Free breathing myocardial perfusion data sets for performance analysis of motion compensation algorithms

GigaScience

Peter Kellman²  Gert Wollny¹ 

[1] Ciber BBN, Zaragoza, Spain;Laboratory of Cardiac Energetics, National Heart, Lung and Blood Institute, National Institutes of Health, DHHS, Bethesda, MD, USA

关键词: Validation;    Perfusion;    Motion compensation;    Image registration;    Heart;   

Others  :  1118576
DOI  :  10.1186/2047-217X-3-23

 received in 2014-05-21, accepted in 2014-10-13,  发布年份 2014

【 摘 要 】

Background

Perfusion quantification by using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) has proved to be a reliable tool for the diagnosis of coronary artery disease that leads to reduced blood flow to the myocardium. The image series resulting from such acquisition usually exhibits a breathing motion that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. Various algorithms have been presented to facilitate such a motion compensation, but the lack of publicly available data sets hinders a proper, reproducible comparison of these algorithms.

Material

Free breathing perfusion MRI series of ten patients considered clinically to have a stress perfusion defect were acquired; for each patient a rest and a stress study was executed. Manual segmentations of the left ventricle myocardium and the right-left ventricle insertion point are provided for all images in order to make a unified validation of the motion compensation algorithms and the perfusion analysis possible. In addition, all the scripts and the software required to run the experiments are provided alongside the data, and to enable interested parties to directly run the experiments themselves, the test bed is also provided as a virtual hard disk.

Findings

To illustrate the utility of the data set two motion compensation algorithms with publicly available implementations were applied to the data and earlier reported results about the performance of these algorithms could be confirmed.

Conclusion

The data repository alongside the evaluation test bed provides the option to reliably compare motion compensation algorithms for myocardial perfusion MRI. In addition, we encourage that researchers add their own annotations to the data set, either to provide inter-observer comparisons of segmentations, or to make other applications possible, for example, the validation of segmentation algorithms.

