A New Fast Accurate Nonlinear Medical Image Registration Program Including Surface Preserving Regularization

• Published in: IEEE transactions on medical imaging Vol. 33; no. 11; pp. 2118 - 2127
• Main Authors: Gruslys, Audrunas, Acosta-Cabronero, Julio, Nestor, Peter J., Williams, Guy B., Ansorge, Richard E.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Alzheimer Disease - pathology; Biomedical imaging; Brain - anatomy & histology; Brain - pathology; Central processing units; Deformable registration; demons algorithms; Diagnostic Imaging - methods; Filtering algorithms; Graphics processing units; graphics processors; Humans; Image processing; Image processing systems; Image Processing, Computer-Assisted - methods; Image registration; Jacobian matrices; Medical; medical image analysis; multi-core processing; Neurosciences; Preserving; Registration; Regularization; Smoothing methods; Surface treatment
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2014.2332370

Recently inexpensive graphical processing units (GPUs) have become established as a viable alternative to traditional CPUs for many medical image processing applications. GPUs offer the potential of very significant improvements in performance at low cost and with low power consumption. One way in which GPU programs differ from traditional CPU programs is that increasingly elaborate calculations per voxel may not impact of the overall processing time because memory accesses can dominate execution time. This paper presents a new GPU based elastic image registration program named Ezys. The Ezys image registration algorithm belongs to the wide class of diffeomorphic demons but uses surface preserving image smoothing and regularization filters designed for a GPU that would be computationally expensive on a CPU. We describe the methods used in Ezys and present results from two important neuroscience applications. Firstly inter-subject registration for transfer of anatomical labels and secondly longitudinal intra-subject registration to quantify atrophy in individual subjects. Both experiments showed that Ezys registration compares favorably with other popular elastic image registration programs. We believe Ezys is a useful tool for neuroscience and other applications, and also demonstrates the value of developing of novel image processing filters specifically designed for GPUs.