps of 0–2 mm) to cover thesupertentorial brain, and 40–80 time points. A single-dose bolus (0.1 mmol/kg) injection of a gadolinium-based paramagnetic tracer (gadopentetate dimeglu-mine or gadobenate dimeglumine) was administeredtypically 10–15 s after the start of the dynamic scanusing a MR-compatible power injector followed by 20–25 mL of saline flush at the same flow rate.

In addition to the perfusion MRI data, diffusion-weighted MRI data were also obtained as part of theDEFUSE trial. Specifically, single-shot diffusion-weighted spin-echo EPI scans were acquired with thefollowing scan parameters: TR ¼ 4–6 s, TE ¼ 60–80 ms,matrix 1282, field-of-view 24 cm, and 12–24 slices(5 mm thick separated by gaps of 0–2 mm). Diffusion-weighted images were acquired with encoding gradientsplayed out separately along the three principal axes at ab-value of 1000 s/mm2 plus one scan where the diffu-sion-encoding gradients were turned off (b ¼ 0 image),resulting in a total of 4 different diffusion weightings.

Thereafter, the three directionally dependent diffusion-weighted images were combined to generate an isotropicdiffusion-weighted image (b ¼ 1000 image).

The lesion volumes identified by RAPID were com-pared with those identified by a human reader in 63of 74 cases, where both DWI and PWI were availableand of good diagnostic quality. PWI maps generatedby RAPID (that included corrections for motion anddifferent slice times in interleaved-slice EPI acquisi-tions) were used for both the manual and the auto-mated Tmax lesion segmentation. Attention was paidby the human reader to check if the AIF used in PWIprocessing was selected appropriately. Additionally,both methods used identical segmentation criteria(ADC and Tmax thresholds). RAPID and the humanreader used mismatch criteria that were based on theoriginal DEFUSE study (1). A mismatch was consid-ered positive if the difference between the PWI andDWI lesion volume was at least 10 mL and the PWI/DWI volume ratio was at least 1.2. The agreementbetween diffusion-perfusion-mismatch identified bythe human reader and RAPID was analyzed by truepositive and false positive rates. Here, the assessmentof mismatch by the human reader was considered asground truth (the ‘‘gold standard’’). Finally, the inter-reader agreement between the human reader andRAPID was assessed by Cohen’s kappa value.

RESULTS

Current Installation Base

For testing and evaluation purposes, RAPID has beenintegrated into the clinical routine imaging pipeline atour institution. This has allowed processing of morethan 2000 cases with PWI and/or DWI abnormalities

Figure 8. RAPID processing-time benchmark. The majorityof the processing time in RAPID is currently spent perform-ing motion correction, DWI/PWI coregistration, DICOM datatransfer, and image annotation. The PWI processing algo-rithms, due to parallelized implementation are not a bottle-neck. Total processing time is typically 5–7 min, dependingon number of slices, imaging matrix, and severity of patientmotion. Color figure can be viewed in the online issue, whichis available at wileyonlinelibrary.com.

1032 Straka et al.

in question (15% acute strokes, 30% subacutestrokes, 30% cerebral tumors, 10% moya-moya dis-ease, 15% unspecified) between July 2008 and March2010. The processing of a typical dataset takes cur-rently 5–7 min (Fig. 8), depending on spatial resolu-tion, number of time-points, and possible patientmotion. From Figure 8 it is evident that most of theprocessing time is currently taken up by applicationof the motion-correction algorithms to PWI time seriesdata before computation of perfusion maps, and alsoby data transfer and image annotation routines.

RAPID was also selected as tool for immediate eval-uation of scan results in two large ongoing clinical tri-als due to its fully automatic nature, capability todeliver PWI maps and ability to assess mismatchshortly after the MRI scan (DEFUSE2 trial, personalcommunication with G.W. Albers, Stanford StrokeCenter, Stanford, CA, and EXTEND trial, personalcommunication with S.M. Davis, University of Mel-bourne, Melbourne, Australia).

For the DEFUSE2 study, RAPID is being tested atfollowing clinical centers: Stanford University MedicalCenter, Stanford, CA; University of Pittsburgh MedicalCenter, Pittsburgh, PA, Northwestern Memorial Hospi-tal, Chicago, IL; St. Luke’s Hospital, Kansas City, MO;Health Sciences Center, Salt Lake City, UT; TampaGeneral Hospital, Tampa, FL; University of TexasSouthwestern Medical Center, Dallas, TX; OregonHealth Sciences University Hospital, Portland, OR;Queen’s Medical Center, Honolulu, HI; Swedish Medi-cal Center, Seattle, WA; Section on Stroke Diagnosticsand Therapeutics, National Institute of NeurologicalDisorders and Stroke (NINDS), National Institutes ofHealth (NIH), Bethesda, MD; Graz General Hospital,Graz, Austria, and University Hospital, Leuven,Belgium.

In a recent study that compared accuracy of perfu-sion parameter maps computation using a digitalphantom (40), RAPID performed on par with severalother academic and commercial perfusion packages.In another study (41), RAPID was found to be asaccurate as a human reader in outlining of perfusionlesions on Tmax maps.

