Original Research

Anatomical Distribution of 3He Apparent DiffusionCoefficients in Severe Chronic ObstructivePulmonary Disease

Andrea Evans, BScE,1,2 David McCormack, MD,3 Alexei Ouriadov, PhD,1

Roya Etemad-Rezai, MD,4 Giles Santyr, PhD,1,2 and Grace Parraga, PhD1,2,4*

Purpose: To evaluate the anatomical distribution of appar-ent diffusion coefficients (ADC) using hyperpolarized heli-um-3 (3He) MRI in chronic obstructive pulmonary disease(COPD).

Materials and Methods: Hyperpolarized 3He MRI was per-formed in eight healthy and seven COPD subjects underbreathhold conditions in the supine position to determineADC values from diffusion-weighted images and evaluateanterior–posterior (AP) and superior–inferior (SI) differ-ences.

Results: ADC differences between anterior and posteriorslices, �AP, was 0.06 �0.01 cm2/second for healthy volun-teers and 0.04 �0.02 cm2/second for COPD subjects andwas significant for each subject (P � 0.01). The AP ADCgradient was –3.98 � 10–3 �0.59 cm2/second/cm forhealthy volunteers and –2.04 � 10–3 �0.89 cm2/sec-ond/cm for COPD subjects. The difference in ADC betweensuperior and inferior regions of interest (ROIs), �SI, was0.02 �0.02 cm2/second for healthy volunteers and 0.10�0.09 cm2/second for COPD subjects, which was signifi-cant for each subject (P � 0.05). The SI ADC gradient was–0.63 � 10–3 �2.23 cm2/second/cm for healthy volunteersand –6.61 � 10–3 �6.68 cm2/second/cm for COPD sub-jects. �AP, AP-gradient, and SI-gradient were significantlydifferent between healthy volunteers and COPD subjects(P � 0.05).

Conclusion: In all subjects, ADC anatomical differenceswere significant and mean ADC was dependent on ana-tomic location and disease status.

Key Words: hyperpolarized helium-3 MRI; COPD; emphy-sema; apparent diffusion coefficient; anatomical distribu-tionJ. Magn. Reson. Imaging 2007;26:1537–1547.© 2007 Wiley-Liss, Inc.

CHRONIC OBSTRUCTIVE PULMONARY DISEASE(COPD) is the fourth leading cause of death worldwideand the disease continues to increase in incidence,morbidity and mortality rates, and direct and indirectcosts (1). The increasing prevalence and economic bur-den due to COPD has motivated the advancement ofnew ways to provide earlier diagnosis, better patientrisk assessment, and improved patient monitoring ofdisease progression and treatment. While both pulmo-nary function tests and high-resolution computed to-mography (HRCT) have been well-established as nonin-vasive diagnostic tools for COPD, the limitations ofthese approaches are accelerating the development ofnew tools to diagnose and monitor COPD lung changes.In this regard, hyperpolarized 3He MRI techniques haveemerged as promising approaches for the evaluation ofthe regional distribution of anatomical and functionallung changes associated with COPD (2–5). In particu-lar, pulsed gradient methods (6) allow for the measure-ment of the 3He apparent diffusion coefficient (ADC) inthe lung. ADC values have been exploited to probe themicrostructure of the lung in patients with COPD (3–7),in animal models of emphysema (8), and in ex vivoexplanted lungs (9). Increases in ADC in COPD andemphysema were consistent with expected and as-sumed increases in acinar size due to destruction ofalveoli accompanying COPD (3,7,10). Whole lung ADCmeasurements have also recently been shown to besensitive to early disease in asymptomatic smokerswith potential for use as a sensitive and specific bi-omarker of emphysematous lung changes associatedwith COPD (11,12).

Recently, the quantitative regional assessment ofADC has been exploited to understand the relationship

1Imaging Research Laboratories, Robarts Research Institute, London,Ontario, Canada.2Department of Medical Biophysics, The University of Western Ontario,London, Ontario, Canada.3Division of Respirology, Department of Medicine, The University ofWestern Ontario, London, Ontario, Canada.4Department of Diagnostic Radiology and Nuclear Medicine, The Uni-versity of Western Ontario, London, Ontario, Canada.Contract grant sponsor: Ontario Research and Development ChallengeFund; Contract grant sponsor: Canadian Institutes of Health Research;Contract grant sponsor: Robarts Research Institute; Contract grantsponsor: Merck Frosst Canada Limited; Contract grant sponsor: MerckResearch Laboratories (USA).*Address reprint requests to: G.P., Imaging Research Laboratories, Ro-barts Research Institute, 100 Perth Drive, London, Ontario, CanadaN6A 5K8. E-mail: gep@imaging.robarts.caReceived February 9, 2007; Accepted September 5, 2007.DOI 10.1002/jmri.21205Published online 26 October 2007 in Wiley InterScience (www.interscience.wiley.com).

