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MEDICAMUNDI 47/2 August 2003 41

Analysis of volumetric cardiac CT and MR image dataM. Breeuwer1, P. Johnson2 and M. Kouwenhoven3

Substantial research and development have beenconcentrated on new technologies and methodsthat can aid the diagnosis and monitoring of cardiacdisease, and on planning appropriate treatment.Much of this effort has been focused on theimprovement of cardiovascular imaging and thedevelopment of computer-assisted analysis of theresulting images.

A variety of imaging modalities can be used to deter-mine structural, functional and perfusion character-istics of the heart [1]. Ideally, a thorough cardiacevaluation should meet the following clinical require-ments: First, provide high spatial resolution images for theassessment of anatomical structures. Second, detectand quantify stenotic, calcified and non-calcifiedlesions in the coronary arteries. Third, evaluate thefunctional performance of the left ventricle in terms ofejection fraction and wall motion (contractility).Fourth, detect regions with abnormal myocardialperfusion and non-viable tissue. Fifth, provide bloodflow information e.g. for valvular analysis.

Conventionally, X-ray angiography is used to detectand quantify stenosis in the coronary arteries.Echocardiography is applied to assess left ventricularand valvular function and to measure flow. Nuclearmedicine is used to quantify the perfusion of bloodinto the myocardium and to assess the viability ofmyocardial tissue [2]. However, during the lastdecade computed tomography (CT) and magneticresonance (MR) imaging have made substantialprogress in addressing the clinical requirements forcardiac imaging.

The substantial increase in spatio-temporal resolutionof CT and MR imaging has made it possible toacquire high-resolution volumetric cardiac image dataas a function of time. The achieved increase inresolution does have a drawback in that the analysisof the image data becomes a much more tedious task.Advanced computer-assisted image processing isneeded to derive diagnostically relevant information,while automation of this processing is required toreduce the required analysis time.

Cardiovascular CT imaging

Traditionally, the utility of computed tomography(CT) for cardiac imaging has been limited.Developments in the past four years have enabledCT to more accurately and consistently assess cardiacmorphology, function, and vasculature. Thesetechnological developments include the introductionof multislice scanners (ranging from 2 to 16 slices),the creation of advanced sub-millimeter detectortechnology, the increase in gantry rotation speed(from 1 to 0.42 seconds rotation period), theintegration of electrocardiogram (ECG) signals intothe scan protocol and reconstruction, and theimplementation of specialized acquisition andreconstruction algorithms. The combination ofthese developments provide the ability to acquireECG-gated, sub-millimeter isotropic 3D datasetswith a temporal resolution between 53 and 210 mswithin a single breath-hold. The 3D dataset coversthe full volume of the heart.

Cardiac imaging with CT can be performed in twomodes: prospective ECG gating and retrospectiveECG tagging. During a prospective gating protocol,axial images are acquired after a programmed delayfollowing a trigger initiated by the R-wavedetection from the ECG (static table position foreach acquired RR interval). In a retrospectivetagging protocol, a spiral acquisition is performed(continuously moving table) with simultaneousacquisition of the ECG. Using the projection dataand the ECG signal, a series of images arereconstructed at a consistent physiological phase inthe cardiac cycle. Retrospectively determinedreconstructions of various physiological phases,relative to the ECG, can be performed, accordingto the clinical question, e.g. coronary arteries, orfunction. The optimal phases for coronary arteryevaluation are the quiet phases of the cardiaccycles, while end-diastole (ED) and end-systole(ES) are chosen for function.

Cardiovascular (CV) CT applications includecalcium scoring, morphological evaluation,ventricular functional assessment, coronary artery

1Medical IT - AdvancedDevelopment, PhilipsMedical Systems, Best,the Netherlands.

2CT Clinical Science,Philips MedicalSystems, ClevelandOH, USA.

3MR Clinical SciencePhilips MedicalSystems, Best, the Netherlands.

Much effort has

been focused on

Improved cardio-

vascular imaging

and computer-

assisted analysis of

the results.

42 MEDICAMUNDI 47/2 August 2003

evaluation of the great vessels, atria and ventricles[5]. Additionally, retrospective tagging provides theability to reconstruct 3D volumes at multiplephases in the cardiac cycle with a single scan/injection, thus enabling ventricular functionalassessment. For the evaluation of the coronaryarteries, a number of recent studies have shown highnegative predictive values for CV CT in comparisonwith conventional X-ray angiography [6-8].Furthermore, CV CT has been used for evaluationof treatment effectiveness in bypass surgery andPTCA intervention. Figures 1 and 2 showexamples of cardiac CT images, while Figure 3shows a volume-rendered visualization of theheart and coronary arteries made from a 3Dcardiac CT examination.

