4th International Conference on Medical Imaging with Deep Learning Jul 07, 2021 - Jul 09, 2021 — Virtual Meeting (online)
Medical Image Understanding and Analysis Conference 2021 Jul 12, 2021 - Jul 14, 2021 — Virtual Meeting (online)
Medical Imaging Convention [rescheduled] Sep 15, 2021 - Sep 16, 2021 — National Exhibition Centre, Birmingham, England
2021 SINAPSE ASM Sep 16, 2021 - Sep 17, 2021 — Technology & Innovation Centre, University of Strathclyde, 99 George Street, Glasgow
Total Body PET 2021 conference [rescheduled] Sep 22, 2021 - Sep 24, 2021 — Virtual Meeting (online)

eLearning

SINAPSE experts from around Scotland have developed ten online modules designed to explain medical imaging. They are freely available and are intended for non-specialists.


Edinburgh Imaging Academy at the University of Edinburgh offers the following online programmes through a virtual learning environment:

Neuroimaging for Research MSc/Dip/Cert

Imaging MSc/Dip/Cert

PET-MR Principles & Applications Cert

Applied Medical Image Analysis Cert

Online Short Courses

Reliability of Two Techniques for Assessing Cerebral Iron Deposits With Structural Magnetic Resonance Imaging

Author(s): M. C. V. Hernandez, T. H. Jeong, C. Murray, M. E. Bastin, F. M. Chappell, I. J. Deary, J. M. Wardlaw

Abstract:
Purpose: To test the reliability of two computational methods for segmenting cerebral iron deposits (IDs) in the aging brain, given that its measurement in magnetic resonance imaging (MRI) is challenging due to the similar effect produced by other minerals, especially calcium, on T2*-weighted sequences. Materials and Methods: T1-, T2*-weighted, and fluid-attenuated inversion recovery (FLAIR) MR brain images obtained at 1.5T from 70 subjects in their early 70s who displayed a wide range of brain IDs were analyzed. The first segmentation method used a multispectral approach based on the fusion of two or more structural sequences registered and mapped in the red/green color space followed by Minimum Variance Quantization. The second method employed a combined thresholding, size and shape analysis using T2*-weighted images augmented with visual information from T1-weighted data. Results: Both segmentation techniques had high intra- and interobserver agreement (95% confidence interval [CI] = +/- 57 voxels in a range from 0 to 1800), which decreased in subjects with significant microbleeds and/or IDs. However, the thresholding method was more observer dependent in identifying microbleeds and IDs boundaries than the multispectral approach. Conclusion: Both techniques proved to be in agreement and have good intra- and interobserver reliability. However, they have limitations, specifically with regard to automation and observer independence, so further work is required to develop fully user-independent methods of identifying cerebral IDs.

Full version: Available here

Click the link to go to an external website with the full version of the paper


ISBN: 1053-1807
Publication Year: 2011
Periodical: Journal of Magnetic Resonance Imaging
Periodical Number: 1
Volume: 33
Pages: 54-61
Author Address: