Technical Note: A comparison of point set registration methods for electromagnetic tracking.

PURPOSE: High dose rate brachytherapy applies intense and destructive radiation. A treatment plan defines radiation source dwell positions to avoid irradiating healthy tissue. The study discusses methods to quantify any positional changes of source locations along the various treatment sessions. METHODS: Electromagnetic tracking (EMT) localizes the radiation source during the treatment sessions. But in each session the relative position of the patient relative to the filed generator is changed. Hence, the measured dwell point sets need to be registered onto each other to render them comparable. Two point set registration techniques are compared: a probabilistic method called coherent point drift (CPD) and a multidimensional scaling (MDS) technique. RESULTS: Both enable using EMT without external registration and achieve very similar results with respect to dwell position determination of the radiation source. Still MDS achieves smaller grand average deviations (CPD-rPSR: MD = 2.55 mm, MDS-PSR: MD = 2.15 mm) between subsequent dwell position determinations, which also show less variance (CPD-rPSR: IQR = 4 mm, MDS-PSR: IQR = 3 mm). Furthermore, MDS is not based on approximations and does not need an iterative procedure to track sensor positions inside the implanted catheters. CONCLUSION: Although both methods achieve similar results, MDS is to be preferred over rigid CPD while nonrigid CPD is unsuitable as it does not preserve topology.

[Multiparametric and molecular imaging of breast tumors with MRI and PET/MRI]. / Multiparametrische und molekulare Bildgebung von Brusttumoren mit MRT und PET­MRT.

CLINICAL/METHODICAL ISSUE: Magnetic resonance imaging (MRI) of the breast is an indispensable tool in breast imaging for many indications. Several functional parameters with MRI and positron emission tomography (PET) have been assessed for imaging of breast tumors and their combined application is defined as multiparametric imaging. Available data suggest that multiparametric imaging using different functional MRI and PET parameters can provide detailed information about the hallmarks of cancer and may provide additional specificity. STANDARD RADIOLOGICAL METHODS: Multiparametric and molecular imaging of the breast comprises established MRI parameters, such as dynamic contrast-enhanced MRI, diffusion-weighted imaging (DWI), MR proton spectroscopy ((1)H-MRSI) as well as combinations of radiological and MRI techniques (e. g. PET/CT and PET/MRI) using radiotracers, such as fluorodeoxyglucose (FDG). METHODICAL INNOVATIONS: Multiparametric and molecular imaging of the breast can be performed at different field-strengths (range 1.5-7 T). Emerging parameters comprise novel promising techniques, such as sodium imaging ((23)Na MRI), phosphorus spectroscopy ((31)P-MRSI), chemical exchange saturation transfer (CEST) imaging, blood oxygen level-dependent (BOLD) and hyperpolarized MRI as well as various specific radiotracers. ACHIEVEMENTS: Multiparametric and molecular imaging has multiple applications in breast imaging. Multiparametric and molecular imaging of the breast is an evolving field that will enable improved detection, characterization, staging and monitoring for personalized medicine in breast cancer.

Inter- and intra-observer agreement of BI-RADS-based subjective visual estimation of amount of fibroglandular breast tissue with magnetic resonance imaging: comparison to automated quantitative assessment.

PURPOSE: To evaluate the inter-/intra-observer agreement of BI-RADS-based subjective visual estimation of the amount of fibroglandular tissue (FGT) with magnetic resonance imaging (MRI), and to investigate whether FGT assessment benefits from an automated, observer-independent, quantitative MRI measurement by comparing both approaches. MATERIALS AND METHODS: Eighty women with no imaging abnormalities (BI-RADS 1 and 2) were included in this institutional review board (IRB)-approved prospective study. All women underwent un-enhanced breast MRI. Four radiologists independently assessed FGT with MRI by subjective visual estimation according to BI-RADS. Automated observer-independent quantitative measurement of FGT with MRI was performed using a previously described measurement system. Inter-/intra-observer agreements of qualitative and quantitative FGT measurements were assessed using Cohen's kappa (k). RESULTS: Inexperienced readers achieved moderate inter-/intra-observer agreement and experienced readers a substantial inter- and perfect intra-observer agreement for subjective visual estimation of FGT. Practice and experience reduced observer-dependency. Automated observer-independent quantitative measurement of FGT was successfully performed and revealed only fair to moderate agreement (k = 0.209-0.497) with subjective visual estimations of FGT. CONCLUSION: Subjective visual estimation of FGT with MRI shows moderate intra-/inter-observer agreement, which can be improved by practice and experience. Automated observer-independent quantitative measurements of FGT are necessary to allow a standardized risk evaluation. KEY POINTS: • Subjective FGT estimation with MRI shows moderate intra-/inter-observer agreement in inexperienced readers. • Inter-observer agreement can be improved by practice and experience. • Automated observer-independent quantitative measurements can provide reliable and standardized assessment of FGT with MRI.

Robust stability analysis and design under consideration of multiple feedback loops of the tryptophan regulatory network of Escherichia coli.

The tryptophan system present in Escherichia coli represents an important regulatory unit described by multiple feedback loops. The role of these feedback loops is crucial for the analysis of the dynamical behavior of the tryptophan synthesis. We analyze the robust stability of this system which models the dynamics of both fast state, such as transcription and synthesis of free operator, and slow state, such as translation and tryptophan synthesis under consideration of nonlinear uncertainties. In addition, we analyze the role of these feedback loops as key design components of this regulatory unit responsible for its physiological performance. The range of allowed parameter perturbations and the conditions that ensure the existence of asymptotically stable equilibria of the perturbed system are determined. We also analyze two important alternate regulatory designs for the tryptophan synthesis pathway and derive the stability conditions.