JOURNAL OF MAGNETIC RESONANCE IMAGING 26:1537–1547 (2007)

© 2007 Wiley-Liss, Inc. 1537

between posture and ADC values in healthy volunteers(11–15) and COPD patients (11). The effect of postureon ADC measurements is of interest because conven-tional clinical MRI is performed in the supine or proneposition and subject positioning has been reported toalter pulmonary function test results (spirometry), dueto position-dependent changes in respiratory dynamics(16). The anatomical distribution of ADC in healthyyoung subjects and some subjects with COPD as pre-viously assessed (10–15), suggests an alveolar (or aci-nar) size gradient in the superior–inferior (SI)(10,12,14,15) and anterior–posterior (AP) direction(11,13–15). ADC gradients in the gravitational directionin healthy volunteers have been attributed to the com-pression of tissue due to gravity, whereas an increase inADC in the superior, or apical, regions of the lungs hasbeen observed as disease-dependent. Here, we extendthese initial findings and assess the effect of COPD onthe anatomical distribution of 3He ADC in older COPDsubjects and age-matched healthy volunteers in thesupine position using hyperpolarized 3He MRI at 3.0T.We quantified SI and AP gradients and differences be-tween healthy and diseased subjects and assessed therelationship between mean ADC, disease status andlung anatomical position. To test the predictive power ofADC-derived measurements with respect to lung func-tion, we employed discriminant analysis to cluster sub-jects based on quantitative ADC gradient data.

MATERIALS AND METHODS

Imaging

A total of 15 volunteers were included in the analysisand baseline demographic characteristics for all sub-jects by subgroup are provided in Table 1. Subjects withsevere COPD were recruited based on the Global Initia-tive for Chronic Obstructive Lung Disease (GOLD)stratification criteria (17) (forced expiratory volume inone second [FEV1] between 30% and 50% predicted).Age-matched healthy volunteers were recruited basedon smoking history (less than one pack-year) and cur-rent FEV1 above 80% predicted. Only subjects withsevere COPD were included in this analysis as theywere expected to provide largest ventilatory defects anddisease-related ADC changes as a background to mea-sure posture-related ADC changes. All subjects wererecruited from the general population of the tertiaryhealth care center in London, Canada as well as directly

from the COPD clinics at St. Joseph’s Hospital (London,Canada), Victoria Hospital, and University Hospital(London Health Sciences Centre, London, Canada). Allsubjects provided written informed consent to thestudy protocol approved by The University of WesternOntario Standing Board of Human Research Ethics.COPD subjects required a disease diagnosis of at leastone year, having had a history of at least 10 pack-yearssmoking and fewer than three exacerbations within thelast 12 months. Healthy subjects were included if theyhad no history of chronic respiratory disease, less thanone pack-year smoking history and no significant un-derlying cardiac disease. Throughout the duration ofthe study, COPD subjects were withdrawn from thestudy if they had experienced a COPD exacerbation.

After subjects provided written informed consent,they were screened for MRI and coil compatibility andunderwent a physical exam prior to plethysmographyand spirometry. Spirometry and plethysmography wereperformed in the morning after patients withheld in-haled bronchodilators and corticosteroids for approxi-mately 12 hours as previously described (18); thesevalues are provided in Table 1 by subject subgroup.Medications were delayed prior to scanning in order toprovide prebronchodilator pulmonary function dataand also to assess all subjects at approximately thesame time after their last dose of medication. Prior toscanning, subjects were administered a practice dosecontaining a mixture of 4He and nitrogen that exactlysimulated the gas mixture of 3He administered duringthe MRI. A standard 14-second breathhold was per-formed with the subject seated upright, while arterialoxygen saturation was continuously monitored bypulse oximetry.

Imaging was performed on a whole body 3.0T Excite12M5 MRI system (General Electric Health Care[GEHC], Milwaukee, WI, USA) with broadband imagingcapability as described previously (18). All helium im-aging employed a whole-body gradient set with maxi-mum gradient amplitude of 0.194 mT/cm. A single-channel, elliptical linear transmit/receive chest coil(RAPID Biomedical GmbH Wuerzburg Germany) wasused for all studies. The basis frequency of the coil was97.3 MHz and excitation power was 3.2 kW using anAMT 3T90 radio frequency (RF) power amplifier(GEHC). A nominal flip angle of 6.7° was used andmultislice images were obtained in the coronal planeusing a fast gradient-recalled echo (FGRE) method withcentric k-space sampling. Flip angle calibration wasperformed as previously described (18) using themethod of Ref. 19 on both phantoms and human vol-unteers. Two interleaved acquisitions (TE � 3.7 msec,TR � 7.6 msec, matrix size � 128 � 128, number ofslices � 7; slice thickness � 30 mm, and FOV � 40 � 40cm) with and without diffusion sensitization (G � 0.194mT/cm, rise and fall time � 0.5 msec, and duration �0.46 msec) were acquired for a given line of k-space toensure that RF depolarization and T1 relaxation effectswere minimal. Total acquisition time was 14 seconds.Hyperpolarized 3He gas was provided by a turnkey,spin-exchange optical pumping system (HeliSpin™;GEHC, Durham, NC, USA) as previously described (18).In a typical study this system provided 30% to 40%

