ADC measurements at low and high b values: insight into normal brain structure with clinical DWI

Magnetic Resonance

ADC measurements at low and high b values: insight into normal brain

structure with clinical DWI

Jose Marıa Garcıa Santos4, Cristina Ordonez, Silvia Torres del RıoArea de Neurorradiologıa, Cabeza y Cuello, Servicio de Radiodiagnostico, Hospital General Universitario Morales Meseguer, Murcia, Spain

Received 18 January 2007; revised 5 April 2007; accepted 21 April 2007

Abstract

Purpose: To demonstrate drop in brain ADC measurements from low to high b values; to evaluate the structural information provided based

on those changes; and to discuss the anatomical reasons for ADC differences.

Methods: Four cerebral ROI (precuneus-PRC, hippocampus-HIP, and the genu-GCC and splenium-SCC of the corpus callosum-CC) were

drawn for ADC measurements with low (1000) and high (3000) b-value DWI in 50 normal subjects. ANOVA and Bonferroni correction

tested ADC differences between areas, between both hemispheres, between GCC and SCC, and between b-value related ADC drop within

areas. Pearson test evaluated dependence of interhemispheric and intercallosum ADC measurements obtained with the same b-value,

dependence between areas of intrazonal drop, and the interhemispheric and intercallosum dependence of intrazonal drop.

Results: ADCs differed between areas (Pb.0001). Interhemispheric ADC only differed in PRC with low b-value (Pb.027). No HIP

asymmetries occurred regardless the b-value. ADC drop within PRC and HIP was similar but differed (Pb.0001) from ADC drop within

both CC ROI. ADC drop was also different between GCC and SCC (Pb.0001). In PRC and HIP, ADC showed a significant interhemispheric

and intrazonal dependence (Pb.0001). There was no GCC to SCC ADC dependence. Intrazonal dependence in the CC was only significant

in the SCC (Pb.001). Interhemispheric dependence of intrazonal drop was significant (PRC P=.007; HIP Pb.0001) but failed to reach

significance in the CC.

Conclusion: Low and high b-value measurements show different diffusion behaviours within different tissues, especially in a highly

anisotropic structure as the corpus callosum. This fact can provide valuable information about brain structure and different diffusion

compartments in clinical DWI.

D 2008 Elsevier Inc. All rights reserved.

Keywords: b value; Diffusion-weighted imaging; Brain; White matter; Gray matter; Corpus callosum

1. Introduction

Diffusion-weighted imaging (DWI) is a magnetic reso-

nance imaging (MRI) technique in which contrast between

tissues depends mainly on the random motion of water

molecules [1]. However, the final determinants of diffusion

are not well understood [2–6]. The degree of diffusion

weight in DWI determines MRI signal and depends on b

value [1,7,8]. The relationship between the motion of water

molecules, the MRI signal and the b value is described by

the Stejskal–Tanner equation [9], which assesses diffusion

by calculating the apparent diffusion coefficient (ADC). The

standard b value used in conventional DWI is ~1000 s/mm2,

and b values of z3000 s/mm2 are considered high [2,8,10].

0730-725X/$ – see front matter D 2008 Elsevier Inc. All rights reserved.

doi:10.1016/j.mri.2007.04.004

4 Corresponding author. Hospital General Universitario Morales,

Meseguer, C/ Marques de los Velez s/n, 30008 Murcia, Spain.

E-mail address: [email protected] (J.M. Garcıa Santos).

As the b value increases, a progressive change in visual

contrast between brain regions and an overall drop in ADC

are noticed [2,7,8,10,11]. The drop in ADC cannot be

adequately interpreted by monoexponential diffusion in

tissues and is better explained by a biexponential model

[2,8,11–16]. Supporting this model, the results of Niendorf

et al. [14] and subsequently of others [2,7,8,10] demon-

strated that there are fast and slow components of diffusion

in the random motion of water molecules in brain tissues.

On the basis of their results, Niendorf et al. [14] suggested

that, using a low b value, DWI signal is dominated by the

fast component, and using a higher b value, the DWI signal

is dominated by the slow component. According to them

[14], the fast and slow components corresponded to the

extracellular and intracellular compartments, respectively.

However, other studies have questioned whether the two

components of diffusion are specifically related to those

Imaging 26 (2008) 35–44

J.M. Garcıa Santos et al. / Magnetic Resonance Imaging 26 (2008) 35–4436

compartments [15,16]. Furthermore, they suggest that there

are fast and slow components of diffusion in both

intracellular and extracellular spaces that are determined

by the distance between membranes and membrane perme-

ability [17,18]. Despite this controversy, it seems that the

use of different b values in DWI can provide valuable

microstructural information [2,12–16].

Many studies on the subject have been performed in

animals or small samples of patients. In the present study,

we used conventional clinical DWI and different b values to

obtain microstructural information from a large group of

patients. Our objectives were to: (a) demonstrate quantita-

tive differences in brain ADCs obtained with high and low

b values; (b) evaluate the structural information provided by

DWI based on those ADC differences; and (c) discuss the

anatomical reasons for ADC differences.

2. Materials and methods

2.1. Subjects

This retrospective study was conducted on a group of

700 consecutive patients who underwent conventional brain

MRI and DWI between 2003 and 2005. The same imaging

parameters were used for all patients. All patients provided

informed consent for the procedure. A sample of 50 subjects

with normal study was obtained by simple randomization.

