Feasibility Study of Signal Similarity Measurements for Improving Morphological Evaluation of Human Brain with Images from Multi-Echo T2-Star Weighted MR Sequences

Signal correlation measurement has been widely used for segmenting specific tissues, localizing abnormal regions and analyzing functional areas in dynamic imaging modalities. In this paper, we discussed the feasibility of similarity mappings derived from six signal coefficient measurements in improving morphological evaluation of human brain. These images are from a digital phantom and four normal volunteers scanned by multi-echo T2-star weighted MR sequences. Simulation studies have shown that similarity mappings from cross-correlation, normalized cross-correlation, mean square error and cubed sum coefficient are not helpful in distinguishing the reference region from its surrounding tissues. Clinical experiments were focused on similarity coefficient mapping (SCM) and improved SCM (iSCM). Final results have demonstrated comparative capacity of SCM and iSCM in improving image quality from quantitative metrics and visual analysis.

[1]  D. Louis Collins,et al.  Robust Rician noise estimation for MR images , 2010, Medical Image Anal..

[2]  谢耀钦,et al.  Automatic mapping extraction from multi-echo T2-star weighted magnetic resonance images for improving morphological evaluations in human brain , 2013 .

[3]  Yu-Chung N. Cheng,et al.  Magnetic Resonance Imaging: Physical Principles and Sequence Design , 1999 .

[4]  K. Brockow.  Contrast media hypersensitivity--scope of the problem. , 2005, Toxicology.

[5]  Siegfried Trattnig,et al.  T2 star relaxation times for assessment of articular cartilage at 3 T: a feasibility study , 2012, Skeletal Radiology.

[6]  Li Meng,et al.  Feasibility of similarity coefficient map for improving morphological evaluation of T2* weighted MRI for renal cancer , 2013, Chinese Physics B.

[7]  Yaoqin Xie,et al.  Automatic Mapping Extraction from Multiecho T2-Star Weighted Magnetic Resonance Images for Improving Morphological Evaluations in Human Brain , 2013, Comput. Math. Methods Medicine.

[8]  E Mark Haacke,et al.  Tissue similarity maps (TSMs): a new means of mapping vascular behavior and calculating relative blood volume in perfusion weighted imaging. , 2013, Magnetic resonance imaging.

[9]  Kendall Preston,et al.  Applications of similarity mapping in dynamic MRI , 1995, IEEE Trans. Medical Imaging.

[10]  D. Louis Collins,et al.  Quantitative Magnetic Resonance Imaging of Cortical Multiple Sclerosis Pathology , 2012, Multiple sclerosis international.

[11]  Bejoy Thomas,et al.  Principles, techniques, and applications of T2*-based MR imaging and its special applications. , 2009, Radiographics : a review publication of the Radiological Society of North America, Inc.

[12]  Joanna M. Wardlaw,et al.  Lesion Area Detection Using Source Image Correlation Coefficient for CT Perfusion Imaging , 2013, IEEE Journal of Biomedical and Health Informatics.

[13]  K.,et al.  Temporal Correlation Analysis of Penumbral Dynamics in Focal Cerebral Ischemia , 2010 .

[14]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

[15]  George Kontaxakis,et al.  Feasibility study of the use of similarity maps in the evaluation of oncological dynamic positron emission tomography images , 2006, Medical and Biological Engineering and Computing.

[16]  W Zidek,et al.  Prevention of radiographic-contrast-agent-induced reductions in renal function by acetylcysteine. , 2000, The New England journal of medicine.