Related Researcher

Author's Photo

Chun, Se Young
Bio-Medical Image Processing Lab (BMIPL)
Research Interests
  • Inverse problem, sparse signal, multimodal information, diffeomorphic alignment, statistical learning, medical imaging


Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging

DC Field Value Language Guerin, B. ko Cho, S. ko Chun, Se Young ko Zhu, X. ko Alpert, N. M. ko El Fakhri, G. ko Reese, T. ko Catana, C. ko 2014-10-30T00:09:27Z - 2014-10-29 ko 2011-06 ko
dc.identifier.citation MEDICAL PHYSICS, v.38, no.6, pp.3025 - 3038 ko
dc.identifier.issn 0094-2405 ko
dc.identifier.uri -
dc.description.abstract Purpose: We propose a novel approach for PET respiratory motion correction using tagged-MRI and simultaneous PET-MRI acquisitions.Methods: We use a tagged-MRI acquisition followed by motion tracking in the phase domain to estimate the nonrigid deformation of biological tissues during breathing. In order to accurately estimate motion even in the presence of noise and susceptibility artifacts, we regularize the traditional HARP tracking strategy using a quadratic roughness penalty on neighboring displacement vectors (R-HARP). We then incorporate the motion fields estimated with R-HARP in the system matrix of an MLEM PET reconstruction algorithm formulated both for sinogram and list-mode data representations. This approach allows reconstruction of all detected coincidences in a single image while modeling the effect of motion both in the emission and the attenuation maps. At present, tagged-MRI does not allow estimation of motion in the lungs and our approach is therefore limited to motion correction in soft tissues. Since it is difficult to assess the accuracy of motion correction approaches in vivo, we evaluated the proposed approach in numerical simulations of simultaneous PET-MRI acquisitions using the NCAT phantom. We also assessed its practical feasibility in PET-MRI acquisitions of a small deformable phantom that mimics the complex deformation pattern of a lung that we imaged on a combined PET-MRI brain scanner.Results: Simulations showed that the R-HARP tracking strategy accurately estimated realistic respiratory motion fields for different levels of noise in the tagged-MRI simulation. In simulations of tumors exhibiting increased uptake, contrast estimation was 20 more accurate with motion correction than without. Signal-to-noise ratio (SNR) was more than 100 greater when performing motion-corrected reconstruction which included all counts, compared to when reconstructing only coincidences detected in the first of eight gated frames. These results were confirmed in our proof-of-principle PET-MRI acquisitions, indicating that our motion correction strategy is accurate, practically feasible, and is therefore ready to be tested in vivo.Conclusions: This work shows that PET motion correction using motion fields measured with tagged-MRI in simultaneous PET-MRI acquisitions can be made practical for clinical application and that doing so has the potential to remove motion blur in whole-body PET studies of the torso. ko
dc.description.statementofresponsibility close -
dc.language 영어 ko
dc.title Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-79958840226 ko
dc.identifier.wosid 000291405200021 ko
dc.type.rims ART ko
dc.description.wostc 37 *
dc.description.scopustc 33 * 2015-05-06 * 2014-10-29 *
dc.identifier.doi 10.1118/1.3589136 ko
dc.identifier.url ko
Appears in Collections:
AI_Journal Papers
Files in This Item:
There are no files associated with this item.

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show simple item record


  • mendeley


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.