Fig. 1. The images on the right are ADC maps upon which the ROI were drawn, u

information, see Garcıa Santos et al. [22].

The criterion for normal study was the absence of

morphological and signal abnormalities in all imaging

sequences. For patients older than 50 years, a maximum

of three supratentorial hyperintense T2 foci with a maximum

diameter of b3 mm (negligible in both T1-weighted and

DWI images) were considered normal. The demographic

variables analyzed were age, gender, presence of vascular

risk factors (VRFs) and relevant medical history.

The study group consisted of 26 men and 24 women with

a mean age of 41.9F17 years (men, 45.3F17.9; women,

38.3F15.5). None of the patients was cognitively impaired,

according to the criteria of Petersen et al. [19]. Fourteen

subjects had one or more VRFs. Eight of those had

medically controlled hypertension. Of those, two had

associated dyslipidemia, one had coexistent type 2 diabetes

mellitus and one had a history of chemotherapy. Of the

remaining six patients with VRF, one had type 2 diabetes

mellitus and dyslipidemia, four had dyslipidemia or

hypercholesterolemia and one was diagnosed with systemic

lupus erythematosus and antiphospholipid syndrome. Since

VRF could bias ADC assessment, a statistical comparison of

the ADCs of non-VRF patients and VRF patients was

performed prior to a full analysis of the data.

2.2. Imaging protocol

All patientswere studiedwith a high-fieldMRI system1.5T

(Signa MRi LX, Milwaukee, WI) using a double protocol.

sing the corresponding T2-weighted images for reference. For more detailed

Table 1

Results of ANOVA comparing ADCs of patients with and without VRFs

ROI DWI b =1000 DWI b =3000

ADC (meanFS.D.) P ADC (meanFS.D.) P

PCR

Non-VRF 0.7602F0.0493 .128 0.6122F0.0460 .468

VRF 0.7835F0.0435 0.6024F0.0306

HIP

Non-VRF 0.8619F0.0515 .249 0.7261F0.0454 .860

VRF 0.8818F0.0603 0.7285F0.0348

GCC

Non-VRF 0.8965F0.1024 .911 0.5634F0.0515 .972

VRF 0.8927F0.1660 0.5628F0.0494

SCC

Non-VRF 0.7910F0.0920 .770 0.5191F0.0510 .287

VRF 0.7821F0.1064 0.5024F0.0448

The mean values and standard deviations of the ADC for each clinical

group are shown. The column on the left corresponds to quantitative data

obtained with a low b value and the P value for the comparison between

clinical groups in each particular ROI. On the right, diffusion data and

P values correspond to ADCs obtained with a high b value.

Table 2A

Results of ANOVA comparing interzonal, interhemispheric and intercallo-

sum ADCs

The upper part of the table presents diffusion data obtained with a low b

value, and the lower part of the table presents diffusion data obtained with a

high b value. The P value for interzonal comparisons is shown on the third

column. The column on the right shows the P values for interhemispheric

and intercallosum comparisons.

J.M. Garcıa Santos et al. / Magnetic Resonance Imaging 26 (2008) 35–44 37

Conventional protocol included a sagittal T1-weighted spin-

echo sequence (TR/TE=525/10 ms), an axial double-echo

long TR fast spin-echo sequence (TR/TE=2000/30ef ms;

TR/TE=2000/112ef ms), an axial T2-weighted fast Fluid

Attenuation Inversion Recovery (FLAIR) spin-echo sequence

(TR/TE/TI=8000/120ef/2200 ms) and a coronal T2-weighted

fast spin-echo sequence (TR/TE=4500/80ef ms). All images

obtained in the conventional protocol were 5 mm thick with a

1-mm gap.

The DWI protocol included two sets of axial single-shot

spin-echo echo-planar sequences, one with TR/TE=9999/

82 ms and a b value of 1000 s/mm2, and the other with TR/

TE=9999/106 ms and a b value of 3000 s/mm. Both

sequences were carried out with a 128�128 matrix, a

36�22 field of view, 5-mm thickness and no gap between

sections. The number of averages was 1 and 4, respectively.

Each DWI set included an axial sequence with a b value of

0 s/mm2 and three independent diffusion-sensitive sequen-

ces in each of the three main spatial axes (xx, yy and zz).

Subsequently, diffusion data were automatically computed

to obtain the final isotropic diffusion images.

2.3. Data acquisition

Data were collected with commercially available software

(GE Functool ADW 3.1, Milwaukee, WI). Variable ADC

values calculated by different MRI systems [20] and

interobserver variability in measuring the region(s) of interest

(ROI) [21] inhibit us from comparing our results with those

obtained in other MRI facilities. However, our goal was to

make a structural assessment with a widely accessible basic

research tool that is applicable in any MRI center, and not to

establish reference values of brain diffusivity.

