Computer-aided detection in breast MRI: a systematic review and meta-analysis

To evaluate the additional value of computer-aided detection (CAD) in breast MRI by assessing radiologists' accuracy in discriminating benign from malignant breast lesions. A literature search was performed with inclusion of relevant studies using a commercially available CAD system with automatic colour mapping. Two independent researchers assessed the quality of the studies. The accuracy of the radiologists' performance with and without CAD was presented as pooled sensitivity and specificity. Of 587 articles, 10 met the inclusion criteria, all of good methodological quality. Experienced radiologists reached comparable pooled sensitivity and specificity before and after using CAD (sensitivity: without CAD: 89%; 95% CI: 78-94%, with CAD: 89%; 95%CI: 81-94%) (specificity: without CAD: 86%; 95% CI: 79-91%, with CAD: 82%; 95% CI: 76-87%). For residents the pooled sensitivity increased from 72% (95% CI: 62-81%) without CAD to 89% (95% CI: 80-94%) with CAD, however, not significantly. Concerning specificity, the results were similar (without CAD: 79%; 95% CI: 69-86%, with CAD: 78%; 95% CI: 69-84%). CAD in breast MRI has little influence on the sensitivity and specificity of experienced radiologists and therefore their interpretation remains essential. However, residents or inexperienced radiologists seem to benefit from CAD concerning breast MRI evaluation

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