title: A comparison of reconstruction and viewing parameters on image quality and accuracy of stress myocardial CT perfusion.
abstract: BACKGROUND
  Myocardial stress computed tomography perfusion (CTP) has similar diagnostic accuracy for detecting perfusion defects (PDs) versus single-photon emission computed tomography (SPECT). However, the optimal diagnostic viewing and image processing parameters for CTP are unknown.
  
  
  OBJECTIVE
  We sought to compare the diagnostic accuracy of different image processing techniques, cardiac phases, slice thicknesses, and viewing parameters for detection of PDs.
  
  
  METHODS
  A stress and rest dual-source CTP protocol was performed with adenosine. Twelve subjects with severe stenosis proven by quantitative coronary angiography (QCA), with corresponding territorial defects at SPECT, were selected as well as 7 controls (subjects with similar clinical suspicion but negative QCA and SPECT). Short-axis stress images were processed with 3 techniques: minimum intensity projection (MinIP), maximum intensity projection, and average intensity multiplanar reconstruction (MPR), 3 thicknesses (1, 3, 8 mm), and 2 phases (systolic, mid-diastolic). The resulting images (n = 1026) were randomized and interpreted by independent readers.
  
  
  RESULTS
  Diastolic reconstructions (8-mm MPR) showed the highest sensitivity (81%) to detect true PDs. The highest accuracy was achieved with the 8-mm (61%) and 1-mm (61%) MPR diastolic images. The most sensitive and accurate systolic reconstructions were 3-mm MinIP images. These findings related to viewing in relatively narrow window width and window level settings.
  
  
  CONCLUSIONS
  Viewing parameters for optimal accuracy in detection of perfusion defects on CTP differ for systolic and diastolic images.
authors.affiliation: Cardiac MR PET CT Program, Department of Radiology and Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA. bghoshhajra@partners.org
authors.forename: Brian B
authors.initials: BB
authors.lastname: Ghoshhajra
authors.forename: Ian S
authors.initials: IS
authors.lastname: Rogers
authors.forename: Pal
authors.initials: P
authors.lastname: Maurovich-Horvat
authors.forename: Tust
authors.initials: T
authors.lastname: Techasith
authors.forename: Daniel
authors.initials: D
authors.lastname: Verdini
authors.forename: Manavjot S
authors.initials: MS
authors.lastname: Sidhu
authors.forename: Nicola K
authors.initials: NK
authors.lastname: Drzezga
authors.forename: Hector M
authors.initials: HM
authors.lastname: Medina
authors.forename: Ron
authors.initials: R
authors.lastname: Blankstein
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Brady
authors.forename: Ricardo C
authors.initials: RC
authors.lastname: Cury
mesh_terms: D000241:Adenosine; D000368:Aged; D001900:Boston; D016022:Case-Control Studies; D017023:Coronary Angiography; D003326:Coronary Circulation; D023921:Coronary Stenosis / Q000000981:diagnostic imaging* / Q000503:physiopathology; D005240:Feasibility Studies; D005260:Female; D006439:Hemodynamics; D006801:Humans; D008297:Male; D008875:Middle Aged; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D011857:Radiographic Image Interpretation, Computer-Assisted; D012720:Severity of Illness Index; D015899:Tomography, Emission-Computed, Single-Photon; D014057:Tomography, X-Ray Computed; D014665:Vasodilator Agents
chemical_list: D014665:Vasodilator Agents; D000241:Adenosine