Paper Info
Title
Comparison of quantitative myocardial perfusion imaging CT to fluorescent microsphere-based flow from high-resolution cryo-images.
Abstract
Myocardial perfusion imaging using CT (MPI-CT) has the potential to provide quantitative measures of myocardial blood flow (MBF) which can aid the diagnosis of coronary artery disease. We evaluated the quantitative accuracy of MPI-CT in a porcine model of balloon-induced LAD coronary artery ischemia guided by fractional flow reserve (FFR). We quantified MBF at baseline (FFR=1.0) and under moderate ischemia (FFR=0.7) using MPI-CT and compared to fluorescent microsphere-based MBF from high-resolution cryo-images. Dynamic, contrast-enhanced CT images were obtained using a spectral detector CT (Philips Healthcare). Projection-based mono-energetic images were reconstructed and processed to obtain MBF. Three MBF quantification approaches were evaluated: singular value decomposition (SVD) with fixed Tikhonov regularization (ThSVD), SVD with regularization determined by the L-Curve criterion (LSVD), and Johnson-Wilson parameter estimation (JW). The three approaches over-estimated MBF compared to cryo-images. JW produced the most accurate MBF, with average error 33.3 +/- 19.2mL/min/100g, whereas LSVD and ThSVD had greater over-estimation, 59.5 +/- 28.3mL/min/100g and 78.3 +/- 25.6 mL/min/100g, respectively. Relative blood flow as assessed by a flow ratio of LAD-to-remote myocardium was strongly correlated between JW and cryo-imaging, with R-2=0.97, compared to R-2=0.88 and 0.78 for LSVD and ThSVD, respectively. We assessed tissue impulse response functions (IRFs) from each approach for sources of error. While JW was constrained to physiologic solutions, both LSVD and ThSVD produced IRFs with non-physiologic properties due to noise. The L-curve provided noise-adaptive regularization but did not eliminate non-physiologic IRF properties or optimize for MBF accuracy. These findings suggest that model-based MPI-CT approaches may be more appropriate for quantitative MBF estimation and that cryo-imaging can support the development of MPI-CT by providing spatial distributions of MBF.
Year
DOI
Venue
2016
10.1117/12.2217027
Proceedings of SPIE
Keywords
Field
DocType
Myocardial perfusion imaging,CT,perfusion modeling,myocardial blood flow,microsphere-based blood flow
Tikhonov regularization,Coronary artery disease,Biomedical engineering,Computer vision,Blood flow,Fractional flow reserve,Ischemia,Artificial intelligence,Medical diagnostics,Myocardial perfusion imaging,Microsphere,Physics
Conference
Volume
ISSN
Citations 
9788
0277-786X
0
PageRank 
References 
Authors
0.34
0
10
Name
Order
Citations
PageRank
Brendan L Eck100.34
Rachid Fahmi200.68
Jacob Levi302.37
Anas Fares401.01
Hao Wu59238.83
Yuemeng Li600.68
M Vembar7517.27
A. Dhanantwari893.27
Hiram G. Bezerra9106.64
David L. Wilson1017436.04