Mapping extraction is useful in medical image analysis. Similarity coefficient mapping (SCM) replaced signal response to time course in tissue similarity mapping with signal response to TE changes in multiecho T2-star weighted magnetic resonance imaging without contrast agent. Since different tissues are with different sensitivities to reference signals, a new algorithm is proposed by adding a sensitivity index to SCM. It generates two mappings. One measures relative signal strength (SSM) and the other depicts fluctuation magnitude (FMM). Meanwhile, the new method is adaptive to generate a proper reference signal by maximizing the sum of contrast index (CI) from SSM and FMM without manual delineation. Based on four groups of images from multiecho T2-star weighted magnetic resonance imaging, the capacity of SSM and FMM in enhancing image contrast and morphological evaluation is validated. Average contrast improvement index (CII) of SSM is 1.57, 1.38, 1.34, and 1.41. Average CII of FMM is 2.42, 2.30, 2.24, and 2.35. Visual analysis of regions of interest demonstrates that SSM and FMM show better morphological structures than original images, T2-star mapping and SCM. These extracted mappings can be further applied in information fusion, signal investigation, and tissue segmentation. 1. Introduction As a routine examination technique, magnetic resonance imaging (MRI) has been extensively used in clinical diagnosis. Pixel intensities on conventional MR images are dependent on a complex mix of proton density (PD), longitudinal relaxation time (T1), and transverse relaxation time (T2) or T2-star relaxation time based on the initial scan setting [1–3]. Many types of MRI have been invented to reflect physical and physiological properties, such as T2-star weighted MRI and susceptibility weighted imaging (SWI) [4, 5]. Among these techniques, T2-star weighted MRI has been widely used to reveal functional and morphological characteristics by taking advantage of differences in tissue properties [6–10]. As an essential modality, T2-star weighted MRI is capable of producing a large number of medical images by selecting optimal cross section and imaging parameters for specific emphasis. How to dig out valuable messages from a series of MR images is an important project for various applications. Quantitative MRI (Q-MRI) is one way to extract tissue-intrinsic information from a series of MR images [6–18]. Conventional MRI focuses on qualitative visual assessment of anatomy and disease. It interprets anatomic changes when there is visibly detectable difference in signal
References
[1]
E. M. Haccke, R. W. Brown, et al., MRI: Basic Principles and Application, Wiley-Blackwell, 1999.
[2]
J. C. Wood, C. Enriquez, N. Ghugre et al., “MRI R2 and R2* mapping accurately estimates hepatic iron concentration in transfusion-dependent thalassemia and sickle cell disease patients,” Blood, vol. 106, no. 4, pp. 1460–1465, 2005.
[3]
M. A. Brown and R. C. Semelka, MRI: Basic Principles and Applications, John Wiley & Sons, 2011.
[4]
E. M. Haacke, Y. Xu, Y. N. Cheng, and J. R. Reichenbach, “Susceptibility weighted imaging (SWI),” Magnetic Resonance in Medicine, vol. 52, no. 3, pp. 612–618, 2004.
[5]
D. Haddar, E. M. Haacke, V. Sehgal et al., “Susceptibility weighted imaging. Theory and applications,” Journal de Radiologie, vol. 85, no. 11, pp. 1901–1908, 2004.
[6]
H. Hatabu, D. C. Alsop, J. Listerud, M. Bonnet, and W. B. Gefter, “T2* and proton density measurement of normal human lung parenchyma using submillisecond echo time gradient echo magnetic resonance imaging,” European Journal of Radiology, vol. 29, no. 3, pp. 245–252, 1999.
[7]
L. J. Anderson, S. Holden, B. Davis et al., “Cardiovascular T2-star (T2*) magnetic resonance for the early diagnosis of myocardial iron overload,” European Heart Journal, vol. 22, no. 23, pp. 2171–2179, 2001.
[8]
E. Elolf, V. Bockermann, T. Gringel, M. Knauth, P. Dechent, and G. Helms, “Improved visibility of the subthalamic nucleus on high-resolution stereotactic MR imaging by added susceptibility (T2*) contrast using multiple gradient echoes,” American Journal of Neuroradiology, vol. 28, no. 6, pp. 1093–1094, 2007.
[9]
S. Ropele, W. De Graaf, M. Khalil et al., “MRI assessment of iron deposition in multiple sclerosis,” Journal of Magnetic Resonance Imaging, vol. 34, no. 1, pp. 13–21, 2011.