Table 1Subject Demographics

Healthyvolunteers

(N � 8)

SevereCOPD(N � 7)

All subjects(N � 15)

Age, years (� SD) 67 � 6 66 � 8 67 � 7Male sex (N) 5 6 11FEV1

a, % (� SD) 106 � 19 42 � 7 73 � 35FEV1/FVC, % (� SD) 75 � 6 38 � 10 60 � 21FRC, % (� SD) 57 � 8 69 � 8 59 � 12TLC, liters (� SD) 6.2 � 1.3 7.8 � 1.2 7.0 � 1.5

aPercent predicted.FRC � functional residual capacity, TLC � total lung capacity.

1538 Evans et al.

polarization in 12 hours. Doses (5 mL/kg) were deliv-ered in 1-liter plastic bags (Tedlar®; Jensen Inert Prod-ucts, Coral Springs, FL, USA) diluted with ultrahighpurity, medical grade nitrogen (Spectra Gases, Alpha,NJ, USA). Polarization of the diluted dose was quanti-fied by a polarimetry station (GEHC, Durham, NC,USA).

Data Analysis

All images were analyzed by a single trained observer ina constant image visualization environment withdimmed and consistent room lighting. ADCs and ADCmaps were calculated using in-house software pro-grammed in the IDL Virtual Machine platform (Re-search Systems Inc., Denver, CO, USA). ADC mapswere calculated on a pixel-by-pixel basis using:

ADC �

ln�S0

S �b

, (1)

where S and S0 represent the pixel signal intensities ofthe diffusion-weighted and subsequent non-diffusion-weighted image, respectively, and b � 1.6 seconds/cm2

as recently described (20), to allow for direct compari-son with previous studies. Following window and leveladjustment, the trachea and major airways were man-ually segmented and these ADC values were excludedfrom further analysis. Figure 1 shows a schematic ofthe regions of interest (ROIs) analyzed for each ADCmap. ROIs for the center slice were manually seg-mented from the ADC maps for each subject using thecarina as an anatomical reference point for each sub-ject. The remaining three centermost slices were man-ually segmented using the same location of the original

center slice ROI. Mean ADC was calculated for eachcoronal slice and for each of the superior, middle, andinferior lung ROI of both the right and left lung asshown in Fig. 1. ADC histograms representing the ADCvalues and number of pixels having that specific ADCvalue were calculated from the ADC map on a pixel-by-pixel basis. Manual segmentation of ROIs was per-formed using a three-dimensional (3D) image visualiza-tion software (21) for the four centermost slices usingthe carina of the center slice as an anatomical referencepoint as previously described (18). This approachyielded a mean ADC and SD for each coronal slice, anda matrix size of 24 (six ROI � four slices) mean ADC andSD values for each subject, representing the four cen-termost slices.

Anatomical differences in ADC were quantified in thefour centermost slices by: 1) calculating the absoluteADC difference for AP slices (�AP) and SI ROI (�SI); and2) measuring the ADC gradient for AP slices (AP-gradi-ent) and for SI ROI (SI-gradient) in cm2/second/cm.�AP was defined as the difference in ADC between themost anterior center slice and the most posterior centerslice. �AP was calculated using each upper, middle,and lower ROI for both the right and left lung. �SI wasdefined as the absolute difference in ADC between themost superior ROI and the most inferior ROI. �SI wascalculated for both the right and left lung. AP-gradientwas defined as the slope of the line that described thechange in ADC as a function of distance in centimetersover the four central slices (with a slice thickness of 3cm each) and was calculated using mean ADC of wholeslices. The SI-gradient was defined as the slope of theline that described the change in ADC over the upper,middle, and lower ROIs in the center slice and averag-ing the ADC values of the right and left lung as a func-tion of the height of the lung in the center coronal slicefor each subject (between �20–25 cm). AP-gradient andSI-gradient were obtained from the slope of four pointsand three points, respectively, using linear regression.

