Reduced respiratory motion artifacts using structural similarity in fast 2D dynamic contrast enhanced MRI of liver lesions

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Publication year
2016Source
NMR in Biomedicine, 29, 11, (2016), pp. 1526-1535ISSN
Publication type
Article / Letter to editor

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Organization
Medical Imaging
Radboudumc Extern
Surgery
Medical Oncology
Journal title
NMR in Biomedicine
Volume
vol. 29
Issue
iss. 11
Page start
p. 1526
Page end
p. 1535
Subject
Radboudumc 14: Tumours of the digestive tract RIHS: Radboud Institute for Health Sciences; Radboudumc 15: Urological cancers RIMLS: Radboud Institute for Molecular Life Sciences; Radboudumc 2: Cancer development and immune defence RIHS: Radboud Institute for Health SciencesAbstract
The purpose of this work was to improve dynamic contrast enhanced MRI (DCE-MRI) of liver lesions by removing motion corrupted images as identified by a structural similarity (SSIM) algorithm, and to assess the effect of this correction on the pharmacokinetic parameter Ktrans using automatically determined arterial input functions (AIFs). Fifteen patients with colorectal liver metastases were measured twice with a T1 weighted multislice 2D FLASH sequence for DCE-MRI (time resolution 1.2 s). AIFs were automatically derived from contrast inflow in the aorta of each patient. Thereafter, SSIM identified motion corrupted images of the liver were removed from the DCE dataset. From this corrected data set Ktrans and its reproducibility were determined. Using the SSIM algorithm a median fraction of 46% (range 37-50%) of the liver images in DCE time series was labeled as motion distorted. Rejection of these images resulted in a significantly lower median Ktrans (p < 0.05) and lower coefficient of repeatability of Ktrans in liver metastases compared with an analysis without correction. SSIM correction improves the reproducibility of the DCE-MRI parameter Ktrans in liver metastasis and reduces contamination of Ktrans values of lesions by that of surrounding normal liver tissue.
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- Academic publications [232155]
- Electronic publications [115359]
- Faculty of Medical Sciences [89071]
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