Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration

Many traditional computer vision tasks, such as segmentation, have seen large step-changes in accuracy and/or speed with the application of Convolutional Neural Networks (CNNs). Image registration, the alignment of two or more images to a common space, is a fundamental step in many medical imaging workflows. In this paper we investigate whether these techniques can also bring tangible benefits to the registration task. We describe and evaluate the use of convolutional neural networks (CNNs) for both mono- and multi- modality registration and compare their performance to more traditional schemes, namely multi-scale, iterative registration. This paper also investigates incorporating inverse consistency of the learned spatial transformations to impose additional constraints on the network during training and investigate any benefit in accuracy during detection. The approaches are validated with a series of artificial mono-modal registration tasks utilizing T1-weighted MR brain i mages from the Open Access Series of Imaging Studies (OASIS) study and IXI brain development dataset and a series of real multi-modality registration tasks using T1-weighted and T2-weighted MR brain images from the 2015 Ischemia Stroke Lesion segmentation (ISLES) challenge. The results demonstrate that CNNs give excellent performance for both mono- and multi- modality head and neck registration compared to the baseline method with significantly fewer outliers and lower mean errors

Measurement of cerebral perfusion volume and 99mTc-HMPAO uptake using SPECT in controls and patients with Alzheimer's disease

Methods for quantifying the changes in brain function observed in single photon emission computed tomography (SPECT) using hexamethylenepropylene amine oxime (HMPAO) for patients with Alzheimer's disease have the potential of improving the diagnostic accuracy of the procedure and its ability to monitor response to treatment. The absolute percentage uptake of HMPAO and the cerebral perfusion volume (CPV) of the brain were assessed using SPECT in 26 patients with mild to moderate Alzheimer's disease (AD) and 24 control subjects. A subset of 15 control subjects, which was age-matched to the AD patients, was selected to allow fair statistical comparison of parameters between groups. The percentage of brain volume with reduced perfusion (R) and a volume loss index (VLI), given by R1/2/CPV, were also calculated. Eight of the control subjects were studied on a second occasion after a mean period of 6 months. There was no significant difference in percentage uptake between controls and AD patients, the mean value being 5.8%. Cerebral perfusion volume in controls was found to depend on sex (mean value in males and females being 1327 ml and 1222 ml, respectively) and on age. The volume loss index corrected for age and sex provided good discrimination between controls and AD subjects giving a sensitivity and specificity of 81% and 96%, respectively. The repeatability coefficient, the 95% confidence limit for the difference between repeat measurements, on controls was 67 ml (5%). The measurement of cerebral perfusion volume and related indices may be of value in identifying patients with early Alzheimer's disease and in following their response to treatment