Original Research

Quantitative Myocardial Infarction on DelayedEnhancement MRI. Part I: Animal Validation of anAutomated Feature Analysis and CombinedThresholding Infarct Sizing Algorithm

Li-Yueh Hsu, DSc, Alex Natanzon, MD, Peter Kellman, PhD, Glenn A. Hirsch, MD,Anthony H. Aletras, PhD, and Andrew E. Arai, MD*

Purpose: To develop a computer algorithm to measuremyocardial infarct size in gadolinium-enhanced magneticresonance (MR) imaging and to validate this method usinga canine histopathological reference.

Materials and Methods: Delayed enhancement MR wasperformed in 11 dogs with myocardial infarction (MI) deter-mined by triphenyltetrazolium chloride (TTC). Infarct sizeon in vivo and ex vivo images was measured by a computeralgorithm based on automated feature analysis and com-bined thresholding (FACT). For comparison, infarct size byhuman manual contouring and simple intensity threshold-ing (based on two standard deviation [2SD] and full width athalf maximum [FWHM]) were studied.

Results: Both in vivo and ex vivo MR infarct size measuredby the FACT algorithm correlated well with TTC (R � 0.95–0.97) and showed no significant bias on Bland Altman anal-ysis (P � not significant). Despite similar correlations (R �0.91–0.97), human manual contouring overestimated invivo MR infarct size by 5.4% of the left ventricular (LV) area(equivalent to 55.1% of the MI area) vs. TTC (P � 0.001).Infarct size measured by simple intensity thresholdingswas less accurate than the proposed algorithm (P � 0.001and P � 0.007).

Conclusion: The FACT algorithm accurately measured MIsize on delayed enhancement MR imaging in vivo and exvivo. The FACT algorithm was also more accurate thanhuman manual contouring and simple intensity threshold-ing approaches.

Key Words: myocardial infarction; magnetic resonanceimaging; computer algorithm; image processing; expertsystem; contrast agent; gadoliniumJ. Magn. Reson. Imaging 2006;23:298–308.Published 2006 Wiley-Liss, Inc.†

THERE IS A NEED to develop objective image analysismethods that can accurately quantify the size of myo-cardial infarction (MI) on delayed enhancement mag-netic resonance (MR) images. Landmark studies of Kimet al (1) and Fieno et al (2) validated that gadoliniumdelayed contrast-enhancement closely tracks the areaof irreversible myocardial injury due to acute or chronicinfarction. However, these studies did not evaluate theaccuracy of in vivo images, did not use the inversionrecovery techniques (3) popular for clinical exams, andonly validated ex vivo images obtained at resolutionsthat are currently impractical in patients (0.5 � 0.5 �0.5 mm). Furthermore, quantification of MI in thesestudies were based on an empirical simple intensitythresholding of more than two SD (2SD) (1) or morethan three SD (3SD) (2) above the mean of the normalmyocardial intensity for infarct size measurements.

Prior studies suggested errors in estimating infarctsize may be partially explained by slice thickness andpartial volume effects (1,4–6). Quantification of MI sizehas been performed using human manual contouringand by computerized simple intensity thresholdingbased on the SD of normal myocardial signal intensities(1,2,7–12). Considering partial volume effects, an alter-native threshold value at 50% of the maximum inten-sity, sometimes referred as full width at half maximum(FWHM) (13), theoretically should be more accurate todichotomize normal and infarcted myocardial pixels.Although simple intensity thresholding techniques re-duce intraobserver or interobserver variability com-pared to human manual contouring, they lack robust-ness due to the use of empirical thresholds, varied bightpixel intensity across different myocardial regions, andsurface coil intensity variation.

Laboratory of Cardiac Energetics, National Heart Lung and Blood In-stitute, National Institutes of Health, Department of Health and HumanServices, Bethesda, Maryland, USA.Contract grant sponsor: National Heart, Lung, and Blood Institute,National Institutes of Health.*Address reprint requests to: A.E.A., Laboratory of Cardiac Energetics,National Heart, Lung, and Blood Institute, National Institutes of Health,10 Center Dr., MSC 1061, Building 10, Room B1D-416, Bethesda, MD20892-1061. E-mail: araia@nih.govReceived February 19, 2005; Accepted October 28, 2005.DOI 10.1002/jmri.20496Published online 31 January 2006 in Wiley InterScience (www.interscience.wiley.com).