Cardiovascular MR imaging

Over the past decade, cardiovascular MR imaginghas made a great leap forward [9]. Importantadvances are the introduction of prospective andretrospective ECG-triggered scanning using thevector cardiogram (VCG), the development of

evaluation, full vascular run-offs, hepatic and renalassessment and treatment follow-up. Calcium scoringstudies are typically prospectively ECG-gated, non-contrast examinations that identify and quantifycoronary artery calcium [4]. Contrast enhancedCV CT angiography with retrospective ECGtagging has proven to be useful in the morphological

Figure 1a.Image generated using Cardiac Review(MxView 5.0) with the slab MIP feature.The arrow indicates the anomalous RCA.

� Figure 1.Anomalous right coronary artery (RCA), originating from the left anterior descending(LAD) coronary artery (Images by courtesy of Indiana University Purdue UniversityIndianapolis, Indianapolis IN, USA).

�Figure 2.

CT can generatetraditional cardiac viewsincluding vertical long

axis, horizontal longaxis, and short axis

images. Images createdusing Cardiac Review

(MxView 5.0)(Courtesy of

University Hospitalsof Cleveland,

Cleveland OH, USA).

Figure 1b.Image generated using Cardiac CTA(MxView 5.0). Arrows indicate the RCA.


� Figure 5. Acquisition of a first-pass MR myocardial perfusion image series (Image material courtesy of the German Heart Institute Berlin).

� Figure 3. Visualization of thecoronary arteries usingCT volume rendering.Image generated usingCardiac CTA (MxView5.0) (Courtesy ofUniversité Catholiquede Louvain, Brussels,Belgium).

� Figure 4. Functional (cine) MR:two heartphases (outof 32) from a retro-spectively triggeredBalanced-TFE sequence.End diastole (left) andend systole (right)(Courtesy of PhilipsMedical Systems).

respiratory navigators for free-breathing scanning,the use of surface receiver coils to improve signal-to-noise ratio and/or allow parallel imaging (SENSE),the introduction of stronger gradient systemsallowing fast scanning protocols, and theincorporation of inhomogeneity correction intothe scanning process (CLEAR).

Thanks to these developments, in particular thepossibility of fast parallel imaging, MRI is now ableto image morphology, function (wall motion),perfusion, viability and the vasculature around theheart. MRI is also able to quantify blood flow. Forthese reasons, MRI is often considered as thepotential comprehensive imaging modality, capableof acquiring most of the required information duringa single scanning session of less than one hour.

MRI is an extremely flexible technique, and has alarge number of user-defined examinationparameters which, for example, allow the imagecontrast to be adapted to specific needs. This abilityto fine-tune acquisitions typically results in adifferent type of scan for each specific imagingquestion. A typical characteristic of all MRI scansis that the scan time depends on the spatial andtemporal resolution, as well as on the volumecoverage; the higher the spatial and/or temporalresolution, and the larger the volume coverage(field of view or volume thickness), the longer thescan time will be. The temporal resolution of MR(typically around 40 ms for functional imaging, andaround 100 ms for coronary imaging) is currentlybetter than that of CT. However, the spatialresolution is generally somewhat lower.

Figures 4-8 show examples of the various types ofcardiac MR images that can now be acquired with

dedicated Philips cardiac MR scanners. Highly-detailed morphological and functional imaging canbe performed in a relatively short time (a fewseconds per slice), 10 short-axis and a few long-axisslices are typically acquired with 20-30 phases inthe heart cycle (Figure 4). Dobutamine-inducedfunctional stress testing at 4-5 stress levels isincreasingly being considered as the method ofchoice for accurately measuring ejection fraction andfor assessing wall motion. Specific protocols havebeen introduced for visualizing myocardial perfusion(first-pass perfusion imaging, Figure 5) and myocar-dial viability (late-enhancement imaging, Figure 6).Finally, protocols have been developed for coronary

of the heart, in particular of the left ventricle, atvarious stress levels [10,11]. Global parameters forleft ventricular functioning are the stroke volume(volume of the ejected blood per heart beat), theejection fraction (percentage of the left ventricularblood volume ejected per heart beat) and cardiacoutput (volume of blood pumped out of the heartper unit of time). More local information aboutventricular functioning can be obtained by quanti-fying the local wall motion, thickness and thickening.A decrease in wall motion with increasing stress may bean indication of ischemia, whereas a total absenceof motion at all stress levels may indicate infarction.

