Biomedical subjects

Magnus Borga

Publications and source records attributed to Magnus Borga.

7 recordsLinked to original sources

PubMed2006

A hip surgery simulator based on patient specific models generated by automatic segmentation.

The use of surgical simulator systems for education and preoperative planning is likely to increase in the future. A natural course of development of these systems is to incorporate patient specific anatomical models. This step requires some kind of segmentation process in which the different anatomical parts are extracted. Anatomical datasets are, however, usually very large and manual processing would be too demanding. Hence, automatic, or semi-automatic, methods to handle this step are required. The framework presented in this paper uses nonrigid registration, based on the morphon method, to automatically segment the hip anatomy and generate models for a hip surgery simulator system.

Computer Simulation↗

PubMed2005

On rotational invariance in adaptive spatial filtering of fMRI data.

Canonical correlation analysis (CCA) has previously been shown to work well for detecting neural activity in fMRI data. The reason is that CCA enables simultaneous temporal modeling and adaptive spatial filtering of the data. This article introduces a novel method for adaptive anisotropic filtering using the CCA framework and compares it to a previously proposed method. Isotropic adaptive filtering, which is only able to form isotropic filters of different sizes, is also presented and evaluated. It is shown that a new feature of the proposed method is invariance to the orientation of activated regions, and that the detection performance is superior to both that of the previous method and to isotropic filtering.

Artifacts↗

PubMed2005

Canonical correlation analysis of risk factors and clinical outcomes in cardiac surgery.

Assessment of the association between risk factors and outcomes in cardiac surgery is a complex problem. The aim of this study was to explore the relationship between possible risk factors and several clinical outcomes in cardiac surgery by using canonical correlation analysis (CCA). This retrospective study of 2605 consecutive adult patients who underwent cardiac surgery, evaluated 74 potential risk factors and up to 12 outcomes by canonical correlation analysis. For three serious outcomes, sternal wound complications/mediastinitis, cerebral complications, and perioperative myocardial infarctions, CCA was preceded by univariate analyses and backward stepwise multivariate logistic regression analyses. The CCA suggests that the major risk factors for complications in these models are intraoperative and postoperative risk factors. The power of risk prediction models developed with multivariate regression analysis can be enhanced by application of canonical correlation analysis, thereby offering new ways of analyzing and interpreting sets of potential risk factors in relation to sets of clinical outcomes.

Aged↗

PubMed2004

Detection and detrending in fMRI data analysis.

This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a novel exploratory method for producing drift models that efficiently capture trends and drifts in the fMRI data is introduced. A comparison to currently employed detrending approaches is presented. It is shown that the novel exploratory model is able to remove a major part of the slowly varying drifts that are abundant in fMRI data. The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series.

Brain↗

PubMed2003

Adaptive analysis of fMRI data.

This article introduces novel and fundamental improvements of fMRI data analysis. Central is a technique termed constrained canonical correlation analysis, which can be viewed as a natural extension and generalization of the popular general linear model method. The concept of spatial basis filters is presented and shown to be a very successful way of adaptively filtering the fMRI data. A general method for designing suitable hemodynamic response models is also proposed and incorporated into the constrained canonical correlation approach. Results that demonstrate how each of these parts significantly improves the detection of brain activity, with a computation time well within limits for practical use, are provided.

Algorithms↗

PubMed2002

Detection of neural activity in fMRI using maximum correlation modeling.

A technique for detecting neural activity in functional MRI data is introduced. It is based on a novel framework termed maximum correlation modeling. The method employs a spatial filtering approach that adapts to the local activity patterns, which results in an improved detection sensitivity combined with good specificity. A spatially varying hemodynamic response is simultaneously modelled by a sum of two gamma functions. Comparisons to traditional analysis methods are made using both synthetic and real data. The results indicate that the maximum correlation modeling approach is a strong alternative for analyzing fMRI data.

Brain↗

PubMed2002

Exploratory fMRI analysis by autocorrelation maximization.

A novel and computationally efficient method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The relation to Principal Component Analysis and Independent Component Analysis is discussed and the performance of the methods is compared using both simulated and real data.

Brain↗