Isotropic images and T2-weighted images (b=0 s/mm2)

were transferred to a workstation (GE Advantage Windows

4.0, Milwaukee, WI). ADC maps were computed automat-

ically for each patient with the GE Functool software,

calculating the ADC pixel by pixel according to the

Stejskal–Tanner equation. Once the ADC map had been

obtained, the same radiologist (J.M.G.S.) drew six ROI

(Fig. 1). In each hemisphere, two structurally well-known

areas were selected: (a) the subcortical white matter of the

medial parietal lobe/precuneus (PRC), behind the cingulate

sulcus and anterior to the parieto-occipital sulcus and made

up of intertwined fibers, and (b) the gray matter of the

hippocampus (HIP), immediately behind the anterior edge

of the temporal horn of the lateral ventricle. In addition to

those ROI (hemispheric ROI), two additional ROI were

drawn in the corpus callosum (CC; callosum ROI): genu of

the corpus callosum (GCC) and splenium of the corpus

callosum (SCC), including white matter comprised of

compact fiber bundles.

Pixelated ADC maps were used to more easily identify

those pixels that may be contaminated with cerebrospinal

fluid (CSF). Accordingly, ROI were moved away from edge

pixels that showed an intermediate signal between the CSF

and the brain parenchyma. Since all DWI studies were

performed in strict axial planes that were not corrected for

craniocaudal tilt of the head, a simple previously reported

method [22] was used to draw each ROI, assuring a similar

position in all individuals.

2.4. Statistical analysis

Prior to analyzing the results, patients were assigned to

one of two clinical groups: the VRF group and the non-VRF


group. Since VRF could bias the final results, after verifying

the homogeneity of variances through the Levene test, the

mean ADCs of the VRF and non-VRF groups were

compared with one-factor analysis of variance (ANOVA).

Quantitative variables were expressed as means and

standard deviations. They were first evaluated with Kolmo-

gorov–Smirnov normality test. After verifying normality, we

assessed the structure of the three selected areas by taking

advantage of the expected nonmonoexponential behavior of

diffusion. To that end, we analyzed diffusion behavior with

two b factors in each of the tissues, in two steps:

(a) Quantitative variables, considered dependent data,

were compared with one-factor ANOVA. Statistical

significance was set at Pb.05. Following ANOVA,

multiple comparisons of all ADC ROI were carried

out with Bonferroni correction. We assessed the

diffusion differences between PRC, HIP and CC

(interzonal comparisons) and between the ROI of

symmetrical areas, including right versus left PRC,

right versus left HIP, and GCC versus SCC

(interhemispheric and intercallosum comparisons).

Subsequently, the relationships between diffusion

trends in both hemispheres and CC were assessed

with parametric Pearson regression test. For this

purpose, the interhemispheric dependence of ADC

values and the correlation of GCC and SCC ADCs

(interhemispheric and intercallosum correlation)

Table 2B

Bonferroni corrections for interhemispheric, intercallosum and interzonal compar

The column on the left and the row on top correspond to analyzed ROI. Each of the

mean difference of ADC means (in parentheses) and the P value for the particula

represent high-b-value comparisons.

were analyzed for the same b values (right PRC

ADC b1000 vs. left PRC b1000; right PRC ADC

b3000 vs. left PRC b3000; right HIP ADC b1000 vs.

left HIP ADC b1000; right HIP ADC b3000 vs. left

HIP ADC b3000; GCC ADC b1000 vs. SCC ADC

b1000; GCC ADC b3000 vs. SCC ADC b3000). Next,

the relationships between diffusion trends within

each ROI (intra-ROI correlations: right PRC ADC

b1000 vs. ADC b3000; left PRC ADC b1000 vs. ADC

b3000; right HIP ADC b1000 vs. ADC b3000; left HIP

ADC b1000 vs. ADC b3000; GCC ADC b1000 vs.

ADC b3000; SCC ADC b1000 vs. ADC b3000) were

analyzed with the same statistical test.

(b) Because data were obtained with two different b

values and a drop in ADC was expected when DWI

was carried out with a high b value, we first assessed

the significance of ADC drop within each ROI

(intrazonal changes) by means of Student’s paired

t test. Furthermore, intrazonal changes in different

brain areas were compared with each other (PRC vs.

HIP vs. GCC vs. SCC). For that purpose, in each

anatomical area, low-b-value and high-b-value ADC

means were subtracted, and the results were com-

pared with one-factor ANOVA and Bonferroni

correction in order to assess differences in the ADC

drop between areas. Finally, the trend of intrazonal

diffusion change within an ROI was compared with

its corresponding ROI with Pearson parametric

isons

boxes represents one of the multiple comparisons performed and shows the

r comparison. White boxes represent low-b-value comparisons. Gray boxes

ig. 2. Intrazonal differences in PRC, HIP and CC ADCs according to

value. The graph represents the mean ADC values (vertical axis; in

m2/ms) of the left and right hemisphere ROI and callosum ROI (horizontal

xis). White bars, PRC; gray bars, HIP; black bars, CC. High-b-value

DCs are represented by outer bars. Low-b-value ADCs are represented by

ner bars. Besides the interzonal (white vs. gray vs. black bars) and

terhemispheric and intercallosum (white vs. white; gray vs. gray; black

s. black) differences, note the different ADC values for low and high

values in the hemispheric ROI and the callosum ROI. While the intra-ROI

DC drop is similar for both the PRC and the HIP, it is far greater in the

CC and GCC. Dotted double arrows (*) indicate significant intrazonal

hanges ( P b.0001 for all comparisons).


