IMR Press / JIN / Special Issues / 1443024096878579713

Interpretation of Neuroimaging Data from Serious Brain Diseases by Fractals Theory Application

Submission deadline: 20 December 2022
Special Issue Editor
  • Viorel-Puiu Paun, MD
    Physics Department, Faculty of Applied Sciences, University Politehnica of Bucharest, 313 Splaiul Independentei, sector 6, Bucharest, Romania; Academy of Romanian Scientists, 54 Splaiul Independentei, sector 5, Bucharest, Romania
    Interests: nonlinear dynamics theory; brain images (MRI, computer tomography); pulmonary radiology interpretation; composite materials; oral rehabilitation
Special Issue Information

Dear Colleagues,

Over the past decade, sustained progress in neuroimaging has provided an additional type of data that can be used to discover and interpret the evolution of peculiar/distinctive cerebral diseases. Complete observation of brain imaging has great epistemic significance since these can be viewed as relating to actual photographs of brain activity.

With this aim, it is necessary to apply advanced algorithms based on heuristic techniques. The suggested algorithms will be used in image processing, such as the investigation of magnetic resonance images and for image remaking similarity in brain computed tomography.

To stimulate the application of these algorithms as soon as possible, the suggested methodology and approaches presented here will be expanded in additional papers submitted to the newly launched Special Issue.

In the past, the strategy used in medical classes for this phenomenon has demonstrated admirable outcomes that are superior to those obtained by standard techniques. Currently, dedicated investigators use various novel procedures for in-depth analysis aimed at increasing the detection of cancers following screening by computed tomography and magnetic resonance imaging. These include the identification and localization of anomalies referred to as tumourets, or mini-tumors in medical vocabulary, that are only several pixels in size and which escape detection even by experienced radiologists.

The fractal analysis applied to medical images may be useful for the purpose stated above. The evaluation of fractal dimensions of the Hurst exponent (Hausdorff dimension, calculated for example using box-counting algorithms) and of lacunarity on pathological and/or healthy tissue samples can be performed using these qualified methods.

A general requirement for papers submitted to this Special Issue is a description of the organization and interpretation of the calculated fractal parameters and their correlation with the interesting aspects that initiated the study. In our case, the correlation was made with physiological or anatomic-pathological characters and with the diagnostic and prognostic elements necessary to complete the study.

The investigations proposed here should contribute to our current understanding of various neurological disorders and their evolution, as well as of neuroscience in general. They may be useful for disease staging and for monitoring disease progression (staging biomarker), and possibly also as a way to monitor pathophysiological aspects of disease and the response to treatment, i.e., theranostic marker. Magnetic resonance imaging also has the potential to serve as a biomarker for Parkinson's disease and Alzheimer's disease.

In this Special Issue, we aim to bring together cutting-edge research on advanced image processing of the brain, as well as self-representation in brain research performed in a quantitative rather than qualitative manner. Specifically, we are calling for the submission of scientifically rigorous and novel papers that include the processes discussed above and neuroimaging data, as well as reviews and meta-analyses in this field.

Prof. Viorel-Puiu Paun

Guest Editor

brain images
fractal analysis
complex systems
pattern recognition
brain disease
neurological disorders
biomarker for parkinson’s and alzheimer’s diseases
Manuscript Submission Information

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Published Paper (1 Paper)
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