Feasibility of similarity coefficient map for improving morphological evaluation ofT₂* weighted MRI for renal cancer

Abstract

The purpose of this paper is to investigate the feasibility of using a similarity coefficient map (SCM) in improving the morphological evaluation of T2* weighted (T2*W) magnatic resonance imaging (MRI) for renal cancer. Simulation studies and in vivo 12-echo T2*W experiments for renal cancers were performed for this purpose. The results of the first simulation study suggest that an SCM can reveal small structures which are hard to distinguish from the background tissue in T2*W images and the corresponding T2* map. The capability of improving the morphological evaluation is likely due to the improvement in the signal-to-noise ratio (SNR) and the carrier-to-noise ratio (CNR) by using the SCM technique. Compared with T2*W images, an SCM can improve the SNR by a factor ranging from 1.87 to 2.47. Compared with T2* maps, an SCM can improve the SNR by a factor ranging from 3.85 to 33.31. Compared with T2*W images, an SCM can improve the CNR by a factor ranging from 2.09 to 2.43. Compared with T2* maps, an SCM can improve the CNR by a factor ranging from 1.94 to 8.14. For a given noise level, the improvements of the SNR and the CNR depend mainly on the original SNRs and CNRs in T2*W images, respectively. In vivo experiments confirmed the results of the first simulation study. The results of the second simulation study suggest that more echoes are used to generate the SCM, and higher SNRs and CNRs can be achieved in SCMs. In conclusion, an SCM can provide improved morphological evaluation of T2*W MR images for renal cancer by unveiling fine structures which are ambiguous or invisible in the corresponding T2*W MR images and T2* maps. Furthermore, in practical applications, for a fixed total sampling time, one should increase the number of echoes as much as possible to achieve SCMs with better SNRs and CNRs.

Automatic Mapping Extraction from Multiecho T2-Star Weighted Magnetic Resonance Images for Improving Morphological Evaluations in Human Brain

“…The optimal number of echoes is determined as 12 and its effect in image quality has been discussed [ 25 ]. All MR imaging was done on a 3 Tesla Scanner system (Siemens) with GRE sequence (FA: 15 degree; FOV: 220 mm × 220 mm; acquisition matrix: 384 × 384; slice thickness: 3.0 mm; TR: 200 ms).…”

“…SCM is proposed [ 25 ] by replacing signal response to time course in TSM with signal response to TE changes in multiecho T2-star weighted MR without contrast agent. SCM is able to improve image quality and morphological evaluation.…”

Linear-fitting-based similarity coefficient map for tissue dissimilarity analysis in -w magnetic resonance imaging

“…Signal correlation analysis from tissue similarity (or dissimilarity) plays an important role in medical image analysis, such as tissue classification and segmentation, [1][2][3] functional region localization, [3][4][5] morphological evaluation, [6][7][8] abnormality detection, [8][9][10] and others. [11][12][13] As to similarity coefficient mapping (SCM), it is a postprocessing method and analyzes signal correlation from tissue similarity.…”

“…[11][12][13] As to similarity coefficient mapping (SCM), it is a postprocessing method and analyzes signal correlation from tissue similarity. [6] It enhances visual perceptibility of anatomical structures and helps clinical diagnosis with improved morphological evaluation. Also, it can compress a T * 2 -w magnetic resonance image series into a map which shows high image quality and non-sensitivity to the choice of reference tissues.…”

“…Wang et al [6] has fully demonstrated how to derive the similarity map and what is the effect of the selection of the reference tissue and the echo number on map quality. However, how to interpret the SCM map is still unknown.…”