F

b

Aa

A

in

in

v

b

A

S

c

Fig. 3. Regression plots of interhemispheric and intercallosum correlations. (A and

and (C) ADC values of the GCC (columnwise) and SCC (rowwise) ROI. In each a

value measurements, respectively. The black box within each plot shows the r and

interhemispheric ADC dependence at both b values. In contrast, the CC ROI did

regression test. The dependence of intrazonal changes

(interhemispheric and intercallosum drop correlation:

right to left PRC; right to left HIP; GCC to SCC) was

evaluated in order to analyze the relationship of the

ADC drop between left and right PRC; left and right

HIP; and CC ROI. Statistical analyses were performed

with SPSS for Windows software (Chicago, IL),

version 13.0.

3. Results

In the preliminary analysis of the effect of VRF, ANOVA

(Table 1) did not show significant ADC differences between

the two clinical groups in any of the ROI compared.

Therefore, it was assumed that VRF did not introduce

changes in ADC measurements.

3.1. Interzonal, interhemispheric and

intercallosum comparisons

The ANOVA for interzonal comparisons (PRC vs. HIP

vs. CC) demonstrated significant differences between the

ADCs of the three brain areas studied (Pb.0001 for both

b values) (Table 2A). Bonferroni correction (Table 2B) also

showed a significant difference between the ADCs of PRC

B) ADC values of right (columnwise) and left (rowwise) hemispheric ROI,

natomical area, the upper and lower plots represent low-b-value and high-b-

P values for each analysis. The plots show significant moderate-to-strong

not show significant dependence with either low or high b values.

Table 4

Results of ANOVA comparing intrazonal changes of the ADC

ROI ADC b1000–b3000 (meanFS.D.) P

PRC

Right 0.1458F0.0432 .0001

Left 0.1688F0.0527

HIP

Right 0.1401F0.0506 .0001

Left 0.1413F0.0523

CC

Genu 0.3387F0.1035 .0001

Splenium 0.2741F0.0850

ANOVA results demonstrate a significant difference in the ADC drop of

different anatomical areas when the b value increases.


and HIP. For both b values, the ADC was lower in the PRC

than in the HIP (all differences Pb.0001). However,

diffusion values in the CC were different from those in

the PRC and HIP, depending on the applied b value (Fig. 2).

As a result, Bonferroni correction (Table 2B) showed that,

with a low b value, the GCC, together with the HIP, had the

greatest diffusion, showing a nonsignificant difference with

the right HIP (P=1.000) and the left HIP (P=.931), and a

significant difference with the right and left PRC (both Ps

b .0001). However, the ADC of the GCC was significantly

lower than the ADCs of the PRC and HIP, with a high

b value (all Ps b .0001). On the other hand, with a low

b value, the ADC of the SCC was lower than the ADCs of

the right and left HIP (all Ps b .0001), and was comparable

to the ADC of the PRC (right PRC, P=.884; left PRC,

P=1.000). However, with a high b value, the SCC showed

the lowest diffusion (all Ps b .0001).

One-factor ANOVA for interhemispheric comparisons

(Table 2A) indicated that, with a low b value, diffusion in

the PRC was significantly higher in the left hemisphere than

in the right hemisphere (P=.027). However, the ADCs of

the left and right PRC did not differ with a high b value

(P=.227). Bonferroni correction (Table 2B) did not

demonstrate a significant interhemispheric difference in

PRC (P=1.0), regardless of the b value. Disagreement

between ANOVA and Bonferroni correction results may

indicate that ADC asymmetry was scarce in PRC or that

sample size was not large enough to obtain significance with

the test of multiple comparisons. Although it is interesting

for the analysis of asymmetries, we did not initially consider

a possible handedness effect in our design. Right and left

HIP ADC measurements were not significantly different

according to ANOVA (b1000, P=.787; b3000, P=.498) and

Bonferroni correction (for both b values, P=1.000).

Diffusion within the CC (GCC vs. SCC) was always

different (intercallosum comparisons) regardless of the b

value (Ps b .0001 for ANOVA and Bonferroni correction)

(Tables 2A and 2B).

The interhemispheric correlations of the PRC and HIP

were positive and significant with both b values (Fig. 3). For

a low b value, Pearson regression parametric test showed

moderate dependence between the right and left HIP

Table 3

Intra-ROI correlations

1000 s/mm2 r P 3000 s/mm2

PRC right .612 b .0001 PRC right

PRC left .510 b .0001 PRC left

HIP right .638 b .0001 HIP right

HIP left .542 b .0001 HIP left

GCC .277 .052 GCC

SCC .455 b .001 SCC

The second and third columns show the r and P values, respectively, of the

correlation analysis of ADC measurements in each ROI. The hemispheric

ROI shows significant and moderate-to-strong dependence. The correlation

in the SCC is weaker than that in the HIP and PRC. The GCC failed to show a

significant correlation between low-b-value and high-b-valuemeasurements.

(r=.485, Pb.0001), and strong dependence between the

right and left PRC (r=.620, Pb.0001). For a high b value,

right-to-left dependence was moderate in the PRC (r=.566,

Pb.0001) and strong in the HIP (r=.675, Pb.0001).