Comparison of Automated and ManualSegmentation of Lesion Volumes andMismatch Identification

Results for the 63 cases analyzed both manually andwith RAPID are presented in Figure 9. The correlationbetween DWI lesion volumes using the manual andthe automatic approach was r2 ¼ 0.99 (Fig. 9a). Theslope of the regression line was 0.99 and its interceptvalue was �0.05. The correlation between PWI lesionvolumes using the manual and the automatic methodwas r2 ¼ 0.96 (Fig. 9b). The slope of the regressionline was 1.11 and its intercept value was �0.99. Notethat the automatic method was sensitive only toobjects of size above 1 mL for DWI and above 3 mL forPWI due to the thresholding operation applied forrobustness to noise and image artifacts.

The diffusion-perfusion-mismatch identified byRAPID was in agreement with the observation of a

human reader in 60 cases (95.2%) and in disagree-ment in 3 cases (4.8%, 3 false positives). Specifically,the agreement in mismatch assessment between thehuman reader and RAPID, as assessed by Cohen’skappa, was k ¼ 0.90. RAPID was able to identify mis-match with 100% sensitivity and 91% specificity (falsepositive rate ¼ 9.1%, false negative rate ¼ 0%) in anal-ysis with observations of the human reader as aground truth. Note, however, that the segmentationcriteria used for the identification of stroke core (ADC< 600 � 10�6 mm2/s) and critically hypoperfused tis-sue (Tmax > 6 s @ 1 s sampling resolution) were differ-ent from those used in the DEFUSE trial (1) (Sb¼1000

> mean(Sb¼1000) þ 3.0 � SD(Sb¼1000); Tmax > 4 s @1.4–2.0 s sampling resolution) and reflect recentadvances and adjustments for robust automatedlesion identification.

DISCUSSION

We have developed a novel system to perform real-time, fully automated identification of diffusion-perfu-sion mismatch in acute stroke patients. This systemhas been deployed at our institution on five 1.5T andthree 3.0T scanners and integrated into the routinediagnostic pipeline where it currently processes 6–10cases per day. The system processes all clinical casesperformed with DWI and GRE-EPI DSC-MRI acquisi-tions. Although not validated, echo-shifted SSFPsequences (such as PRESTO) and diffusion-weightedFSE sequences (such as PROPELLER) have been alsosuccessfully processed using the RAPID pipeline.RAPID is also the processing tool used in two largeacute stroke trials that are currently underway(DEFUSE2, EXTEND) and this manuscript shouldserve as a future reference basis for the processingmethods used in these trials.

Currently, one case is processed in approximately5–7 min, depending in part on the hardware platformon which RAPID is implemented. RAPID could easilybe installed on a more powerful machine; however, asmall form factor computer was chosen for the pres-ent implementation to save space in the usually verytight MR control rooms. For example, one case can beprocessed in 1:30 min on a current state-of-the artIntel Xeon E5570 or AMD Opteron 8378 based plat-form with 16 CPU cores processing data threads inparallel. Although RAPID requires a separate dedi-cated computer, no additional training is required forneuroradiologists and other clinicians, and the proc-essed data are readily available in PACS.

To benchmark and to evaluate RAPID, a large num-ber of research datasets were processed and analyzed.The system was tested across a wide range of imagingparameters and variable data quality. It has beendemonstrated that RAPID is robust and accurateenough for a reliable identification of diffusion-perfu-sion mismatch. The differences in PWI and DWI vol-umes identified by manual and automatic approaches(Fig. 9) can be attributed to two main factors. First, ahuman reader could ignore certain imaging artifactsthat the automatic segmentation could not, leading to

Real-Time DWI/PWI Mismatch (RAPID) 1033

overestimation of volumes by RAPID. Second, theautomated processing ignored parts of DWI or PWIlesions present in locations not covered by both DWIand PWI scans (e.g., cerebellum), which led to under-estimation of volumes in automatic assessment com-pared with results of the human reader. The false pos-itive identification of mismatch was caused byimaging artifacts in the raw PWI data sets, particu-larly, through-plane patient motion and susceptibil-

ity-induced signal loss. These artifacts caused regionswith artificially high Tmax that were erroneously addedto the segmented lesion, thus increased the seg-mented lesion volumes. The majority of the discrepan-cies in segmentation and mismatch assessments canbe attributed to data quality; however, this wasmainly a problem with earlier existing data from theDEFUSE study. Forthcoming studies using betterimaging techniques and optimized imaging protocols

Figure 9. Comparison of stroke core and hypoperfused region volumes identified by a human reader and by RAPID. Correla-tion between manual and automated methods for determining stroke core volume on ADC maps (a) and volumes of hypoper-fused tissue on Tmax (b). Difference between the methods with respect to mean of the results (Bland-Altman plots), for strokecore on ADC maps (c) and hypoperfused tissue on Tmax (d).

1034 Straka et al.

will have better data quality and fewer confoundingartifacts. Nevertheless, even in the original DEFUSEpatients, the automated processing delivered subopti-mal results for only 3 of 63 cases.

