Performance of an efficient image-registration algorithm in processing MR renography data

Christopher C. Conlin, Jeff L. Zhang, Florian Rousset, Clement Vachet, Yangyang Zhao, Kathryn A. Morton, Kristi Carlston, Guido Gerig, Vivian S. Lee

    Research output: Contribution to journalArticle

    Abstract

    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. J. Magn. Reson. Imaging 2016;43:391-397.

    Original languageEnglish (US)
    Pages (from-to)391-397
    Number of pages7
    JournalJournal of magnetic resonance imaging : JMRI
    Volume43
    Issue number2
    DOIs
    StatePublished - Feb 1 2016

    Fingerprint

    Radioisotope Renography
    Software
    Glomerular Filtration Rate
    Artifacts
    Magnetic Resonance Spectroscopy
    Respiration
    Renal Plasma Flow
    Kidney
    Nuclear Medicine
    Reference Values

    Keywords

    • glomerular filtration rate
    • Hough transform
    • image registration
    • magnetic resonance renography

    ASJC Scopus subject areas

    • Radiology Nuclear Medicine and imaging

    Cite this

    Conlin, C. C., Zhang, J. L., Rousset, F., Vachet, C., Zhao, Y., Morton, K. A., ... Lee, V. S. (2016). Performance of an efficient image-registration algorithm in processing MR renography data. Journal of magnetic resonance imaging : JMRI, 43(2), 391-397. https://doi.org/10.1002/jmri.25000

    Performance of an efficient image-registration algorithm in processing MR renography data. / Conlin, Christopher C.; Zhang, Jeff L.; Rousset, Florian; Vachet, Clement; Zhao, Yangyang; Morton, Kathryn A.; Carlston, Kristi; Gerig, Guido; Lee, Vivian S.

    In: Journal of magnetic resonance imaging : JMRI, Vol. 43, No. 2, 01.02.2016, p. 391-397.

    Research output: Contribution to journalArticle

    Conlin, CC, Zhang, JL, Rousset, F, Vachet, C, Zhao, Y, Morton, KA, Carlston, K, Gerig, G & Lee, VS 2016, 'Performance of an efficient image-registration algorithm in processing MR renography data', Journal of magnetic resonance imaging : JMRI, vol. 43, no. 2, pp. 391-397. https://doi.org/10.1002/jmri.25000
    Conlin, Christopher C. ; Zhang, Jeff L. ; Rousset, Florian ; Vachet, Clement ; Zhao, Yangyang ; Morton, Kathryn A. ; Carlston, Kristi ; Gerig, Guido ; Lee, Vivian S. / Performance of an efficient image-registration algorithm in processing MR renography data. In: Journal of magnetic resonance imaging : JMRI. 2016 ; Vol. 43, No. 2. pp. 391-397.
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    abstract = "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. J. Magn. Reson. Imaging 2016;43:391-397.",
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