Literature DB >> 29568147

Comparison of quantitative myocardial perfusion imaging CT to fluorescent microsphere-based flow from high-resolution cryo-images.

Brendan L Eck1, Rachid Fahmi1, Jacob Levi2, Anas Fares3, Hao Wu1, Yuemeng Li1, Mani Vembar4, Amar Dhanantwari4, Hiram G Bezerra3, David L Wilson1,5.   

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 R2=0.97, compared to R2=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.

Entities:  

Keywords:  CT; Myocardial perfusion imaging; microsphere-based blood flow; myocardial blood flow; perfusion modeling

Year:  2016        PMID: 29568147      PMCID: PMC5859322          DOI: 10.1117/12.2217027

Source DB:  PubMed          Journal:  Proc SPIE Int Soc Opt Eng        ISSN: 0277-786X


  15 in total

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Authors:  S L Bernard; J R Ewen; C H Barlow; J J Kelly; S McKinney; D A Frazer; R W Glenny
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5.  Quantification of myocardial perfusion using dynamic 64-detector computed tomography.

Authors:  Richard T George; Michael Jerosch-Herold; Caterina Silva; Kakuya Kitagawa; David A Bluemke; Joao A C Lima; Albert C Lardo
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6.  Quantitative myocardial perfusion measurement using CT perfusion: a validation study in a porcine model of reperfused acute myocardial infarction.

Authors:  Aaron So; Jiang Hsieh; Jian-Ying Li; Jennifer Hadway; Hua-Fu Kong; Ting-Yim Lee
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7.  Low diagnostic yield of elective coronary angiography.

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8.  Accuracy of dynamic computed tomography adenosine stress myocardial perfusion imaging in estimating myocardial blood flow at various degrees of coronary artery stenosis using a porcine animal model.

Authors:  Fabian Bamberg; Rabea Hinkel; Florian Schwarz; Torleif A Sandner; Elisabeth Baloch; Roy Marcus; Alexander Becker; Christian Kupatt; Bernd J Wintersperger; Thorsten R Johnson; Daniel Theisen; Ernst Klotz; Maximilian F Reiser; Konstantin Nikolaou
Journal:  Invest Radiol       Date:  2012-01       Impact factor: 6.016

9.  Detection and quantification of fluorescent cell clusters in cryo-imaging.

Authors:  Grant J Steyer; Feng Dong; Lehar Kanodia; Debashish Roy; Marc Penn; David L Wilson
Journal:  Int J Biomed Imaging       Date:  2012-03-18

10.  Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details.

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  6 in total

1.  The role of acquisition and quantification methods in myocardial blood flow estimability for myocardial perfusion imaging CT.

Authors:  Brendan L Eck; Raymond F Muzic; Jacob Levi; Hao Wu; Rachid Fahmi; Yuemeng Li; Anas Fares; Mani Vembar; Amar Dhanantwari; Hiram G Bezerra; David L Wilson
Journal:  Phys Med Biol       Date:  2018-09-13       Impact factor: 3.609

2.  SLIC robust (SLICR) processing for fast, robust CT myocardial blood flow quantification.

Authors:  Hao Wu; Brendan L Eck; Jacob Levi; Anas Fares; Yuemeng Li; Di Wen; Hiram G Bezerra; David L Wilson
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2018-03-12

3.  Effect of Beam Hardening on Transmural Myocardial Perfusion Quantification in Myocardial CT Imaging.

Authors:  Rachid Fahmi; Brendan L Eck; Jacob Levi; Anas Fares; Hao Wu; Mani Vembar; Amar Dhanantwari; Hiram G Bezerra; David L Wilson
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2016-03-29

4.  SLICR super-voxel algorithm for fast, robust quantification of myocardial blood flow by dynamic computed tomography myocardial perfusion imaging.

Authors:  Hao Wu; Brendan L Eck; Jacob Levi; Anas Fares; Yuemeng Li; Di Wen; Hiram G Bezerra; Raymond F Muzic; David L Wilson
Journal:  J Med Imaging (Bellingham)       Date:  2019-11-06

5.  Static CT myocardial perfusion imaging: image quality, artifacts including distribution and diagnostic performance compared to 82Rb PET.

Authors:  João R Inácio; Sriraag Balaji Srinivasan; Terrence D Ruddy; Robert A deKemp; Frank Rybicki; Rob S Beanlands; Benjamin J W Chow; Girish Dwivedi
Journal:  Eur J Hybrid Imaging       Date:  2022-01-04

6.  Calibration-free beam hardening correction for myocardial perfusion imaging using CT.

Authors:  Jacob Levi; Brendan L Eck; Rachid Fahmi; Hao Wu; Mani Vembar; Amar Dhanantwari; Anas Fares; Hiram G Bezerra; David L Wilson
Journal:  Med Phys       Date:  2019-03-07       Impact factor: 4.071

  6 in total

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