Showing papers by "Gemma Piella published in 2009"

Image Fusion for Enhanced Visualization: A Variational Approach

Abstract: We present a variational model to perform the fusion of an arbitrary number of images while preserving the salient information and enhancing the contrast for visualization. We propose to use the structure tensor to simultaneously describe the geometry of all the inputs. The basic idea is that the fused image should have a structure tensor which approximates the structure tensor obtained from the multiple inputs. At the same time, the fused image should appear `natural' and `sharp' to a human interpreter. We therefore propose to combine the geometry merging of the inputs with perceptual enhancement and intensity correction. This is performed through a minimization functional approach which implicitly takes into account a set of human vision characteristics.

Multi-sequence Registration of Cine, Tagged and Delay-Enhancement MRI with Shift Correction and Steerable Pyramid-Based Detagging

Abstract: In this work, we present a registration framework for cardiac cine MRI (cMRI), tagged (tMRI) and delay-enhancement MRI (deMRI), where the two main issues to find an accurate alignment between these images have been taking into account: the presence of tags in tMRI and respiration artifacts in all sequences. A steerable pyramid image decomposition has been used for detagging purposes since it is suitable to extract high-order oriented structures by directional adaptive filtering. Shift correction of cMRI is achieved by firstly maximizing the similarity between the Long Axis and Short Axis cMRI. Subsequently, these shift-corrected images are used as target images in a rigid registration procedure with their corresponding tMRI/deMRI in order to correct their shift. The proposed registration framework has been evaluated by 840 registration tests, considerably improving the alignment of the MR images (mean RMS error of 2.04mm vs. 5.44mm).

Myocardial deformation from tagged MRI in hypertrophic cardiomyopathy using an efficient registration strategy

Abstract: This paper combines different parallelization strategies for speeding up motion and deformation computation by non-rigid registration of a sequence of images. The registration is performed in a two-level acceleration approach: (1) parallelization of each registration process using MPI and/or threads, and (2) distribution of the sequential registrations over a cluster. On a 24-node double quad-core Intel Xeon (2.66 GHz CPU, 16 GB RAM) cluster, the method is demonstrated to efficiently compute the deformation of a cardiac sequence reducing the computation time from more than 3 hours to a couple of minutes (for low downsampled images). It is shown that the distribution of the sequential registrations over the cluster together with the parallelization of each pairwise registration by multithreading lowers the computation time towards values compatible with clinical requirements (a few minutes per patient). The combination of MPI and multithreading is only advantageous for large input data sizes. Performances are assessed for the specific scenario of aligning cardiac sequences of taggedMagnetic Resonance (tMR) images, with the aim of comparing strain in healthy subjects and hypertrophic cardiomyopathy (HCM) patients. In particular, we compared the distribution of systolic strain in both populations. On average, HCM patients showed lower average values of strain with larger deviation due to the coexistence of regions with impaired deformation and regions with normal deformation.