Intercallosum correlations (GCC b1000 vs. SCC b1000;

GCC b3000 vs. SCC b3000) were not significant at either

b value (b1000, r=.232, P=.106; b3000, r=�.071, P=.625).Accordingly, we concluded that the ADC in one hemisphere

depends on its counterpart, while the ADC at one end of the

CC is independent of the ADC at the other end.

Intra-ROI correlation was positive and significant in both

hemispheres (Table 3). Pearson test showed strong depen-

dence in the right PRC and HIP (r=.612, Pb.0001; r=.638,

Pb.0001), and moderate dependence in the left PRC and

HIP (r=.510, Pb.0001; r=.542, Pb.0001). For the CC,

intra-ROI dependence was moderate in the SCC (r=.455,

Pb.001) but was not significant in the GCC (r=.277,

P=.052). As a result, unlike hemispheric and SCC ROI,

Fig. 4. White and dark bars represent ADCs (vertical axis) at low and high

b values, respectively, in each selected anatomical area (horizontal axis).

Right and left measurements were averaged in the PRC and HIP. Double-

arrowed (A), (B), (C) and (D) represent the ADC drop in each region (PCR,

HIP, GCC and SCC) and are shown with the area between joining lines to

demonstrate different diffusion behaviors in the CC and hemispheric ROI.

The table on the right shows Bonferroni correction results for each

comparison. The upper value in each box corresponds to differences

between low-b-value and high-b-value subtractions, and the lower value

corresponds to the P value of statistical comparison.

Fig. 6. The two upper longitudinal axons represent the right PRC, and the

Fig. 5. Regression plots of interhemispheric and intercallosum ADC drop correlations. The axes in each plot represent the subtraction of the mean ADC for the

corresponding ROI. Within each plot, the black box shows the r and P values for each analysis. The plots show a significant interhemispheric correlation that is

stronger in the HIP than in the PRC. There was no significant dependence in the intercallosum ADC drop.


diffusion in the GCC appears to have different character-

istics when the b value is increased.

3.2. Intrazonal changes

In all the anatomical areas assessed, when the applied

b value was shifted from 1000 to 3000 s/mm2, the ADC

measurement dropped significantly (Pb.0001) (Fig. 2).

One-factor ANOVA comparing intrazonal ADCs showed

significant differences between the PRC, HIP, GCC and

SCC (Pb.0001) (Table 4). Bonferroni correction showed

significant differences between hemispheric and CC

intrazonal changes (Pb.0001) and between GCC and

SCC intrazonal changes (Pb.0001) (Fig. 4). There were

no differences between PRC and HIP (Fig. 4). As a result,

we can conclude that, unlike the two brain areas (i.e., PRC

and HIP) that did not differ in their b-value-related drop

in diffusion measurements, the GCC and SCC, which

are parts of the same structure, had quantitatively differ-

ent diffusion drops from the PRC and HIP and also from

each other.

Regarding the interhemispheric and intercallosum drop

correlation, Pearson parametric regression showed weak

positive dependence between the right and left PRC

(r=.374, P=.007), and moderate dependence between the

right and left HIP (r=.546, Pb.0001). On the contrary, there

was no significant dependence between GCC and SCC

(r=.000, P=.999) (Fig. 5).
two lower axons represent the left PRC. Darker circles represent
perpendicular axons in a tissue that is comprised of crossed fibers.

Considering the difference in distance between the axons of the two PRCs

(the right PRC is represented by a narrower extracellular space), the fast

components of diffusion (represented by Molecule 1a) in the right PRC are

more restricted than those in the left PRC (represented by Molecule 1b). On

the other hand, because axons are of similar size in both PRCs, intracellular

diffusion would be comparable in the two (Molecules 2a and 2b). However,

if the slow components in clinical DWI also correspond to the extracellular

space, the diffusion behaviors of Molecules 3a and 3b would be similar.

4. Discussion

Our results point out that, regardless of the b value used,

different brain areas have different ADC measurements, and

the use of different b values can provide information about

subtle structural differences. Increasing the b value always

produced a drop in ADC that varied depending on brain

area. Accordingly, diffusion with low and high b values can

be remarkably different in different tissues.

In agreement with previous reports [5,23,24], brain

diffusion in our patients was different across the several

areas analyzed. Since we assessed diffusion in gray matter

and two different white matter regions, ADC differences

also reveal differences in anatomical structure. As in other

reports, interhemispheric asymmetries are occasionally

Fig. 7. In the CC, the GCC and the SCC differ in intracellular and

extracellular spaces. The wider and less tortuous extracellular space in the

GCC allows Molecule 1a to diffuse more freely than Molecule 1b in the

extracellular space of the SCC. In fact, the anisotropy of the fast component

would be much higher in the SCC. Accordingly, the GCC ADC is higher

than the SCC ADC. On the other hand, the intracellular space is wider in

the SCC. As a result, the intracellular diffusion of Molecule 2b in the SCC

should be higher than that of Molecule 2a in the GCC. As the extracellular

space is narrower and more tortuous in the SCC and the fibers in the SCC

are highly myelinated, the slow components outside the axons must be

more restricted in the SCC (Molecule 3b) than in the GCC (Molecule 3a).