Discriminant analysis of three representative ADCand ADC-derived independent variables was performedusing the statistical package SPSS 14.00 (SPSS, Inc.,Chicago, IL, USA). Discriminant analysis is a statisticalapproach that can be used to classify data by predictingmembership of subjects to a particular group based ona set of variables (22). Discriminant analysis performsthe reverse of a multivariate analysis of variance(ANOVA) by using mean differences in variables to de-termine membership of data to a certain group. Sepa-ration of the groups is expressed by the discriminantfunction, and the significance of this function is alsotested. Here we applied discriminant analysis to predictthe membership of subjects into either healthy or dis-eased groups, using three variables derived from ADCmeasurements. As shown in Fig. 7, using the unstand-ardized (nonscaled) canonical discriminant function co-efficients, the equation of a plane was used to separatedata points representing subject data plotted in 3Dspace. Unstandardized coefficients were used in themethod to separate the data into subgroups as dis-cussed in Materials and Methods and shown in Fig. 7.However, the standardized coefficients are used to de-termine the significance of the contribution of the dif-

Figure 1. ROI analysis. Four centermost coronal slices weresegmented into six ROIs. �AP is the difference in ADC betweenthe most anterior and most posterior slice calculated for all sixROIs. �SI is the difference in ADC between the most superiorand most inferior ROIs for all four center slices and both theright and left lung.

Distribution of 3He MRI ADC in COPD 1539

ferent variables to the separation of the data, since thisallows comparison of variables on the same scale. Thedata points, plotted with the x-y-z axes correspondingto three independent variables, were projected onto thisplane and subsequently rotated for 2D representationin Matlab 6.5 (The Mathworks Inc, Natick, MA, USA).

Comparison of ADC means was performed using theone-way ANOVA in SPSS 14.00. For multiple compari-sons, the Fisher’s least significant difference post-hoctest was used. A multivariate ANOVA was used to as-sess statistical significance of differences of mean ADCbetween healthy volunteers and subjects with COPD forthe upper, middle, and lower ROIs of both the right andleft lung within the center four coronal slices. The fourvariables assessed in the multivariate ANOVA wereslice, ROI, lung side (R or L), and subject group. To testthe significance of �AP and �SI, a one-sample t-test wasperformed for all �AP and �SI values for all individualsas compared to the test value of 0.0 cm2/second. Lev-ene’s test was employed to ensure equality of variancebetween groups, and the Mann-Whitney U-test (thenonparametric equivalent to the Student’s t-test) wasused where this assumption was found to be invalid. Inall statistical analyses, results were considered signifi-cant when the probability of making a type I error wasless than 5% (P � 0.05). In addition, 95% confidenceintervals as well as SD were calculated for all results.

RESULTS

Subject demographic and pulmonary function charac-teristics reported during the screening visit are pro-vided in Table 1. Pulmonary function measurementsshown for the two subgroups are reflective of the inclu-sion criteria based on screening measurements of post-bronchodilator FEV1 and FEV1/forced vital capacity(FVC). Representative example diffusion-weighted im-ages with their respective ADC maps and ADC histo-grams are presented in Fig. 2 for a single healthy vol-unteer and a single subject with severe COPD.

As shown in the schematic in Fig. 1, mean ADC val-ues for each of six ROI in each of the four centermostslices were calculated. Figure 3a shows mean ADC foreach ROI measured in the center coronal slice and Fig.3b shows the relevant ADC �SI values for all subjectsby subgroup (and lung side) within each of the fourcentermost slices. As summarized in Table 2, meancenter slice �SI was 0.02 � 0.02 cm2/second forhealthy subjects and 0.1 � 0.09 cm2/second for sub-jects with severe COPD. Mean �SI was statistically sig-nificant for healthy volunteers (P � 0.05) and subjectswith severe COPD (P � 0.05), indicating that mean ADCis different between superior and inferior lung regionsfor all subjects. The Mann-Whitney U-test showed �SIto be nonsignificant between groups (P � 0.15).

Figure 4a shows mean ADC for each of the four cen-termost coronal slices by subgroup, and Fig. 4b shows�AP across the four centermost coronal slices for eachROI by subgroup. �AP (using the entire coronal slice asan ROI) was significant for all subjects within thehealthy volunteer subgroup (P � 0.001) and the severeCOPD subgroup (P � 0.01), indicating that a differencein mean ADC in the AP direction exists in all subjects.

Additionally, the difference in �AP between healthysubjects (0.06 � 0.01 cm2/second) and those with se-vere COPD (0.04 � 0.02 cm2/second) was significantlydifferent (P � 0.05).

In Fig. 5, SI-gradients are shown for all subjects, andalso by subgroup, with example images for a singlehealthy volunteer and a subject with severe COPD pro-vided along with corresponding ADC histograms show-ing ADC values for most inferior and superior ROI. Asshown in Table 2 and Fig. 5a, SI-gradients had highvariance as indicated by the SDs of each group. TheMann-Whitney U-test showed that SI-gradients inhealthy volunteers were significantly smaller thanthose in subjects with severe COPD (P � 0.05).