【 授权许可】

   
2014 Wollny and Kellman; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]Hsu LY, Groves DW, Aletras AH, Kellman P, Arai AE: A quantitative pixel-wise measurement of myocardial blood flow by contrast-enhanced first-pass CMR perfusion imaging: microsphere validation in dogs and feasibility study in humans. JACC Cardiovasc Imaging 2012, 5(2):154-166. http://mia.sourceforge.net/ webcite
• [2]Jerosch-Herold M: Quantification of myocardial perfusion by cardiovascular magnetic resonance. J Cardiov Magn Reson 2010, 12:57. BioMed Central Full Text
• [3]Gupta SN, Solaiyappan M, Beache GM, Arai AE, Foo TKF: Fast method for correcting image misregistration due to organ motion in time-series mri data. Magn Reson Med 2003, 49:506-514.
• [4]Dornier C, Ivancevic MK, Thevenaz P, Vallee J-P: Improvement in the quantification of myocardial perfusion using an automatic spline-based registration algorithm. J Magn Reson Imag 2003, 18(2):160-168.
• [5]Milles J, van der Geest RJ, Jerosch-Herold M, Reiber JH, Lelieveldt BP: Fully automated registration of first-pass myocardial perfusion MRI using independent component analysis. Inf Process Med Imaging 2007, 20:544-555.
• [6]Gupta V, Giessen M, Kirisli H, Kirschbaum SW, Niessen WJ, Lelieveldt BPF: Robust motion correction in the frequency domain of cardiac mr stress perfusion sequences. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2012, Lecture Notes in Computer Science, vol. 7510. Edited by Ayache N, Delingette H, Golland P, Mori K. Nice: Springer; 2012:667-674. doi:10.1007/978-3-642-33415-3_82
• [7]Wong KK, Yang ES, Wu EX, Tse H-F, Wong ST: First-pass myocardial perfusion image registration by maximization of normalized mutual information. J Magn Reson Imaging 2008, 27:529-537.
• [8]Breeuwer M, Spreeuwers L, Quist M: Automatic quantitative analysis of cardiac MR perfusion images. In Proc. SPIE 4322. Edited by Sonka M, Hanson KM. San Diego: Medical Imaging 2001, Image Processing 733; 2001:733-742. doi:10.1117/12.431152
• [9]Delzescaux T, Frouin F, Cesare AD, Philipp-Foliguet S, Todd-Pokropek A, Herment A, Janier M: Using an adaptive semiautomated self-evaluated registration technique to analyze mri data for myocardial perfusion assessment. J Magn Reson Imag 2003, 18:681-690.
• [10]Ólafsdóttir H: Nonrigid Registration of Myocardial Perfusion MRI. In Proc. Svenska Symposium i Bildanalys, SSBA 2005. Malmø: SSBA; 2005.
• [11]Xue H, Zuehlsdorff S, Kellman P, Arai A, Nielles-Vallespin S, Chefdhotel C, Lorenz CH, Guehring J: Unsupervised inline analysis of cardiac perfusion mri. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009. LNCS, vol. 5762/2009. Berlin, Heidelberg: Springer; 2009:741-749.
• [12]Wollny G, Ledesma-Carbayo MJ, Kellman P, Santos A: On breathing motion compensation in myocardial perfusion imaging. In New Developments in Biomedical Engineering. Edited by Campolo D. Vukovar: In-Teh; 2010:235-247.
• [13]Wollny G, Ledesma-Carbayo M-J, Kellman P, Santos A: Exploiting quasiperiodicity in motion correction of free-breathing myocardial perfusion MRI. IEEE Trans Med Imag 2010, 29(8):1516-1527.
• [14]Tautz L, Hennemuth A, Andersson M, Seeger A, Knutsson H, Friman O: Phase-based non-rigid registration of myocardial perfusion mri image sequences. Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium On 2010, 516-519. doi:10.1109/ISBI.2010.5490297
• [15]Milles J, van der Geest RJ, Jerosch-Herold M, Reiber J, Lelieveldt B: Fully automated motion correction in first-pass myocardial perfusion MR image sequences. IEEE Trans Med Imag 2008, 27(11):1611-1621. doi:10.1109/TMI.2008.928918
• [16]Gupta V, Hendriks E, Milles J, Van Der Geest R, Jerosch-Herold M, Reiber J, Lelieveldt B: Fully automatic registration and segmentation of first-pass myocardial perfusion mr image sequences. Acad Radiol 2010, 17(11):1375-1385.
• [17]Weng AM, Ritter C, Lotz J, Beer MJ, Hahn D, Köstler H: Automatic postprocessing for the assessment of quantitative human myocardial perfusion using mri. Eur Radiol 2010, 20(6):1356-1365. doi:10.1007/s00330-009-1684-z
• [18]Li C, Sun Y, Chai P: Pseudo ground truth based nonrigid registration of myocardial perfusion MRI. Med Image Anal 2011, 15(4):449-459. doi:10.1016/j.media.2011.02.001
• [19]Wollny G, Kellman P, Santos A, Ledesma M-J: Nonrigid motion compensation of free breathing acquired myocardial perfusion data. In Bildverarbeitung fur̈ die Medizin 2011. Informatik aktuell. Edited by Handels H, Ehrhardt L, Deserno TM, Meinzer HP, Tolxdorff T. Berlin Heidelberg; 2011:84-88.
• [20]Wollny G, Kellman P, Santos A, Ledesma-Carbayo M-J: Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis. Med Image Anal 2012, 16(5):1015-1028. doi:10.1016/j.media.2012.02.004
• [21]Gupta V, Kirisli H, Hendriks E, van der Geest R, van de Giessen M, Niessen W, Reiber J, Lelieveldt B: Cardiac MR perfusion image processing techniques: a survey. Med Image Anal 2012, 16(4):767-785. doi:10.1016/j.media.2011.12.005
• [22]Wollny G: Segmentation software for Android. [http://tabseg.sourceforge.net webcite]
• [23]Wollny G, Hublin J, Ledesma-Carbayo M-J, Skinner M, Kellman P, Hierl T: MIA - a free and open source software for gray scale medical image analysis. Source Code Biol Med 2013, 8:20. BioMed Central Full Text
• [24]Vlcek J, Luksan L: Shifted limited-memory variable metric methods for large-scale unconstrained minimization. J Comput Appl Math 2006, 186:365-390.
• [25]Hyvarinen A: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Networks 1999, 10(3):626-634.
• [26]Daubechies I: Orthonormal bases of compactly supported wavelets. Comm Pure Appl Math 1988, 41:909-996.
• [27]Haber E, Modersitzki J: beyond mutual information: a simple and robust alternative. In Bildverarbeitung Für die Medizin 2005. Informatik Aktuell. Edited by Meinzer H-P, Handels H, Horsch A, Tolxdorff T. Heidelberg: Springer; 2005:350-354.
• [28]Tortora SR, J-Grabowski G: Principles of Anatomy and Physiology. Hoboken: John Wiley & Sons; 2002.
• [29]Lee DC, Johnson NP: Quantification of absolute myocardial blood flow by magnetic resonance perfusion imaging. JACC Cardiovasc Imaging 2009, 2(6):761-770. doi:10.1016/j.jcmg.2009.04.003
• [30]Miller C, Naish J, Ainslie M, Tonge C, Tout D, Arumugam P, Banerji A, Egdell R, Clark D, Weale P, Steadman C, McCann G, Ray S, Parker G, Schmitt M: Voxel-wise quantification of myocardial blood flow with cardiovascular magnetic resonance: effect of variations in methodology and validation with positron emission tomography. J Cardiovasc Magn Reson 2014, 16(1):11. doi:10.1186/1532-429X-16-11 BioMed Central Full Text
• [31]Wollny G, Kellman P: Supporting material for: Free Breathingly acquired Myocardial Perfusion Data sets for Performance Analysis of Motion Compensation Algorithms. 2014. GigaScience Database, [http://dx.doi.org/10.5524/100106 webcite]