title: Low dose dynamic CT myocardial perfusion imaging using a statistical iterative reconstruction method.
abstract: OBJECTIVE
  Dynamic CT myocardial perfusion imaging has the potential to provide both functional and anatomical information regarding coronary artery stenosis. However, radiation dose can be potentially high due to repeated scanning of the same region. The purpose of this study is to investigate the use of statistical iterative reconstruction to improve parametric maps of myocardial perfusion derived from a low tube current dynamic CT acquisition.
  
  
  METHODS
  Four pigs underwent high (500 mA) and low (25 mA) dose dynamic CT myocardial perfusion scans with and without coronary occlusion. To delineate the affected myocardial territory, an N-13 ammonia PET perfusion scan was performed for each animal in each occlusion state. Filtered backprojection (FBP) reconstruction was first applied to all CT data sets. Then, a statistical iterative reconstruction (SIR) method was applied to data sets acquired at low dose. Image voxel noise was matched between the low dose SIR and high dose FBP reconstructions. CT perfusion maps were compared among the low dose FBP, low dose SIR and high dose FBP reconstructions. Numerical simulations of a dynamic CT scan at high and low dose (20:1 ratio) were performed to quantitatively evaluate SIR and FBP performance in terms of flow map accuracy, precision, dose efficiency, and spatial resolution.
  
  
  RESULTS
  Forin vivo studies, the 500 mA FBP maps gave -88.4%, -96.0%, -76.7%, and -65.8% flow change in the occluded anterior region compared to the open-coronary scans (four animals). The percent changes in the 25 mA SIR maps were in good agreement, measuring -94.7%, -81.6%, -84.0%, and -72.2%. The 25 mA FBP maps gave unreliable flow measurements due to streaks caused by photon starvation (percent changes of +137.4%, +71.0%, -11.8%, and -3.5%). Agreement between 25 mA SIR and 500 mA FBP global flow was -9.7%, 8.8%, -3.1%, and 26.4%. The average variability of flow measurements in a nonoccluded region was 16.3%, 24.1%, and 937.9% for the 500 mA FBP, 25 mA SIR, and 25 mA FBP, respectively. In numerical simulations, SIR mitigated streak artifacts in the low dose data and yielded flow maps with mean error <7% and standard deviation <9% of mean, for 30 × 30 pixel ROIs (12.9 × 12.9 mm(2)). In comparison, low dose FBP flow errors were -38% to +258%, and standard deviation was 6%-93%. Additionally, low dose SIR achieved 4.6 times improvement in flow map CNR(2) per unit input dose compared to low dose FBP.
  
  
  CONCLUSIONS
  SIR reconstruction can reduce image noise and mitigate streaking artifacts caused by photon starvation in dynamic CT myocardial perfusion data sets acquired at low dose (low tube current), and improve perfusion map quality in comparison to FBP reconstruction at the same dose.
authors.affiliation: Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53705.
authors.forename: Yinghua
authors.initials: Y
authors.lastname: Tao
authors.affiliation: Department of Medical Physics and Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin 53705.
authors.forename: Guang-Hong
authors.initials: GH
authors.lastname: Chen
authors.affiliation: Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin 53792.
authors.forename: Timothy A
authors.initials: TA
authors.lastname: Hacker
authors.affiliation: Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin 53792.
authors.forename: Amish N
authors.initials: AN
authors.lastname: Raval
authors.affiliation: Department of Medical Physics and Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin 53705.
authors.forename: Michael S
authors.initials: MS
authors.lastname: Van Lysel
authors.affiliation: Department of Medical Physics and Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin 53705.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Speidel
mesh_terms: D000465:Algorithms; D000641:Ammonia; D000818:Animals; D016477:Artifacts; D003198:Computer Simulation; D054059:Coronary Occlusion / Q000000981:diagnostic imaging; D008954:Models, Biological; D015233:Models, Statistical; D055414:Myocardial Perfusion Imaging / Q000295:instrumentation / Q000379:methods*; D009590:Nitrogen Radioisotopes; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000295:instrumentation / Q000379:methods*; D011829:Radiation Dosage; D019275:Radiopharmaceuticals; D013552:Swine; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*
chemical_list: D009590:Nitrogen Radioisotopes; D019275:Radiopharmaceuticals; D000641:Ammonia