The AP-gradients for all subjects and mean AP-gradi-ents by subgroups (Fig. 6) shows that mean ADC AP-gradients for healthy volunteers was –3.98 � 10–3 �0.59 � 10–3 cm2/second/cm, which is significantlygreater (P � 0.05) from the mean ADC AP-gradient forsubjects with severe COPD (–2.04 � 10–3 � 0.89 � 10–3

cm2/second/cm). Although the difference in slope isnot visually large in Fig. 6b, it is significant.

To assess the relationship of the mean ADC valuefor each of the 24 mean ADC values calculated for thefour centermost coronal slices for all subjects, a mul-tivariate ANOVA was performed. The multivariateanalysis probes the significance of the relationshipsbetween the four variables (ROI [SI direction], slice[AP direction], lung side [right or left], and subjectsubgroup [healthy or severe COPD]) while simulta-neously assessing the interactions of the variableswith one another. Multivariate ANOVA of mean ADCfor all subjects for the superior, middle, and inferiorROI within each of the four centermost slices showedsignificant main effects for slice (P � 0.001), ROI (P �0.001), and subject subgroup (P � 0.001). Additionalsignificant two-way interactions (Table 3) betweenROI and slice, lung side, and group where observed(P � 0.01), as well as subject subgroup and slice (P �0.01). Additionally, a significant three-way interac-tion was observed for mean ADC (P � 0.05) based onsubject subgroup, slice, and ROI.

Discriminant analysis was performed for all subjectdata using center slice mean ADC, ADC SD, and one of:�AP, AP-gradient, �SI, and SI-gradient. The discrimi-nant function was significant (P � 0.01) when �AP orAP-gradient was used as the third independent vari-able. The discriminant function was significant (P �0.01) and standardized coefficients were 0.814, –0.572,

Figure 2. 3He MR images for a healthy volunteer and subjectwith severe COPD. a–d: Diffusion-weighted MR images of fourcentermost slices for a healthy volunteer (74 years old, FEV1 �91% predicted, functional residual capacity [FRC] � 58% oftotal lung capacity [TLC]). e–h: ADC maps for respective diffu-sion-weighted MR images. i–l: ADC histograms for respectiveADC maps. m–p: Diffusion-weighted MR images of four cen-termost slices for a subject with severe COPD (65 years old,FEV1 � 42% predicted, FRC � 66% of TLC). q–t: ADC maps forrespective diffusion-weighted MR images. u–x: ADC histo-grams for respective ADC maps.

1540 Evans et al.

Figure 2.

Distribution of 3He MRI ADC in COPD 1541

and 0.263 when the variables center slice mean ADC,AP-gradient, and ADC SD, were used, respectively. Thestandardized coefficients indicate the relative contribu-tion of the variable to the overall discrimination, andthat AP-gradient contributed to the overall discrimina-tion. Figure 7 presents discriminant analysis resultsusing AP-gradient, mean ADC, and ADC SD as an ex-ample of the clustering of subjects achieved using thesevariables. The discriminant function was not signifi-cant (P � 0.1) when �SI or SI-gradient was used as thethird independent variable, and did not appear to clus-ter subjects into their respective subgroups.

DISCUSSION

Hyperpolarized 3He MRI provides the possibility of di-rectly imaging and quantifying posture-related regionalchanges in ADC within healthy and diseased lungs.ADC gradients in the AP and SI direction in healthysubjects (10–15) and subjects with COPD (11) havebeen previously reported. The AP gradient has beenattributed to a gravity dependence in the supine andother positions which affects ADC by the compressionof lung tissue in the posterior regions by virtue of pa-renchymal weight.

Here we report the analysis of 15 older subjects withsevere COPD and age-matched healthy volunteers andprovide evidence of ADC differences in the SI directionand across all coronal slices in the AP direction. In thisstudy we show for all subjects: 1) SI differences mea-sured using �SI and SI-gradient; 2) AP differences mea-sured using �AP and AP-gradient; 3) multivariateANOVA analysis of mean ADC for 24 lung regions; and,4) discriminant analysis based on ADC-derived mea-surements.