The main image-processing issue in functionalanalysis is the segmentation (i.e. delineation) of theepi- and endocardial boundaries (contours) of themyocardium of the left ventricle in each of theacquired images. These contours are required for thecalculation of the above mentioned parameters.Since about 50-200 short-axis images and severaltens of long-axis images are acquired per stresslevel, manual contour delineation is a very tediousjob, which can take several tens of minutes.

Contour delineation is an obvious candidate forautomation, but fully automatic identification of theepi-and endocardial borders presents several problems,particularly where the transitions are unclear, orwhere the myocardium has to be distinguished fromother structures. An approach that has proveduseful in practice is the active contouring techniqueproposed by Lobregt and Viergever [12].

The Philips EasyVision Cardiac MR Analysispackage uses the active contouring technique forsemi-automatic contour segmentation of short-axisimages. The user has to draw an initial epi- andendocardial contour on one of the images (i.e. onephase of a selected slice). These contours are thenautomatically propagated to all remaining slicesand phases. The iterative repositioning of thecontours per image is driven by so-called externalforces (image features, such as bright-to-darktransitions) and internal features (restrictions on theshape of the contours). This approach generallyworks well in areas with clear image features, e.g.clear transitions (edges) from the bright left-ventricular blood pool to the darker myocardium,but manual correction is usually required forpositioning the contours in areas where features areweak or lacking, such as on parts of the epicardialcontour (e.g. adjacent to lungs) and in locations

artery imaging (Figure 7) and for imaging the bloodflow in the arteries around the heart (Figure 8).

Cardiac image analysis

Each of the image types that can be acquired withCT or MR has its own specific clinical purposeand, consequently, its own specific image-analysisrequirements. This section discusses theserequirements and reviews which image-analysisalgorithms are available and/or under investigation.

Functional analysis

The main goal of functional analysis is thevisualization and quantification of the functioning


� Figure 6. CMR images showinglate-enhancement/viability. 4 Slices (outof 20) from a 3Dsingle breathhold LateEnhancement scan,15 minutes postinjection. Viablemyocardial tissue isdark; non-viablemyocardial tissue(scar) appears bright(Images courtesy ofGasthuisbergUniversity Hospital,Leuven, Belgium).

� Figure 7. CMR images of the coronary arteries. LAD (left) and RCA(right) coronary artery using a free-breathing 3D BalancedTFE technique. Each coronary artery was imaged with aseparate, targeted 3D volume. Images were created using aMIP projection in a thin, curved volume (Image by courtesyof Philips Medical Systems).

� Figure 8.MR Quantitative Flow. Example of an axial slice throughthe ascending (white) and descending (black) aorta showingpeak flow during systole. Flow sensitivity was chosen to beperpendicular to the slice. In these flow images, white pixelsindicate positive flow (towards the head in this case), whileblack pixels indicate negative flow (Image by courtesy ofPhilips Medical Systems)

EasyVision

Cardiac MR

Analysis provides

semi-automatic

contour

segmentation.


�Figure 9. Short-axis functionalCMR images at enddiastole (top left) andend systole (top right)with manually drawnendo- and epicardialcontours. Thecorresponding wall-thickening graph for 1 slice is shown(bottom left) togetherwith a wall-thickeningbull’s-eye plot for 3slices (bottom right).The red areas in thebull’s-eye plotindicate reduced wallthickening (Imagematerial by courtesyof the German HeartInstitute Berlin). Theanalysis was performedwith the PhilipsEasyVision CardiacMR Analysis package.

are still under investigation. Active contouringitself can be improved by creating better featureimages and by incorporating more anatomicalknowledge in the contour propagation process. Anexample is the approach proposed by Spreeuwersand Breeuwer [13], which includes joint optimizationof the placement of the endo- and epicardialcontours (see Figure 10). Research into the extensionof the technique to 3D is ongoing (‘3D ActiveObjects’) so that 3D anatomical shape constraintscan be better taken into account [14].