observed [24,25]. However, it is noteworthy that, in the

present study, interhemispheric differences depended on the

applied b value. In this sense, the analysis of diffusion with

low and high b values in any of the three ROI selected

provided us with much more information than that with low

(i.e., standard) or high b values alone. Accordingly, we

found that in the PRC and HIP, ADC measurements

acquired with low and high b values (i.e., intra-ROI

correlation) had similar moderate-to-strong significant

dependence. In contrast, for the SCC ROI, intra-ROI

dependence was weak, and the correlation was not

significant. Unlike the PRC and HIP, the CC is a highly

anisotropic structure, and differences in intra-ROI correla-

tions may be related to structural differences. On the whole,

the information achieved by changing the b value supports

the idea that the result of increasing the b factor is not

simply a strengthening of diffusion. Indeed, it may

emphasize the nonmonoexponential behavior of signal

attenuation represented by the fast (stressed by the low

b value) and slow (stressed by the high b value) components

of diffusion [2,7,12–16].

The nature of the fast and slow components is

controversial. One of the first hypotheses on the subject

stated that the slow component represents diffusion in the

intracellular space and that the fast component represents

diffusion in the extracellular space [14]. Using DWI with a

q-space technique, recent work partially supports that idea

and suggests that high b values emphasize intracellular

diffusion, but only within axons [2,12]. However, other

reports do not support the intracellular and extracellular

compartments hypothesis [17,18] and suggest that, regard-

less of how water molecules move, cell membranes and the

distance between them are the main restrictive barriers of

diffusion [18,26]. Accordingly, intra-axonal diffusion tends

toward isotropy and fast motion in large-diameter axons,

and the slow components of diffusion stand out in areas of

thin grouped axons where intracellular and extracellular

spaces are limited [18]. Although water intrinsic to the

myelin sheath could represent another independent com-

partment in the MRI, we believe that, given its fast decay,

the myelin effect in conventional long TE DWI is

negligible [27]. Nevertheless, from a structural point of

view, it is likely that myelin itself could emphasize or

modulate the influence of the slow component on the

image [28–30].

The ultrastructural reasons for ADC differences related to

the b value have not been elucidated. However, based on the

present findings, double b value DWI analysis may provide

a useful investigative approach. We know from previous

research that, in the PRC, the subcortical white matter is

highly isotropic and is comprised of crossed fibers, with

interhemispheric asymmetries in fractional anisotropy

[23,28]. Thus, the weak interhemispheric drop dependence

we detected in the PRC supports a right-to-left anisotropy

difference. If we consider that the size of neuronal fibers is

similar in both PRCs, the intracellular and extracellular

compartments hypothesis could explain why the ADCs in

both PRCs were similar with a high b value (i.e., similar

intracellular diffusion) but different with a low b value (i.e.,

different extracellular diffusion) (Fig. 6). However, our

findings could also be explained by a hypothesis based on

extracellular diffusion, since the extracellular space did not

hinder the slow component of diffusion in the PRCs, despite

their asymmetry (Fig. 6). By the same reasoning and in

agreement with the symmetry of HIP neuronal density

[31,32], both theories would also explain the constant

symmetric HIP diffusion and the stronger interhemispheric

drop dependence we observed.

More than the hemispheric ROI, fiber differences in the

CC can provide information about the final determinants of

diffusion in conventional DWI. The GCC is comprised of

thin poorly myelinated fibers, while thicker highly myelin-

ated fibers comprise the SCC. As a result, the SCC has a

wider intra-axonal space and more constricted and tortuous

extracellular space [33,34]. The wider extracellular space in

the GCC can facilitate parallel and perpendicular diffusion

much more than the compact SCC. However, beyond

mechanical confines, other proposed forms of compartmen-

tation of extracellular water (linked to the fast exchange of

water molecules in the myelin sheath or surface-versus-

volume water) can also modulate signal differences in

different types of tissue [35,36].

In summary, less restriction of the fast and slow diffusion

components in the wider and less tortuous extracellular

space of the less myelinated GCC can explain why its ADC


was greater than that of SCC, independent of the b factor. It

can also explain why GCC and SCC ROI had such different

diffusion behaviors. On the other hand, if slow components

of diffusion were representative of intracellular space and

were detected by increasing the b value, they would have

been more conspicuous in wider SCC fibers. Therefore, the

hypothesis of intracellular and extracellular compartmental

diffusion does not explain why, with a high b value, the

GCC ADC is higher than the SCC ADC (Fig. 7). Thus, in

conventional DWI using standard (i.e., ~1000 s/mm2) and

high (i.e., ~3000 s/mm2) b values, it can be deduced that

fiber density and degree of myelination, shaping extracel-

lular space amplitude and perhaps other membrane-related

variables, are critical factors that determine the diffusion

signal. That idea is supported by previous findings

suggesting that water diffusion is only multicompartmental

(i.e., intracellular and extracellular) when detected at very

large b values; that measurements at smaller b values

mainly reflect restricting boundaries, such as relative

volume fractions and extracellular tortuosity; and that

intracellular diffusion has minor influence [37,38].

