OBJECTIVE: To compare six different scenarios for correcting for breathing motion in abdominal dual-energy CT (DECT) perfusion measurements. METHODS: Rigid [RRComm(80 kVp)] and non-rigid [NRComm(80 kVp)] registration of commercially available CT perfusion software, custom non-rigid registration [NRCustom(80 kVp], demons algorithm) and a control group [CG(80 kVp)] without motion correction were evaluated using 80 kVp images. Additionally, NRCustom was applied to dual-energy (DE)-blended [NRCustom(DE)] and virtual non-contrast [NRCustom(VNC)] images, yielding six evaluated scenarios. After motion correction, perfusion maps were calculated using a combined maximum slope/Patlak model. For qualitative evaluation, three blinded radiologists independently rated motion correction quality and resulting perfusion maps on a four-point scale (4 = best, 1 = worst). For quantitative evaluation, relative changes in metric values, R(2) and residuals of perfusion model fits were calculated. RESULTS: For motion-corrected images, mean ratings differed significantly [NRCustom(80 kVp) and NRCustom(DE), 3.3; NRComm(80 kVp), 3.1; NRCustom(VNC), 2.9; RRComm(80 kVp), 2.7; CG(80 kVp), 2.7; all p < 0.05], except when comparing NRCustom(80 kVp) with NRCustom(DE) and RRComm(80 kVp) with CG(80 kVp). NRCustom(80 kVp) and NRCustom(DE) achieved the highest reduction in metric values [NRCustom(80 kVp), 48.5%; NRCustom(DE), 45.6%; NRComm(80 kVp), 29.2%; NRCustom(VNC), 22.8%; RRComm(80 kVp), 0.6%; CG(80 kVp), 0%]. Regarding perfusion maps, NRCustom(80 kVp) and NRCustom(DE) were rated highest [NRCustom(80 kVp), 3.1; NRCustom(DE), 3.0; NRComm(80 kVp), 2.8; NRCustom(VNC), 2.6; CG(80 kVp), 2.5; RRComm(80 kVp), 2.4] and had significantly higher R(2) and lower residuals. Correlation between qualitative and quantitative evaluation was low to moderate. CONCLUSION: Non-rigid motion correction improves spatial alignment of the target region and fit of CT perfusion models. Using DE-blended and DE-VNC images for deformable registration offers no significant improvement. ADVANCES IN KNOWLEDGE: Non-rigid algorithms improve the quality of abdominal CT perfusion measurements but do not benefit from DECT post processing.
OBJECTIVE: To compare six different scenarios for correcting for breathing motion in abdominal dual-energy CT (DECT) perfusion measurements. METHODS: Rigid [RRComm(80 kVp)] and non-rigid [NRComm(80 kVp)] registration of commercially available CT perfusion software, custom non-rigid registration [NRCustom(80 kVp], demons algorithm) and a control group [CG(80 kVp)] without motion correction were evaluated using 80 kVp images. Additionally, NRCustom was applied to dual-energy (DE)-blended [NRCustom(DE)] and virtual non-contrast [NRCustom(VNC)] images, yielding six evaluated scenarios. After motion correction, perfusion maps were calculated using a combined maximum slope/Patlak model. For qualitative evaluation, three blinded radiologists independently rated motion correction quality and resulting perfusion maps on a four-point scale (4 = best, 1 = worst). For quantitative evaluation, relative changes in metric values, R(2) and residuals of perfusion model fits were calculated. RESULTS: For motion-corrected images, mean ratings differed significantly [NRCustom(80 kVp) and NRCustom(DE), 3.3; NRComm(80 kVp), 3.1; NRCustom(VNC), 2.9; RRComm(80 kVp), 2.7; CG(80 kVp), 2.7; all p < 0.05], except when comparing NRCustom(80 kVp) with NRCustom(DE) and RRComm(80 kVp) with CG(80 kVp). NRCustom(80 kVp) and NRCustom(DE) achieved the highest reduction in metric values [NRCustom(80 kVp), 48.5%; NRCustom(DE), 45.6%; NRComm(80 kVp), 29.2%; NRCustom(VNC), 22.8%; RRComm(80 kVp), 0.6%; CG(80 kVp), 0%]. Regarding perfusion maps, NRCustom(80 kVp) and NRCustom(DE) were rated highest [NRCustom(80 kVp), 3.1; NRCustom(DE), 3.0; NRComm(80 kVp), 2.8; NRCustom(VNC), 2.6; CG(80 kVp), 2.5; RRComm(80 kVp), 2.4] and had significantly higher R(2) and lower residuals. Correlation between qualitative and quantitative evaluation was low to moderate. CONCLUSION: Non-rigid motion correction improves spatial alignment of the target region and fit of CT perfusion models. Using DE-blended and DE-VNC images for deformable registration offers no significant improvement. ADVANCES IN KNOWLEDGE: Non-rigid algorithms improve the quality of abdominal CT perfusion measurements but do not benefit from DECT post processing.
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