JOURNAL OF MAGNETIC RESONANCE IMAGING 23:298–308 (2006)

Published 2006 Wiley-Liss, Inc. †This article is aUS Government work and, as such, is in the publicdomain in the United States of America.

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Thus, the purpose of this study was to develop acomputer algorithm to improve MI size measurement ingadolinium-enhanced MR imaging and to validate thismethod using a canine histopathological reference. Wehypothesized that computer algorithms that incorpo-rate expert knowledge (expert system) could objectivelyanalyze infarct regions with user independent thresh-olds, and advanced image processing techniques thatuse feature analysis to eliminate false positive brightregions and to compensate dark areas of microvascularobstruction. Such a system should be more accuratethan human manual contouring or simple intensitythresholding. It is particularly beneficial if these meth-ods were validated on images acquired with practical invivo resolution.

MATERIALS AND METHODS

Study Design

After developing the feature analysis and combinedthresholding (FACT) computer expert system for mea-suring infarct size on delayed enhancement images, ananimal model with triphenyltetrazolium chloride (TTC)histopathology was used to validate MR infarct sizemeasured by the FACT algorithm and human manualcontouring on both in vivo and ex vivo images. Intraob-server and interobserver agreements of the FACT algo-rithm and human manual contouring were assessed.Finally, the FACT algorithm and simple intensitythresholding methods (based on 2SD and FWHM) werecompared against the TTC reference standard.

FACT Algorithm

Figure 1 illustrates graphically how the FACT infarct siz-ing algorithm applied a series of pre-determined steps to

automatically calculate intensity thresholds, perform fea-ture analysis, and classify infarcted and normal pixels.The algorithm analyzed image features derived from ex-pert knowledge to exclude false positive areas such asdiscrete bright patches that were not contiguous withother bright regions. Additional post-processing proce-dures were applied to include areas of microvascular ob-struction. For all images, the epicardial and endocardialborders were manually contoured using custom imagedisplay and analysis software written in Interactive Dis-play Language (Research Systems Inc., Boulder, CO,USA) before applying the FACT algorithm, which was im-plemented in Microsoft Visual C�� Language (MicrosoftCorporation, Redmond, WA, USA).

Figure 2 shows the flow diagram of the FACT algorithmfor myocardial infarct sizing. From the analysis of thepixel intensity histogram (Fig. 1), a bi-modal distributionis observed from the myocardial regions of interest. Incontrast-enhanced infarct imaging with phase sensitiveinversion recovery (PSIR) reconstruction (14) and surfacecoil intensity correction, normal myocardial pixel valuesappear in the low intensity portion of the histogram andfollow a Gaussian distribution. Due to the T1 weighting ofcontrast enhancement, the infarct tissue values are at thehigher intensity portion of the histogram.

The algorithm automatically estimated an initialthreshold to separate the infarction from the normalmyocardium. Both median filtering and histogramclustering (15) were used to smooth the random noiseon the spatial and the histogram spaces before thethreshold estimation.

2SD Thresholding

The mean and SD of the normal tissue intensities werefirst estimated by the maximum value of the lower part

Figure 1. Data flow of the FACT infarct sizing algorithm. After manual segmentation of epicardial and endocardial borders, thealgorithm automatically selects optimal intensity thresholds, performs region-based feature analysis, compensates microvas-cular obstruction areas, and finally contours the contrast-enhanced regions of interest.

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of the intensity histogram. The threshold value wasthen calculated as 2 SD above the mean. Pixels darkerthan this threshold value were excluded from furtheranalysis. This, however, left aggregates of bright pixelsthat included infarct, imperfectly excluded pixels of epi-cardial fat or right ventricular blood, randomly brightpixels, and potential artifacts. An initial threshold set at2 SD above the mean may seem relatively low since itonly covers 95% of a normal distribution. However, it isdesirable to maintain a lower threshold to increase theprobability of detecting more bright infarcts and thendiscounting false positive infarct pixels during follow upprocessing.

