Computational fractal-based analysis of MR susceptibility-weighted imaging (SWI) in neuro-oncology and neurotraumatology

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Abstract

Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) technique able to depict the magnetic susceptibility produced by different substances, such as deoxyhemoglobin, calcium, and iron. The main application of SWI in clinical neuroimaging is detecting microbleedings and venous vasculature. Quantitative analyses of SWI have been developed over the last few years, aimed to offer new parameters, which could be used as neuroimaging biomarkers. Each technique has shown pros and cons, but no gold standard exists yet. The fractal dimension (FD) has been investigated as a novel potential objective parameter for monitoring intratumoral space-filling properties of SWI patterns. We showed that SWI patterns found in different tumors or different glioma grades can be represented by a gradient in the fractal dimension, thereby enabling each tumor to be assigned a specific SWI fingerprint. Such results were especially relevant in the differentiation of low-grade versus high-grade gliomas, as well as from malignant gliomas versus lymphomas. Therefore FD has been suggested as a potential image biomarker to analyze intrinsic neoplastic architecture in order to improve the differential diagnosis within clinical neuroimaging, determine appropriate therapy, and improve outcome in patients. These promising preliminary findings could be extended into the field of neurotraumatology, by means of the application of computational fractal-based analyses for the qualitative and quantitative imaging of microbleedings in traumatic brain injury patients. In consideration of some evidences showing that SWI signals are correlated with trauma clinical severity, FD might offer some objective prognostic biomarkers. In conclusion, fractal-based morphometrics of SWI could be further investigated to be used in a complementary way with other techniques, in order to form a holistic understanding of the temporal evolution of brain tumors and follow-up response to treatment, with several further applications in other fields, such as neurotraumatology and cerebrovascular neurosurgery as well.
LanguageEnglish
Title of host publicationThe Fractal geometry of the brain
EditorsAntonio Di Ieva
Place of PublicationNew York
PublisherSpringer, Springer Nature
Pages311-332
Number of pages22
ISBN (Print)9781493939954
DOIs
Publication statusPublished - 2016

Publication series

NameSpringer Series in Computational Neuroscience
PublisherSpringer

Fingerprint

Fractals
Neuroimaging
Glioma
Biomarkers
Dermatoglyphics
Neurosurgery
Brain Neoplasms
Lymphoma
Neoplasms
Differential Diagnosis
Iron
Magnetic Resonance Imaging
Calcium
Wounds and Injuries
Therapeutics

Keywords

  • brain tumors
  • fractal dimension
  • SWI
  • susceptibility-weighted imaging
  • traumatic brain injury

Cite this

Di Ieva, A. (2016). Computational fractal-based analysis of MR susceptibility-weighted imaging (SWI) in neuro-oncology and neurotraumatology. In A. Di Ieva (Ed.), The Fractal geometry of the brain (pp. 311-332). (Springer Series in Computational Neuroscience). New York: Springer, Springer Nature. https://doi.org/10.1007/978-1-4939-3995-4_20
Di Ieva, Antonio. / Computational fractal-based analysis of MR susceptibility-weighted imaging (SWI) in neuro-oncology and neurotraumatology. The Fractal geometry of the brain. editor / Antonio Di Ieva. New York : Springer, Springer Nature, 2016. pp. 311-332 (Springer Series in Computational Neuroscience).
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Di Ieva, A 2016, Computational fractal-based analysis of MR susceptibility-weighted imaging (SWI) in neuro-oncology and neurotraumatology. in A Di Ieva (ed.), The Fractal geometry of the brain. Springer Series in Computational Neuroscience, Springer, Springer Nature, New York, pp. 311-332. https://doi.org/10.1007/978-1-4939-3995-4_20

Computational fractal-based analysis of MR susceptibility-weighted imaging (SWI) in neuro-oncology and neurotraumatology. / Di Ieva, Antonio.

The Fractal geometry of the brain. ed. / Antonio Di Ieva. New York : Springer, Springer Nature, 2016. p. 311-332 (Springer Series in Computational Neuroscience).

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

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N2 - Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) technique able to depict the magnetic susceptibility produced by different substances, such as deoxyhemoglobin, calcium, and iron. The main application of SWI in clinical neuroimaging is detecting microbleedings and venous vasculature. Quantitative analyses of SWI have been developed over the last few years, aimed to offer new parameters, which could be used as neuroimaging biomarkers. Each technique has shown pros and cons, but no gold standard exists yet. The fractal dimension (FD) has been investigated as a novel potential objective parameter for monitoring intratumoral space-filling properties of SWI patterns. We showed that SWI patterns found in different tumors or different glioma grades can be represented by a gradient in the fractal dimension, thereby enabling each tumor to be assigned a specific SWI fingerprint. Such results were especially relevant in the differentiation of low-grade versus high-grade gliomas, as well as from malignant gliomas versus lymphomas. Therefore FD has been suggested as a potential image biomarker to analyze intrinsic neoplastic architecture in order to improve the differential diagnosis within clinical neuroimaging, determine appropriate therapy, and improve outcome in patients. These promising preliminary findings could be extended into the field of neurotraumatology, by means of the application of computational fractal-based analyses for the qualitative and quantitative imaging of microbleedings in traumatic brain injury patients. In consideration of some evidences showing that SWI signals are correlated with trauma clinical severity, FD might offer some objective prognostic biomarkers. In conclusion, fractal-based morphometrics of SWI could be further investigated to be used in a complementary way with other techniques, in order to form a holistic understanding of the temporal evolution of brain tumors and follow-up response to treatment, with several further applications in other fields, such as neurotraumatology and cerebrovascular neurosurgery as well.

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Di Ieva A. Computational fractal-based analysis of MR susceptibility-weighted imaging (SWI) in neuro-oncology and neurotraumatology. In Di Ieva A, editor, The Fractal geometry of the brain. New York: Springer, Springer Nature. 2016. p. 311-332. (Springer Series in Computational Neuroscience). https://doi.org/10.1007/978-1-4939-3995-4_20