Other proposed segmentation approaches includematching with models which provide a statisticaldescription of the local shape (geometry) and theappearance (brightness) [15], and the non-rigidregistration of the myocardium in adjacent phasesfollowed by contour propagation from phase tophase using the derived non-rigid transformation[16]. Considerable effort is still needed to optimize,compare and validate each of these newer techniques.

where the papillary muscles touch the endocardialcontour. Figure 9 shows an example of thequantification of wall thickening with the EasyVisionCardiac MR Analysis package.

For the analysis of cardiac CT studies, the axiallyacquired images must first be re-oriented to thetraditional short-axis planes before segmentation.In the Philips MxView LV/RV Analysis application,ventricular cavity calculations (ejection fraction, strokevolume, cardiac output) are defined by the volumewithin the endocardial border, as defined by the 3Dregion-growing algorithm with heuristic constraints.Ventricular muscle measurements (wall motion, wallthickness, wall thickening, muscle mass) areadditionally calculated based on the volume betweenthe endocardial and epicardial borders as definedby a 2D active contouring algorithm.

Several alternative segmentation approaches havebeen proposed in the last few years, most of which

Philips MxView

LV/RV Analysis

calculates

dynamic data on

the ventricular

cavity and wall.

Determination of the MPRI is not a simpleprocedure, largely due to the properties of first-passperfusion MR image series. Most importantly, theseimages may contain a significant amount ofrespiratory motion. Without motion compensation,the myocardial contours must be individuallyrepositioned for each image in the series.Furthermore, the images usually contain a significantamount of noise, and they may also containsignificant intensity inhomogeneity due to thespecific placement of the MR surface receiver coils.

Figure 11 is a diagram of a semi-automatic first-passperfusion analysis method that was recentlydeveloped by Philips Medical Systems [19]. First,respiratory (and other) motion is automaticallycompensated by using the technique of rigidregistration between successive images in the imageseries. Next, manual or semi-automatic detection

First-pass myocardial perfusion analysis

The purpose of first-pass myocardial perfusionanalysis is the visualization and quantification ofthe regional inflow of blood into the myocardium.This type of study is usually performed with contrast-enhanced MRI, but investigations into CT-basedperfusion analysis have recently started [17].

Several studies have shown that the maximumupslope of the local myocardial time/intensity profilesin MR first-pass perfusion image series is a goodindicator of myocardial perfusion [18]. For normallyperfused myocardial tissue, the ratio of the maximumupslopes under stress and at rest should exceed acertain threshold. Myocardial segments with a valuebelow the threshold may be ischemic [18]. Thestress/rest maximum upslope ratio is usually denotedas the myocardial perfusion reserve index (MPRI).


�Figure 10.

Result of segmentationwith coupled active

contours as describedby Spreeuwers et al.

[13]. The contours inthe top left-hand image

(end diastole) werespecified manually. All

the other contourswere derived fully

automatically, basedon these initial

contours. Note thatthis method can cope

very well with thepapillary muscles

(Courtesy of L.Spreeuwers,

University MedicalCenter, Image SciencesInstitute, Utrecht, theNetherlands) (Imagematerial by courtesy

of the German HeartInstitute Berlin).

�Figure 11.

Block diagram of thefirst-pass myocardial

perfusion analysismethod described byBreeuwer et al. [19].

Philips has

developed a semi-

automatic first-

pass perfusion

analysis method.


� Figure 12. First pass myocardial perfusion analysis, performed using the method described byBreeuwer et al. [21].

Figure 12a.Maximum upslope at restrepresented as a colouroverlay on the 1st-passperfusion image. The color scale rangesfrom blue (low perfusion)to red (high perfusion).

Figure 12b.Maximum upslope at stressrepresented as a coloroverlay on the 1st-passperfusion image.

Figure 12c.Myocardial perfusionreserve index afterthresholding (red = sufficiently perfused,blue = insufficientlyperfused).

� Figure 13.The perfusogram: a new method of visualizing the inflow of contrast into themyocardium as a function of time. The display was produced with a prototype PhilipsEasyVision Cardiac Perfusion Analysis package currently undergoing clinical validation.

Figure 13a.Schematic representation of the leftventricle (LV) and the right ventricle (RV)in a short-axis cardiac image, with the LVmyocardium divided into 5 segments bya ‘spoked wheel’.