Region-Based Feature Analysis

To remove false positive pixels, the following image fea-tures were computed and classified on each aggregatedpotential infarct region in three-dimensional space: vol-ume mass, subendocardial distance, and mean inten-sity. Before the feature analysis, all slices were regis-tered to the center of the myocardial regions of interest.This ensures a proper three-dimensional propagation ofmyocardial regions through the entire image stack.

To calculate the infarct mass, all potential two-di-mensional regions were first grouped to three-dimen-sional volumes using connected component analysis(15). This technique aggregates isolated two-dimen-sional regions into three-dimensional volumes basedon the definition of neighbor connectivity. We imple-mented a 10-neighbor connectivity based on the largeslice thickness of our dataset. After the process, eachpotential three-dimensional volume was then convertedto units of mass based on the density of myocardium(1.05 g/mm3). Any bright volume that had a masssmaller than a minimum setting (0.1 g) was consideredas noise and removed.

To obtain the subendocardial distance, the shortestpath of each potential three-dimensional infarcted vol-ume to the endocardial boundary was computedamongst all border pixels. Any bright volume more than2 mm away from the endocardial border was considereda potential artifact and removed. Additionally, the in-

tensity value of each potential three-dimensional vol-ume was examined for homogeneity. For each volume,the mean intensity value was computed as a ratio to theaverage intensity of all potential infarcted volumes. Aminimum setting of 50% was used to remove volumesthat have a darker average intensity.

FWHM Thresholding

While the above steps effectively remove disjointed falsepositive bright regions, overestimation of infarct size isstill possible due to the tissue partial volume effect. Thenext step was to determine a final threshold to classifypartial volume tissues. We defined a 50% of the maxi-mum intensity threshold as the mid point between theaverage normal myocardial pixel intensity and the peakinfarct pixel intensity. Any residual bright pixels lessthan this value were excluded from the infarct region ofinterest. After the thresholding, the region-based fea-ture analysis was repeated to further reduce false pos-itive infarct regions.

Inclusion of Microvascular Obstruction

Additional post-processing steps were applied to takeaccount of dark pixel regions that may represent micro-vascular obstruction. An image morphology closing op-eration (15) with a crossbar shape kernel was used toconnect small concave borders and fill small dark cav-ities surrounded by bright infarcted pixels. A small ker-nel (5 mm) of such process can also smooth the borderof infarct regions without excessively altering the over-all shape. Next, remaining regions of isolated dark pix-els were further analyzed to identify possible areas ofmicrovascular obstruction. Each dark region was re-classified as infarct if its border was completely encom-passed by either endocardial or infarct pixels. Theseprocesses compensated for dark areas of microvascularobstruction that were too low in signal intensity to beclassified as infarct by the thresholding steps. Finally,an edge-following step using an m-neighbor path (15)was applied to obtain the contour of infarcted regionsfor qualitative representation.

Figure 2. Flow diagram of the FACT infarctsizing algorithm. There are a series of imageprocessing steps to classify the myocardial re-gions of interest into normal and infarct pixels.

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Animal Preparation

Eleven mongrel dogs (average weight 18 kg) underwenta 90-minute occlusion of the left anterior descendingcoronary artery in an open chest model followed byreperfusion. Five animals were imaged two dayspost-MI and six were studied two months post-MI. Allprocedures and imaging studies were approved by theAnimal Care and Use Committee of the National Insti-tutes of Health.