We observed a significant decrease in mean ADCfrom the superior to the inferior regions of the lungexpressed as �SI and SI-gradient in all subjects, andthat the SI-gradient was significantly greater in sub-jects with COPD. Although �SI was not significantlydifferent between healthy volunteers and subjectswith severe COPD, the significant interaction betweenROI and group (P � 0.01) indicated in the multivari-ate ANOVA suggests that there is a difference in theADC distribution in the SI direction between groups,and this is supported by the fact that the SI-gradientwas significantly different (P � 0.05) between healthyvolunteers and subjects with COPD. It is possiblethat higher variance in ADC in the severe subgroupaccounted for the fact that �SI differences between

Figure 3. Mean ADC and �SI.a: Mean ADC values in cm2/second for ROIs in center slicefor entire slice and for each ROIin center slice. Round brack-eted values are SDs of meanADC for the subjects in eachsubgroup. Square bracketedvalues are the lower and upper95% confidence intervals of themean, respectively. b: �SI foreach of four center slices (Rand L), where �SI is the changein ADC from the most superiorto the most inferior ROI.Bracketed values are SD of �SIfor each subgroup.

Table 2ADC SI and AP Differences in Healthy Subjects and Subjects With Severe COPD

Healthy volunteers(N � 8)

Severe COPD(N � 7)

Difference betweensubject groups

(P value)

Center-slice mean ADC (cm2/second) 0.25 � 0.02 NA 0.44 � 0.09 NA �0.001Center-slice SD of ADC (cm2/second) 0.18 � 0.02 NA 0.24 � 0.04 NA �0.01Center-slice �SI (cm2/second) 0.02 � 0.02 P � 0.05 0.10 � 0.09 P � 0.05 NSSI-gradient (10–3 cm2/second/cm) �0.63 � 2.23 R2 � 0.82 �6.61 � 6.68 R2 � 0.62 �0.05�AP (cm2/second) 0.06 � 0.01 p � .001 0.04 � 0.02 P � 0.01 �0.05AP-gradient (10–3 cm2/second/cm) �3.98 � 0.59 R2 � 0.91 �2.04 � 0.89 R2 � 0.65 �0.05

�SI � averaged for the right and left lung, �AP � average across all ROI � the intersubject SD, NA � not applicable, NS � nonsignificant.

1542 Evans et al.

subgroups were not statistically significant. In-creased �SI variance observed for the severe COPDsubgroup may be due to the fact that within thepopulation, large airspaces in COPD are less likely tobe ventilated and hence less likely to be probed by

3He to provide ADC values. Large areas of unventi-lated lung in severe COPD may be due to airwayobstruction with mucous, stenotic airways, and areasof lung involved with bullae. This study did not ex-amine the cause of these focal ventilation defects;

Figure 4. Mean ADC and �AP.a: Mean ADC for center sliceand each of the four center-most individual coronal slices.Round bracketed values areSDs of mean ADC for the sub-jects in each subgroup. Squarebracketed values are the lowerand upper 95% confidence in-tervals of the mean, respec-tively. b: �AP for mean ADC forROIs where �AP is the changein ADC from the most anteriorto the most posterior slice.

Figure 5. Mean ADC SI-gradi-ent (center slice). a: SI-gradientfor eight healthy volunteersand seven subjects with severeCOPD. Stars indicate mean ofsubgroups, and error bars areSD of the mean (see Table 2). b:Mean ADC for healthy volun-teers and subjects with severeCOPD from the superior to theinferior regions. Error bars in-dicate standard error of themean. c,d: ADC histogram ofinferior (left and right lung) andsuperior (left and right lung)ROIs. ADC maps of ROIs fromwhich ADC values were calcu-lated are provided above thehistogram. c: Healthy volun-teer (58-year-old male, FEV1 �108% predicted, FEV1/FVC �80.70%, SI-gradient � –1.39 �10–3 cm2/second/cm). d: Sub-ject with severe COPD (62-year-old male, FEV1 � 35%predicted, FEV1/FVC �30.35%, SI-gradient � –3.54 �10–3 cm2/second/cm).

Distribution of 3He MRI ADC in COPD 1543

however, further evaluation and validation using CTand pathology is required.

We observed a significant decrease in mean ADC fromthe most anterior to posterior slices of the lung ex-pressed as �AP and AP-gradient. For all subjects, �APwas significant, indicating that a difference in meanADC in the AP direction exists in all subjects. Addition-ally, the difference in �AP between healthy subjects andthose with severe COPD was significantly different (P �0.05) as was the difference in mean ADC AP-gradients.One possible explanation for the significantly larger�AP in healthy volunteers as compared to subjects withCOPD is that the airspaces are less compressible inCOPD in the dependent portions of the lung due toair-trapping in these regions. If this is the case it isexpected that changes in �AP in patients may providepredictive power with respect to extent of regional air-trapping.

