Pointwise adaptive estimation for robust and quantile regression

A nonparametric procedure for robust regression estimation and for quantile regression is proposed which is completely data-driven and adapts locally to the regularity of the regression function. This is achieved by considering in each point M-estimators over different local neighbourhoods and by a local model selection procedure based on sequential testing. Non-asymptotic risk bounds are obtained, which yield rate-optimality for large sample asymptotics under weak conditions. Simulations for different univariate median regression models show good finite sample properties, also in comparison to traditional methods. The approach is extended to image denoising and applied to CT scans in cancer research

Pointwise adaptive estimation for quantile regression

A nonparametric procedure for quantile regression, or more generally nonparametric M-estimation, is proposed which is completely data-driven and adapts locally to the regularity of the regression function. This is achieved by considering in each point M-estimators over different local neighbourhoods and by a local model selection procedure based on sequential testing. Non-asymptotic risk bounds are obtained, which yield rate-optimality for large sample asymptotics under weak conditions. Simulations for different univariate median regression models show good finite sample properties, also in comparison to traditional methods. The approach is the basis for denoising CT scans in cancer research.M-estimation, median regression, robust estimation, local model selection, unsupervised learning, local bandwidth selection, median filter, Lepski procedure, minimax rate, image denoising

Laplace deconvolution and its application to Dynamic Contrast Enhanced imaging

In the present paper we consider the problem of Laplace deconvolution with noisy discrete observations. The study is motivated by Dynamic Contrast Enhanced imaging using a bolus of contrast agent, a procedure which allows considerable improvement in {evaluating} the quality of a vascular network and its permeability and is widely used in medical assessment of brain flows or cancerous tumors. Although the study is motivated by medical imaging application, we obtain a solution of a general problem of Laplace deconvolution based on noisy data which appears in many different contexts. We propose a new method for Laplace deconvolution which is based on expansions of the convolution kernel, the unknown function and the observed signal over Laguerre functions basis. The expansion results in a small system of linear equations with the matrix of the system being triangular and Toeplitz. The number $m$ of the terms in the expansion of the estimator is controlled via complexity penalty. The advantage of this methodology is that it leads to very fast computations, does not require exact knowledge of the kernel and produces no boundary effects due to extension at zero and cut-off at $T$. The technique leads to an estimator with the risk within a logarithmic factor of $m$ of the oracle risk under no assumptions on the model and within a constant factor of the oracle risk under mild assumptions. The methodology is illustrated by a finite sample simulation study which includes an example of the kernel obtained in the real life DCE experiments. Simulations confirm that the proposed technique is fast, efficient, accurate, usable from a practical point of view and competitive

Laplace deconvolution on the basis of time domain data and its application to Dynamic Contrast Enhanced imaging

In the present paper we consider the problem of Laplace deconvolution with noisy discrete non-equally spaced observations on a finite time interval. We propose a new method for Laplace deconvolution which is based on expansions of the convolution kernel, the unknown function and the observed signal over Laguerre functions basis (which acts as a surrogate eigenfunction basis of the Laplace convolution operator) using regression setting. The expansion results in a small system of linear equations with the matrix of the system being triangular and Toeplitz. Due to this triangular structure, there is a common number $m$ of terms in the function expansions to control, which is realized via complexity penalty. The advantage of this methodology is that it leads to very fast computations, produces no boundary effects due to extension at zero and cut-off at $T$ and provides an estimator with the risk within a logarithmic factor of the oracle risk. We emphasize that, in the present paper, we consider the true observational model with possibly nonequispaced observations which are available on a finite interval of length $T$ which appears in many different contexts, and account for the bias associated with this model (which is not present when $T\rightarrow\infty$). The study is motivated by perfusion imaging using a short injection of contrast agent, a procedure which is applied for medical assessment of micro-circulation within tissues such as cancerous tumors. Presence of a tuning parameter $a$ allows to choose the most advantageous time units, so that both the kernel and the unknown right hand side of the equation are well represented for the deconvolution. The methodology is illustrated by an extensive simulation study and a real data example which confirms that the proposed technique is fast, efficient, accurate, usable from a practical point of view and very competitive.Comment: 36 pages, 9 figures. arXiv admin note: substantial text overlap with arXiv:1207.223

Dynamic Three-Dimensional Shoulder Mri during Active Motion for Investigation of Rotator Cuff Diseases.

