diffusion and perfusion mri: basic physics

European Journal of Radiology 38 (2001) 19–27

Diffusion and perfusion MRI: basic physics

R. Luypaert *, S. Boujraf, S. Sourbron, M. OsteauxMR-Centre, Academisch Ziekenhuis, Vrije Uni6ersiteı́t Brussels, Laarbeeklaan 101, 1090 Brussels, Belgium

Received 3 January 2001; received in revised form 9 January 2001; accepted 10 January 2001

Abstract

Diffusion and perfusion MR imaging are now being used increasingly in neuro-vascular clinical applications. While diffusionweighted magnetic resonance imaging exploits the translational mobility of water molecules to obtain information on themicroscopic behaviour of the tissues (presence of macromolecules, presence and permeability of membranes, equilibriumintracellular–extracellular water,…), perfusion weighted imaging makes use of endogenous and exogenous tracers for monitoringtheir hemodynamic status. The combination of both techniques is extremely promising for the early detection and assessment ofstroke, for tumor characterisation and for the evaluation of neurodegenerative diseases. This article provides a brief review of thebasic physics principles underlying the methodologies followed. © 2001 Elsevier Science Ireland Ltd. All rights reserved.

Keywords: Diffusion MRI; Perfusion MRI; Magnetic resonance imaging; Apparent diffusion coefficient; Hemodynamics

www.elsevier.nl/locate/ejrad

1. Introduction

Over the past 20 years, MR imaging has become apowerful tool for the evaluation of the anatomic char-acteristics of various organs. More recently, a numberof techniques have been introduced that allow addi-tional evaluation of functional parameters. Diffusionand perfusion imaging are typical examples that havegained considerable clinical acceptance in neuro-vascu-lar imaging.

MR diffusion weighted imaging (DWI) uses the sig-nal loss associated with the random thermal motion ofwater molecules in the presence of magnetic field gradi-ents to derive a parameter (the so-called apparent diffu-sion coefficient) that directly reflects the translationalmobility of the water molecules in the tissues. Applica-tions of this technique in the context of neuro-vascularimaging include the early detection and assessment ofstroke, tumor characterisation, evaluation of multiplesclerosis. MR perfusion weighted imaging (PWI) refersto methods that make use of the effect of endogenousor exogenous tracers on the MR images for derivingvarious hemodynamic quantities such as cerebral bloodvolume, cerebral blood flow and mean transit time.Potential applications include the identification of tissue

at risk after acute stroke, assessment of tumors, evalua-tion of neurodegenerative conditions.

Although DWI and PWI on their own can answer anumber of questions, the information they provide is toa large extent complementary and their combination,for instance in application to stroke, appears to beextremely promising. The purpose of this article is toprovide a basic understanding of the methodology un-derlying both imaging techniques and their applicationin the neuro-vascular clinical environment.

2. Diffusion weighted imaging

2.1. Background

While the signal attenuation caused by moleculardiffusion in the presence of magnetic field gradients wasrecognized in MR spectroscopy as early as 1954 byCarr and Purcell [1], the pulsed gradient techniquedeveloped by Stejskal and Tanner in 1965 [2] forms thebasis of today’s diffusion weighted imaging methods.Interest in the potential medical usefulness of the tech-nique was further stimulated by the 1990 discovery byMoseley and co-workers that the apparent diffusioncoefficient of cat brain decreased by up to 50% within30 min after the onset of focal ischemia, while theconventional MR images remained normal [3].* Corresponding author. Fax: +32-2-4775362.

0720-048X/01/$ - see front matter © 2001 Elsevier Science Ireland Ltd. All rights reserved.PII: S0720-048X(01)00286-8

R. Luypaert et al. / European Journal of Radiology 38 (2001) 19–2720

Molecular diffusion is the result of brownian motion,the constant random walk of the individual moleculesin a fluid due to thermal agitation. Although the meandisplacement of the molecules remains zero, as timegoes by, there is a non-zero probability of finding anindividual molecule at a distance from its point oforigin. In fact, the root-mean-square displacement canbe shown to increase in proportion to the square rootof time, the constant of proportionality being a diffu-sion constant D characterising the fluid studied. At 25°,for instance, the diffusion coefficient of pure water isabout 2.2×10−3 mm2/s. Soft tissues tend to behavelike aqueous protein solutions and, due to the reducedmobility of the water molecules, the correspondingdiffusion coefficient is generally smaller than that ofpure water. In many tissues, boundaries with variousdegrees of permeability hinder the free diffusion ofwater, further decreasing the diffusion coefficient. Ap-plying the brownian motion model in these circum-stances leads to an ‘apparent diffusion coefficient’ orADC, to be distinguished from the diffusion coefficientof free water molecules. In tissues like white brainmatter, an additional complication arises from the factthat molecular mobility is not the same in all directions,i.e. the diffusion process is anisotropic and the scalardiffusion coefficient must be replaced by a tensor quan-tity [4,5]. Diffusion imaging thus provides a window onthe microscopic structures and processes (presence andpermeability of membranes, equilibrium intracellularextracellular water, …) inside the tissues as reflected bythe motion of the water molecules.

