Fully automated framework for the analysis of myocardial first-pass perfusion MR images


  • Beache Garth M.,

    Corresponding author
    1. Department of Radiology, School of Medicine, University of Louisville, Louisville, Kentucky 40202
    • Author to whom correspondence should be addressed. Electronic mail: aselba01@louisville.edu; https://louisville.edu/speed/bioengineering/faculty/bioengineering-full/dr-ayman-el-baz/elbazlab.html/.

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  • Khalifa Fahmi,

    1. BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292 and Electronics and Communication Engineering Department, Faculty of Engineering Mansoura University, Mansoura 35516, Egypt
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    • a)

      Shared first authorship.

  • El-Baz Ayman,

    1. BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292
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  • Gimel'farb Georgy

    1. Department of Computer Science, University of Auckland, Auckland 1142, New Zealand
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To develop an automated framework for accurate analysis of myocardial perfusion using first-pass magnetic resonance imaging.


The proposed framework consists of four processing stages. First, in order to account for heart deformations due to respiratory motion and heart contraction, a two-step registration methodology is proposed, which has the ability to account for the global and local motions of the heart. The methodology involves an affine-based registration followed by a local B-splines alignment to maximize a new similarity function based on the first- and second-order normalized mutual information. Then the myocardium is segmented using a level-set function, its evolution being constrained by three features, namely, a weighted shape prior, a pixelwise mixed object/background image intensity distribution, and an energy of a second-order binary Markov–Gibbs random field spatial model. At the third stage, residual segmentation errors and imperfection of image alignment are reduced by employing a Laplace-based registration refinement step that provides accurate pixel-on-pixel matches on all segmented frames to generate accurate parametric perfusion maps. Finally, physiology is characterized by pixel-by-pixel mapping of empirical indexes (peak signal intensity, time-to-peak, initial upslope, and the average signal change of the slowly varying agent delivery phase), based on contrast agent dynamics.


The authors tested our framework on 24 perfusion data sets from 8 patients with ischemic damage who are undergoing a novel myoregeneration therapy. The performance of the processing steps of our framework is evaluated using both synthetic and in-vivo data. First, our registration methodology is evaluated using realistic synthetic phantoms and a distance-based error metric, and an improvement of registration is documented using the proposed similarity measure (P-value ≤10−4). Second, evaluation of our segmentation using the Dice similarity coefficient, documented an average of 0.910 ± 0.037 compared to two other segmentation methods that achieved average values of 0.862 ± 0.045 and 0.844 ± 0.047. Also, the receiver operating characteristic (ROC) analysis of our multifeature segmentation yielded an area under the ROC curve of 0.92, while segmentation based intensity alone showed low performance (an area of 0.69). Moreover, our framework indicated the ability, using empirical perfusion indexes, to reveal regional perfusion improvements with therapy and transmural perfusion differences across the myocardial wall.


By quantitative and visual assessment, our framework documented the ability to characterize regional and transmural perfusion, thereby it augmenting the ability to assess follow-up treatment for patients undergoing myoregeneration therapy. This is afforded by our framework being able to handle both global and local deformations of the heart, segment accurately the myocardial wall, and provide accurate pixel-on-pixel matches of registered perfusion images.