Magnetic resonance fusion imaging of chronic myocardial ischemia

M. Nahrendorf, K. H. Hiller, Andreas Greiser, Sascha Köhler, Thomas Neuberger, K. Hu, C. Waller, G. Ertl, A. Haase, W. R. Bauer

Research output: Contribution to journalArticle


Aim of the study was to evaluate the concept of fusion imaging by magnetic resonance spectroscopy and imaging in the model of chronic coronary stenosis in the rat. 3D coronary angiograms were fused with ATP metabolite maps, which were acquired by P localized MR spectroscopy in the isolated rat heart. Stenosis was induced by a ligation including a 300μm wire placed next to the left coronary artery. The wire was taken away imediately after the suture was closed. 2 weeks later localized 3D 31P Chemical Shift imaging was performed in 8 isolated perfused hearts on a Bruker 12 T AMX. (voxel size 4 x 4 x 6 mm). 1H gradient echo images were acquired to correlate position of the stenotic region in the metabolite maps and the angiography data, which was segmented and used for volume correction of spectroscopy. PCr/ATP was determined in a control and the ischemic region. MR angiography was performed with a flow weighted 3D gradient echo (TE 1.0ms, matrix 1283). Metabolite maps of ATP were fused with the coronary angiogram using the Amira software. After MR, fraction of scarring within ischemic region was determined in histology. 3D MRA enabled detection of coronary stenosis. In the ischemic region, PCr/ATP was decreased when compared to control region (1.24 ± 0.38 vs 1.45±-0.49, p<0.05). Fraction of fibrosis in histology was 12.8±1.4%, and was correlated to ATP signal reduction in the ischemic region (r=0.71, p<0.05). In future this kind of image fusion might be of help in fast characterisation of the severity of a stenosis and might aid decision making concerning revascularisation, because not only anatomy, but also metabolic information can be given at a glance.

Original languageEnglish (US)
Pages (from-to)272-277
Number of pages6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - Dec 1 2003


All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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