2.2. Imaging diffusion

2.2.1. Scalar diffusion modelIn an isotropic environment molecular mobility can

be described by a scalar diffusion coefficient, reflectingthe fact that the brownian motion is similar in allspatial directions. Description of the effect of isotropicdiffusion on the spin echo signal is relatively simple inthis case. In the absence of magnetic field gradients, thesignal is unaffected by the presence of incoherent mo-tion. As soon as field gradients are switched on duringany stage of the signal preparation, the motion leads tospin dephasing that, due to the random nature of thesuccessive trajectories of each individual molecule, can-not be undone. The result is an exponential attenuationof the original signal S0(N(H), T1, T2) obtained in theabsence of field gradients:

S=S0(N(H), T1, T2) e−bD, (1)

where D is the (apparent) diffusion coefficient of themedium and b is a scalar reflecting the properties of thegradient G(t) that was present during the experiment[2,5]:

b=g2& TE

0

�& t

0

G(t %) dt %�2

dt. (2)

In this expression, G(t %) is replaced by −G(t %) forgradients switched on after the 180° pulse at t=TE/2.

For a constant linear gradient of strength G [1],

b=g2G2TE3/12, (3)

while for sensitization using two identical rectangularpulses (duration d, spacing D) placed on either side ofthe 180° pulse [2]:

b=g2G2d2(D−d/3). (4)

Both schemes lead to complete rephasing of static spins.The second arrangement (due to Stejskal and Tanner) isnow routinely employed for quantitative diffusionwork. It has the advantage that no strong gradientneeds to be present during the echo sampling leading toimproved signal-to-noise ratios. Using several (at leasttwo) diffusion weighted images obtained for differentb-values, the local ADC values can be calculated byfitting the signal values to Eq. (1).

2.2.2. Tensor diffusion modelAs a consequence of their morphology, many tissues

exhibit anisotropic diffusion behavior: the ADC valuesmeasured using the Stejskal–Tanner sequence dependon the direction of the sensitizing gradient. For ananisotropic diffusion process, Eq. (1) must be replacedby a more complicated one:

S=S0(N(H), T1, T2) e−% bij Dij, (5)

where i and j can be any of the three spatial directionsx, y, z in an orthogonal frame of reference.

The bij factors characterize the sensitizing gradientsalong the i and j directions [6]:

bij=g2& TE

0

�& t

0

Gi(t %) dt %� �& t

0

Gj(t %) dt %�

dt, (6)

while the Dij are elements of the apparent diffusiontensor. This tensor is symmetrical and contains only sixindependent elements, the determination of which needsthe acquisition of images with at least two differentdiffusion weightings for each of at least six independentdirections of the sensitizing gradient. The informationin the diffusion tensor may be conceptualized using the‘diffusion ellipsoid’ picture: the portion of space withinwhich we can expect a molecule to end up due to itsaleatory motion expands around the point of origin astime goes by and, in general, has the shape of aflattened cigar, reflecting anisotropic mobility [7].

2.3. DWI in neuro-6ascular imaging

Neuro-vascular applications are in general not inter-ested in the anisotropy aspects of the diffusion pro-

R. Luypaert et al. / European Journal of Radiology 38 (2001) 19–27 21

cesses. However, as some important structures (e.g. thewhite matter) exhibit anisotropic diffusion, the resultsobtained with a scalar Stejskal–Tanner approach canbe expected to depend on the orientation of the sensitiz-ing gradient. A way to overcome this problem is torepeat the scalar diffusion measurement for three inde-pendent sensitizing directions x, y, z and calculate themean of the resulting ADC’s:

�D�= (Dxx+Dyy+Dzz)/3. (7)

As this average is proportional to the trace of thediffusion tensor, it is independent of the choice of thesensitizing directions. The same information is ofcourse available after any measurement following thefull tensor approach.

