RATIONALE AND OBJECTIVES: Deconvolution-based software can be used to calculate quantitative maps of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) from first-pass computed tomography perfusion (CTP) datasets. The application of this software requires the user to select multiple input variables. The purpose of this study was to investigate the degree to which both major and minor variations of these user-defined inputs would affect the final quantitative values of CBF, CBV, and MTT. MATERIALS AND METHODS: A neuroradiologist constructed CBF, CBV, and MTT maps using standard methodology with commercially available software (GE Functool Version 1.9s) from CTP datasets of three acute stroke patients. Each map was reconstructed multiple times by systematically and independently varying the following parameters: postenhancement and preenhancement cutoff values, arterial and venous region-of-interest (ROI) placement, and arterial and venous ROI size. The resulting quantitative CTP values were compared using identical ROIs placed at the infarct core. RESULTS: Major variations of either arterial ROI placement or arterial and venous ROI size had no significant effect on the mean CBF, CBV, and MTT values at the infarct core (p > .05). Even minor variations, however, in the choice of venous ROI placement or in pre- and postenhancement cutoff values significantly altered the quantitative values for each of the CTP maps, by as much as threefold. CONCLUSION: Even minor variations of user-defined inputs can significantly influence the quantitative, deconvolution-based CTP map values of acute stroke patients. Although quantitation was robust to the choice of arterial ROI placement and arterial or venous ROI size, it was strongly dependent on the choice of venous ROI location and pre- and postenhancement cut-off values. Awareness of these results by clinicians may be important in the creation of quantitatively accurate CTP maps.
RATIONALE AND OBJECTIVES: Deconvolution-based software can be used to calculate quantitative maps of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) from first-pass computed tomography perfusion (CTP) datasets. The application of this software requires the user to select multiple input variables. The purpose of this study was to investigate the degree to which both major and minor variations of these user-defined inputs would affect the final quantitative values of CBF, CBV, and MTT. MATERIALS AND METHODS: A neuroradiologist constructed CBF, CBV, and MTT maps using standard methodology with commercially available software (GE Functool Version 1.9s) from CTP datasets of three acute strokepatients. Each map was reconstructed multiple times by systematically and independently varying the following parameters: postenhancement and preenhancement cutoff values, arterial and venous region-of-interest (ROI) placement, and arterial and venous ROI size. The resulting quantitative CTP values were compared using identical ROIs placed at the infarct core. RESULTS: Major variations of either arterial ROI placement or arterial and venous ROI size had no significant effect on the mean CBF, CBV, and MTT values at the infarct core (p > .05). Even minor variations, however, in the choice of venous ROI placement or in pre- and postenhancement cutoff values significantly altered the quantitative values for each of the CTP maps, by as much as threefold. CONCLUSION: Even minor variations of user-defined inputs can significantly influence the quantitative, deconvolution-based CTP map values of acute strokepatients. Although quantitation was robust to the choice of arterial ROI placement and arterial or venous ROI size, it was strongly dependent on the choice of venous ROI location and pre- and postenhancement cut-off values. Awareness of these results by clinicians may be important in the creation of quantitatively accurate CTP maps.
Authors: Edward D Greenberg; Y Pierre Gobin; Howard Riina; Carl E Johnson; Apostolos J Tsiouris; Joseph Comunale; Pina C Sanelli Journal: Imaging Med Date: 2011-06-01
Authors: Aquilla S Turk; Allison Grayev; Howard A Rowley; Aaron S Field; Patrick Turski; Kari Pulfer; Rajat Mukherjee; Victor Haughton Journal: Neuroradiology Date: 2007-07-24 Impact factor: 2.804
Authors: R P Killeen; A Gupta; H Delaney; C E Johnson; A J Tsiouris; J Comunale; M E Fink; H S Mangat; A Z Segal; A I Mushlin; P C Sanelli Journal: AJNR Am J Neuroradiol Date: 2013-11-07 Impact factor: 3.825
Authors: P C Sanelli; A Pandya; A Z Segal; A Gupta; S Hurtado-Rua; J Ivanidze; K Kesavabhotla; D Mir; A I Mushlin; M G M Hunink Journal: AJNR Am J Neuroradiol Date: 2014-05-08 Impact factor: 3.825