The cohort size required in epidemiological imaging genetics studies often mandates the pooling of data from multiple hospitals. Patient data, however, is subject to strict privacy protection regimes, and physical data storage may be legally restricted to a hospital network. To enable biomarker discovery, fast data access and interactive data exploration must be combined with high-performance computing resources, while respecting privacy regulations. We present a system using fast and inherently secure light-paths to access distributed data, thereby obviating the need for a central data repository. A secure private cloud computing framework facilitates interactive, computationally intensive exploration of this geographically distributed, privacy sensitive data. As a proof of concept, MRI brain imaging data hosted at two remote sites were processed in response to a user command at a third site. The system was able to automatically start virtual machines, run a selected processing pipeline and write results to a user accessible database, while keeping data locally stored in the hospitals. Individual tasks took approximately 50% longer compared to a locally hosted blade server but the cloud infrastructure reduced the total elapsed time by a factor of 40 using 70 virtual machines in the cloud. We demonstrated that the combination light-path and private cloud is a viable means of building an analysis infrastructure for secure data analysis. The system requires further work in the areas of error handling, load balancing and secure support of multiple users.
Neurological pathologies are often reflected in brain magnetic resonance images as abnormal global or local anatomical
changes. These variations can be computed using non-rigid registration and summarized using Jacobian determinant
maps of the resulting deformation field, which characterise local volume changes. We propose a new approach which
exploits the information contained in Jacobian determinant maps of the whole brain in Alzheimer’s disease (AD)
classification by means of texture analysis. Textural features were derived from whole-brain Jacobian determinant maps
based on 3D Grey Level Co-occurrence Matrix. The large number of features obtained depicts anatomical variations at
different resolution, allowing retaining both global and local information. Principle component analysis was applied for
feature reduction such that 95% of the data variance was retained. Classification was performed using a linear support
vector machine. We evaluated our approach using a bootstrapping procedure in which 92 subjects were randomly split
into separate training and testing sets. For comparison purposes, we implemented two dissimilarity-based classification
approaches, one based on pairwise registration and the other based on registration to a single template. Our new
approach significantly outperformed the other approaches. The results of this study showed that pairwise registration did
not bring added value compared to registration to a single template and textural features were more informative than
dissimilarity-based features. This study demonstrates the potential of texture analysis on whole brain Jacobian
determinant map for diagnosis of AD subjects.
Catheter ablation is an important option to treat ventricular tachycardias (VT). Scar-related VT is among the most
difficult to treat, because myocardial scar, which is the underlying arrhythmogenic substrate, is patient-specific and
often highly complex. The scar image from preprocedural late gadolinium enhancement magnetic resonance
imaging (LGE- MRI) can provide high-resolution substrate information and, if integrated at the early stage of the
procedure, can largely facilitate the procedure with image guidance. In clinical practice, however, early MRI
integration is difficult because available integration tools rely on matching the MRI surface mesh and
electroanatomical mapping (EAM) points, which is only possible after extensive EAM has been performed.
In this paper, we propose to use a priori information on patient posture and a multi-sequence MRI integration
framework to achieve accurate MRI integration that can be accomplished at an early stage of the procedure. From
the MRI sequences, the left ventricular (LV) geometry, myocardial scar characteristics, and an anatomical landmark
indicating the origin of the left main coronary artery are obtained preprocedurally using image processing techniques.
Thereby the integration can be realized at the beginning of the procedure after acquiring a single mapping point. The
integration method has been evaluated postprocedurally in terms of LV shape match and actual scar match.
Compared to the iterative closest point (ICP) method that uses high-intensity mapping (225±49 points), our method
using one mapping point reached a mean point-to-surface distance of 5.09±1.09 mm (vs. 3.85±0.60 mm, p<0.05),
and scar correlation of -0.51±0.14 (vs. -0.50±0.14, p=NS).
This work investigates knowledge driven segmentation of cardiac MR perfusion sequences. We build upon
previous work on multi-band AAMs to integrate into the segmentation both spatial priors about myocardial
shape as well as temporal priors about characteristic perfusion patterns. Different temporal and spatial features
are developed without a strict need for temporal correspondence across the image sequences. We also investigate
which combination of spatial and temporal features yields the best segmentation performance. Our evaluation
criteria were boundary errors wrt manual segmentations, area overlap, and convergence envelope. From a
quantitative evaluation on 19 perfusion studies, we conclude that a combination of the maximum intensity
projection feature and gradient orientation map yields the best segmentation performance, with an average
point-to-curve error of 0.9-1 pixel wrt manual contours. We also conclude that addition of different temporal
features does not necessarily increase performance.
Myocardial perfusion MRI has emerged as a suitable imaging technique for the detection of ischemic regions of the heart. However, manual post-processing is labor intensive, seriously hampering its daily clinical use. We propose a novel, data driven analysis method based on Independent Component Analysis (ICA). By performing ICA on the complete perfusion sequence, physiologically meaningful feature images, representing events occurring during the perfusion sequence, can be factored out. Results obtained using our method are compared with results obtained using manual contouring by a medical expert. The estimated weight functions are correlated against the perfusion time-intensity curves from manual contours, yielding promising results.
A novel approach for correcting intensity nonuniformity in magnetic resonance imaging (MRI) is presented.
This approach is based on the fusion of spatial and gray-level histogram information. Spatial information
about intensity nonuniformity is obtained using cubic B-spline smoothing. Gray-level histogram information
of the image corrupted by intensity nonuniformity is exploited from a frequential point of view. The proposed
correction method is illustrated using both physical phantom and human brain images. The results are consistent
with theoretical prediction, and demonstrate a new way of dealing with intensity nonuniformity problems.
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