Image Acquisition

Infarct imaging was performed on a 1.5-T scanner (GEMedical Systems, Waukesha, WI, USA) approximately20–30 minutes following gadolinium diethyltriamine-pentaacetic acid (Gd-DTPA) administration using aninversion recovery fast gradient-echo sequence trig-gered every other heartbeat. Imaging used multipletwo-dimensional acquisitions of the left ventricle (LV) ina short axis plane from base to apex. All images werecorrected for surface coil intensity variation and usedPSIR reconstruction (14). Typical imaging parametersof the study included TE 3.4 msec, TR 7.8 msec, ap-proximately optimized inversion time (TI typically 300msec), bandwidth � 31.25 kHz, and acquisition time125 msec per RR interval, which achieved a spatialresolution of 1.1 � 1.8 � 8.0 mm in vivo and 0.6 � 0.6 �4.0 mm ex vivo.

Histopathology Preparation

MI was assessed on both in vivo and ex vivo MR imagesand compared with histopathology. Excised heartswere rinsed with normal saline and sliced into 4-mmsections using a commercial meat slicer (Globe FoodEquipment, Dayton, OH, USA). Each slice was stainedwith TTC at 37°C for 5–10 minutes to demarcate theinfarct territory. TTC stained slices were then sub-merged in 0.9% normal saline and photographed usinga digital camera (Nikon D100, Melville, NY, USA) at aresolution about 0.065 mm per pixel. Epicardial, endo-cardial, and infarct borders were manually contouredby one observer. Both MR and TTC images were visuallymatched based on anatomic landmarks, which in-cluded LV shape, papillary muscle shape and location,and insertion point of the right ventricle. For each an-imal, five best-matched consecutive MR images withcorresponding TTC slices were selected. Overall, 55 exvivo MR-to-TTC and 55 in vivo MR-to-TTC image pairswere studied to validate and compare infarct size mea-surements by human manual contouring and variouscomputerized methods.

Statistical Analysis

For comparison of infarct size measurements using theFACT algorithm, human manual contouring, and sim-ple intensity thresholding methods, the same epicardialand endocardial contours of each slice were used tominimize bias. All the ex vivo and in vivo MR imageswere processed using the same computer parametersettings for the entire study.

Validations of the FACT algorithm and human man-ual contouring on both ex vivo and in vivo MR imagesagainst TTC were performed by two observers usinglinear correlation (16) and Bland Altman analysis (17).A paired t-test with Bonferroni correction for multiplecomparisons was performed to determine if there was asignificant difference (P � 0.05) between different meth-ods. The result was reported as both percent of the LVarea and percent of the MI area. The use of percent slicemeasurements was an important consideration in theintermodality comparison of MR-to-TTC to reduce reg-istration error due to physiological distortions and dur-ing the histopathological preparation. TTC results wereused as the independent variable for all analyses, andwere taken as the reference standard for defining nor-mal and infarcted myocardium.

Comparisons of intraobserver and interobserver vari-ability were performed to test the reproducibility of theFACT algorithm and human manual contouring of in-farct size on both ex vivo and in vivo MR images. Theerrors and differences in measuring infarct size werereported in both percent of LV area and percent of MIarea using TTC as the reference standard. Intraob-server and interobserver agreements were also studiedon a pixel-by-pixel comparison of infarcted and normalmyocardium classifications using Cohen’s kappa sta-tistics (18).

Comparisons of the FACT algorithm vs. simple inten-sity thresholding techniques widely used by other stud-ies (1,2,7–13) were performed to evaluate the efficacy ofdifferent computer based methods in infarct sizing. Wecompared 2SD intensity thresholding and FWHM in-tensity thresholding to the FACT algorithm, which com-bines both thresholdings and additional image featureanalysis. The infarct size of ex vivo and in vivo MRimages was studied using all these computerized meth-ods and compared to the TTC reference standard.

RESULTS

Details at the edges of infarcts on TTC slices can appearblurred or disappear on MR due to the partial volumeeffect or motion, particularly on in vivo images (Fig. 3).Sample contours of a matched set of MR and TTC im-ages show good agreement between the infarct regioncontoured by a human observer and the FACT algo-rithm (Fig. 4). Qualitatively, the shape and location ofinfarcted regions appear similar between human andcomputer contouring. The algorithm is also capable offollowing highly detailed infarct contours as shown onthe example TTC image. However, the TTC referencestandard used in MR validation was contoured by hu-man planimetry.

