A Semi-parametric Technique for the Quantitative Analysis of Dynamic Contrast-enhanced MR Images Based on Bayesian P-splines

Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) is an important tool for detecting subtle kinetic changes in cancerous tissue. Quantitative analysis of DCE-MRI typically involves the convolution of an arterial input function (AIF) with a nonlinear pharmacokinetic model of the contrast agent concentration. Parameters of the kinetic model are biologically meaningful, but the optimization of the non-linear model has significant computational issues. In practice, convergence of the optimization algorithm is not guaranteed and the accuracy of the model fitting may be compromised. To overcome this problems, this paper proposes a semi-parametric penalized spline smoothing approach, with which the AIF is convolved with a set of B-splines to produce a design matrix using locally adaptive smoothing parameters based on Bayesian penalized spline models (P-splines). It has been shown that kinetic parameter estimation can be obtained from the resulting deconvolved response function, which also includes the onset of contrast enhancement. Detailed validation of the method, both with simulated and in vivo data, is provided

A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization.

Density assessment and lesion localization in breast MRI require accurate segmentation of breast tissues. A fast, computerized algorithm for volumetric breast segmentation, suitable for multi-centre data, has been developed, employing 3D bias-corrected fuzzy c-means clustering and morphological operations. The full breast extent is determined on T1-weighted images without prior information concerning breast anatomy. Left and right breasts are identified separately using automatic detection of the midsternum. Statistical analysis of breast volumes from eighty-two women scanned in a UK multi-centre study of MRI screening shows that the segmentation algorithm performs well when compared with manually corrected segmentation, with high relative overlap (RO), high true-positive volume fraction (TPVF) and low false-positive volume fraction (FPVF), and has an overall performance of RO 0.94 ± 0.05, TPVF 0.97 ± 0.03 and FPVF 0.04 ± 0.06, respectively (training: 0.93 ± 0.05, 0.97 ± 0.03 and 0.04 ± 0.06; test: 0.94 ± 0.05, 0.98 ± 0.02 and 0.05 ± 0.07)

An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data

Twellmann T, Lichte O, Nattkemper TW. An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data. IEEE TRANSACTIONS ON MEDICAL IMAGING. 2005;24(10):1256-1266.Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid cancer diagnosis. Nevertheless, due to the multi-temporal nature of the three-dimensional volume data obtained from DCE-MRI, evaluation of the image data is a challenging task and tools are required to support the human expert. We investigate an approach for automatic localization and characterization of suspicious lesions in DCE-MRI data. It applies an artificial neural network (ANN) architecture which combines unsupervised and supervised techniques for voxel-by-voxel classification of temporal kinetic signals. The algorithm is easy to implement, allows for fast training and application even for huge data sets and can be directly used to augment the display of DCE-MRI data. To demonstrate that the system provides a reasonable assessment of kinetic signals, the outcome is compared with the results obtained from the model-based three-time-points (3TP) technique which represents a clinical standard protocol for analysing breast cancer lesions. The evaluation based on the DCE-MRI data of 12 cases indicates that, although the ANN is trained with imprecisely labeled data, the approach leads to an outcome conforming with 3TP without presupposing an explicit model of the underlying physiological process

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

IRM de perfusion T1 dans le cancer de la prostate, analyse quantitative et étude de l’impact de la fonction d’entrée artérielle sur les capacités diagnostiques des paramètres pharmacocinétiques