A further consideration for chosing a protocol forDW imaging is the role played by motion artifacts. Asin diffusion weighted imaging the signals have beensensitized to microscopic motion of the watermolecules, other sources of motion (e.g. macroscopicpatient motion) may lead to large phase errors andcorresponding artifacts in the images. One solutionconsists in measuring and correcting the phase errors inthe raw data before image calculation (the navigatorecho technique [8]). It allows the use of conventionalspin echo sequences, yielding high signal-to-noise ra-tios. Unfortunately, the scheme does not work for alltypes of motion to be expected in the clinical setting.An alternative but technically more demanding solutionis to use rapid imaging in order to minimize potentialmacroscopic motion during image acquisition. The se-quence of choice has become single shot echo planarimaging, with a Stejskal–Tanner preparation part (Fig.1).

Typical results obtained on a standard clinical imager(here a Siemens Magnetom Vision) are shown in Fig. 2.The diffusion weighted images in Fig. 2a (images withsensitizing gradient along the x, y and z axes of thescanner, respectively, being shown in the consecutiverows) were part of the results obtained by a full tensoracquisition using EPI with following sequence settings:TR=800 ms, TE=123 ms, slice thickness 6 mm, FOV240×240 mm, matrix 128×128, five slices and b-val-ues of: (a) 0 s/mm2; (b) 300 s/mm2; (c) 1200 s/mm2 forsensitizing gradients along six directions. The acquisi-tion time was 3 minutes for five measurements of eachimage. As expected, on the diffusion weighted imagesthe tissues with very mobile water (e.g. cerebro–spinalfluid) are dark while structures with reduced mobility(e.g. white matter tracts perpendicular to the sensitizinggradient) are bright (weak dephasing and less signalattenuation).

Fig. 2b shows the ADC maps Dxx, Dyy and Dzz

obtained on the basis of these diffusion weighted im-ages using pixel-by-pixel fitting to Eq. (1). In the ADCmaps tissues with mobile water are bright (high ADC),

while structures with reduced mobility are dark (lowADC). One of the advantages of these calculated mapsis that they contain pure diffusion information: theso-called ‘T2 shine-through’ effect, due to the presenceof T2 contrast in the diffusion weighted signals (Eq.(1)), is completely absent. Note that structures withanisotropic diffusion show marked variation in bright-ness from one map to the other, reflecting the fact thatwater diffusion is stronger along the nerve fibers thanperpendicular to them. Finally, Fig. 2b also presentsthe trace image calculated following Eq. (7) and illus-trating that in this image all directional aspects of thediffusion process are averaged out.

3. Perfusion weighted imaging

3.1. Background

One of the early approaches to perfusion weightedMRI was proposed by Le Bihan [9]. Comparing thealeatory nature of the motion of blood through therandomly oriented capillaries to that of brownian mo-tion, this approach tried with some success to use theprinciples of diffusion weighted MR for estimatingblood flow. This ‘intravoxel incoherent motion’ (IVIM)technique has more recently been replaced by methodsrelying on magnetic susceptibility and inflow effects.

Susceptibility PWI is based on the passage of in-travascular tracers like Gd-DTPA through the capil-laries, producing a transient signal loss due to

Fig. 1. Schematic overview of diffusion weighted MRI methodology.


Fig. 2. Typical images obtained for a healthy volunteer using DW-EPI: (a) Diffusion weighted images corresponding to three different diffusionweighting gradients (b-factors indicated expressed in s/mm2) along each of the three spatial axes x (1st row), y (2nd row), z (3rd row); (b) ADCmaps for sensitization along x, y and z, respectively, and the corresponding mean diffusion (trace) map.

susceptiblity effects and allowing first-pass kinetics ofthe agent to be applied [10]. PWI with arterial spintagging uses the blood itself, with suitably preparedmagnetization, as an endogenous tracer [11]. With anumber of hypotheses and limitations to be discussedlater, perfusion imaging allows the estimation of severalimportant hemodynamic parameters: cerebral bloodvolume (CBV), defined as the fraction of the total tissuevolume within a voxel occupied by blood; cerebralblood flow (CBF) or perfusion, defined as the volumeof arterial blood delivered to the tissue per minute pertissue volume and the mean transit time (MTT), whichcorresponds to the average time it takes a tracermolecule to pass through the tissue studied.

