Applications of fractional calculus in magnetic resonance imaging (MRI) have increased over the last twenty years. From the mathematical, computational, and biophysical perspectives, fractional calculus provides new tools for describing the complexity of biological tissues (cells, organelles, membranes and macromolecules). Specifically, fractional order models capture molecular dynamics (transport, rotation, and vibration) by incorporating power law convolution kernels into the time and space derivatives appearing in the equations that govern nuclear magnetic resonance (NMR) phenomena. Hence, it is natural to expect fractional calculus models of relaxation and diffusion to be applied to problems in NMR and MRI. Early studies considered the fractal dimensions of multi-scale materials in the non-linear growth of the mean squared displacement, assumed power-law decays of the spectral density, and suggested stretched exponential signal relaxation to describe non-Gaussian behavior. Subsequently, fractional order generalization of the Bloch, and Bloch-Torrey equations were developed to characterize NMR (and MRI) relaxation and diffusion. However, even for simple geometries, analytical solutions of fractional order equations in time and space are difficult to obtain, and predictions of the corresponding changes in image contrast are not always possible. Currently, a multifaceted approach using coarse graining, simulation, and accelerated computation is being developed to identify 'imaging' biomarkers of disease. This review surveys the principal fractional order models used to describe NMR and MRI phenomena, identifies connections and limitations, and finally points to future applications of the approach.
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http://dx.doi.org/10.1615/CritRevBiomedEng.2020033925 | DOI Listing |
PLoS One
January 2025
Department of Mathematics, University of Peshawar, Peshawar, Khyber Pakhtunkhwa, Pakistan.
In biology and life sciences, fractal theory and fractional calculus have significant applications in simulating and understanding complex problems. In this paper, a compartmental model employing Caputo-type fractional and fractal-fractional operators is presented to analyze Nipah virus (NiV) dynamics and transmission. Initially, the model includes nine nonlinear ordinary differential equations that consider viral concentration, flying fox, and human populations simultaneously.
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January 2025
Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.
In applied research, fractional calculus plays an important role for comprehending a wide range of intricate physical phenomena. One of the Klein-Gordon model's peculiar case yields the Phi-four equation. Additionally, throughout the past few decades it has been utilized to explain the kink and anti-kink solitary waveform contacts that occur in biological systems and in the field of nuclear mechanics.
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January 2025
Biology Department, Faculty of Science, Islamic University of Madinah, Madinah, Saudi Arabia.
This study presents a novel approach to modeling breast cancer dynamics, one of the most significant health threats to women worldwide. Utilizing a piecewise mathematical framework, we incorporate both deterministic and stochastic elements of cancer progression. The model is divided into three distinct phases: (1) initial growth, characterized by a constant-order Caputo proportional operator (CPC), (2) intermediate growth, modeled by a variable-order CPC, and (3) advanced stages, capturing stochastic fluctuations in cancer cell populations using a stochastic operator.
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January 2025
IT4Innovations, VSB - Technical University of Ostrava, Ostrava, Czech Republic.
Malware is a common word in modern era. Everyone using computer is aware of it. Some users have to face the problem known as Cyber crimes.
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December 2024
School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132022, China.
This paper proposes a joint multi-innovation fractional gradient descent identification algorithm for fractional order systems. First, the flexibility of fractional calculus is leveraged to design a joint fractional gradient descent algorithm capable of estimating system parameters and unknown orders. The estimated system parameters are used as the initial conditions to identify the unknown order, and the identified order is used as the update conditions for the system parameters.
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