2020
DOI: 10.1093/noajnl/vdaa049
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Abstract: The use of magnetic resonance imaging (MRI) in healthcare and the emergence of radiology as a practice are both relatively new compared with the classical specialties in medicine. Having its naissance in the 1970s and later adoption in the 1980s, the use of MRI has grown exponentially, consequently engendering exciting new areas of research. One such development is the use of computational techniques to analyze MRI images much like the way a radiologist would. With the advent of affordable, powerful computing … Show more

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Cited by 18 publications
(3 citation statements)
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“…Medical imaging technologies including magnetic resonance imaging (MRI) and computed tomography (CT) scans, are one of newer technologies increasingly used in translational imaging research 3 . Due to its complex nature, the brain tissue environment offers a rich opportunity for translational research.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Medical imaging technologies including magnetic resonance imaging (MRI) and computed tomography (CT) scans, are one of newer technologies increasingly used in translational imaging research 3 . Due to its complex nature, the brain tissue environment offers a rich opportunity for translational research.…”
Section: Introductionmentioning
confidence: 99%
“…Due to its complex nature, the brain tissue environment offers a rich opportunity for translational research. MRI can provide a comprehensive view of the abnormal regions in the brain 4 therefore, its applications in the translational brain cancer research is considered essential for the diagnosis, monitoring, and management of the disease 3 .…”
Section: Introductionmentioning
confidence: 99%
“…Although the use of radial distance-based shape features, such as the tumor boundary roughness and zero-crossing count, has shown success in classifying malignant and benign breast tumors (Kilday, Palmieri and Fox, 1993;Georgiou et al, 2007;Li et al, 2013;Rahmani Seryasat, Haddadnia and Ghayoumi Zadeh, 2016) and predicting brain tumor prognosis (Sanghani et al, 2019;Vadmal et al, 2020) from radiology (e.g. CT and MRI) images, we found that they performed poorly to characterize tumor border irregularity in pathology images.…”
mentioning
confidence: 84%