PURPOSE: To evaluate the performance of an edge-based registration technique in correcting for respiratory motion artifacts in magnetic resonance renographic (MRR) data and to examine the efficiency of a semiautomatic software package in processing renographic data from a cohort of clinical patients. MATERIALS AND METHODS: The developed software incorporates an image-registration algorithm based on the generalized Hough transform of edge maps. It was used to estimate glomerular filtration rate (GFR), renal plasma flow (RPF), and mean transit time (MTT) from 36 patients who underwent free-breathing MRR at 3T using saturation-recovery turbo-FLASH. The processing time required for each patient was recorded. Renal parameter estimates and model-fitting residues from the software were compared to those from a previously reported technique. Interreader variability in the software was quantified by the standard deviation of parameter estimates among three readers. GFR estimates from our software were also compared to a reference standard from nuclear medicine. RESULTS: The time taken to process one patient's data with the software averaged 12 ± 4 minutes. The applied image registration effectively reduced motion artifacts in dynamic images by providing renal tracer-retention curves with significantly smaller fitting residues (P < 0.01) than unregistered data or data registered by the previously reported technique. Interreader variability was less than 10% for all parameters. GFR estimates from the proposed method showed greater concordance with reference values (P < 0.05). CONCLUSION: These results suggest that the proposed software can process MRR data efficiently and accurately. Its incorporated registration technique based on the generalized Hough transform effectively reduces respiratory motion artifacts in free-breathing renographic acquisitions.
PURPOSE: To evaluate the performance of an edge-based registration technique in correcting for respiratory motion artifacts in magnetic resonance renographic (MRR) data and to examine the efficiency of a semiautomatic software package in processing renographic data from a cohort of clinical patients. MATERIALS AND METHODS: The developed software incorporates an image-registration algorithm based on the generalized Hough transform of edge maps. It was used to estimate glomerular filtration rate (GFR), renal plasma flow (RPF), and mean transit time (MTT) from 36 patients who underwent free-breathing MRR at 3T using saturation-recovery turbo-FLASH. The processing time required for each patient was recorded. Renal parameter estimates and model-fitting residues from the software were compared to those from a previously reported technique. Interreader variability in the software was quantified by the standard deviation of parameter estimates among three readers. GFR estimates from our software were also compared to a reference standard from nuclear medicine. RESULTS: The time taken to process one patient's data with the software averaged 12 ± 4 minutes. The applied image registration effectively reduced motion artifacts in dynamic images by providing renal tracer-retention curves with significantly smaller fitting residues (P < 0.01) than unregistered data or data registered by the previously reported technique. Interreader variability was less than 10% for all parameters. GFR estimates from the proposed method showed greater concordance with reference values (P < 0.05). CONCLUSION: These results suggest that the proposed software can process MRR data efficiently and accurately. Its incorporated registration technique based on the generalized Hough transform effectively reduces respiratory motion artifacts in free-breathing renographic acquisitions.
Authors: Vivian S Lee; Henry Rusinek; Louisa Bokacheva; Ambrose J Huang; Niels Oesingmann; Qun Chen; Manmeen Kaur; Keyma Prince; Ting Song; Elissa L Kramer; Edward F Leonard Journal: Am J Physiol Renal Physiol Date: 2007-01-09
Authors: Paul A Skluzacek; Robert G Szewc; Charles R Nolan; Daniel J Riley; Shuko Lee; Pablo E Pergola Journal: Am J Kidney Dis Date: 2003-12 Impact factor: 8.860
Authors: P S Tofts; G Brix; D L Buckley; J L Evelhoch; E Henderson; M V Knopp; H B Larsson; T Y Lee; N A Mayr; G J Parker; R E Port; J Taylor; R M Weisskoff Journal: J Magn Reson Imaging Date: 1999-09 Impact factor: 4.813
Authors: Christopher C Conlin; Niels Oesingmann; Bradley Bolster; Yufeng Huang; Vivian S Lee; Jeff L Zhang Journal: Magn Reson Imaging Date: 2016-11-15 Impact factor: 2.546
Authors: Jeff L Zhang; Chris C Conlin; Kristi Carlston; Luke Xie; Daniel Kim; Glen Morrell; Kathryn Morton; Vivian S Lee Journal: NMR Biomed Date: 2016-05-20 Impact factor: 4.044
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
Authors: Dimitra Flouri; Daniel Lesnic; Constantina Chrysochou; Jehill Parikh; Peter Thelwall; Neil Sheerin; Philip A Kalra; David L Buckley; Steven P Sourbron Journal: MAGMA Date: 2021-06-23 Impact factor: 2.310