Validations of the FACT Algorithm and HumanManual Contouring

Using the TTC measurements as the reference stan-dard, the errors of either observer using the FACT al-gorithm and with manual planimetry to measure theMR infarct size were calculated in both percent of LVand percent of MI area. Since acute and chronic infarctsin our study exhibited similar correlation, slope, inter-

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Figure 3. Despite good registration ofMR with TTC, the lower spatial resolu-tion of the in vivo image (in the middle)blurs details such as the hook shapedlateral edge of the infarct as evident onTTC and the ex vivo image (Dog 4).

Figure 4. Examples of myocardial in-farct borders contoured by human andcomputer (FACT) demonstrate that thealgorithm is capable of highly detailedcontours (Dog 6).

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cept, and scatter, all results were simplified by combin-ing both data sets.

For the FACT algorithm, the correlations betweenoverall infarct size on MR and the TTC reference stan-dard were good for both in vivo and ex vivo images (Fig.5, R � 0.95 to 0.97). Bland Altman analysis also showedno consistent bias as a function of infarct size. Whenresults from a given animal were summarized into asingle volumetric measurement, the correlations be-tween FACT and TTC were even higher (ex vivo observer1: y � 0.96x – 0.00, R � 0.99; ex vivo observer 2: y �1.01x – 0.00, R � 0.98; in vivo observer 1: y � 1.05x �0.01, R � 0.97; in vivo observer 2: y � 0.96x � 0.02, R �0.98).

While good correlations (R � 0.91 to 0.97) betweenhuman manual contouring of infarct size on MR andTTC images were achieved (Fig. 6), human contouringoverestimated infarct size on in vivo images. Bland Alt-man analysis also demonstrated relatively increasedscatter from the human contouring. When results weresummarized into global volumetric measurements, thecorrelations between human contouring and TTC wereimproved but still overestimated the infarct size (ex vivoobserver 1: y � 0.99x � 0.03, R � 0.99; ex vivo observer2: y � 1.03x � 0.03, R � 0.97; in vivo observer 1: y �1.21x � 0.01, R � 0.97; in vivo observer 2: y � 1.07x �0.05, R � 0.94).

There were even tighter correlations when comparinghuman measurements of infarct size with the FACTalgorithm on the same MR images since there were noregistration errors (Fig. 7). Although there was almostno difference between human and computer measure-ments of infarct size on ex vivo slices, human contour-ing of in vivo images overestimated infarct size whencompared with the FACT algorithm.

While the error of human contouring using percent ofthe LV area as the measurement looks small relative tothe entire ventricle, the error is considerably larger us-ing the area of MI as the denominator. The humancontouring of MR overestimated infarct size on ex vivoimages by 3.2% of the LV (equivalent to 22.2% of the MI)area and to a greater degree on in vivo images by 5.4%of the LV (equivalent to 55.1% of the MI) area (all P �0.001 vs. TTC). Using the FACT algorithm, the averageerror was 0.5% of the LV (equivalent to 3.5% of the MI)area on ex vivo images, and 1.2% of the LV (equivalentto 11.9% of the MI) area on in vivo images (all P notsignificant vs. TTC).

Comparisons of Intraobserver and InterobserverVariability

Both observers had similar errors and moderate differ-ences (1.2% of the LV, or 7.9% of the MI area, P � 0.001

Figure 5. Ex vivo and in vivoMR infarct size measured bythe computer (FACT) corre-lates well with TTC (intermo-dality comparison, MR vs.TTC). Bland Altman analysisshows no systematic bias. Thefirst and second observers areindicated in open circles(dashed line) and triangles(dotted line), respectively.

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vs. TTC) when manually tracing the infarct size on exvivo images. This difference was even smaller using theFACT algorithm (0.2% of the LV, or 1.8% of the MI area,P not significant vs. TTC).

The difference between the two observers’ manualcontouring of in vivo images was considerably larger(4.0% of the LV, or 41.2% of the MI area, P � 0.001 vs.TTC). These interobserver differences were reducedmore than 10 fold by using the FACT algorithm (0.3% ofthe LV, or 2.9% of the MI area, P not significant vs. TTC).

