Literature DB >> 12741495

The design and implementation of a motion correction scheme for neurological PET.

Peter M Bloomfield1, Terry J Spinks, Johnny Reed, Leonard Schnorr, Anthony M Westrip, Lefteris Livieratos, Roger Fulton, Terry Jones.   

Abstract

A method is described to monitor the motion of the head during neurological positron emission tomography (PET) acquisitions and to correct the data post acquisition for the recorded motion prior to image reconstruction. The technique uses an optical tracking system, Polaris, to accurately monitor the position of the head during the PET acquisition. The PET data are acquired in list mode where the events are written directly to disk during acquisition. The motion tracking information is aligned to the PET data using a sequence of pseudo-random numbers, which are inserted into the time tags in the list mode event stream through the gating input interface on the tomograph. The position of the head is monitored during the transmission acquisition, and it is assumed that there is minimal head motion during this measurement. Each event, prompt and delayed, in the list mode event stream is corrected for motion and transformed into the transmission space. For a given line of response, normalization, including corrections for detector efficiency, geometry and crystal interference and dead time are applied prior to motion correction and rebinning in the sinogram. A series of phantom experiments were performed to confirm the accuracy of the method: (a) a point source located in three discrete axial positions in the tomograph field of view, 0 mm, 10 mm and 20 mm from a reference point, (b) a multi-line source phantom rotated in both discrete and gradual rotations through +/- 5 degrees and +/- 15 degrees, including a vertical and horizontal movement in the plane. For both phantom experiments images were reconstructed for both the fixed and motion corrected data. Measurements for resolution, full width at half maximum (FWHM) and full width at tenth maximum (FWTM), were calculated from these images and a comparison made between the fixedand motion corrected datasets. From the point source measurements, the FWHM at each axial position was 7.1 mm in the horizontal direction, and increasing from 4.7 mm at the 0 mm position, to 4.8 mm, 20 mm offset, in the vertical direction. The results from the multi-line source phantom with +/- 5 degrees rotations showed a maximum degradation in FWHM, when compared with the stationary phantom, of 0.6 mm, in the horizontal direction, and 0.3 mm in the vertical direction. The corresponding values for the larger rotation, +/- 15 degrees, were 0.7 mm and 1.1 mm, respectively. The performance of the method was confirmed with a Hoffman brain phantom moved continuously, and a clinical acquisition using [11C]raclopride (normal volunteer). A visual comparison of both the motion and non-motion corrected images of the Hoffman brain phantom clearly demonstrated the efficacy of the method. A sample time-activity curve extracted from the clinical study showed irregularities prior to motion correction, which were removed after correction. A method has been developed to accurately monitor the motion of the head during a neurological PET acquisition, and correct for this motion prior to image reconstruction. The method has been demonstrated to be accurate and does not add significantly to either the acquisition or the subsequent data processing.

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Year:  2003        PMID: 12741495     DOI: 10.1088/0031-9155/48/8/301

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  47 in total

1.  Accuracy of Head Motion Compensation for the HRRT: Comparison of Methods.

Authors:  Xiao Jin; Tim Mulnix; Beata Planeta-Wilson; Jean-Dominique Gallezot; Richard E Carson
Journal:  IEEE Nucl Sci Symp Conf Rec (1997)       Date:  2009-10-24

Review 2.  FDG-PET Contributions to the Pathophysiology of Memory Impairment.

Authors:  Shailendra Segobin; Renaud La Joie; Ludivine Ritz; Hélène Beaunieux; Béatrice Desgranges; Gaël Chételat; Anne Lise Pitel; Francis Eustache
Journal:  Neuropsychol Rev       Date:  2015-08-30       Impact factor: 7.444

3.  MRI-assisted PET motion correction for neurologic studies in an integrated MR-PET scanner.

Authors:  Ciprian Catana; Thomas Benner; Andre van der Kouwe; Larry Byars; Michael Hamm; Daniel B Chonde; Christian J Michel; Georges El Fakhri; Matthias Schmand; A Gregory Sorensen
Journal:  J Nucl Med       Date:  2011-01       Impact factor: 10.057

4.  Kinetic analysis in human brain of [11C](R)-rolipram, a positron emission tomographic radioligand to image phosphodiesterase 4: a retest study and use of an image-derived input function.

Authors:  Paolo Zanotti-Fregonara; Sami S Zoghbi; Jeih-San Liow; Elise Luong; Ronald Boellaard; Robert L Gladding; Victor W Pike; Robert B Innis; Masahiro Fujita
Journal:  Neuroimage       Date:  2010-10-26       Impact factor: 6.556

5.  An event-driven motion correction method for neurological PET studies of awake laboratory animals.

Authors:  Victor W Zhou; Andre Z Kyme; Steven R Meikle; Roger Fulton
Journal:  Mol Imaging Biol       Date:  2008-08-01       Impact factor: 3.488

6.  Use of MRI to assess the prediction of heart motion with gross body motion in myocardial perfusion imaging by stereotracking of markers on the body surface.

Authors:  Michael A King; Joyoni Dey; Karen Johnson; Paul Dasari; Joyeeta M Mukherjee; Joseph E McNamara; Arda Konik; Cliff Lindsay; Shaokuan Zheng; Dennis Coughlin
Journal:  Med Phys       Date:  2013-11       Impact factor: 4.071

7.  A method to synchronize signals from multiple patient monitoring devices through a single input channel for inclusion in list-mode acquisitions.

Authors:  J Michael O'Connor; P Hendrik Pretorius; Karen Johnson; Michael A King
Journal:  Med Phys       Date:  2013-12       Impact factor: 4.071

8.  Evaluation of motion correction methods in human brain PET imaging--a simulation study based on human motion data.

Authors:  Xiao Jin; Tim Mulnix; Jean-Dominique Gallezot; Richard E Carson
Journal:  Med Phys       Date:  2013-10       Impact factor: 4.071

9.  Movement correction method for human brain PET images: application to quantitative analysis of dynamic 18F-FDDNP scans.

Authors:  Mirwais Wardak; Koon-Pong Wong; Weber Shao; Magnus Dahlbom; Vladimir Kepe; Nagichettiar Satyamurthy; Gary W Small; Jorge R Barrio; Sung-Cheng Huang
Journal:  J Nucl Med       Date:  2010-01-15       Impact factor: 10.057

10.  Population-based input function and image-derived input function for [¹¹C](R)-rolipram PET imaging: methodology, validation and application to the study of major depressive disorder.

Authors:  Paolo Zanotti-Fregonara; Christina S Hines; Sami S Zoghbi; Jeih-San Liow; Yi Zhang; Victor W Pike; Wayne C Drevets; Alan G Mallinger; Carlos A Zarate; Masahiro Fujita; Robert B Innis
Journal:  Neuroimage       Date:  2012-08-10       Impact factor: 6.556

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