Figure 13b.The perfusogram shows a color-codedrepresentation of the inflow of contrastagent into the myocardium for the 5 segments shown in 13a (blue: low inflow;red: high inflow).

of the myocardial boundaries [20] is applied, andthe maximum upslopes of the local myocardialtime/intensity profiles are measured automatically.The analysis is performed both at rest and understress, and the stress/rest upslope ratio is calculated.

Figure 12 shows examples of color-coded maximumupslopes at rest and stress with the correspondingperfusion reserve index. Figure 13 shows the‘perfusogram’: a new method of visualizing the inflowof contrast agent into the myocardium as a functionof time [21]. These results were produced with aprototype Philips Medical Systems EasyVisionCardiac Perfusion Analysis package that is currentlyundergoing clinical validation.

Myocardial viability analysis

Myocardial viability analysis aims at the detectionand quantification of infarcted (necrotic) myocardialtissue. At present, this is usually performed withcontrast-enhanced ECG-triggered late-enhancementMRI. However, some initial experiments in late-enhancement CT imaging have recently beenpresented [17].

The main image-processing issues are the segmentationof myocardial boundaries, the discriminationbetween normal and infarcted tissue, thequantification of volume of the infarcted tissue,and the comprehensive visualization of the resultsfrom all processed slices.

Figure 14 shows example analysis results that wereproduced with a recently developed prototype: thePhilips EasyVision Myocardial Viability Analysispackage [27]. In this case, the myocardial contourswere manually delineated, automatic histogram-based thresholding was performed to detectinfarcted tissue, and the results were visualized in abull’s-eye plot. Research into automatic segmentationof myocardial contours is ongoing. The need forfully automatic contour segmentation is howeverless urgent than for functional images, since a late-enhancement image data set consists of only 10-20short-axis and possibly a few long-axis images.

Coronary artery analysis

Imaging and analysis of the coronary arteries servesseveral purposes: visualization of the coronary tree,detection and quantification of stenosis,quantification of the coronary flow reserve, and

analysis of the vessel walls (identification andclassification of plaque, determination of plaquerupture risk).

Although the resolution of MR coronary arteryimaging is gradually increasing, it is still relativelylow when compared with X-ray angiography.Consequently, MR image processing has beenprimarily focused on methods for the improvedvisualization of the coronary arteries, so that a stenosiscan be more easily identified.

The resolution of CT coronary artery imaging ishigher than that of MR, enabling more accuratequantification of stenoses. Furthermore, CT can be

Coronary artery

analysis includes

detection and

quantification of

stenosis, and

classification of

plaque.

determined by the algorithm to calculate theindividual plaque score. The summation of theplaque score constitutes the total calcium score forthe coronary arteries [22].

A variety of MR-based coronary-artery visualizationapproaches have been proposed. A simple,straightforward approach is the construction ofmultiplanar reformatted images (MPR) and/or slabmaximum-intensity projections (slab-MIP) inoptimally chosen planes, so that the major segmentsof the coronary tree are visualized (see the examplein Figure 15) [23]. More complex approachesinclude the semi-automatic detection of the axis ofthe coronary tree (from a number of user-specifiedseed points) [24], followed by slab-MIP reformattingbased on this axis (see the example in Figure 16) [25].

used for calcium scoring [4]. Coronary arterycalcification is a specific marker of coronaryatherosclerosis. Traditional analysis of studies hasfocused on a scoring system developed by Agatston.Newer scoring algorithms that could provide morereproducible results, including the volume scoreand the mass score, have been proposed asalternatives to the Agatston system. The primarybasis of all of these algorithms is the detection andquantification of calcium. Voxels are classified innon-contrast CT images as calcium if theHounsfield Unit (HU) or intensity is greater than agiven threshold (typically 90 HU for non-ECGgated studies and 130 HU for ECG gated studies).These calcium voxels are summed into a singleplaque region based on connectivity. The plaque isthen multiplied by a weighting factor that is


� Figure 14.Myocardial viability analysis. The results were produced with a recently developed prototype Philips EasyVision MyocardialViability Analysis package. The image material was analyzed using the method described in a previous publication [27] (Imagematerial by courtesy of the German Heart Institute Berlin).

Figure 14a.Late enhancement slice

with scar.

Figure 14b. Result after thres-holding (scar areas

indicated intransparent red).

Figure 14c. Bull’s-eye plot of the

original myocardialsignal intensity for a

set of short-axis slices.

Figure 14d.Bull’s-eye plot of the

thresholdedmyocardial intensity

(scar indicated in red).

Coronary artery

calcification is a

specific marker of

coronary

atherosclerosis.


