+ Site Statistics
References:
54,258,434
Abstracts:
29,560,870
PMIDs:
28,072,757
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Qualitative and quantitative evaluation of rigid and deformable motion correction algorithms using dual-energy CT images in view of application to CT perfusion measurements in abdominal organs affected by breathing motion



Qualitative and quantitative evaluation of rigid and deformable motion correction algorithms using dual-energy CT images in view of application to CT perfusion measurements in abdominal organs affected by breathing motion



British Journal of Radiology 88(1046): 20140683



To compare six different scenarios for correcting for breathing motion in abdominal dual-energy CT (DECT) perfusion measurements. 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. 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. 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. Non-rigid algorithms improve the quality of abdominal CT perfusion measurements but do not benefit from DECT post processing.

(PDF emailed within 0-6 h: $19.90)

Accession: 058668634

Download citation: RISBibTeXText

PMID: 25465353

DOI: 10.1259/bjr.20140683


Related references

Abdominal DCE-MRI reconstruction with deformable motion correction for liver perfusion quantification. Medical Physics 45(10): 4529-4540, 2018

Motion correction with PROPELLER MRI: Application to head motion and free-breathing cardiac imaging. Magnetic Resonance in Medicine 42(5): 963-969, 1999

Semi-automatic motion compensation of contrast-enhanced ultrasound images from abdominal organs for perfusion analysis. Computers in Biology and Medicine 63: 229-237, 2016

Evaluation of a New Motion-correction Algorithm Using On-rigid Registration in Respiratory-gated PET/CT Images of Liver Tumors. Nihon Hoshasen Gijutsu Gakkai Zasshi 72(11): 1067-1073, 2017

Quantitative assessment of motion artifacts and validation of a new motion-correction program for myocardial perfusion SPECT. Journal of Nuclear Medicine 42(5): 687-694, 2001

Objective evaluation of the correction by non-rigid registration of abdominal organ motion in low-dose 4D dynamic contrast-enhanced CT. Physics in Medicine and Biology 57(6): 1701-1715, 2012

Regularized B-spline deformable registration for respiratory motion correction in PET images. Physics in Medicine and Biology 54(9): 2719-2736, 2009

Estimation of and correction for finite motion sampling errors in small animal PET rigid motion correction. Medical and Biological Engineering and Computing 2018, 2018

Free-breathing myocardial T2* mapping using GRE-EPI and automatic non-rigid motion correction. Journal of Cardiovascular Magnetic Resonance 17: 113, 2016

A new similarity measure for non-rigid breathing motion compensation of myocardial perfusion MRI. Conference Proceedings 2008: 3389-3392, 2009

Robust non-rigid motion compensation of free-breathing myocardial perfusion MRI data. IEEE Transactions on Medical Imaging 2019, 2019

A new quantitative method for the evaluation of regional wall motion and thickening on gated sestamibi perfusion images. Journal of Nuclear Medicine 36(5 SUPPL ): 139P, 1995

Evaluation of rigid and non-rigid motion compensation of cardiac perfusion MRI. Medical Image Computing and Computer-Assisted Intervention 11(Pt 2): 35-43, 2008

Respiratory lung motion analysis using a nonlinear motion correction technique for respiratory-gated lung perfusion SPECT images. Annals of Nuclear Medicine 21(3): 175-183, 2007

Free-breathing myocardial perfusion MRI using SW-CG-HYPR and motion correction. Magnetic Resonance in Medicine 64(4): 1148-1154, 2011