comparison of RBF estimates between DCE and PC MRI techniques in patients with type-2 diabetes

dc.contributor.advisorDavid Buckley
dc.contributor.authorKHALID SALEH ALI ALGHAMDI
dc.date2021
dc.date.accessioned2022-05-30T07:03:19Z
dc.date.available2022-05-30T07:03:19Z
dc.degree.departmentMedical imaging
dc.degree.grantorSchool of medicine
dc.description.abstractObjectives To investigate the association and agreement between dynamic contrast enhancement (DCE) and phase contrast (PC) MRI techniques in estimating renal blood flow (RBF) ml/min. Methods MRI data of 22 patients with type-2 diabetes and eGFR > 30 mL/min/1.73m2 was obtained using PET-MRI scanner. PC was performed on both renal arteries using 2D gradient echo (GE) pulse sequence. DCE-MRI was acquired using 2-dimensional (2D), T1 weighted gradient echo (GR) sequence. Statistical tests were performed on RBF estimates per kidney (right vs right, left vs left) between the two methods. Results Average RBF measurements for left kidney was 427 ± 161 ml/min for PC and 325 ± 216 ml/min for DCE. In the right kidney, average RBF measurements was 306 ± 111 ml/min for PC and 411 ± 258 ml/min for DCE. no significant differences were observed between the two methods for each kidney, (P = 0.11 for right kidney, P = 0.06 for left kidney). A large bias was found between the two methods in both right (102 ml/min) and left (-105 ml/min) kidneys; however, it is not systemic. variation of RBF measurements was found between the two MRI methods for right (-603, 393) and left (-338, 542) kidneys. Conclusion The study demonstrated no significant difference between PC and DCE estimating RBF. However, at the individual level, there was a variation between RBF measurements of the two MRI methods. Future studies with larger sample size are needed to confirm the findings of this study.
dc.identifier.urihttps://drepo.sdl.edu.sa/handle/20.500.14154/50672
dc.language.isoen
dc.titlecomparison of RBF estimates between DCE and PC MRI techniques in patients with type-2 diabetes
sdl.thesis.levelMaster
sdl.thesis.sourceSACM - United Kingdom

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