Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs

A new methodology based on tensor algebra that uses a higher order singular value decomposition to perform three-dimensional voxel reconstruction from a series of temporal images obtained using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. Principal component analysis (PCA) is used to robustly extract the spatial and temporal image features and simultaneously de-noise the datasets. Tumour segmentation on enhanced scaled (ES) images performed using a fuzzy C-means (FCM) cluster algorithm is compared with that achieved using the proposed tensorial framework. The proposed algorithm explores the correlations between spatial and temporal features in the tumours. The multi-channel reconstruction enables improved breast tumour identification through enhanced de-noising and improved intensity consistency. The reconstructed tumours have clear and continuous boundaries; furthermore the reconstruction shows better voxel clustering in tumour regions of interest. A more homogenous intensity distribution is also observed, enabling improved image contrast between tumours and background, especially in places where fatty tissue is imaged. The fidelity of reconstruction is further evaluated on the basis of five new qualitative metrics. Results confirm the superiority of the tensorial approach. The proposed reconstruction metrics should also find future applications in the assessment of other reconstruction algorithms

Data driven analysis of dynamic contrast-enhanced magnetic resonance imaging data in breast cancer diagnosis

In the European Union, breast cancer is the most common type of cancer affecting women. If diagnosed in an early stage, breast cancer has an encouraging cure rate. Thus, early detection of breast cancer continues to be the key for an effective treatment. Recently, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been identified as a valuable complementary technique for breast imaging. DCE-MRI has demonstrated to be highly sensitive for the detection of cancer, motivating the initiation of several ongoing studies evaluating the potential of DCE-MRI as a screening tool for young women or women at high risk by virtue of genetic predispositions. In DCE-MRI, a temporal sequence of 3D MRI images of the female breast is recorded, depicting the temporal course of the concentration of a contrast agent in breast tissue. The temporal dynamics of the concentration enable radiologists to infer valuable information not only for differentiating between healthy and pathologically affected tissue, but also for distinguishing innocuous benign disorders from life-threatening carcinoma. This new type of information is inherent in the multi-temporal image sequence, but does not become evident to the observer by means of the individual images. For detecting and characterising pathological disorders of tiny tissue regions, radiologists are required to simultaneously consider the entire data; a challenging task due to the multi-temporal nature and the huge amount of 3D image data. Hence, there is a substantial demand for Computer-Aided Diagnosis (CAD) systems for supporting radiologists in the time consuming diagnosis process. The aim of the work as presented in this thesis is to develop computational approaches for DCE-MRI data analysis in breast cancer diagnosis. Central component of the presented approaches will be techniques from the field of artificial neural networks (ANN) and machine learning. ANNs allow for analysing DCE-MRI data from a data-driven and initially model-free perspective differing from the model-based perspective predominant in clinical practice. A central concept of the data-driven approaches to DCE-MRI analysis is example-based learning: Training signals reflecting the temporal courses of contrast agent concentrations in single voxels are exposed to unsupervised ANNs which in turn autonomously reveal categories of similar signals by virtue of their statistical features. Supervised ANNs are able to derive knowledge for the distinction of predetermined classes of signals from a sequence of training examples which were assigned by e.g. a human expert to one of the considered classes. After adaptation, the trained predictor is able to generalise from the seen to unseen examples and can be applied for detecting signals of the corresponding classes in DCE-MRI sequences of new cases. The initial identification of tissue masses affected by pathological disorders is considered as a binary classification problem. Linear discriminant analysis as well as state-of-the-art support vector machines for kernel-based learning are applied for voxel-by-voxel classification of temporal kinetic signals or textural features. The outcome is visualised as a new grey value image, enabling radiologists to identify suspicious tissue masses by means of a single 3D image. Analysis of the tumour masses themselves is supported by pseudo-colour representation of the DCE-MRI data. A hierarchical architecture of an ANN as well as a multi-class support vector machine with dedicated post-processing of the output is trained to distinguish temporal kinetic signals of healthy, benign and malignant tissue. The visual presentation of the outcome as a 3D RGB image reveals the heterogeneity of tumour tissue and provides valuable information about the tumour architecture. For further examination of the relation between pseudo-colour and multi-temporal signals, the adaptive colour-scale technique is proposed for simultaneous presentation of signal and colour space. In the last application, efficient visualisation of DCE-MRI data is considered as a dimensionality reduction problem. Principal component analysis and the non-linear kernel principal component analysis are applied to reduce the dimension of the signal space of temporal kinetic signals. The depiction of the reduced signal data allows for displaying DCE-MRI sequences by a reduced number of images and reveals information which is inherent in the data but not perceivable in the original images. The applicability of the different modules is demonstrated by means of DCE-MRI sequences recorded during routine examinations at the City Centre Hospital of the University of Munich, Germany, as well as sequences recorded within the MARIBS breast cancer screening study by the Institute of Cancer Research, UK. The validity of the results is demonstrated by means of ROC analyses as well as detailed qualitative comparison with established model-based techniques