Previously reported differences in ADC values in theAP direction for healthy subjects scanned in the supine

position were reported as approximately 0.01 cm2/sec-ond, which was approximately 5% of the whole-lungmean ADC reported in that study population (15). Thisvalue was based on the difference in mean of two aver-aged ROIs that comprise the total lung volume in slicesacquired in the transverse plane. In the current analy-sis of older subjects scanned in the supine position withcoronal slice acquisition, the mean �AP values forhealthy volunteers was approximately 0.06 cm2/sec-ond (0.06/0.26 cm2/second � 23% of whole-lung ADC)and this was significantly larger (P � 0.05) than themean �AP values for severe subjects, which was 0.04cm2/second (0.04/0.45 cm2/second � 9% of whole-lung ADC). However, the current results cannot be di-rectly compared with the results previously reportedbecause our study employed slices acquired in thecoronal plane and additionally we expressed �AP overfour slices (of seven acquired in total) that comprise thebulk center of the lung volume. Differences in ADCvalues in the SI direction also previously reported (15)were approximately 0.05 cm2/second for COPD sub-jects (0.05/0.30cm2/second � 17% of whole-lung ADC)and 0.01 cm2/second for healthy volunteers (0.01/0.21cm2/second � 5% of whole-lung ADC). In our analysis,�SI for subjects with severe COPD was 0.10 cm2/sec-ond, corresponding to 20% of the whole-lung meanADC value and for healthy subjects, 0.02 cm2/second(approximately 8% of the whole-lung mean ADC). Thedifferences between our results in �SI and those previ-ously reported may reflect differences in patient ages,disease severity, and ROI (three smaller regions for this

Figure 6. Mean ADC AP-gradi-ent (whole slice). a: AP-gradientfor eight healthy volunteersand seven subjects with severeCOPD. Stars indicate mean ofsubgroups, and error bars areSD of the mean (see Table 2). b:Mean ADC for healthy volun-teers and subjects with severeCOPD from the anterior to theposterior slices. Error bars in-dicate standard error of themean. c,d: ADC histogram ofslice-specific ADC values.Sample diffusion-weighted im-ages of slices from which ADCvalues were calculated are pro-vided above the histogram. c:Healthy volunteer (58-year-oldmale, FEV1 � 108% predicted,FEV1/FVC � 80.70%, AP-gra-dient � –3.44 � 10–3 cm2/sec-ond/cm. d: Subject with severeCOPD (62-year-old male,FEV1 � 35% predicted, FEV1/FVC � 30.35%, AP-gradient �–1.97 � 10–3 cm2/second/cm).

Table 3Multivariate ANOVA Two-Way Interaction Results

Region ofinterest

Lungside

Coronalslice

Subjectgroup

Region of interestLung side P � 0.01Coronal slice P � 0.01 NSSubject group P � 0.01 NS P � 0.001

NS � not significant.

1544 Evans et al.

study and two larger ones for the previous study). Thesignificant difference in ADC gradients between healthyvolunteers and diseased individuals reported here indi-cate that there are alterations in ADC imposed by dis-ease, which is in addition to the underlying anatomicalvariations in healthy volunteers.

Mean ADC values for six superior, middle, and infe-rior ROIs within the four centermost slices were ana-lyzed using a multivariate ANOVA and showed a signif-icant main interaction between mean ADC, SI locationof the ROI (P � 0.001), the AP location of the ROI (P �0.001), and disease status (P � 0.001). This indicatesthat mean ADC is significantly influenced by anatomicposition in the AP and SI direction as well as diseasestatus. Numerous significant bidirectional relation-ships between the ADC values, slice, ROI, disease sta-tus, and lung side also provide evidence that ADC val-ues are influenced and sensitive to lung side, coronalslice, ROI, and disease.

The use of discriminant analysis to classify or clustersubjects using regional differences in ADC (�AP, AP-gradient, �SI, and SI-gradient) was tested in the sub-jects assessed in this study. Discriminant analysis us-ing these variables resulted in the grouping of subjectsinto healthy volunteer and COPD subgroups. This sug-gests that independent of pulmonary function mea-sures, MRI-derived measurements might be useful forcategorizing age-matched subjects into lung diseasecategories, with the results being similar to those re-sulting from the use of established pulmonary functionmeasures. It is possible that further analysis of differ-ent MRI-derived variables using more subjects will pro-vide clues as to differences among subjects diagnosed

with COPD. The approach might also be used to cate-gorize subjects for different treatment options or riskcategories.