BACKGROUND: MRI is the standard methodology in diagnosis of rotator cuff diseases. However, many patients continue to have pain despite treatment, and MRI of a static unloaded shoulder seems insufficient for best diagnosis and treatment. This study evaluated if Dynamic MRI provides novel kinematic data that can be used to improve the understanding, diagnosis and best treatment of rotator cuff diseases. METHODS: Dynamic MRI provided real-time 3D image series and was used to measure changes in the width of subacromial space, superior-inferior translation and anterior-posterior translation of the humeral head relative to the glenoid during active abduction. These measures were investigated for consistency with the rotator cuff diseases classifications from standard MRI. RESULTS: The study included: 4 shoulders with massive rotator cuff tears, 5 shoulders with an isolated full-thickness supraspinatus tear, 5 shoulders with tendinopathy and 6 normal shoulders. A change in the width of subacromial space greater than 4mm differentiated between rotator cuff diseases with tendon tears (massive cuff tears and supraspinatus tear) and without tears (tendinopathy) (p = 0.012). The range of the superior-inferior translation was higher in the massive cuff tears group (6.4mm) than in normals (3.4mm) (p = 0.02). The range of the anterior-posterior translation was higher in the massive cuff tears (9.2 mm) and supraspinatus tear (9.3 mm) shoulders compared to normals (3.5mm) and tendinopathy (4.8mm) shoulders (p = 0.05). CONCLUSION: The Dynamic MRI enabled a novel measure; 'Looseness', i.e. the translation of the humeral head on the glenoid during an abduction cycle. Looseness was better able at differentiating different forms of rotator cuff disease than a simple static measure of relative glenohumeral position.The authors received no specific funding for this work

In Vivo Detection of Succinate by Magnetic Resonance Spectroscopy as a Hallmark of SDHx Mutations in Paraganglioma

International audiencePurpose: Germline mutations in genes encoding mitochon-drial succinate dehydrogenase (SDH) are found in patients with paragangliomas, pheochromocytomas, gastrointestinal stromal tumors, and renal cancers. SDH inactivation leads to a massive accumulation of succinate, acting as an oncometabolite and which levels, assessed on surgically resected tissue are a highly specific biomarker of SDHx-mutated tumors. The aim of this study was to address the feasibility of detecting succinate in vivo by magnetic resonance spectroscopy. Experimental Design: A pulsed proton magnetic resonance spectroscopy (1 H-MRS) sequence was developed, optimized, and applied to image nude mice grafted with Sdhb À/À or wild-type chromaffin cells. The method was then applied to patients with paraganglioma carrying (n ¼ 5) or not (n ¼ 4) an SDHx gene mutation. Following surgery, succinate was measured using gas chromatography/mass spectrometry, and SDH protein expression was assessed by immunohistochemistry in resected tumors. Results: A succinate peak was observed at 2.44 ppm by 1 H-MRS in all Sdhb À/À-derived tumors in mice and in all paragangliomas of patients carrying an SDHx gene mutation, but neither in wild-type mouse tumors nor in patients exempt of SDHx mutation. In one patient, 1 H-MRS results led to the identification of an unsus-pected SDHA gene mutation. In another case, it helped define the pathogenicity of a variant of unknown significance in the SDHB gene. Conclusions: Detection of succinate by 1 H-MRS is a highly specific and sensitive hallmark of SDHx mutations. This non-invasive approach is a simple and robust method allowing in vivo detection of the major biomarker of SDHx-mutated tumors. Clin Cancer Res; 22(5); 1120–9. Ó2015 AACR

