E. Nagel

H.B.W. Larsson

R.H. Davies

T.F. Cootes

In this paper, a method based on Active Appearance Models (AAM) is applied for automatic registration of myocardial perfusion MRI. A semi-quantitative perfusion assessment of the registered image sequences is presented. This includes the formation of perfusion maps for three parameters; maximum up-slope, peak and time-to-peak

Ólafsdóttir, Hildur

Stegmann, Mikkel Bille

Larsson, Henrik B.W.

Online Research Database In Technology

Automatic Assessment of Cardiac PerfusionMRIHildur O´lafsdo´ttir1, Mikkel B. Stegmann1,2, and Henrik B.W. Larsson2,31 Informatics and Mathematical Modelling, Technical University of Denmark2 Danish Research Centre for Magnetic Resonance, H:S Hvidovre Hospital, Denmark3 Dept. of Diagnostic Imaging, St. Olavs Hospital, Trondheim University, NorwayAbstract. In this paper, a method based on Active Appearance Mod-els (AAM) is applied for automatic registration of myocardial perfusionMRI. A semi-quantitative perfusion assessment of the registered imagesequences is presented. This includes the formation of perfusion maps forthree parameters; maximum up-slope, peak and time-to-peak.1 IntroductionMyocardial perfusion MRI has proven to be a powerful method to assess coronaryartery diseases. The ultimate goal of the analysis is to obtain a full quantiﬁca-tion of the perfusion in ml/(g·min), see e.g. [3]. A step towards this goal is asemi-quantitative perfusion assessment obtained by generating perfusion mapsfrom a registered sequence of images, see e.g. [4]. This paper presents an auto-matic registration of multi-slice perfusion sequences and a preliminary clinicalvalidation in terms of perfusion maps.2 Methods and ResultsThe data material comprises 500 myocardial perfusion, multi-slice, short-axis,magnetic resonance images (MRI) obtained from ten freely breathing patientswith acute myocardial infarction. Each image is composed of four spatial slices.The registration method is based on Active Appearance Models (AAMs) [1].AAMs establish a compact parameterisation of object variability, as learned froma representative training set. Objects are deﬁned by marking up each trainingexample with points of correspondence, i.e. landmarks. Here, landmark positionsare optimised by an MDL approach [2]. Subsequently, AAMs can be registeredrapidly to unseen images. Modiﬁcations to the standard AAM framework includeslice-coupled modelling, sequence priors and clustering of texture vectors. Forfurther details, refer to [5].Given the per-pixel point correspondences of each slice of the myocardium, asemi-quantitative perfusion assessment is carried out. Signal-intensity (SI) curves(plot of intensity vs. time frame) for each pixel position are generated and fromthose, three perfusion parameters are derived, the maximum upslope, peak andtime-to-peak.Registration of the perfusion data was carried out in a leave-one-out cross val-idation. Quantitative comparison to ground-truth (manual) registration showedC. Barillot, D.R. Haynor, and P. Hellier (Eds.): MICCAI 2004, LNCS 3217, pp. 1060–1061, 2004.c© Springer-Verlag Berlin Heidelberg 2004Automatic Assessment of Cardiac Perfusion MRI 1061Maximum upslopePeakTime-to-peakFig. 1. Above: Registration of multi-slice time frame 17 from patient 8 (Slice 1–4 left toright). Below: Perfusion maps for patient 8 generated from the automatic registration.a mean registration accuracy of 1.25± 0.36 pixels in terms of point to curve dis-tance. Qualitative registration results and perfusion maps for patient 8 are givenin Figure 1. The ﬁgure reveals a severe perfusion deﬁcit at the anteroseptal andinferoseptal wall judged from the original multi-slice frame. This is conﬁrmed bythe perfusion maps for all slices for the maximum upslope parameter and slices1, 3 and 4 for the remaining two parameters. Same regions were identiﬁed bythe perfusion maps for the ground-truth registration.Based on the preliminary clinical validation, it is concluded that the auto-matic registration method holds great promise for the automation of quantitativeperfusion examinations.References1. T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. IEEETrans. on Pattern Analysis and Machine Intelligence, 23(6):681–685, 2001.2. R. H. Davies, C. J. Twining, T. F. Cootes, J. C. Waterton, and C. J. Taylor.A minimum description length approach to statistical shape modeling. MedicalImaging, IEEE Transactions on, 21(5):525–537, 2002.3. H. B. W. Larsson, T. Fritz-Hansen, E. Rostrup, L. Søndergaard, P. Ring, andO. Henriksen. Myocardial perfusion modeling using MRI. Magnetic Resonancein Medicine, 35:716–726, 1996.4. E. Nagel, N. Al-Saadiand, and E. Fleck. Cardiovascular magnetic resonance: My-ocardial perfusion. Herz, 25(4):409–416, 2000.5. M. B. Stegmann, H. O´lafsdo´ttir, and H. B. W. Larsson. Unsupervised motion-compensation of multi-slice cardiac perfusion MRI. Invited contribution for theFIMH special issue in Medical Image Analysis, 2004 (accepted for publication).

Automatic assessment of cardiac perfusion MRI

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Automatic Assessment of Cardiac Perfusion MRI

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Automatic assessment of cardiac perfusion MRI

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