Data driven analysis of dynamic contrast-enhanced magnetic resonance imaging data in breast cancer diagnosis

In the European Union, breast cancer is the most common type of cancer affecting women. If diagnosed in an early stage, breast cancer has an encouraging cure rate. Thus, early detection of breast cancer continues to be the key for an effective treatment. Recently, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been identified as a valuable complementary technique for breast imaging. DCE-MRI has demonstrated to be highly sensitive for the detection of cancer, motivating the initiation of several ongoing studies evaluating the potential of DCE-MRI as a screening tool for young women or women at high risk by virtue of genetic predispositions. In DCE-MRI, a temporal sequence of 3D MRI images of the female breast is recorded, depicting the temporal course of the concentration of a contrast agent in breast tissue. The temporal dynamics of the concentration enable radiologists to infer valuable information not only for differentiating between healthy and pathologically affected tissue, but also for distinguishing innocuous benign disorders from life-threatening carcinoma. This new type of information is inherent in the multi-temporal image sequence, but does not become evident to the observer by means of the individual images. For detecting and characterising pathological disorders of tiny tissue regions, radiologists are required to simultaneously consider the entire data; a challenging task due to the multi-temporal nature and the huge amount of 3D image data. Hence, there is a substantial demand for Computer-Aided Diagnosis (CAD) systems for supporting radiologists in the time consuming diagnosis process. The aim of the work as presented in this thesis is to develop computational approaches for DCE-MRI data analysis in breast cancer diagnosis. Central component of the presented approaches will be techniques from the field of artificial neural networks (ANN) and machine learning. ANNs allow for analysing DCE-MRI data from a data-driven and initially model-free perspective differing from the model-based perspective predominant in clinical practice. A central concept of the data-driven approaches to DCE-MRI analysis is example-based learning: Training signals reflecting the temporal courses of contrast agent concentrations in single voxels are exposed to unsupervised ANNs which in turn autonomously reveal categories of similar signals by virtue of their statistical features. Supervised ANNs are able to derive knowledge for the distinction of predetermined classes of signals from a sequence of training examples which were assigned by e.g. a human expert to one of the considered classes. After adaptation, the trained predictor is able to generalise from the seen to unseen examples and can be applied for detecting signals of the corresponding classes in DCE-MRI sequences of new cases. The initial identification of tissue masses affected by pathological disorders is considered as a binary classification problem. Linear discriminant analysis as well as state-of-the-art support vector machines for kernel-based learning are applied for voxel-by-voxel classification of temporal kinetic signals or textural features. The outcome is visualised as a new grey value image, enabling radiologists to identify suspicious tissue masses by means of a single 3D image. Analysis of the tumour masses themselves is supported by pseudo-colour representation of the DCE-MRI data. A hierarchical architecture of an ANN as well as a multi-class support vector machine with dedicated post-processing of the output is trained to distinguish temporal kinetic signals of healthy, benign and malignant tissue. The visual presentation of the outcome as a 3D RGB image reveals the heterogeneity of tumour tissue and provides valuable information about the tumour architecture. For further examination of the relation between pseudo-colour and multi-temporal signals, the adaptive colour-scale technique is proposed for simultaneous presentation of signal and colour space. In the last application, efficient visualisation of DCE-MRI data is considered as a dimensionality reduction problem. Principal component analysis and the non-linear kernel principal component analysis are applied to reduce the dimension of the signal space of temporal kinetic signals. The depiction of the reduced signal data allows for displaying DCE-MRI sequences by a reduced number of images and reveals information which is inherent in the data but not perceivable in the original images. The applicability of the different modules is demonstrated by means of DCE-MRI sequences recorded during routine examinations at the City Centre Hospital of the University of Munich, Germany, as well as sequences recorded within the MARIBS breast cancer screening study by the Institute of Cancer Research, UK. The validity of the results is demonstrated by means of ROC analyses as well as detailed qualitative comparison with established model-based techniques