This study has several limitations. First, patients with

VRF were included and may have introduced uncontrolled

bias to ADC measurements. However, no statistically

significant ADC differences were detected between patients

with and without VRF. Second, although ROI were carefully

drawn, we cannot rule out CSF contamination, particularly in

the PRC. Nevertheless, in contrast to more sophisticated and

less accessible analytic models for the elimination of CSF

effect, the use of conventional software makes our study

easily reproducible in any MRI system. Third, we did not use

diffusion tensor images, which would provide us with more

information [39]. However, the simplicity of our design does

not invalidate the results, particularly now that diffusion

tensor imaging is less available, postprocessing is more

complicated and data acquisition is longer than that of DWI

[40,41]. Fourth, both DWI sequences were carried out using

a different TE. Since the TE in the high-b-value sequence was

longer, there was a difference in the signal/noise ratio in

sequences that could interfere with ADC measurements.

However, this effect would not account for the differences in

diffusion that we observed. Finally, our two-point measuring

method cannot achieve a full biexponential parameterization,

and ADC results may differ depending on the specific b value

selected. Parameterization is needed to explain the complex

phenomenon of diffusion, including diffusion constants,

relative fractions of fast and slow components, exchange

time, extracellular tortuosity and intracellular diffusion

restriction. However, parameterization is not easy to

perform, is difficult to interpret and is beyond the scope of

our design.

In conclusion, the analysis of ADC measurements with b

values of 1000 and 3000 s/mm2 makes the assessment of

different brain structures possible in clinical DWI, probably

emphasizing the distance between membranes, their degree

of myelinization and the complexity of extracellular space.

Acknowledgments

We would like to give thanks to Dr. Laura Oleaga for her

comments to the final version of this article, and Dr. Andres

Carrillo, for his invaluable statistical support.

References

[1] Bammer R. Basic principles of diffusion-weighted imaging. Eur J

Radiol 2003;45:169–84.

[2] Bashat DB, Sira LB, Graif M, Pianka P, Hendler T, Cohen Y, et al.

Normal white matter development from infancy to adulthood:

comparing diffusion tensor and high b value diffusion weighted MR

images. J Magn Reson Imaging 2005;21:503–11.

[3] Romero JM, Schaefer PW, Grant PE, Becerra L, Gonzalez G.

Diffusion MR imaging of acute ischemic stroke. Neuroimaging Clin

N Am 2002;12:35–53.

[4] Moseley ME, Cohen Y, Kucharczik J, Mintorovitch J, Asgari HS,

Wendland MF, et al. Diffusion-weighted MR imaging of anisotropic

water diffusion in cat central nervous system. Radiology 1990;176:

439–45.

[5] Takahashi M, Ono J, Harada K, Maeda M, Hackney DB. Diffusional

anisotropy in cranial nerves with maturation: quantitative evaluation

with diffusion MR imaging in rats. Radiology 2000;216:881–5.

[6] Le Bihan D, Turner R, Douek P, Patronas N. Diffusion MR imaging:

clinical applications. AJR Am J Roentgenol 1992;159:591–9.

[7] Burdette JH, Durden DD, Elster AD, Yen YF. High b-value diffusion-

weighted MRI of normal brain. J Comput Assist Tomogr 2001;25:

515–9.

[8] DeLano M, Cooper TG, Siebert JE, Potchen MJ, Kuppusamy K.

High-b-value diffusion-weighted MR imaging of adult brain: image

contrast and apparent diffusion coefficient map features. AJNR Am J

Neuroradiol 2000;21:1830–6.

[9] Stejskal EO, Tanner JE. Spin diffusion measurements: spin echoes in

the presence of a time dependent field gradient. J Chem Phys 1965;42:

288–92.

[10] DeLano MC, Cao Y. High b-value diffusion imaging. Neuroimaging

Clin N Am 2002;12:21–34.

[11] Yoshiura T, Wu O, Zaheer A, Reese TG, Sorensen AG. Highly

diffusion-sensitized MRI of brain: dissociation of gray and white

matter. Magn Reson Med 2001;45:734–40.

[12] Assaf Y, Cohen Y. Assignment of the water slow-diffusing component

in the central nervous system using q-space diffusion MRS:

implications for fiber tract imaging. Magn Reson Med 2000;43:

191–9.

[13] Cohen Y, Assaf Y. High b-value q-space analyzed diffusion-weighted

MRS and MRI in neuronal tissues. A technical review. NMR Biomed

2002;15:516–42.

[14] Niendorf T, Dijkhuizen RM, Norris DG, van Lookeren Campagne M,

Nicolay K. Biexponential diffusion attenuation in various states of

brain tissue: implications for diffusion-weighted imaging. Magn

Reson Med 1996;36:847–57.

[15] Mulkern RV, Gudbjartsson H, Westin CF, Zengingonul HP, Gartner W,

GuttmannCR, et al. Multi-component apparent diffusion coefficients in

human brain. NMR Biomed 1999;12:51–62.

[16] Clark CA, Le Bihan DL. Water diffusion compartmentation and

anisotropy at high b values in the human brain. Magn Reson Med

2000;44:852–9.

[17] Chin CL, Wehrli FW, Hwang SN, Takahashi M, Hackney DB.

Biexponential diffusion attenuation in the rat spinal cord: computer

simulations based on anatomic images of axonal architecture. Magn

Reson Med 2002;47:455–60.