� Figure 15. Coronary imaging with MR (Image material by courtesy of the German Heart Institute Berlin)

� Figure 16. MR coronary artery ‘soap bubble’ visualization.

� Figure 17. Volume renderingshowing the cardiacvolume of interest,including a LIMAbypass to the LAD, aRIMA bypass to theleft circumflex, and avenous graft to theRCA. The image wasgenerated using PhilipsMxView Cardiac CTA(MxView 5.0)(Courtesy ofUniversité Catholiquede Louvain, Brussels,Belgium).

Figure 15a.One slice of a coronary artery MR scan.A centerline has been defined throughpart of a coronary artery.

Figure 15b.Straightened planar reformatting in acurved plane defined by the centerline in15a.

Figure 16a.A sphere is constructed containing thecoronary arteries to be visualized, and athin external layer is defined withthickness d.

Figure 16b.A slap-MIP is made from the thin layerdefined in 16a.

At this time, there is no standardized technique forcoronary-artery analysis using CT. Manyvisualization techniques and quantificationstrategies are currently in use. In the PhilipsMxView Cardiac Review application, two of themost commonly used review tools are assessable.Fast leafing or slab scrolling through axial imagesprovides speed and accuracy, as no post-processingis required. Thin sliding slab maximum intensityprojections (slab-MIP) provide critical visualizationof the vessel continuity within a single plane.Additionally, the value of CT's ability to acquireisotropic 3D volumes can be exploited usingvolume rendering features and sophisticated vesseltracking algorithms. In the Philips MxViewCardiac CTA application, volume rendering is usedfor visualizing complex anatomy such as bypassgrafts and coronary anomalies (Figure 17). The ribcage and other non-cardiac regions can be removedquickly using automated segmentation tools, thusleaving the cardiac volume of interest. Theintegration of advanced vessel tracking algorithmscan simplify centerline detection for a vessel to twoseed points. Following the detection of the vesselcenterline, orthogonal or cross-sectional images areavailable for review and quantification. The vesselluminal area can be calculated using traditionalborder extraction techniques (Figure 18).

Quantitative flow analysis

Quantitative flow analysis aims at measuring bloodflow (as a function of position and time) in thecoronary arteries and the arteries and vesselssurrounding the heart. Specific MR acquisitionprotocols are available for flow measurement [26].

The image data acquired usually consists of amplitudeimages, which clearly show the vessel lumen,together with phase images in which the pixelintensity is proportional to the blood flow. The mainimage-processing issue is the segmentation of theboundary of the lumen as a function of time. Inthe Philips EasyVision Quantitative Flow packagethis is performed by applying the technique of 2DActive Contouring to the amplitude images andsubsequently copying the detected contours to theflow images (see Figure 19).

Combination of analysis results

The different types of analysis described aboveprovide a large amount of information, which

must be presented to the clinical user in acomprehensible form. Figure 20 shows an exampleof such a presentation. Results from first-passmyocardial perfusion analysis were registered tothose of myocardial viability analysis, so that theycould be combined in one presentation [27, 30]. Thepresentation could be extended with information onwall motion and information on the coronaryarteries (geometry, percentage stenosis, flow reserve,presence of calcifications, etc.). Similarly, analysisresults from multiple slices could be jointlyrepresented on the visualization of a 3Dsegmentation or a 3D model of the heart.

Future expectations

Investigations into the improvement of cardiac CTand MR imaging are ongoing. The spatial andtemporal resolution of MR is expected to furtherincrease, and the motion blurring will be reduced,both of which may enable more accurate coronaryartery analysis in the near future, including moredetailed imaging of the vessel walls (e.g. usingcontrast agents that specifically target various typesof plaque).


�Figure 18.

Quantification of theluminal diameters for

the RCA from theostium to the distal

segment. Contouringof the orthogonal

slices to the vesselcenterline providesinformation on the

lumen areas anddiameters. The Tableshows a reduction of

60% of the lumen areain the mid segment of

the RCA. The imagewas generated using

the Stenosis Measure-ment feature of the

Philips MxView CardiacCTA (MxView 5.0)

(Courtesy ofUniversity Hospitals

of Cleveland,Cleveland, OH, USA.).

� Figure 19.Segmented amplitude and corresponding phase image, with agraph of the measured flow as a function of time in one cardiaccycle (produced with the EasyVision Cardiac MR Qflow package)(Images by courtesy of Philips Medical Systems Best).


