SINAPSE Virtual Happy Hour May 19, 2021 04:30 PM - 05:30 PM — Virtual Happy Hour (online)
9th Annual Scottish Radiotherapy Research Forum Jun 03, 2021 12:30 PM - 05:00 PM — Virtual Meeting (online)
Scottish Dementia Research Consortium Annual Conference 2021 Jun 16, 2021 10:00 AM - 03:30 PM — 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

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

A histogram-based similarity measure for quantitative magnetic resonance imaging: application in acute mild traumatic brain injury

Author(s): B. S. Aribisala, C. J. Cowie, J. He, J. Wood, D. A. Mendelow, P. Mitchell, A. M. Blamire

Abstract:
OBJECTIVES: The most commonly used summary metric in neuroimaging is the mean value, but this pays little attention to the shape of the data distribution and can therefore be insensitive to subtle changes that alter the data distribution. METHODS: We propose a distributional-based metric called the normalized histogram similarity measure (HSM) for characterization of quantitative images. We applied HSM to quantitative magnetic resonance imaging T1 relaxation data of 44 patients with mild traumatic brain injury and compared with data of 43 age-matched controls. RESULTS: Significant differences were found between the patients and the controls in 8 gray matter regions using the HSM whereas in only 1 gray matter region based on the mean values. CONCLUSIONS: Our results show that HSM is more sensitive than the standard mean values in detecting brain tissue changes. Future studies on brain tissue properties using quantitative magnetic resonance imaging should consider the use of HSM to properly capture any tissue changes.

Full version: Available here

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


ISBN: 1532-3145 (Electronic) 0363-8715 (Linking)
Publication Year: 2014
Periodical: J Comput Assist Tomogr
Periodical Number: 6
Volume: 38
Pages: 915-23
Author Address: From the *Institute of Cellular Medicine & Newcastle Magnetic Resonance Centre, Newcastle University, Newcastle upon Tyne; daggerBrain Research Imaging Centre, University of Edinburgh, Edinburgh; double daggerInstitute of Neuroscience, Newcastle University, Newcastle upon Tyne; and section signDepartment of Neurosurgery, Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, UK.