To our knowledge, this is the first 3He ADC study at3T, and thus it is important to consider any potentialeffects the higher field strength may have on the ADCvalues, because intrapulmonary susceptibility varia-tions are more pronounced at higher fields.. Magneticfield nonuniformity, either through applied gradients(as embodied in the “b-value”) or background gradients(potentially through susceptibility gradients) would beexpected to affect the hyperpolarized gas gradient-echosignal, particularly in the lung, which presents strongsusceptibility at the tissue-gas interface. Indeed, theimportance of background gradients on helium ADC ismeasurable using pulsed gradient experiments withvarying delay between bipolar wave-forms (23). In ad-dition, susceptibility would be expected to be higher (byroughly a factor of 2) at 3T compared to 1.5T. In thisregard, we have assessed the change in ADC, whichincludes a diffusion coefficient (DC) offset to our bipolardiffusion gradient originating from background gradi-ents of 2.3% (doubling that previously estimated at1.5T) (23), and it can be shown to be about 2%. There-fore, at 3T, the background effect on measured ADC issmall for our method, though it would be expected to belarger with different diffusion gradient approaches (e.g.,double bipolar schemes, longer and weaker gradientpulses). The agreement of our mean ADC values andADC gradients in normal and diseased patients at 3Twith values obtained by others at 1.5T shown here andelsewhere (18) is further confirmation that the effect ofsusceptibility on ADC is not measurable by our pulse

Figure 7. Discriminant analysis results. a: Data points were graphed on a scatter plot with axes corresponding to theindependent variables: center-slice mean ADC, AP-gradient, and center-slice SD of ADC. b: Following discriminant analysis,data points were projected onto the plane defined by the unstandardized coefficients of the first discriminant function. c:Following rotation of the plane, a 2D representation of the clustered data is achieved, which shows grouping of subjects into twoclusters, independent of pulmonary function measurements.

Distribution of 3He MRI ADC in COPD 1545

gradient method, consisting of a single bipolar gradientwith minimum duration.

Shortcomings of our approach include the small sam-ple size and the use of large ROIs (whole slices or su-perior, middle, and inferior regions) to calculate meanADC. The optimal location and size of ROIs must still beinvestigated to quantify regional information and re-gional heterogeneity of ADC while including a suffi-ciently large sample of ADC values in the regional anal-ysis. Linearity of ADC gradients was assumed for thisinitial analysis and these were approximated using lin-ear regression of mean ADC values for ROI (four ROIsfor AP-gradient and three ROIs for SI-gradient). In theCOPD subgroup, however, SI gradients have corre-sponding lower R2 values, possibly due to significantventilation defects in apical regions of the lung in thesesubjects. Using smaller ROIs would perhaps allow amore robust linear regression model of ADC gradients.In addition, �AP values reported here were based onfour thick slices (3-cm each) and we expect implemen-tation of 3D approaches (24) will provide a more com-plete analysis of ADC gradients. A general limitation ofthe use of 3He ADC values to probe lung function in thisstudy and others is the fact that focal ventilation defectsin the lung, representing those areas of the lung thatcannot participate in ventilation within the timeframeof the scan, result in zero values on ADC maps, result-ing in no ADC value from these important ROIs. Thismost certainly provides a specific ADC value bias to-ward only those areas of the lung that can participate inventilation within the timeframe of the breathhold ex-periment. Finally, the analysis of more subjects withvery severe and mild-moderate COPD may identify spe-cific MRI-derived differences between these subjectsand for correlations with pulmonary function mea-sures.

In conclusion, this study showed that 3He imagingcan provide ADC-derived measurements that are highlysensitive to lung position in the AP and SI directions, innormal and COPD lungs. The results suggest that thereis a significant gradient in alveolar size in the AP and SIdirection that is also dependent on disease status. APADC gradient differences used in discriminant analysisresulted in the clustering of subjects into groups thatare directly comparable to categorization of subjectsbased on pulmonary function measurements. The an-atomical distribution and potential gravitational effectson ADC values and the dependence of these values ondisease status must be taken into consideration whenplanning clinical studies of disease progression ortreatment effects that include ADC measurements.

ACKNOWLEDGMENTS

Ms. Andrea Evans gratefully acknowledges salary sup-port from the Western Graduate Research Fund pro-vided by The University of Western Ontario (LondonCanada); she is also a Fellow of the Canadian Institutesof Health Research Vascular Research Training Pro-gram (London, Canada). We thank Dr. Aaron Fensterfor insightful discussions and Dr. Yves Bureau for as-sistance with the statistical analyses. We also thankMrs. Sandra Halko, Mrs. Shayna McKay, and Ms.

Christine Piechowicz for clinical coordination and clin-ical database management, Mr. Wilfred Lam for pro-duction and dispensing of 3He gas, and Mrs. ElisabethLorusso, RMT, and Mrs. Cyndi Harper-Little, RMT, forMR scanning of research subject volunteers. We grate-fully acknowledge Dr. Sean Fain (University of Wiscon-sin, Madison WI, USA) for the provision of the FGREpulse sequence. This work contains parts of the grad-uate thesis of Ms. Andrea Evans. We are indebted toRobarts Research Institute for pursuing and providingcapital and operating funding for the London Lung Im-aging Research Team and continuing support of thisresearch program.

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