Both intraobserver and interobserver agreement onclassifying pixels as infarcted generally showed excel-lent agreement (kappa � 0.8) on ex vivo and in vivo MRimages (Table 1). However, human manual contouringtended to overestimate the number of infarcted pixelsas evidenced by more pixels designated by human con-touring but not the FACT algorithm. The interobserveragreement of myocardial segmentation was excellent(kappa � 0.9) on both ex vivo and in vivo MR images.

Comparisons of FACT Algorithm Vs. SimpleIntensity Thresholding Techniques

For the group data overall, the FWHM intensity thresh-olding produced a smaller error than the 2SD intensitythresholding (Fig. 8). However, the FACT algorithm fur-ther reduced the error compared to both 2SD andFWHM intensity thresholding techniques (P � 0.001

and P � 0.007). This was true on both ex vivo and invivo MR to TTC validations.

The results of infarct size measurements by the FACTalgorithm, human manual contouring, and simple in-tensity thresholding based on 2SD and FWHM weresummarized (Table 2). While human contouring of in-farct size on MR was similar to the FWHM intensitythresholding, both had a smaller error than the 2SDintensity thresholding. The FACT algorithm howeverproduced the smallest error compared to all othermethods.

DISCUSSION

While MR acquisition methods have markedly improvedimage quality (3), quantitative analysis methods mustbe established that measure MI objectively and accu-rately on in vivo images. It is important to distinguishthe current paper from prior validation studies (1,2)that aimed to prove gadolinium distribution differenti-ates normal myocardium from MI—a question an-swered by high resolution ex vivo images. The currentstudy takes on the difficult task of accomplishing ahigher accuracy in measuring infarct size, particularlyon images obtained at clinically relevant image resolu-tion. The systematic errors of infarct size measured byhuman manual contouring and simple intensity

Figure 6. Despite good corre-lations between MR and TTCinfarct size as measured byhuman manual contouring,Bland-Altman analysis showssystematic overestimation onMR images (intermodalitycomparison, MR vs. TTC). Thefirst and second observers areindicated in open circles(dashed line) and triangles(dotted line), respectively.

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thresholding as delineated in this paper do not questionthe accuracy of gadolinium-enhanced infarct imaging.Rather, this study indicates that infarct size can bedetermined accurately using delayed enhancementtechniques at practical in vivo resolutions with the as-sistance of computerized methods that incorporate ex-

pert knowledge as the FACT algorithm presented in thisstudy.

Several factors can lead to systematic errors in quan-tifying infarct size even if the gadolinium concentra-tions accurately reflect the underlying pathology. Theseinclude partial volume errors, imperfect myocardialsegmentation, inconsistencies introduced by differentdisplay setup or intensity thresholds, differences re-lated to human visual perception, bright imaging arti-facts on the myocardium, and dark but infarcted pixelsdue to microvascular obstruction (19). The proposedFACT algorithm takes into account all of these issues,though some of the sources of error warrant furtherdiscussion.

Another potential source of infarct sizing error is theinclusion of the peri-infarction zone, which results inintermediate pixel intensities (20). The peri-infarctionzone can affect several pixels near the infarct bordersand thus can lead to larger measurement errors thanmight be predicted by image resolution and an assump-tion of smooth infarct borders. It is likely that theseperipheral zones are more difficult for a human to cor-rectly quantify than a computer algorithm. Since theinfarct territory exhibits a wavefront phenomenon (21),pixels that are further away from the core of the infarctare more likely to have increasing numbers of viablecells than those on the borders.

Figure 7. There are eventighter correlations when com-paring infarct size measuredby the FACT algorithm and hu-man manual contouring on thesame MR images (intramodal-ity comparisons, MR vs. MR).Bland Altman analysis showsa trend of overestimation onhuman contouring. The firstand second observers are indi-cated in open circles (dashedline) and triangles (dotted line),respectively.