Future improvements in cardiac CT will focus onworkflow solutions, spatial resolution, temporalresolution and dose efficiency. Furthermore, first-pass perfusion and viability imaging with CT areunder investigation.

Considerable effort has already been spent on thecomparison of cardiac analysis based on CT and/orMR with analysis results derived via other imagingmodalities. For example, it is becoming more andmore clear that cardiac MR perfusion and viabilityanalysis have a higher resolution than similar analysesbased on nuclear medicine. This means that MRalso allows small sub-endocardial perfusion deficitsand scar areas to be identified. Multicenter clinicalvalidation studies will be needed in order to confirmthese findings [28, 29].

Cardiac image processing research will have to focuson the further automation of each of the stepsinvolved in the analysis, and on the improvement of

� Figure 20.Combined analysisresults (Image materialby courtesy of theGerman HeartInstitute Berlin).

Figure 20a.Maximum upslope atrest (from 1st-passperfusion analysis).

Figure 20b.Maximum upslopeunder stress (from1st-pass perfusionanalysis).

Figure 20c.Myocardial perfusionreserve index (MPRI)(from 1st-passperfusion analysis).

Figure 20d.Combined visualizationof the MPRI and scartissue resulting fromlate-enhancementanalysis (red = normal, blue = ischemic, grey = necrotic tissue).

the analysis accuracy, in order to achieve a higherdiagnostic sensitivity and specificity. Considerableeffort will be needed to achieve fully automaticsegmentation of the myocardial contours infunctional, first-pass perfusion and viability images,and to be able to fully automatically track andsegment the coronary arteries.

Intra- and intermodality registration betweendifferent types of cardiac images and associatedanalysis results will be needed to come to acomprehensive combined representation of allanalysis results. A relatively new area of research isthe analysis of cardiovascular diseases by means ofhemodynamic modeling. Based on the estimationof the geometry, flow, pressure and tissue propertiesof the heart and surrounding vessels, this type ofanalysis attempts to gain more insight into thepatient-specific local blood flow and local pressure,and into derived parameters such as wall shear stressand wall strain. This analysis may, for example,

Cardiac analysis

based on CT and

MR compares

well with results

from other

modalities.

image material that was used in the studies reportedin this paper.

Our thanks are also due to Luuk Spreeuwers(University Medical Center, Image Sciences Institute,Utrecht, the Netherlands), Nick Noble and DerekHill (Guy's Hospital, London, United Kingdom)and Marcel Quist (Philips Medical Systems, Best,the Netherlands) for cooperation in research intosegmentation and/or registration of cardiac MR1st-pass perfusion and functional image data.

In addition, we would like to acknowledge thecontributions to this article from KrishnaSubramanyan, Alain Vlassenbroek, Shalabh Chandra,Jonathan Lessick, Guy Lavi, Amnon Steinberg,Haim Gelman, Phillip Prather, Shosh Rubinstein,Arie Amara (Philips Medical Systems), RobertGilkeson (University Hospitals of Cleveland),Robert Choplin (Indiana University) and BernhardGerber (Université Catholique de Louvain).

enable a more accurate prediction of the risk ofplaque rupture and may supply a better prognosisof the long-term development of cardiovasculardisease.

Acknowledgements

We are grateful to Eike Nagel and Ingo Paetsch(German Heart Institute Berlin, Germany),Bernhard Schnackenburg (Philips Medical Systems,Germany), Raja Muthupillai (Philips MedicalSystems North America / Texas Heart Institute,Houston, USA), Scott Flamm (Texas Heart Institute,Houston, USA), Steven Dymarkowski and JanBogaert (Gasthuisberg University Hospital, Leuven,Belgium), John Ridgway and Sven Plein (LeedsGeneral Infirmary, United Kingdom) and HolgerThiele (University Hospital, Leipzig, Germany)for the fruitful cooperation in the study of cardiacMR 1st-pass perfusion and/or late-enhancementanalysis, and we thank those who supplied the


Hemodynamic

modeling may

enable more

accurate prediction

of the risk of

plaque rupture.

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CHIEF OF CARDIOLOGY . . . . . . DR. DAN RODRIGUEZ

HOSPITAL CFO . . . . . . . . . . . . . MIKE TAYLOR

SUPPORTING CAST . . . . . . . . . .PHILIPSCARDIOVASCULAR SOLUTIONS

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