Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4–5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15–60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of as open source under the BSD license.

To develop a deep learning-based method for fully automated quantification of left ventricular (LV) function from shortaxis cine MR images and to evaluate its performance in a multivendor and multicenter setting. Materials and Methods: This retrospective study included cine MRI data sets obtained from three major MRI vendors in four medical centers from 2008 to 2016. Three convolutional neural networks (CNNs) with the U-NET architecture were trained on data sets of increasing variability: (a) a single-vendor, single-center, homogeneous cohort of 100 patients (CNN1); (b) a single-vendor, multicenter, heterogeneous cohort of 200 patients (CNN2); and (c) a multivendor, multicenter, heterogeneous cohort of 400 patients (CNN3). All CNNs were tested on an independent multivendor, multicenter data set of 196 patients. CNN performance was evaluated with respect to the manual annotations from three experienced observers in terms of (a) LV detection accuracy, (b) LV segmentation accuracy, and (c) LV functional parameter accuracy. Automatic and manual results were compared with the paired Wilcoxon test, Pearson correlation, and Bland-Altman analysis. Results: CNN3 achieved the highest performance on the independent testing data set. The average perpendicular distance compared with manual analysis was 1.1 mm 6 0.3 for CNN3, compared with 1.5 mm 6 1.0 for CNN1 (P , .05) and 1.3 mm 6 0.6 for CNN2 (P , .05). The LV function parameters derived from CNN3 showed a high correlation (r 2  0.98) and agreement with those obtained by experts for data sets from different vendors and centers. Conclusion: A deep learning-based method trained on a data set with high variability can achieve fully automated and accurate cine MRI analysis on multivendor, multicenter cine MRI data.

In conclusion, image matching and subsequent analysis of differences according to the proposed lung models (i) has good local registration accuracy on patient data, (ii) effectively eliminates a dependency on inspiration level at acquisition time, (iii) accurately predicts progression in phantom data, and (iv) is reasonably consistent with global results in patient data. It is therefore a potential future tool for assessing local emphysema progression in drug evaluation trials and in clinical practice.

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The possibility to quantitatively characterize the full three-dimensional retinal shape by MRI offers new ophthalmologic possibilities, such as quantitative geometric description of staphyloma. It could in addition be used as a validation technique, independent of standard optical methods, to measure the peripheral retinal shape.