Extraction of hybrid data using intrinsic interactions of relaxation times in MRI

Huge data have been superimposed in the image created by the medical imaging systems in which some scientific aspects are intervened. Theories have been suggested based on related known intrinsic specifications to find out the microscopic details of these data. For instance, assessment of breast cancer in all phases is of great importance in medicine because, in addition to predicting the growth rate, treatment planning must also be considered. In this study, a new parametric function on the SNR is introduced to determine proper indices for the investigation of tissue performance in MRI. The relaxation times along with the convolution operator and fast Fourier transform have a key role in attaining the novel maps named the M[Formula: see text]-map besides its signal-to-noise ratio (SNR), and the new indices such as the mean value of the FD[Formula: see text] function (Ti) and FWHM, which are unique qualities for each MR-image. In other words, this new function on the SNR from a convolutional combination of the relaxation times as [Formula: see text] is suggested which utilizes Fourier transform and differentiating operator. The Ti value is increased by the increase in FWHM. In contrast, the Ti value remains at the constant value of 0.15, while the [Formula: see text]-parameter value increases. The [Formula: see text] function may be computed for all T 1 - and T 2 -weighted images toward the prediction of the growth rate of abnormal tissues. Also, these maps and the indices may contribute to a better diagnosis of breast cancer.