Dynamic contrast enhanced (DCE)-MRI is a T1 weighted sequence performed before, during and after a bolus injection of a contrast agent (CA). It is included in the multi-parametric prostate MRI (mp-MRI) protocol using to assess the extent of prostate cancer (PCa). The rationale for using DCE-MRI in PCa is that on one hand angiogenesis has been shown to play a central role in the PCa development and metastasis and on the other hand that DCE-MRI is a non invasive method able to depict this angiogenesis in vivo. The quantitative analysis of DCE-MRI data is a complex and multi-step process. The principle is to use a pharmacokinetic (PK) model reflecting the theoretical distribution of the CA in a tissue to extract PK parameters that describe the perfusion and capillary permeability. These parameters are of growing interest, especially in the field of oncology, for their use in assessing the aggressiveness, the prognosis and the efficacy of anti-angiogenic or anti-vascular treatments. The potential utility of these parameters is significant; however, the parameters often lack reproducibility, particularly between different quantitative analysis software programs.Firstly, we developed a quantitative analysis software solution using the variable flip angle method to estimate the T1 mapping which is needed to convert the signal-time curves to CA concentration-time curves; using three different arterial input functions (AIF): an individual AIF (Ind) measured manually in a large artery, and two literature population average AIFs of Weinmann (W) and of Fritz-Hansen (FH); and using two PK models (Tofts and modified Tofts). The robustness of the software programs was assessed on synthetic DCE-MRI data set and on a clinical DCE-MRI data set. Secondly, we assessed the impact of the AIF selection on the PK parameters to distinguish PCa from benign tissue. 38 patients with clinically important peripheral PCa (≥0.5cc) were retrospectively included. These patients underwent 1.5T multiparametric prostate MR with PCa and benign regions of interest (ROI) selected using a visual registration with morphometric reconstruction obtained from radical prostatectomy. Using three pharmacokinetic (PK) analysis software programs, the mean Ktrans, ve and vp of ROIs were computed using three AIFs: Ind-AIF, W-AIF and FH-AIF. The Ktrans provided higher area under the receiver operating characteristic curves (AUROCC) than ve and vp. The Ktrans was significantly higher in the PCa ROIs than in the benign ROIs. AUROCCs obtained with W-AIF were significantly higher than FH-AIF (0.002≤p≤0.045) and similar to or higher than Ind-AIF (0.014≤p≤0.9). Ind-AIF and FH-AIF provided similar AUROCC (0.34≤p≤0.81).We have then demonstrated that the selection of AIF can modify the capacity of the PK parameter Ktrans to distinguish PCa from benign tissue and that W-AIF yielded a similar or higher performance than Ind-AIF and a higher performance than FH-AIF.La séquence d’IRM de perfusion pondérée T1 après injection de Gadolinium (Gd), appelée dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) fait partie du protocole d’IRM multiparamétrique (IRM-mp) réalisée pour le bilan d’extension du cancer prostatique (CaP). Le rationnel pour l’utilisation de cette séquence est d’une part le rôle capital de la néoangiogénèse dans le développement et la dissémination du CaP et d’autre part la possibilité d’imager l’angiogénèse in vivo et de façon non invasive. L’analyse quantitative nécessite un post-traitement multi-étapes complexe, dont le principe repose sur la modélisation pharmacocinétique (PC) de la biodistrubtion du Gd. Elle permet de calculer des paramètres PC reflétant la perméabilité capillaire et/ou la perfusion. Dans le CaP, ces paramètres PC ont montré leur potentiel pour évaluer l’agressivité tumorale, le pronostic, l’efficacité d’un traitement et/ou pour déterminer la dose efficace d’une nouvelle molécule anti-angiogéniques ou antivasculaires en cours de développement. Néanmoins, ils manquent de reproductibilité, notamment du fait des différentes techniques de quantifications utilisées par les logiciels de post-traitement.Nous avons développé au sein du laboratoire un outil de quantification capable de calculer une cartographie T1(0) à partir de la méthode des angles de bascule variables, nécessaire pour convertir les courbes du signal en courbe de concentration du Gd (Ct); de déterminer la fonction d’entrée artérielle (AIF – arterial input function) dans l’artère fémorale (Indivuduelle – Ind) ou lorsque cela n’était pas possible, d’utiliser une AIF issue de la littérature, telle que celle de Weinmann (W) ou de Fritz-Hansen (FH) ; et d’utiliser deux modèles PC, celui de Tofts et celui de Tofts modifié. Le logiciel a été validé sur des données simulées et sur une petite série clinique.Nous avons ensuite étudié l’impact du choix de la fonction d’entrée artériel sur les paramètres PC et notamment sur leur capacité à distinguer le CaP du tissu sain. 38 patients avec un CaP (>0,5cc) de la zone périphérique (ZP) ont été rétrospectivement inclus. Chaque patient avait bénéficié d’une IRM-mp sur laquelle deux régions d’intérêt (RI) : une tumorale et une bénigne ont été sélectionnées en utilisant une corrélation avec des cartes histo-morphométriques obtenues après prostatectomie radicale. En utilisant trois logiciels d’analyse quantitative différents, les valeurs moyennes de Ktrans (constante de transfert), ve (fraction du volume interstitiel) and vp (fraction du volume plasmatique) dans les RI ont été calculées avec trois AIF différentes (AIF Ind, AIF de W et AIF de FH). Ktrans était le paramètre PC qui permettait de mieux distinguer le CaP du tissu sain et ses valeurs étaient significativement supérieures dans le CaP, quelque soit l’AIF ou le logiciel. L’AIF de W donnait des aires sous les courbes ROC (Receiver Operating Characteristic) significativement plus grandes que l’AIF de FH (0.002≤p≤0.045) et plus grandes ou égales à l’AIF Ind (0.014≤p≤0.9). L’AIF Ind et de FH avaient des aires sous les courbes ROC comparables (0.34≤p≤0.81). Nous avons donc montré que les valeurs de Ktrans et sa capacité à distinguer CaP du tissu sain variaient significativement avec le choix de l’AIF et que les meilleures performances étaient obtenues avec l’AIF de W

Quality framework for semantic interoperability in health informatics: definition and implementation

Aligned with the increased adoption of Electronic Health Record (EHR) systems, it is recognized that semantic interoperability provides benefits for promoting patient safety and continuity of care. This thesis proposes a framework of quality metrics and recommendations for developing semantic interoperability resources specially focused on clinical information models, which are defined as formal specifications of structure and semantics for representing EHR information for a specific domain or use case. This research started with an exploratory stage that performed a systematic literature review with an international survey about the clinical information modelling best practice and barriers. The results obtained were used to define a set of quality models that were validated through Delphi study methodologies and end user survey, and also compared with related quality standards in those areas that standardization bodies had a related work programme. According to the obtained research results, the defined framework is based in the following models: Development process quality model: evaluates the alignment with the best practice in clinical information modelling and defines metrics for evaluating the tools applied as part of this process. Product quality model: evaluates the semantic interoperability capabilities of clinical information models based on the defined meta-data, data elements and terminology bindings. Quality in use model: evaluates the suitability of adopting semantic interoperability resources by end users in their local projects and organisations. Finally, the quality in use model was implemented within the European Interoperability Asset register developed by the EXPAND project with the aim of applying this quality model in a broader scope to contain any relevant material for guiding the definition, development and implementation of interoperable eHealth systems in our continent. Several European projects already expressed interest in using the register, which will now be sustained by the European Institute for Innovation through Health Data