We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact us so we can address the problem.
Automated detection and quantification of microcirculatory oxygenation changes in the heart
© Tsaftaris et al; licensee BioMed Central Ltd. 2010
Published: 21 January 2010
Blood-oxygen-level dependent (BOLD) MRI may be used for detecting myocardial oxygenation (MO) changes secondary to coronary artery stenosis (CAS). Under pharmacological stress, the myocardial territory affected by CAS appears hypointense relative to healthy regions in BOLD images.
To test a method for automatic quantification of myocardial signal changes reflecting the regional variations in oxygenation against true microsphere flow measurements obtained from controlled canine studies.
Data Acquisition: Short-axis 2D cine SSFP-based myocardial BOLD images were acquired in 7 dogs under adenosine stress with and without hydraulically-controlled left-circumflex CAS in a Siemens 1.5 T scanner. Scan parameters: resolution = 1.2 × 1.2 × 6 mm3; flip-angle = 90°; and TR/TE = 5.7/2.9 ms. Fluorescent microspheres were infused to measure true myocardial perfusion. Following imaging studies dogs were euthanized and the myocardial tissue was processed to ascertain perfusion. The flow within each segment was summed to obtain total flow μ F for each slice. Image Processing: End-systolic images were identified and segmented. Baseline images (BA) were used as reference, while stress without (AD) or with various levels of CAS (SS) were used as targets (TRG). BA myocardial intensities were collected and the mean (μ), variance (σ), and degrees-of-freedom of a Student's t-distribution were found. C M , defined as the size of the largest contiguous hypointense region (pixel intensity below μ-σ) divided by the number of pixels in the myocardium, was computed. C M ratios between BA and TRG images, Q M TRG, BA) = C M (TRG)/C M (BA), were also calculated. Statistical tests were used to show that Q M (AD, BA)<Q M (SS, BA). Finally, Q M was correlated against the ratio of microsphere flow ρ = μ F (TRG)/μ F (BA).
The method is capable of automatically delineating the perfusion deficit territories (Fig. 1). Observe that C M increases as the total flow decreases, establishing the foundation for utilizing the ratio of C M between rest and stress studies as an image-based metric for detecting microcirculatory oxygenation changes. Fig. 2 supports the fact that Q M provides adequate power (0.8) in detecting CAS. The exponential relationship (Fig. 3) between microsphere flow and Q M has 0.99 statistical power. The work forms an initial step in the development of an objective and automated analysis of BOLD MR images and a metric for quantifying microcirculatory oxygen changes in the heart.
This article is published under license to BioMed Central Ltd.