UNLABELLED: The objective of this study was to develop an automatic image registration technique capable of compensating for kidney motion in renal perfusion MRI, to assess the effect of renal artery stenosis on the kidney parenchyma. MATERIALS AND METHODS: Images from 20 patients scheduled for a renal perfusion study were acquired using a 1.5 T scanner. A free-breathing 3D-FSPGR sequence was used to acquire coronal views encompassing both kidneys following the infusion of Gd-BOPTA. A two-step registration algorithm was developed, including a preliminary registration minimising the quadratic difference and a fine registration maximising the mutual information (MI) between consecutive image frames. The starting point for the MI-based registration procedure was provided by an adaptive predictor that was able to predict kidney motion using a respiratory movement model. The algorithm was validated against manual registration performed by an expert user. RESULTS: The mean distance between the automatically and manually defined contours was 2.95 ± 0.81 mm, which was not significantly different from the interobserver variability of the manual registration procedure (2.86 ± 0.80 mm, P = 0.80). The perfusion indices evaluated on the manually and automatically extracted perfusion curves were not significantly different. CONCLUSIONS: The developed method is able to automatically compensate for kidney motion in perfusion studies, which prevents the need for time-consuming manual image registration.
UNLABELLED: The objective of this study was to develop an automatic image registration technique capable of compensating for kidney motion in renal perfusion MRI, to assess the effect of renal artery stenosis on the kidney parenchyma. MATERIALS AND METHODS: Images from 20 patients scheduled for a renal perfusion study were acquired using a 1.5 T scanner. A free-breathing 3D-FSPGR sequence was used to acquire coronal views encompassing both kidneys following the infusion of Gd-BOPTA. A two-step registration algorithm was developed, including a preliminary registration minimising the quadratic difference and a fine registration maximising the mutual information (MI) between consecutive image frames. The starting point for the MI-based registration procedure was provided by an adaptive predictor that was able to predict kidney motion using a respiratory movement model. The algorithm was validated against manual registration performed by an expert user. RESULTS: The mean distance between the automatically and manually defined contours was 2.95 ± 0.81 mm, which was not significantly different from the interobserver variability of the manual registration procedure (2.86 ± 0.80 mm, P = 0.80). The perfusion indices evaluated on the manually and automatically extracted perfusion curves were not significantly different. CONCLUSIONS: The developed method is able to automatically compensate for kidney motion in perfusion studies, which prevents the need for time-consuming manual image registration.
Authors: Florian Lietzmann; Frank G Zöllner; Ulrike I Attenberger; Stefan Haneder; Henrik J Michaely; Lothar R Schad Journal: J Magn Reson Imaging Date: 2011-11-29 Impact factor: 4.813
Authors: Henrik J Michaely; Stefan O Schoenberg; Niels Oesingmann; Carina Ittrich; Christopher Buhlig; Denise Friedrich; Anja Struwe; Johannes Rieger; Cornelia Reininger; Walter Samtleben; Max Weiss; Maximilian F Reiser Journal: Radiology Date: 2006-02 Impact factor: 11.105
Authors: Sheng Li; Frank G Zöllner; Andreas D Merrem; Yinghong Peng; Jarle Roervik; Arvid Lundervold; Lothar R Schad Journal: Comput Med Imaging Graph Date: 2011-06-24 Impact factor: 4.790
Authors: Hao Song; Dan Ruan; Wenyang Liu; V Andrew Stenger; Rolf Pohmann; Maria A Fernández-Seara; Tejas Nair; Sungkyu Jung; Jingqin Luo; Yuichi Motai; Jingfei Ma; John D Hazle; H Michael Gach Journal: Med Phys Date: 2017-02-21 Impact factor: 4.071
Authors: Anneloes de Boer; Tim Leiner; Eva E Vink; Peter J Blankestijn; Cornelis A T van den Berg Journal: Magn Reson Med Date: 2017-11-13 Impact factor: 4.668
Authors: Frank G Zöllner; Amira Šerifović-Trbalić; Gordian Kabelitz; Marek Kociński; Andrzej Materka; Peter Rogelj Journal: MAGMA Date: 2019-10-09 Impact factor: 2.310