KEYWORDS: Cardiovascular magnetic resonance imaging, 3D modeling, Magnetic resonance imaging, Data modeling, Image segmentation, Single photon, Tomography, 3D metrology, Single photon emission computed tomography, Databases
Gated single photon emission tomography (gSPECT) is a well-established technique used routinely in clinical
practice. It can be employed to evaluate global left ventricular (LV) function of a patient. The purpose of this
study is to assess LV systolic and diastolic function from gSPECT datasets in comparison with cardiac magnetic
resonance imaging (CMR) measurements. This is achieved by applying our recently implemented 3D active
shape model (3D-ASM) segmentation approach for gSPECT studies. This methodology allows for generation of
3D LV meshes for all cardiac phases, providing volume time curves and filling rate curves. Both systolic and
diastolic functional parameters can be derived from these curves for an assessment of patient condition even at
early stages of LV dysfunction. Agreement of functional parameters, with respect to CMR measurements, were
analyzed by means of Bland-Altman plots. The analysis included subjects presenting either LV hypertrophy,
dilation or myocardial infarction.
KEYWORDS: Single photon emission computed tomography, 3D modeling, Image segmentation, Data modeling, Statistical modeling, Monte Carlo methods, Error analysis, Statistical analysis, Heart, Gold
Over the course of the last two decades, myocardial perfusion with Single Photon Emission Computed Tomography
(SPECT) has emerged as an established and well-validated method for assessing myocardial ischemia,
viability, and function. Gated-SPECT imaging integrates traditional perfusion information along with global
left ventricular function. Despite of these advantages, inherent limitations of SPECT imaging yield a challenging
segmentation problem, since an error of only one voxel along the chamber surface may generate a huge difference
in volume calculation. In previous works we implemented a 3-D statistical model-based algorithm for Left Ventricle
(LV) segmentation of in dynamic perfusion SPECT studies. The present work evaluates the relevance of
training a different Active Shape Model (ASM) for each frame of the gated SPECT imaging acquisition in terms
of their subsequent segmentation accuracy. Models are subsequently employed to segment the LV cavity of gated
SPECT studies of a virtual population. The evaluation is accomplished by comparing point-to-surface (P2S)
and volume errors, both against a proper Gold Standard. The dataset comprised 40 voxel phantoms (NCAT,
Johns Hopkins, University of of North Carolina). Monte-Carlo simulations were generated with SIMIND (Lund
University) and reconstructed to tomographic slices with ASPIRE (University of Michigan).
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