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

Pseudo-color visualizations of DCE-MR image series for MR mammography

In recent years, dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging has become a valuable tool for detection, diagnosis and management of breast cancer. Several criteria for describing morphologic and dynamic characteristics of suspiciously enhancing tissue regions have been collected in the ACR BIRADS MRI lexicon. However, evaluation of these criteria is nonetheless a challenging task for human observers due to the huge amount and the multitemporal nature of the image data. Therefore, computer aided diagnosis (CAD) tools based on artificial neural networks (ANN) or pharamcokinetic models receive growing attention from the radiologic community. In DCE-MR imaging, each voxel is associated with a vector s = (s1, . . . , st) reflecting the temporal variation of the local signal intensity after intravenous administration of a contrast agent (Gd-DTPA). Due to changes in their vascular structure, benign and malignant tissue expose characteristic intensity-time curves (ITC). These curves enable radiologists to infer information about the tissue state from the image data, a time-consuming task owing to the heterogeneity of cancerous tissue. To aid evaluation of DCE-MR image series, we propose a pseudo-color visualization of the temporal information based on ANNs. An ANN is trained with labeled ITCs sampled from a number of histologically verified training cases to classify each temporal signal sx,y,z as being indicative for malignant (m), normal (n) or benign (b) tissue according to the returned posteriori probabilities p(m|s_x,y,z), p(n|s_x,y,z) and p(b|s_x,y,z). Pseudo-color visualizations of unseen image series are computed by displaying suspiciously enhancing voxels with RGB colors reflecting the ANN based signal assessment: bright red, green and blue voxels indicate high p(m|s_x,y,z), p(n|s_x,y,z) and p(b|s_x,y,z) values, respectively. Therewith, temporal characteristics of tissue regions are revealed, enabling radiologists to assess the architecture of lesions by means of a single 3D color image

Breas

Nattkemper TW, Degenhard A, Twellmann T. Breas. In: Hayat MA, ed. Cancer Imaging - Lung and breast carcinomas. Vol 1. Academic Press; 2007: 309-324

Breast Tumor Classification and Visualization with Machine-Learning Approaches

Nattkemper TW, Degenhard A, Twellmann T. Breast Tumor Classification and Visualization with Machine-Learning Approaches. In: Hayat MA, ed. Cancer Imaging - Lung and breast carcinomas. Vol 1. Amsterdam : Academic Press; 2007: 309-321

Spectral Clustering for Data Categorization based on Self-Organizing Maps

Saalbach A, Twellmann T, Nattkemper TW. Spectral Clustering for Data Categorization based on Self-Organizing Maps. In: Nasser M. N, ed. Applications of Neural Networks and Machine Learning in Image Processing IX. Vol 5673. San Jose, CA; 2005: 12-18.The exploration and categorization of large and unannotated image collections is a challenging task in the field of image retrieval as well as in the generation of appearance based object representations. In this context the Self-Organizing Map (SOM) has shown to be an efficient and scalable tool for the analysis of image collections based on low level features. Next to commonly employed visualization methods, clustering techniques have been recently considered for the aggregation of SOM nodes into groups in order to facilitate category specific data exploration. In this paper, spectral clustering based on graph theoretic concepts is employed for SOM based data categorization. The results are compared with those from the Neural Gas algorithm and hierarchical agglomerative clustering. Using SOMs trained on an eigenspace representation of the Columbia Object Image Library 20 (COIL20), the correspondence of the cluster data to a semantic reference grouping is calculated. Based on the Adjusted Rand Index it is shown that independent from the number of selected clusters, spectral clustering achieves a significantly higher correspondence to the reference grouping than any of the other methods

The influence of temporal resolution and phase-encoding order on shape-based classification of dynamic contrast enhanced (DCE) MRI uptake curves in the breast

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Impact of the arterial input function on the classification of contrast-agent uptake curves in dynamic contrast-enhanced (DCE) MR images based on heuristic shape modeling

Purpose: To demonstrate that inter-patient differences in the spreading of the contrast agent throughout the blood, described by the arterial input function (AIF), should be considered in the classification of contrast-agent uptake curves in the tissue of interest, e.g., a suspicious lesion in the breast. In the application of heuristic shape modeling (Kuhl et al. 1999, three-time-point (3TP) by Weinstein et al. 1999), the AIF is not extracted from the DCE-MR image series and, therefore, not taken into account.Methods and Materials: A two-compartment model (extended Kety) with fixed pharmacokinetic parameters is used to simulate the tissue-response curves for different AIFs. The shape of these curves is classified into benign, suspicious or malignant by means of the 3TP method.Results: While AIFs are known to differ in a wide range, our simulations indicate that already small changes of the AIF considerably alter the shape of the response curve. These changes may even lead to different curve classifications, although the simulated response curves relate to ’tissue’ with fixed pharmacokinetic properties. Conclusion: The shape of contrast-agent uptake curves expressed by simulated tissue with fixed pharmacokinetic properties can get classified differently in different patients owing to inter-patient variations of the AIF. Evaluation of the influence of variation in AIFs in real patient data is work in progress. Validation of our observation with real patient data might suggest that deconvolution with patient-specific AIFs, as it is done in pharmacokinetic modeling, improves reliability of tissue classification derived from the shape of contrast-agent uptake curves

The role of temporal resolution in determining pharmacokinetic parameters from DCE-MR data

In DCE-MRI of the breast, a wide variety in parameter settings is possible. This especially holds for the temporal resolution of the dynamic series. Given thehigh expectations of pharmacokinetic modeling, it is crucial to analyze the effect of temporal resolution in determining pharmacokinetic parameters. Weinvestigated this issue by deriving low-temporal-resolution image-series from a high-temporal-resolution original via a reorganization of the k-space data. The initial experiment, as presented here, was performed on data from model tumors in rats. Fitting of the Kety two-compartment pharmacokinetic modeldemonstrated that with decreasing temporal resolution, Ktrans and ve get progressively under- and overestimated

Image fusion for dynamic contrast enhanced magnetic resonance imaging

Twellmann T, Saalbach A, Gerstung O, Leach MO, Nattkemper TW. Image fusion for dynamic contrast enhanced magnetic resonance imaging. Biomed Eng Online. 2004;3(1): 35.BACKGROUND: Multivariate imaging techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been shown to provide valuable information for medical diagnosis. Even though these techniques provide new information, integrating and evaluating the much wider range of information is a challenging task for the human observer. This task may be assisted with the use of image fusion algorithms. METHODS: In this paper, image fusion based on Kernel Principal Component Analysis (KPCA) is proposed for the first time. It is demonstrated that a priori knowledge about the data domain can be easily incorporated into the parametrisation of the KPCA, leading to task-oriented visualisations of the multivariate data. The results of the fusion process are compared with those of the well-known and established standard linear Principal Component Analysis (PCA) by means of temporal sequences of 3D MRI volumes from six patients who took part in a breast cancer screening study. RESULTS: The PCA and KPCA algorithms are able to integrate information from a sequence of MRI volumes into informative gray value or colour images. By incorporating a priori knowledge, the fusion process can be automated and optimised in order to visualise suspicious lesions with high contrast to normal tissue. CONCLUSION: Our machine learning based image fusion approach maps the full signal space of a temporal DCE-MRI sequence to a single meaningful visualisation with good tissue/lesion contrast and thus supports the radiologist during manual image evaluation