Chapter

Artificial Neural Networks — ICANN 2001

Volume 2130 of the series Lecture Notes in Computer Science pp 1000-1005

Date:

Neural Network Analysis of Dynamic Contrast-Enhanced MRI Mammography

  • Axel WismüllerAffiliated withInstitut für Radiologische Diagnostik, Ludwig-Maximilians-Universität München
  • , Oliver LangeAffiliated withInstitut für Radiologische Diagnostik, Ludwig-Maximilians-Universität München
  • , Dominik R. DerschAffiliated withHypovereinsbank AG
  • , Klaus HahnAffiliated withInstitut für Radiologische Diagnostik, Ludwig-Maximilians-Universität München
  • , Gerda L. LeinsingerAffiliated withInstitut für Radiologische Diagnostik, Ludwig-Maximilians-Universität München

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Abstract

We present a neural network clustering approach to the analysis of dynamic contrast-enhanced magnetic resonance imaging (MRI) mammography time-series. In contrast to conventional extraction of a few voxel-based perfusion parameters, neural network clustering does not discard information contained in the complete signal dynamics time-series data. We performed exploratory data analysis in patients with breast lesions classified as indeterminate from clinical findings and conventional X-ray mammography. Minimal free energy vector quantization provided a self-organized segmentation of voxels w.r.t. fine-grained differences of signal amplitude and dynamics, thus identifying the lesions from surrounding tissue and enabling a subclassification within the lesions with regard to regions characterized by different MRI signal time-courses. We conclude that neural network clustering can provide a useful extension to the conventional visual inspection of interactively defined regions-of-interest. Thus, it can contribute to the diagnosis of indeterminate breast lesions by non-invasive imaging.