Computer-aided classification of lesions by means of their kinetic signatures in dynamic contrast-enhanced MR images

The kinetic characteristics of tissue in dynamic contrast-enhanced magnetic resonance imaging data are an important source of information for the differentiation of benign and malignant lesions. Kinetic curves measured for each lesion voxel allow to infer information about the state of the local tissue. As a whole, they reflect the heterogeneity of the vascular structure within a lesion, an important criterion for the preoperative classification of lesions. Current clinical practice in analysis of tissue kinetics however is mainly based on the evaluation of the "most-suspect curve", which is only related to a small, manually or semi-automatically selected region-of-interest within a lesion and does not reflect any information about tissue heterogeneity. We propose a new method which exploits the full range of kinetic information for the automatic classification of lesions. Instead of breaking down the large amount of kinetic information to a single curve, each lesion is considered as a probability distribution in a space of kinetic features, efficiently represented by its kinetic signature obtained by adaptive vector quantization of the corresponding kinetic curves. Dissimilarity of two signatures can be objectively measured using the Mallows distance, which is a metric defined on probability distributions. The embedding of this metric in a suitable kernel function enables us to employ modern kernel-based machine learning techniques for the classification of signatures. In a study considering 81 breast lesions, the proposed method yielded an Az value of 0.89±0.01 for the discrimination of benign and malignant lesions in a nested leave-one-lesion-out evaluation settin

An Adaptive Tissue Characterisation Network for Model-Free Visualisation of Dynamic Contrast-Enhanced Magnetic Resonance Image Data

Dynamic contrast-enhanced magnetic resonanceimaging (DCE-MRI) has become an important source of informationto aid cancer diagnosis. Nevertheless, due to the multi-temporalnature of the three-dimensional volume data obtained fromDCE-MRI, evaluation of the image data is a challenging task andtools are required to support the human expert. We investigatean approach for automatic localization and characterization ofsuspicious lesions in DCE-MRI data. It applies an artificial neuralnetwork (ANN) architecture which combines unsupervised andsupervised techniques for voxel-by-voxel classification of temporalkinetic signals. The algorithm is easy to implement, allows forfast training and application even for huge data sets and canbe directly used to augment the display of DCE-MRI data. Todemonstrate that the system provides a reasonable assessment ofkinetic signals, the outcome is compared with the results obtainedfrom the model-based three-time-points (3TP) technique whichrepresents a clinical standard protocol for analysing breast cancerlesions. The evaluation based on the DCE-MRI data of 12 casesindicates that, although the ANN is trained with impreciselylabeled data, the approach leads to an outcome conforming with3TP without presupposing an explicit model of the underlyingphysiological process

An Adaptive Tissue Characterisation Network for Model-Free Visualisation of Dynamic Contrast-Enhanced Magnetic Resonance Image Data

Dynamic contrast-enhanced magnetic resonanceimaging (DCE-MRI) has become an important source of informationto aid cancer diagnosis. Nevertheless, due to the multi-temporalnature of the three-dimensional volume data obtained fromDCE-MRI, evaluation of the image data is a challenging task andtools are required to support the human expert. We investigatean approach for automatic localization and characterization ofsuspicious lesions in DCE-MRI data. It applies an artificial neuralnetwork (ANN) architecture which combines unsupervised andsupervised techniques for voxel-by-voxel classification of temporalkinetic signals. The algorithm is easy to implement, allows forfast training and application even for huge data sets and canbe directly used to augment the display of DCE-MRI data. Todemonstrate that the system provides a reasonable assessment ofkinetic signals, the outcome is compared with the results obtainedfrom the model-based three-time-points (3TP) technique whichrepresents a clinical standard protocol for analysing breast cancerlesions. The evaluation based on the DCE-MRI data of 12 casesindicates that, although the ANN is trained with impreciselylabeled data, the approach leads to an outcome conforming with3TP without presupposing an explicit model of the underlyingphysiological process

Breast tumor classification and visualization with machine-learning approaches

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