Table 1Kappa Statistics for Pixel-Wise Comparison of Intraobserver andInterobserver Agreement on MR Images

Ex vivo In vivo

Intraobservera

Observer 1 infarct classification 0.83 0.86Observer 2 infarct classification 0.83 0.80

Interobserverb

Human infarct classification 0.79 0.87Computer (FACT) infarctclassification

0.81 0.90

Myocardial segmentationc 0.91 0.92aIntraobserver comparisons of infarct classification describe theagreement between human contouring and the FACT algorithm foreach observer.bInterobserver comparisons of infarct classification indicate how wellthe two observers agreed in classifying pixels using a given method(human contouring or the FACT algorithm).cInterobserver comparison of myocardial segmentation shows theagreement of two observers contouring myocardial regions.

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The FACT algorithm accurately measured infarct sizeon both ex vivo and in vivo MR images and reducedinterobserver variability in our studies. In comparison,human manual contouring systematically overesti-mated in vivo infarct size despite a better ex vivo mea-surements. We suggest this is due to the finer resolu-tion of the ex vivo images that have 12� smaller pixelvolume (voxel) size and reduced tissue partial volumeeffects.

Partial volume issues warrant further discussion. Al-though TTC is the reference standard for determininginfarct territory, TTC stains the surface of the myocar-dial slice and thus is not a volumetric measurementunless an infinite number of slices are studied. None-theless, thick MR slices introduce the possibility of amixture of infarcted myocardium and normal myocar-dium within an image pixel as illustrated by Kim et al(1). This leads to a signal intensity that is brighter thancompletely normal myocardium but not quite brightenough to qualify as infarct. By analogy, it is knownthat the peri-infarction zone contains mixed popula-tions of viable and nonviable tissues as well as edema(20). Such a combination of factors may result in inter-mediate contrast enhancement that requires an objec-tive criterion to dichotomize normal from infarct pixels.

Ideally, viability could be assessed at the cardiomyo-cyte level. Based on the volume within a pixel (voxel),each ex vivo MR pixel represents on the order of 10,000to 20,000 cardiomyocytes while the in vivo resolution isabout 10 times worse. If a pixel contains 60% viable and40% nonviable cardiomyocytes, it is reasonable to clas-sify that pixel as viable even though it appears brighter

than a completely normal pixel. This concept led us touse a 50% maximum intensity threshold (FWHM) toclassify myocardial pixels within the limits that thepixel is essentially a mixture of a large number of cells.

While quantitative analyses using interactive inten-sity thresholding may reduce intraobserver or interob-server variability (8), these techniques lack robustnessdue to empirical threshold setting and may overesti-mate infarct size by inclusion of all bright pixels. Weshowed the use of FWHM intensity thresholding, whichis based on contrast-enhanced pixels, as opposed to thethreshold based on 2 SD that models the normal myo-cardium, can more accurately dichotomize normal frominfarcted border tissues. We also demonstrated withhigh statistical certainty that the simple intensitythresholding methods used in most validation papers(1,2,7–13) overestimated infarct size on images ac-quired at in vivo resolutions compared to the FACTalgorithm (Fig. 8).

It is interesting to note that the average errors ofsimple intensity thresholding based on 2SD and FWHMin our in vivo study (Table 2), when reported as percentof LV, were very similar to a recent report (13) usingsimple intensity thresholding for infarct sizing. Fur-thermore, the average error of human manual contour-ing in our study was comparable to the FWHM simpleintensity thresholding (Table 2). This suggests that al-though human contouring can be as accurate asFWHM criteria, the accuracy of infarct size measure-ment can still be improved by using the FACT algo-rithm.

The region-based feature analysis of the FACT algo-rithm used expert derived rules to reduce type-II mis-classification errors (false positive; overestimation) thatoccurred with simple thresholding techniques. Thebenefit of image feature analysis is most evident foreliminating epicardial white patches, such as right ven-tricular blood, that are included within imperfectlydrawn epicardial borders. It can also exclude small iso-lated bright regions that result from imaging noise orpotential artifacts provided they were not connected inthree-dimensional space with other areas of infarction.Furthermore, feature analysis can designate dark pix-els corresponding to microvascular obstruction.