The complementary role played by PWI next to DWIis well illustrated by the application of both techniques

to acute stroke. There are strong indications that anarea of decreased CBV, decreased CBF and increasedMTT corresponds to both the infarct core and thesurrounding reversible ischemic tissue, while an area ofdecreased ADC is limited to the irreversibly ischemiccore [12].

3.2. Imaging perfusion

3.2.1. Tracer kineticsIn MR perfusion imaging, the models introduced in

the past for nuclear medicine studies [13] remain useful,except that now the tracer is either an intravascularcontrast agent or a volume of blood that has beentagged using RF excitation. Excellent reviews of theresults of kinetic theory that are of interest for MR


have been presented in the past [14,15]. The reader isreferred to those texts for more detailed discussions ofthe assumptions needed for the conclusions to be valid.Before summarizing these results, we define two addi-tional quantities describing the interaction betweenblood, tracer and tissue. The blood–tissue partitioncoefficient p of a tracer expresses the equilibrium distri-bution of tracer between blood and tissue. For anintravascular tracer, it equals CBV while for a freelydiffusable tracer it is about 1. The residue function R(t)describes the probability that a molecule of tracer, thatentered a voxel at t=0, is still inside that voxel at a latertime t. It depends on the transport of the tracer betweenblood and tissue and the subsequent clearance from thetissue volume.

3.2.2. General assumptionsFollowing assumptions are tacidly made in any anal-

ysis applying tracer kinetics:1. The perfusion must be constant and unaffected by

the tracer.2. The tracer must be thoroughly mixed with the

blood.3. The concentration of the tracer can be monitored

accurately.4. Recirculation of the tracer can be corrected for

when present.

3.2.3. Useful relationships from kinetic theoryFour important expressions provide the foundation

for most of the perfusion applications met in clinical MR:

CT(t)=CBF& t

0

Ca(t %) R(t− t %) dt %, (8)

MTT=&�

0

R(t) dt, (9)

CBF=p/MTT, (10)&�0

CT(t) dt=p&�

0

Ca(t) dt. (11)

The first expression states that the local tissue concentra-tion at time t equals the sum of all amounts of tracer thatentered the voxel at some previous time, weighted by theprobability that these amounts are still there at time t,taking into account imperfect bolus administrationthrough the arterial tracer concentration Ca(t). Note thatthe fact that R(0)=1 and a perfect (i.e. instantaneous)bolus imply:

CT(0)/CBF. (12)

Eq. (9) simply puts the definition of MTT undermathematical form. [10] is the well-known central volumeprinciple. It follows from Eq. (8) for Ca(t) held constant(equilibrium conditions). Finally, Eq. (11), which canbe derived from Eq. (8) using the properties of con-volution integrals, provides a link between the

Fig. 2. (Continued)


area under the arterial and tissue concentration curvesand the partition coefficient.

3.3. Application to microspheres

For tracers consisting of particles that remain stuckin the capillaries after administration, R(t)=1 andfrom Eq. (8) we see that after the bolus has reached thetissue,

CT(t)=CT=CBF&�

0

Ca(t) dt. (13)

The perfusion can be calculated on the basis of localtissue concentration and the area under the arterialconcentration curve. The calculation is robust and pro-vides the gold standard for CBF measurements.

3.4. Application to intra6ascular tracers

Tracers that remain in the blood can yield modelindependent information on CBV on the basis of Eq.(11) and the knowledge that for an intravascular tracerand intact blood–brain barrier p=CBV:

CBV=

&�0

CT(t) dt&�0

Ca(t) dt. (14)

When the arterial concentration time curve Ca(t)cannot be measured, relative CBV values can still becalculated, assuming all the capillaries in the region ofinterest are fed by the same artery. CT(t) and Ca(t) canin principle also lead to estimates of CBF using Eq. (8),but in this case sufficient knowledge about R(t) isnecessary. In addition, for MR imaging of an intravas-cular tracer, there is no simple relationship between theMTT, as defined in Eq. (9), and the first moment of thetissue concentration time course:

MTT"

&�0

t CT(t) dt&�0

CT(t) dt, (15)

which means that only Eq. (9) and a detailed knowl-edge of R(t) can lead to direct estimates of MTT. Mostfrequently, detailed knowledge of the tissue vasculatureand, consequently, R(t) is not available.