[18] Takahashi M, Hackney DB, Zhang G, Wehrli SL, Wright AC, O’Brien

WT, et al. Magnetic resonance microimaging of intraaxonal water

diffusion in live excised lamprey spinal cord. Proc Natl Acad Sci U S A

2002;99:16192–6.


[19] Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen

E. Mild cognitive impairment: clinical characterization and outcome.

Arch Neurol 1999;56:303–8.

[20] Gideon P, Thomsen C, Henriksen O. Increased self-diffusion of brain

water in normal aging. J Magn Reson Imaging 1994;4:185–8.

[21] Bilgili Y, Unal B. Effect of region of interest on interobserver variance

in apparent diffusion coefficient measures. AJNR Am J Neuroradiol

2004;25:109–11.

[22] Garcıa Santos JM, Ordonez Gonzalez C, Lloret Estan F, Torres del Rıo

S. Hippocampal age-dependent ADC changes measured with low and

high b-value DWI. European Congress of Radiology (ECR),

Electronic Poster On-line System (EPOS). http://posters.webges.

com/ecr/epos.

[23] Shimony JS, McKinstry RC, Akbudak E, Aronovitz JA, Snyder AZ,

Lori NF, et al. Quantitative diffusion-tensor anisotropy brain MR

imaging: normative human data and anatomic analysis. Radiology

1999;212:770–84.

[24] Helenius J, Soinne L, Perkio J, Salonen O, Kangasmaki A, Kaste M,

et al. Diffusion-weighted MR imaging in normal human brains in

various age groups. AJNR Am J Neuroradiol 2002;23:194–9.

[25] Park HJ, Westin CF, Kubicki M, Maier SE, Niznikiewicz M, Baer A,

et al. White matter hemisphere asymmetries in healthy subjects and in

schizophrenia: a diffusion tensor MRI study. Neuroimage 2004;23:

213–23.

[26] Schwartz ED, Chin CL, Takahashi M, Hwang SN, Hackney DB.

Diffusion-weighted imaging of the spinal cord. Neuroimag Clin N Am

2002;12:125–46.

[27] MacKay A, Whittall K, Adler J, Li D, Paty D, Graeb D. In vivo

visualization of myelin water in brain by magnetic resonance. Magn

Reson Med 1994;31:673–7.

[28] Tuch DS, Salat DH, Wisco JJ, Zaleta AK, Hevelone ND, Rosas HD.

Choice reaction time performance correlates with diffusion anisotropy

in white matter pathways supporting visuospatial attention. Proc Natl

Acad Sci U S A 2005;102:12212–7.

[29] Yoshiura T, Mihara F, Tanaka A, Ogomori K, Ohyagi Y, Taniwaki T,

et al. High b value diffusion-weighted imaging is more sensitive to

white matter degeneration in Alzheimer’s disease. Neuroimage

2003;20:413–9.

[30] Beaulieu C. The basis of anisotropic water diffusion in the nervous

system — a technical review. NMR Biomed 2002;15:435–55.

[31] Zaidel DW, Esiri MM, Harrison PJ. Size, shape, and orientation of

neurons in the left and right hippocampus: investigation of normal

asymmetries and alterations in schizophrenia. Am J Psychiatry

1997;154:812–8.

[32] Highley JR, Walker MA, McDonald B, Crow TJ, Esiri MM. Size of

hippocampal pyramidal neurons in schizophrenia. Br J Psychiatry

2003;183:414–7.

[33] Aboitiz F, Scheibel AB, Fisher RS, Zaidel E. Fiber composition of the

human corpus callosum. Brain Res 1992;598:143–53.

[34] Aboitiz F. Brain connections: interhemispheric fiber systems and

anatomical brain asymmetries in humans. Biol Res 1992;25:51–61.

[35] Ababneh Z, Beloeil H, Berde CB, Gambarota G, Maier SE, Mulkern

RV. Biexponential parameterization of diffusion and T2 relaxation

decay curves in a rat muscle edema model: decay curve components

and water components. Magn Reson Med 2005;54:524–31.

[36] Ronen I, Moeller S, Ugurbil K, Kim DS. Investigation of multicom-

ponent diffusion in cat brain using a combined MTC-DWI approach.

Magn Reson Imaging 2006;24:425–31.

[37] Pfeuffer J, Dreher W, Sykova E, Leibfritz D. Water signal attenuation

in diffusion-weighted 1H NMR experiments during cerebral ischemia:

influence of intracellular restrictions, extracellular tortuosity, and

exchange. Magn Reson Imaging 1998;16:1023–32.

[38] Pfeuffer J, Provencher SW, Gruetter R. Water diffusion in rat brain in

vivo as detected at very large b values is multicompartmental.

MAGMA 1999;8:98–108.

[39] Castedo J. Anatomıa de la sustancia blanca mediante tractografıa por

tensor de difusion. Radiologia [in press].

[40] Ito R, Mori S, Melhem ER. Diffusion tensor brain imaging and

tractography. Neuroimag Clin N Am 2002;12:1–19.

[41] Basser PJ, Jones DK. Diffusion tensor MRI: theory, experimental

design and data analysis — a technical review. NMR Biomed 2002;

15:456–67.

http://posters.webges.com/ecr/epos

ADC measurements at low and high b values: insight into normal brain structure with clinical DWI

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