In our canine validation, the FACT algorithm per-formed well on slices without infarction, which includesone animal with no TTC evidence of infarction. Thesegmentation of epicardial and endocardial borders byhuman readers was the first step for computer analysis,

Figure 8. The FACT algorithm reducesaverage errors in measuring infarct sizecompared with simple intensity thresh-olding methods based on 2SD andFWHM. Both 2SD and FWHM methodssignificantly overestimate MR infarctsize compared to TTC.

Table 2Summary of Average MR Infarct Sizing Errors Using DifferentMethodsa

Error in % of LV Error in % of MI

Ex vivo In vivo Ex vivo In vivo

FACT 0.5% 1.2% 3.5% 11.9%Human 3.2% 5.4% 22.2% 55.1%FWHM 3.1% 6.7% 21.8% 69.0%2SD 10.4% 9.1% 72.2% 93.0%

aErrors are expressed as % of LV and % of MI for the differencebetween MR and TTC measurements.FACT � Feature analysis and combined thresholding, Human �human manual contouring, FWHM � full width at half maximumintensity thresholding, 2SD � two–standard deviation intensitythresholding.

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a potential source of subjective errors. In our results,interobserver agreement (kappa � 0.9) was excellent formyocardial segmentation (Table 1). The residual errorsin the FACT algorithm infarct quantification suggestedthe difference in myocardial segmentation was not amajor source of error.

The ability of inversion recovery and gadolinium-enhanced MR techniques to suppress the signal ofnormal myocardium leads to high contrast betweennormal and infarcted tissues (3). However, the accu-racy of determining thresholds for infarct quantifica-tion based on simple image intensity, includingFWHM approach (13), can be limited by noise bias(22) in conventional root-sum-square magnitude im-ages unless PSIR reconstruction (14) is used. In ad-dition, the use of FWHM criteria is based on theassumption that the MI contour is at 50% of themaximum intensity and not altered by the surfacecoil profile. Surface coil intensity correction is thusan important aspect for accurate MI sizing, particu-larly with large infarcts. The proposed FACT infarctsizing algorithm is now validated on the PSIR methodbut it can be extended to magnitude reconstructionachieving a similar accuracy as long as normal myo-cardium is properly nulled (23). Since PSIR imaginghas benefits of being insensitive to inversion time andimproved contrast-to-noise ratio compared to magni-tude reconstruction, this algorithm may be furtherexploited over a relatively wide range of inversionrecovery times for accurate myocardial infarct sizing.

Limitations

The algorithm was designed to identify necrosis thatincludes a subendocardial component as typically seenin patients with myocardial infarction due to coronaryartery disease. Other etiologies of necrosis/fibrosissuch as hypertrophic cardiomyopathy, cocaine inducedinfarction, and myocarditis may require modificationsto the algorithm as these other diseases may causefibrosis in other parts of the LV myocardium withoutaffecting the subendocardium.

CONCLUSION

In conclusion, this paper presents a FACT algorithmthat automatically selects optimal thresholds and usesfeature analysis methods to classify normal and in-farcted myocardium. It includes histopathological vali-dations and indicates that in vivo MR accurately de-picts the infarct region. The proposed algorithmmeasures infarct size more precisely than human man-ual contouring and simple intensity thresholding meth-ods. Detailed validations were performed against histo-pathology in a canine infarct model includingintraobserver and interobserver analysis to supportthese conclusions. The FACT algorithm improves thecorrelation, decreases random errors, minimizes in-traobserver and interobserver variability, and reducessystematic overestimation that plagues human manualmeasurements or conventional simple intensity thresh-olding techniques. This computer-assisted methodol-

ogy appears promising for serial infarct sizing in clinicaland basic science studies.

ACKNOWLEDGMENTS

The authors thank Christine H. Lorenz, PhD, Sie-mens Corporate Research, Princeton, NJ, for manyhelpful suggestions regarding the method and studydesign.

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