• Benjamin S Aribisala, Christopher J A Cowie, Jiabao He, Joshua Wood, David A Mendelow, Patrick Mitchell, and Andrew M Blamire.
• From the *Institute of Cellular Medicine & Newcastle Magnetic Resonance Centre, Newcastle University, Newcastle upon Tyne; †Brain Research Imaging Centre, University of Edinburgh, Edinburgh; ‡Institute of Neuroscience, Newcastle University, Newcastle upon Tyne; and §Department of Neurosurgery, Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, UK.
• J Comput Assist Tomogr. 2014 Nov 1;38(6):915-23.

ObjectivesThe 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.MethodsWe 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.ResultsSignificant 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.ConclusionsOur 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.

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