Small lesions evaluation based on unsupervised cluster analysis of signal-intensity time courses in dynamic breast MRI.

An application of an unsupervised neural network-based computer-aided diagnosis (CAD) system is reported for the detection and characterization of small indeterminate breast lesions, average size 1.1 mm, in dynamic contrast-enhanced MRI. This system enables the extraction of spatial and temporal features of dynamic MRI data and additionally provides a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions. Lesions with an initial contrast enhancement >/=50% were selected with semiautomatic segmentation. This conventional segmentation analysis is based on the mean initial signal increase and postinitial course of all voxels included in the lesion. In this paper, we compare the conventional segmentation analysis with unsupervised classification for the evaluation of signal intensity time courses for the differential diagnosis of enhancing lesions in breast MRI. The results suggest that the computerized analysis system based on unsupervised clustering has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography.

COMPUTER-AIDED DIAGNOSIS AND VISUALIZATION BASED ON CLUSTERING AND INDEPENDENT COMPONENT ANALYSIS FOR BREAST MRI.

Computer-aided diagnosis and simultaneous visualization based on independent component analysis and clustering are integrated in an intelligent system for the evaluation of small mammographic lesions in breast MRI. These techniques are tested on biomedical time-series representing breast MRI scans and enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By revealing regional properties of contrast-agent uptake characterized by subtle differences of signal amplitude and dynamics, these methods provide both a set of prototypical time-series and a corresponding set of cluster assignment maps which further provide a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions. Both approaches lead to an increase of the diagnostic accuracy of MRI mammography by improving the sensitivity without reduction of specificity.

Analysis of dynamic susceptibility contrast MRI time series based on unsupervised clustering methods.

We compare five different unsupervised clustering techniques as tools for the analysis of dynamic susceptibility contrast MRI time series. The study included four subjects: two subjects with stroke and two subjects without focal neurological deficit. The goal was to determine the robustness and reliability of clustering methods in providing a self-organized segmentation of perfusion MRI data sharing common properties of signal dynamics. For this purpose, the relative signal reduction time series was computed for each pixel. Clustering of the resulting high-dimensional feature vectors was performed by minimal free-energy deterministic annealing, self-organizing maps, two variants of fuzzy c-means clustering (FVQ and FSM), and the neural gas algorithm. Clustering results were evaluated by visual assessment of cluster assignment maps and corresponding signal time curves as well as by quantitative comparison of cluster assignment maps with conventional pixel-specific perfusion parameter maps based on quantitative receiver operating characteristic (ROC) curve analysis. Clustering methods provided a functional segmentation with respect to vessel size, detected side asymmetries of contrast-agent first pass, and identified regions of perfusion deficits in subjects with stroke. As confirmed by quantitative ROC analysis, the clustering approach can detect regions of reduced brain perfusion with high accuracy when compared to conventional analysis by pixel-specific cerebral blood volume and mean transit time maps. We conclude that by unveiling differences of signal dynamics and amplitude, clustering is a useful tool to analyze and visualize regional properties of brain perfusion. Thus, it may contribute to the computer-aided diagnosis of cerebral circulation deficits by noninvasive neuroimaging.

Cluster analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series.

We performed neural network clustering on dynamic contrast-enhanced perfusion magnetic resonance imaging time-series in patients with and without stroke. Minimal-free-energy vector quantization, self-organizing maps, and fuzzy c-means clustering enabled self-organized data-driven segmentation with respect to fine-grained differences of signal amplitude and dynamics, thus identifying asymmetries and local abnormalities of brain perfusion. We conclude that clustering is a useful extension to conventional perfusion parameter maps.

Comparison of two exploratory data analysis methods for fMRI: unsupervised clustering versus independent component analysis.

Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). In this paper, we present a comparison between unsupervised clustering and ICA in a systematic fMRI study. The comparative results were evaluated by 1) task-related activation maps, 2) associated time-courses, and 3) receiver operating characteristic analysis. For the fMRI data, a comparative quantitative evaluation between the three clustering techniques, self-organizing map, "neural gas" network, and fuzzy clustering based on deterministic annealing, and the three ICA methods, FastICA, Infomax and topographic ICA was performed. The ICA methods proved to extract features relatively well for a small number of independent components but are limited to the linear mixture assumption. The unsupervised Clustering outperforms ICA in terms of classification results but requires a longer processing time than the ICA methods.

Global exponential stability of competitive neural networks with different time scales.

The dynamics of cortical cognitive maps developed by self-organization must include the aspects of long and short-term memory. The behavior of such a neural network is characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural system. We present a new method of analyzing the dynamics of a biological relevant system with different time scales based on the theory of flow invariance. We are able to show the conditions under which the solutions of such a system are bounded being less restrictive than with the K-monotone theory, singular perturbation theory, or those based on supervised synaptic learning. We prove the existence and the uniqueness of the equilibrium. A strict Lyapunov function for the flow of a competitive neural system with different time scales is given and based on it we are able to prove the global exponential stability of the equilibrium point.