Vergleichende Analyse der morphologischen und dynamischen Charakteristika von Brustläsionen, die mittels 1,5 vs. 3 T MRT bioptiert wurden: Unterschiede und Gemeinsamkeiten im diagnostischen Outcome und in der Machbarkeit der Intervention

Ziel der vorliegenden Arbeit war es, etablierte MR-Kriterien zur Analyse von Mamma-Läsionen bei insbesondere kleinen , nur im MR zu visualisierenden Befunden, zu untersuchen und diesbezüglich einen Vergleich zwischen 1,5T und 3T Feldstärke aufzustellen, auch in Bezug auf die Durchführung einer MR-gestützten Biopsie mithilfe beider Feldstärken. Es wurden 345 Läsionen (BI-RADS®4 / 5-Befunde) mit histologischer Sicherung durch MR-Biopsie untersucht und bezüglich der u.a. im Göttingen-Score aufgeführten MR-Kriterien (dynamisch und morphologisch) analysiert. Es lag an beiden Geräten ein identisches Untersuchungsprotokoll und Auswerteverfahren vor und es fanden zur statistischen Prüfung deskriptive Verfahren inkl. Chi-Quadrat-Tests Anwendung, ebenso wie eine Summenscore-Analyse sowie eine nicht-parametrische Korrelation. Es konnten anhand vorliegender Kriterien in der Studie mehr maligne Läsionen mittels 3T detektiert werden, d.h. es konnten bereits zuvor mehr benigne Läsionen mittels 3T ausgeschlossen werden, welche gar nicht erst einer Biopsie zugeführt werden mussten. Desweiteren zeigte sich, dass sich ein kleines Karzinom <1 cm bei 1,5T und 3T Feldstärke signifikant anders darstellt, gerade bei der Beurteilung von Spiculae und ein zuführendes Gefäß konnte mittels 3T signifikant besser beurteilt werden. Zusammenfassend sind die etablierten Kriterien an beiden Geräten nützlich, es sollte hierbei jedoch auf die im Einzelnen ausgearbeiteten Unterschiede und bei einzelnen konkreten Merkmalen der Vorteil von 3T geachtet werden, ebenso aber auch auf die Tatsache, dass die Biopsie mittels 3T konsequenter erscheint, erkennbar durch unter anderem eine deutlich geringere Abbruchquote

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

Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis

This Thesis describes the research work performed in the scope of a doctoral research program and presents its conclusions and contributions. The research activities were carried on in the industry with Siemens S.A. Healthcare Sector, in integration with a research team. Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and complete solutions in the medical sector. The company offers a wide selection of diagnostic and therapeutic equipment and information systems. Siemens products for medical imaging and in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis, magnetic resonance, equipment to angiography and coronary angiography, nuclear imaging, and many others. Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness in the sector. The company owns several patents related with self-similarity analysis, which formed the background of this Thesis. Furthermore, Siemens intended to explore commercially the computer- aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the high knowledge acquired by University of Beira Interior in this area together with this Thesis, will allow Siemens to apply the most recent scienti c progress in the detection of the breast cancer, and it is foreseeable that together we can develop a new technology with high potential. The project resulted in the submission of two invention disclosures for evaluation in Siemens A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index, two other articles submitted in peer-reviewed journals, and several international conference papers. This work on computer-aided-diagnosis in breast led to innovative software and novel processes of research and development, for which the project received the Siemens Innovation Award in 2012. It was very rewarding to carry on such technological and innovative project in a socially sensitive area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas. Um destes métodos foi também adaptado para a classi cação de massas da mama, em cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais, permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram extraídas por análise multifractal características dos tecidos que permitiram identi car os casos tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal 3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a interpretação dos radiologistas