[10]
J. Cohen-Adad, “What can we learn from T2* maps of the cortex?” NeuroImage, 2013.
[11]
K. P. Whittall, A. L. MacKay, D. A. Graeb, R. A. Nugent, D. K. B. Li, and D. W. Paty, “In vivo measurement of T2 distributions and water contents in normal human brain,” Magnetic Resonance in Medicine, vol. 37, no. 1, pp. 34–43, 1997.
[12]
J. Oh, E. T. Han, D. Pelletier, and S. J. Nelson, “Measurement of in vivo multi-component T2 relaxation times for brain tissue using multi-slice T2 prep at 1.5 and 3 T,” Magnetic Resonance Imaging, vol. 24, no. 1, pp. 33–43, 2006.
[13]
A. M. Peters, M. J. Brookes, F. G. Hoogenraad et al., “T2* measurements in human brain at 1.5, 3 and 7 T,” Magnetic Resonance Imaging, vol. 25, no. 6, pp. 748–753, 2007.
[14]
G. B. Chavhan, P. S. Babyn, B. Thomas, M. M. Shroff, and E. Mark Haacke, “Principles, techniques, and applications of T2*-based MR imaging and its special applications,” Radiographics, vol. 29, no. 5, pp. 1433–1449, 2009.
[15]
B. Shah, S. W. Anderson, J. Scalera, H. Jara, and J. A. Soto, “Quantitative MR imaging: physical principles and sequence design in abdominal imaging,” Radiographics, vol. 31, no. 3, pp. 867–880, 2011.
[16]
T. C. Mamisch, T. Hughes, T. J. Mosher et al., “T2 star relaxation times for assessment of articular cartilage at 3 T: a feasibility study,” Skeletal Radiology, vol. 41, no. 3, pp. 287–292, 2012.
[17]
C. L. Tardif, B. J. Bedell, S. F. Eskildsen, et al., “Quantitative magnetic resonance imaging of cortical multiple sclerosis pathology,” Multiple Sclerosis International, vol. 2012, Article ID 742018, 13 pages, 2012.
[18]
C. Lambert, A. Lutti, G. Helms, R. Frackowiak, and J. Ashburner, “Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussian,” Neuroimage, vol. 2, pp. 684–694, 2013.
[19]
J. Rogowska, K. Preston, G. J. Hunter et al., “Applications of similarity mapping in dynamic MRI,” IEEE Transactions on Medical Imaging, vol. 14, no. 3, pp. 480–486, 1995.
[20]
M. Wiart, N. Rognin, Y. Berthezene, N. Nighoghossian, J. C. Froment, and A. Baskurt, “Perfusion-based segmentation of the human brain using similarity mapping,” Magnetic Resonance in Medicine, vol. 45, no. 2, pp. 261–268, 2001.
[21]
T. Thireou, G. Kontaxakis, L. G. Strauss, A. Dimitrakopoulou-Strauss, S. Pavlopoulos, and A. Santos, “Feasibility study of the use of similarity maps in the evaluation of oncological dynamic positron emission tomography images,” Medical and Biological Engineering and Computing, vol. 43, no. 1, pp. 23–32, 2005.
[22]
E. M. Haacke, M. Li, and F. Juvvigunta, “Tissue similarity maps (TSMs): a new means of mapping vascular behavior and calculating relative blood volume in perfusion weighted imaging,” Magnetic Resonance Imaging, vol. 31, no. 4, pp. 481–489, 2012.
[23]
M. Tepel, M. Van Der Giet, C. Schwarzfeld, U. Laufer, D. Liermann, and W. Zidek, “Prevention of radiographic-contrast-agent-induced reductions in renal function by acetylcysteine,” The New England Journal of Medicine, vol. 343, no. 3, pp. 180–184, 2000.
[24]
K. Brockow, “Contrast media hypersensitivity: scope of the problem,” Toxicology, vol. 209, no. 2, pp. 189–192, 2005.
[25]
H. Wang, J. Hu, Y. Xie, et al., “Feasibility of similarity coefficient map for improving morphological evaluation of T2* weighted MRI for renal cancer,” Chinese Physics B, vol. 22, no. 3, Article ID 038702, 2013.
[26]
W. M. Morrow, R. B. Paranjape, R. M. Rangayyan, and J. E. L. Desautels, “Region-based contrast enhancement of mammograms,” IEEE Transactions on Medical Imaging, vol. 11, no. 3, pp. 392–406, 1992.
[27]
P. Sakellaropoulos, L. Costaridou, and G. Panayiotakis, “A wavelet-based spatially adaptive method for mammographic contrast enhancement,” Physics in Medicine and Biology, vol. 48, no. 6, pp. 787–803, 2003.
