Abstract:
PURPOSE:This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model. METHODS:A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed. RESULTS:The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6 mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10 % in most of the organs considered. CONCLUSION:The proposed automated quantification technique is reliable, robust and suitable for fast quantification of preclinical PET data in large serial studies.
journal_name
Eur J Nucl Med Mol Imagingauthors
Gutierrez DF,Zaidi Hdoi
10.1007/s00259-012-2188-7subject
Has Abstractpub_date
2012-11-01 00:00:00pages
1807-20issue
11eissn
1619-7070issn
1619-7089journal_volume
39pub_type
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journal_title:European journal of nuclear medicine and molecular imaging
pub_type: 杂志文章,实务指引
doi:10.1007/s00259-011-1884-z
更新日期:2012-01-01 00:00:00
abstract::After publication of this article we received a request from Dr. Jong Kyun Lee to have his name removed from the author list as he felt he did not fully meet the authorship criteria. The original version of this article was inadvertently published with an incorrect inclusion period of study. ...
journal_title:European journal of nuclear medicine and molecular imaging
pub_type: 已发布勘误
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journal_title:European journal of nuclear medicine and molecular imaging
pub_type: 杂志文章,评审
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journal_title:European journal of nuclear medicine and molecular imaging
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pub_type: 杂志文章
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journal_title:European journal of nuclear medicine and molecular imaging
pub_type: 杂志文章
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pub_type: 杂志文章
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pub_type: 杂志文章
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journal_title:European journal of nuclear medicine and molecular imaging
pub_type: 杂志文章
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journal_title:European journal of nuclear medicine and molecular imaging
pub_type: 临床试验,杂志文章
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更新日期:2011-08-01 00:00:00
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pub_type: 杂志文章
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pub_type: 临床试验,杂志文章
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pub_type: 杂志文章
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journal_title:European journal of nuclear medicine and molecular imaging
pub_type: 临床试验,杂志文章
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journal_title:European journal of nuclear medicine and molecular imaging
pub_type: 杂志文章
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更新日期:2020-11-01 00:00:00
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journal_title:European journal of nuclear medicine and molecular imaging
pub_type: 杂志文章
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更新日期:2014-05-01 00:00:00