A motion correction algorithm for an image realignment programme useful for sequential radionuclide renography

Agostini, Antonio De; Moretti, R.; Belletti, S; Maira, Giovanni Di; Magri, Giancarlo; Bestagno, M

doi:10.1007/bf00185852

Cited by 6 publications

(1 citation statement)

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“…There have been several methods reported for motion correction of radionuclide renograms. In 1992, De Agostini et al reported a method for re-aligning shifted renogram images by using statistical methods to select from a set of shifted ROIs the one that best represents the motion of features within the image[25]. Lee and Barber used affine transformation with decomposition to motion correct renograms [26].…”

Section: Background and Objectivesmentioning

confidence: 99%

Automated Patient Motion Detection and Correction in Dynamic Renal Scintigraphy

Folks¹,

Manatunga²,

Garcia³

et al. 2011

Journal of Nuclear Medicine Technology

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Kidney motion during dynamic renal scintigraphy can cause errors in calculated renal function parameters. Our goal was to develop and validate algorithms to detect and correct patient motion. Methods: We retrospectively collected dynamic images from 86 clinical renal studies (42 women, 44 men), acquired using 99m Tc-mercaptoacetyltriglycine (80 image frames [128 · 128 pixels; 3.2 mm/pixel]: twenty-four 2-s frames, sixteen 15-s frames, and forty 30-s frames). We simulated 10 types of vertical motion in each patient study, resulting in 860 image sets. Motion consisted of up or down shifts of magnitude 0.25 pixel to 4 pixels per frame and was either a gradual shift additive over multiple frames or an abrupt shift of one or more consecutive frames, with a later return to the start position. Additional horizontal motion was added to test its effect on detection of vertical motion. Original and shifted files were processed using a motion detection algorithm. Corrective shifts were applied, and the corrected and original (unshifted) images were compared pixel by pixel. Motion detected in the shifted data was also tabulated before and after correction of motion detected in the original data. A detected shift was considered correct if it was within 0.25 pixel of the simulated magnitude. Software was developed to facilitate visual review of all images and to summarize kidney motion and motion correction using linograms. Results: Overall detection of simulated shifts was 99% (3,068/3,096 frames) when the existing motion in the original images was first corrected. When the original motion was not corrected, overall shift detection was 76% (2,345/3,096 frames). For image frames in which no shift was added (and original motion was not corrected), 87% (27,142/31,132 frames) were correctly detected as having no shift. When corrected images were compared with original images, calculated count recovery was 100% for all shifts that were whole-pixel magnitudes. For fractional-pixel shifts, percentage count recovery varied from 52% to 73%. Visual review suggested that some original frames exhibited true patient motion. Conclusion: The algorithm accurately detected motion as small as 0.25 pixel. Whole-pixel motion can be detected and corrected with high accuracy. Fractional-pixel motion can be detected and corrected, but with less accuracy. Importantly, by accurately identifying unshifted frames, the algorithm helps to prevent the introduction of errors during motion correction.

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Section: Background and Objectivesmentioning

confidence: 99%