However, based on mathematical models and com-puter simulations, Weisskoff et al. [16] have indicatedthat, even in this case, useful semi-quantitative relativevalues for the MTT and CBF may still be derived fromthe tissue concentration data, provided microvasculartopology (hence R(t)) is reasonably constant in theregion of interest (at most variations in the number of

Fig. 3. Schematic overview of perfusion weighted MRI methodologyfor intravascular tracer studies.

perfused capillaries and their diameters, with only mod-erate variation in the capillary length distribution). Ifthis is the case, the first moment of CT(t) is expected tobehave approximatively like the MTT multiplied by aconstant factor common for all pixels, and a relativevalue of CBF may be derived from the relative CBVand this relative MTT using Eq. (10). If this assumptiondoes not apply, large systematic errors can arise whencomparing regions of interest with different residuefunctions [17].

The use of non-parametric deconvolution techniquesin combination with Eq. (8) has been advocated as analternative solution for the failing knowledge aboutR(t). In how far it can be applied in the clinicalsituation remains unclear, although recently publisheddata on animal models seems promising [18].

3.4.1. Dynamic Gd-DTPA perfusion imagingIn order to apply these results to MR, we need first

to be able to derive tissue concentrations from thesignal intensities measured. Villringer et al. [19] haveexperimentally established that the susceptibility relatedchanges in signal intensity introduced by a bolus ofGd-DTPA have the form:

S=S0 exp(−TE DR2), (16)

where S0 is the signal obtained in the absence ofcontrast agent, TE is the echo time of the sequence usedand DR2 is the change in relaxation rate R2=1/T2 dueto the agent. The basic mechanism underlying these


changes is illustrated in Fig. 3. Due to the large differ-ence in susceptibility between the capillaries containingthe paramagnetic Gd-DTPA and the surrounding tis-sues, strong field gradients exist in the neighbourhoodof the vessel walls, leading to direct signal dephasing ingradient echo images and diffusion mediated dephasingin spin echo images. Experimental data [20] show thatfor the concentration range expected in clinicalapplications,

DR2=k2CT. (17)

The proportionality constant k2 can be expected todepend on the particular tissue, field strength and pulsesequence. Spin echo based PWI shows reduced appear-ance of large vessels and may therefore be more repre-sentative of capillary perfusion, while gradient echobased techniques exhibit higher contrast-to-noise ratio[21] and are usually preferred in the clinic for thatreason. Assuming both relationships Eq. (16) and Eq.(17) to be valid, the tissue concentration during bolustransit can be monitored using following expression:

CT=ln(S/S0)k2 TE

. (18)

A bolus of Gd-DTPA administered as a short venousinjection of a few seconds duration will have a width ofup to 10 s by the time it reaches the brain, creating asignal dip of about 15 s or longer. Adequate coverageof the whole brain with T2* weighted images at a timeresolution of B2 s needs rapid imaging sequences likeEPI. In Fig. 4a results for an acute stroke patient areshown obtained on a Philips Gyroscan Intera using aGE-EPI sequence with following settings: TR=650 ms,TE=30 ms, flip angle 30°, slice thickness 7 mm, FOV230×230 mm, matrix 128×128, 11 slices, one acquisi-tion, leading to a total acquisition time of 1 min and 15s and a time resolution of 1.9 s. The figure shows onetime point out of every four for one slice, covering thewhole time course of 32 images and clearly illustratingthe transient signal drop caused by the susceptibilitydifferences introduced by the tracer.

Typical data processing makes use of Eq. (18) todetermine CT(t) on a pixel-by-pixel basis. In practice, k2

is not usually known and assumed to be the same forall tissues of interest, which means that the concentra-tion values will only be relative. A typical problem thatneeds to be solved when doing these calculations is that

Fig. 4. Typical results obtained using T2* weighted EPI on a patient with acute stroke: (a) First pass of a Gd-DTPA bolus: one time point outof each four is shown for a single slice, illustrating the transient signal drop due to the susceptibility effect introduced by the tracer; (b)Corresponding relative CBF, relative CBV and relative MTT maps (color scale from 0 (blue) to 100% (red)).


Fig. 5. Schematic overview of perfusion weighted MRI methodologyfor endogenous tracer studies.

commonly their magnetisation is inverted) at the levelof the large feeding vessels. The resulting image reflectshow, after a delay, these protons reach the capillaries inthe slice of interest and diffuse in the tissue water space.The second image is obtained without inversion. Inideal conditions, the difference signal is proportional tothe amount of blood delivered to the slice during thedelay period and therefore should reflect perfusion.Several variations of this basic scheme have been inves-tigated: in the oldest method due to Williams et al. [22]images with and without continuous adiabatic inversionin the neck are subtracted, Kwong et al. [23] introducedsubtraction of two inversion recovery images obtainedwith a slice-selective and a non-selective 180° pulse,respectively, and Edelman et al. [24] put forward theEPISTAR technique in which images with and withoutan inversion pulse below the slice of interest are sub-tracted. A general kinetic model for analysing the quan-titative application of these and related techniques [25]leads to the following expression for the difference inlongitudinal magnetisation in the tissue due to labeledblood:

DM(t)=2M0bCBF& t

0

Ca(t %) R(t− t %) m(t− t %) dt %,

(20)

indicating that the magnetization difference due to tag-ging is proportional to the equilibrium magnetization ofthe blood and to blood flow. The integral shows that inaddition to the normalized arterial concentration Ca(t)of magnetization arriving in a voxel at time t, theresidue function R(t) of tagged water molecules andtheir clearance m(t) due to (mainly) relaxation will ingeneral affect the result obtained. The basic features ofthese methods can be understood using following sim-plified expressions obtained by just considering theamount of blood entering the voxel and the magnetiza-tion carried by that blood, neglecting the difference inT1 between blood and tissue [15]:

DM=2M0b CBF t e− t/T1 (pulsed tagging at t=0),(21)

DM=2M0b CBF T1 (continuous tagging). (22)

In each case, the measured signal difference, which isproportional to DM, is proportional to CBF, the equi-librium magnetization of blood M0b and a factor withthe dimension of time. Typical values for CBF, t andT1 lead to estimated magnetization differences of about2% of the equilibrium magnetization, stressing the im-portance of sufficient SNR in these methods. Anothercomplication that affects some of the spin taggingmethods is magnetisation transfer. When the waterspins are labeled in a slab preceding the slice of interest,the spins in that slice undergo off-resonance excitationthat selectively saturates the broad resonance peak of

of recirculation. In order to obtain the true first boluspassage concentration time curve, we need to eliminateany contribution from tracer re-entering the volume ofinterest. The standard approach [14] is to fit the datacurve (with recirculation cut-off) using a gamma variatefunction of the form:

C(t)=K(t− t0)r e− (t− t0)/b. (19)

K, r and b are fit parameters and t0 is the time at whichthe tracer first appears in the data. In most clinicalsettings, no Ca(t) curve is obtained and only relativevalues of CBV are calculated using Eq. (14). For situa-tions with relatively uniform microvascular topology,based on the conclusions of Weisskoff [16], relativeMTT values are derived from the tissue concentrationcurve and combined with the relative CBV data usingEq. (10) to yield a relative CBF map. This approachwas followed for the data in Fig. 4b, which show astrong reduction of the relative values for CBV andCBF and prolongation of the relative MTT in a regionof the left brain, caused by the presence of acute strokedue to thrombosis of the arteria cerebri media.

3.4.2. Perfusion imaging using arterial taggingA second family of perfusion techniques makes use

of RF labeled water in the blood as endogenous, freelydiffusable tracer. In all these techniques, subtraction oftwo images isolates the signal of inflowing arterialblood in the slice of interest (Fig. 5). Typically, for oneimage the water protons in the blood are tagged (most


macromolecule-bound protons. As this saturation getstransferred to the free protons, it results in a severe lossof brain signal (up to 60%). This effect can be compen-sated by using RF excitation of a symmetrical slabfollowing the slice of interest during the baseline acqui-sition [24].

Although a short discussion of inflow perfusionmethods has been included here for completeness, theirclinical application has been limited in comparison tothat of intravascular tracer methods, mainly due totheir sensitivity to patient motion and the insufficientsignal-to-noise ratios often met in practice, especially inlow flow conditions.

4. Conclusion

Diffusion and perfusion weighted MR imaging arerapidly gaining acceptance as clinical tools. DWI makesuse of the signal attenuation due to the random motionof water molecules in a strong gradient and allowsinsight in the microscopic behaviour of the tissues asreflected by the mobility of the water molecules (e.g. inthe changes in the intracellular vs extracellular waterbalance in acute stroke). PWI enables assessment ofregional cerebral hemodynamics using a variety ofmethods, among which the first pass endovascular bo-lus studies are presently the most common in the clinic.Typically, they lead to relative values for CBV and, forsituations where the vascular topology can be assumedto be sufficiently constant in the whole region of inter-est, to semi-quantitative relative values for MTT andCBF. Both imaging techniques provide complementaryinformation that is expected to be of prime importancefor the diagnosis and treatment of cerebrovascular dis-ease, tumors and other disorders.

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diffusion and perfusion mri: basic physics

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