Certainly, as with any computer method, the over-all robustness of the system highly depends on thequality of input DWI and PWI data, and can be easilydegraded by patient motion or scanner hardware fail-ure. In our experience, around 10% of acute strokedatasets tend to be corrupted by strong motion.Therefore, the system implements multiple measuresto correct for patient motion or to become insensitiveto signal changes induced by motion. Nevertheless,motion continues to be a problem even in manualdata processing, because heavy through-planemotion cannot currently be corrected sufficientlywith retrospective methods. Fortunately, there areseveral ways to identify cases corrupted by motion.First, the presence of motion artifacts can be ascer-tained from raw PWI data available in PACS. Second,the motion can be detected by computerized methodsand the cost functions used for image registrationcan be used to directly derive an image quality indi-cator as well as a metric for successful registration.The intrinsic vulnerability to out-of-plane motion ofthe 2D acquisition methods used in DSC-MRI couldbe addressed by prospective motion-correction meth-ods, however few vendors currently provide suchcapability and these methods are not yet ready forroutine clinical use.

RAPID currently identifies the ratio between the vol-ume of the infarct core and the volume of the hypo-perfused tissue, also known as the volumetric mis-match. Recent studies indicate that mismatch canalternatively be defined as coregistered mismatch—that is the voxel-wise mismatch between core and pe-numbra (42). Even though RAPID is capable of com-puting this mismatch measure, it is not used in thecurrent approach because of concerns that coregistra-tion inaccuracies combined with imaging artifacts,such as EPI distortions, strongly influence the results.Nevertheless, the coregistered mismatch warrants fur-ther investigation.

Although RAPID has fulfilled a previously unmetclinical need, work is ongoing to improve severalaspects of the system. First, the system currentlyuses an absolute ADC threshold and thus might beinaccurate for subacute strokes where ADC pseudo-normalizes (43). Second, the Tmax does not reflectblood flow directly. For example, prolonged Tmax canbe caused by stenotic vessels or collateral flow aswell. Still, in our experience, in acute stroke the pro-longed Tmax correlates well with lowered CBF (Fig. 3).Third, the computed CBV and CBF values are notabsolute. This is due to the known nonlinearities(18,35) and errors in MR PWI signal acquisitions andis predominantly caused by inaccuracies in the esti-mation of contrast agent concentration in large ves-sels providing the AIF and VOF. Here, the challengesare signal peak clipping (44), fast flow of blood inlarge vessels, T1-effects (45), unknown hematocrit andthe dependency of the tracer effect on vessel orienta-tion relative to the main magnetic field (18,19,46,47).

Signal peak clipping is known to cause errors in CBVand CBF estimation (48), but has much less effect onTmax. Tmax is also independent of the area under theVOF (Eq. [13]). Furthermore, according to our simula-tions (unpublished data), Tmax estimates generallyremain quite unaltered even when AIF amplitude ischanged by clipping, particularly when regularizeddeconvolution is used. The low-pass filtering in theregularization limits the effect of clipping to a changein AIF amplitude only, and hence has little to no effecton Tmax computation. However, this effect certainlydepends on the shape of the bolus curve and dosageof the tracer and should be investigated in futurestudies. Existing artifacts in DSC-MRI signal can beaddressed by using multi-echo acquisitions (36) andusing the MR signal phase instead of signal magni-tude for tracer concentration computation, but thesemethods are not widely available yet and, therefore,we tested RAPID only with conventional DSC-PWIdata. Fourth, if the blood–brain barrier is not intact incertain regions, the PWI computation might deliverinaccurate results of CBV, CBF, and MTT maps dueto extravasation of the contrast agent. In future revi-sions, these effects will be modeled using existingapproaches (49) and thus potentially improve the ac-curacy of the PWI parameter estimation.

The results of this work indicate that automaticprocessing of DWI and PWI in RAPID is sufficientlyfast for identification of the mismatch in an acutestroke setting and is comparable to manual lesionoutlining. The failure rate of RAPID is very low and inour experience it has been outweighed by the conven-ience of having immediate information about diffu-sion-perfusion mismatch without the subjectivity of ahuman operator. In addition to the mismatch assess-ment, the system also provides CBV, CBF, MTT, andTmax maps which are extremely valuable to the stroketreatment team in their daily routine. Stroke neurolo-gists, neuroradiologists, and residents consider thesystem to be extremely useful and a valuable additionto routine PWI and acute stroke patient workup.

ACKNOWLEDGMENTS

The authors thank Dr. Maarten G. Lansberg, Dr. GregZaharchuk, Dr. Michael Mlynash, Dr. Søren Christen-sen, Dr. Jean-Marc Olivot, and Dr. Deidre A. De Silvafor valuable input during the multiple phases of de-velopment and evaluation of RAPID. The authors alsothank Dr. Rafael O’Halloran for proofreading themanuscript. Finally, the authors thank all DEFUSEand EPITHET investigators for providing data as wellas all DEFUSE2 and EXTEND investigators for evalu-ating RAPID and providing feedback. The authors alsoexpress special gratitude to Don Valentine. Withouthis constant support and encouragement this projectwould have never been possible.

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