Pointwise adaptive estimation for quantile regression

A nonparametric procedure for quantile regression, or more generally nonparametric M-estimation, is proposed which is completely data-driven and adapts locally to the regularity of the regression function. This is achieved by considering in each point M-estimators over different local neighbourhoods and by a local model selection procedure based on sequential testing. Non-asymptotic risk bounds are obtained, which yield rate-optimality for large sample asymptotics under weak conditions. Simulations for different univariate median regression models show good finite sample properties, also in comparison to traditional methods. The approach is the basis for denoising CT scans in cancer research

Qualité de la modélisation en imagerie dynamique de la microcirculation avec injection d'un agent de contraste (nouveaux critères et applications en multimodalité)

L'imagerie dynamique de microcirculation dispose d'un potentiel important pour l'étude de nombreuses pathologies in vivo, en complément à l'imagerie conventionnelle. Or pour obtenir des cartes de paramètres microcirculatoires à partir des données dynamiques, une modélisation doit être effectuée. Les méthodes actuelles pour vérifier la qualité de cette modélisation n'étant pas satisfaisantes, le potentiel de l'imagerie dynamique en est fortement réduit. Nous montrons ici que pour étudier la modélisation, tant qualitativement que quantitativement, il est nécessaire de traiter séparément les questions de qualité d'ajustement et de robustesse,. Nous avons mis au point une nouvelle méthode, basée sur l'autocorrélation, pour estimer les amplitudes des composantes corrélées et non corrélées des signaux. Cette méthode nous a permis de corriger le coefficient de corrélation R et la matrice de covariance, et ainsi de définir de nouveaux critères de fiabilité et une matrice de covariance corrigée pour les remplacer. L'amélioration apportée par les nouveaux critères est démontrée sur simulations et sur données IRM dynamiques réelles. La matrice de covariance corrigée estime la robustesse et la redondance locale des paramètres. Elle peut être calculée conjointement pour compléter les nouveaux critères de fiabilité. Les améliorations apportées par les nouveaux indicateurs doivent faciliter le développement de l'imagerie de la microcirculation. L'intérêt des nouveaux indicateurs est illustré sur un grand panel de données d'imagerie. Ils constituent plus généralement de nouveaux outils de traitement du signal.The microcirculation dynamic imaging could be a relevant imaging when used in addition with more conventional medical imaging. The dynamic data are modeled, pixel by pixel, to provide microcirculation parameters maps. However there is no efficient tool to assess the modeling quality. The relevance of the parametric maps provided by the dynamic imaging is then limited. Here, we show that a qualitative and quantitative study of the modeling quality needs first to distinguish two questions : the quality of the data fits and the robusness for the random noise. To separate the questions, we designed a new autocorrelation based method which is able to estimate the amplitude of both the correlated and not correlated component of a signal. This method allowed us to correct the correlation coefficient R and the covariance matrix estimation. It allowed us to define new reliability criteria and a corrected covariance matrix to replace the more conventional indicators. It was shown, on simulated data and in MR data, that new reliabily criteria are obviously better than the R to assess fit quality. The corrected covariance matrix which assess the robustness and the redoundancy can be calculated in addition to the reliability criteria unlike conventional one which is limited to good data fits. Thus the modeling quality is obviously improved by the new indicators. It should improve the clinical use of microcirculation dynamic imaging where guaranties are needed against artefact. The interest of the new criteria is showed on many different dynamic data. More generaly the new indicators appear as new efficient tools for signal analysis.ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF

Effets d'un traitement antiangiogénique sur la microcirculation tumorale (mise en évidence par IRMf chez la souris à l'aide d'un agent de contraste macromoléculaire)

PARIS6-Bibl.Pitié-Salpêtrie (751132101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF