Compressed sensing subtracted rotational angiography with multiple sparse penalty

International audienceDigital Subtraction Rotational Angiography (DSRA) is a clinical protocol that allows three-dimensional (3D) visualization of vasculature during minimally invasive procedures. C-arm systems that are used to generate 3D reconstructions in interventional radiology have limited sampling rate and thus, contrast resolution. To address this particular subsampling problem, we propose a novel iterative reconstruction algorithm based on compressed sensing. To this purpose, we exploit both spatial and temporal sparsity of DSRA. For computational efficiency, we use a proximal implementation that accommodates multiple '1-penalties. Experiments on both simulated and clinical data confirm the relevance of our strategy for reducing subsampling streak artifacts

The Tools for Integrated Management of Childhood Illness (TIMCI) study protocol: a multi-country mixed-method evaluation of pulse oximetry and clinical decision support algorithms.

Effective and sustainable strategies are needed to address the burden of preventable deaths among children under-five in resource-constrained settings. The Tools for Integrated Management of Childhood Illness (TIMCI) project aims to support healthcare providers to identify and manage severe illness, whilst promoting resource stewardship, by introducing pulse oximetry and clinical decision support algorithms (CDSAs) to primary care facilities in India, Kenya, Senegal and Tanzania. Health impact is assessed through: a pragmatic parallel group, superiority cluster randomised controlled trial (RCT), with primary care facilities randomly allocated (1:1) in India to pulse oximetry or control, and (1:1:1) in Tanzania to pulse oximetry plus CDSA, pulse oximetry, or control; and through a quasi-experimental pre-post study in Kenya and Senegal. Devices are implemented with guidance and training, mentorship, and community engagement. Sociodemographic and clinical data are collected from caregivers and records of enrolled sick children aged 0-59 months at study facilities, with phone follow-up on Day 7 (and Day 28 in the RCT). The primary outcomes assessed for the RCT are severe complications (mortality and secondary hospitalisations) by Day 7 and primary hospitalisations (within 24 hours and with referral); and, for the pre-post study, referrals and antibiotic. Secondary outcomes on other aspects of health status, hypoxaemia, referral, follow-up and antimicrobial prescription are also evaluated. In all countries, embedded mixed-method studies further evaluate the effects of the intervention on care and care processes, implementation, cost and cost-effectiveness. Pilot and baseline studies started mid-2021, RCT and post-intervention mid-2022, with anticipated completion mid-2023 and first results late-2023. Study approval has been granted by all relevant institutional review boards, national and WHO ethical review committees. Findings will be shared with communities, healthcare providers, Ministries of Health and other local, national and international stakeholders to facilitate evidence-based decision-making on scale-up.Study registration: NCT04910750 and NCT05065320

Echantillonnage et reconstruction de mouvement en radiologie interventionnelle tridimensionnelle

Medical imaging has known great advances over the past decades to become a powerful tool for the clinical practice. It has led to the tremendous growth of interventional radiology, in which medical devices are inserted and manipulated under image guidance through the vascular system to the pathology location and then used to deliver the therapy. In these minimally-invasive procedures, X-ray guidance is carried out with C-arm systems through two-dimensional real-time projective low-dose images. More recently, three-dimensional visualization via tomographic acquisition has also become available. This work tackles tomographic reconstruction in the aforementioned context. More specifically, it deals with the correction of motion artifacts that originate from the temporal variations of the contrast-enhanced vessels and thus tackles a central aspect of tomography: data (angular) sampling. The compressed sensing theory identifies conditions under which subsampled data can be recovered through the minimization of a least-square data fidelity term combined with sparse constraints. Relying on this theory, an original reconstruction framework is proposed based on iterative filtered backprojection, proximal splitting, `1-minimization and homotopy. This framework is derived for integrating several spatial and temporal penalties. Such a strategy is shown to outperform the analytical filtered backprojection algorithm that is used in the current clinical practice by reducing motion and sampling artifacts in well-identified clinical cases, with focus on cerebral and abdominal imaging. The obtained results emphasize one of the key contributions of this work that is the importance of homotopy in addition to regularization, to provide much needed image quality improvement in the suggested domain of applicability.La pratique clinique a été profondément transformée par l'explosion technologique, ces dernières décades, des techniques d'imagerie médicale. L'expansion de la radiologie interventionnelle a ainsi rendu possible des procédures dites « minimalement invasives » au cours desquelles la thérapie est délivrée directement au niveau de la région pathologique via des micro-outils guidés par imagerie à travers le système vasculaire. Des systèmes dits « C-arm », générant une imagerie rayons X planaire temps-réelle en faible dose, sont utilisés pour le guidage. Ils ont offert plus récemment la possibilité d'une visualisation tridimensionnelle par le biais d'acquisitions tomographiques. C'est dans ce contexte de reconstruction tomographique que s'inscrivent ces travaux de thèse. Ils s'attèlent en particulier à corriger les artefacts de mouvement dus aux variations temporelles des vaisseaux injectés et se concentrent sur un aspect central de la tomographie, à savoir l'échantillonnage angulaire. La théorie du compressed sensing identifie les conditions sous lesquelles des données sous-échantillonnées peuvent être reconstruites en minimisant une fonctionnelle qui combine un terme de fidélité quadratique et une contrainte parcimonieuse. S'appuyant sur cette théorie, un formalisme original de reconstruction est proposé : il repose sur la rétroprojection filtrée itérative, les algorithmes proximaux, la minimisation de normes L1 et l'homotopie. Ce formalisme est ensuite dérivé pour intégrer différentes contraintes spatiales et temporelles. Une telle stratégie s'avère plus performante que la rétroprojection filtrée analytique utilisée dans la pratique clinique, permettant la réduction d'artefacts de mouvement et d'échantillonnage dans des cas cliniques bien identifiés de l'imagerie cérébrale et abdominale. Les résultats obtenus soulignent l'une des principales contributions de ce travail, à savoir : l'importance de l'homotopie, en supplément de la régularisation, pour améliorer la qualité image, un gain indispensable dans le domaine d'applicabilit

Echantillonnage et reconstruction de mouvement en radiologie interventionnelle tridimensionnelle

La pratique clinique a été profondément transformée par l'explosion technologique, ces dernières décades, des techniques d'imagerie médicale. L'expansion de la radiologie interventionnelle a ainsi rendu possible des procédures dites minimalement invasives au cours desquelles la thérapie est délivrée directement au niveau de la région pathologique via des micro-outils guidés par imagerie à travers le système vasculaire. Des systèmes dits C-arm , générant une imagerie rayons X planaire temps-réelle en faible dose, sont utilisés pour le guidage. Ils ont offert plus récemment la possibilité d'une visualisation tridimensionnelle par le biais d'acquisitions tomographiques. C'est dans ce contexte de reconstruction tomographique que s'inscrivent ces travaux de thèse. Ils s'attèlent en particulier à corriger les artefacts de mouvement dus aux variations temporelles des vaisseaux injectés et se concentrent sur un aspect central de la tomographie, à savoir l'échantillonnage angulaire. La théorie du compressed sensing identifie les conditions sous lesquelles des données sous-échantillonnées peuvent être reconstruites en minimisant une fonctionnelle qui combine un terme de fidélité quadratique et une contrainte parcimonieuse. S'appuyant sur cette théorie, un formalisme original de reconstruction est proposé : il repose sur la rétroprojection filtrée itérative, les algorithmes proximaux, la minimisation de normes L1 et l'homotopie. Ce formalisme est ensuite dérivé pour intégrer différentes contraintes spatiales et temporelles. Une telle stratégie s'avère plus performante que la rétroprojection filtrée analytique utilisée dans la pratique clinique, permettant la réduction d'artefacts de mouvement et d'échantillonnage dans des cas cliniques bien identifiés de l'imagerie cérébrale et abdominale. Les résultats obtenus soulignent l'une des principales contributions de ce travail, à savoir : l'importance de l'homotopie, en supplément de la régularisation, pour améliorer la qualité image, un gain indispensable dans le domaine d'applicabilitéMedical imaging has known great advances over the past decades to become a powerful tool for the clinical practice. It has led to the tremendous growth of interventional radiology, in which medical devices are inserted and manipulated under image guidance through the vascular system to the pathology location and then used to deliver the therapy. In these minimally-invasive procedures, X-ray guidance is carried out with C-arm systems through two-dimensional real-time projective low-dose images. More recently, three-dimensional visualization via tomographic acquisition has also become available. This work tackles tomographic reconstruction in the aforementioned context. More specifically, it deals with the correction of motion artifacts that originate from the temporal variations of the contrast-enhanced vessels and thus tackles a central aspect of tomography: data (angular) sampling. The compressed sensing theory identifies conditions under which subsampled data can be recovered through the minimization of a least-square data fidelity term combined with sparse constraints. Relying on this theory, an original reconstruction framework is proposed based on iterative filtered backprojection, proximal splitting, 1-minimization and homotopy. This framework is derived for integrating several spatial and temporal penalties. Such a strategy is shown to outperform the analytical filtered backprojection algorithm that is used in the current clinical practice by reducing motion and sampling artifacts in well-identified clinical cases, with focus on cerebral and abdominal imaging. The obtained results emphasize one of the key contributions of this work that is the importance of homotopy in addition to regularization, to provide much needed image quality improvement in the suggested domain of applicability.CHATENAY MALABRY-Ecole centrale (920192301) / SudocSudocFranceF

Echantillonnage et reconstruction de mouvement en radiologie interventionnelle tridimensionnelle

La pratique clinique a été profondément transformée par l'explosion technologique, ces dernières décades, des techniques d'imagerie médicale. L'expansion de la radiologie interventionnelle a ainsi rendu possible des procédures dites minimalement invasives au cours desquelles la thérapie est délivrée directement au niveau de la région pathologique via des micro-outils guidés par imagerie à travers le système vasculaire. Des systèmes dits C-arm , générant une imagerie rayons X planaire temps-réelle en faible dose, sont utilisés pour le guidage. Ils ont offert plus récemment la possibilité d'une visualisation tridimensionnelle par le biais d'acquisitions tomographiques. C'est dans ce contexte de reconstruction tomographique que s'inscrivent ces travaux de thèse. Ils s'attèlent en particulier à corriger les artefacts de mouvement dus aux variations temporelles des vaisseaux injectés et se concentrent sur un aspect central de la tomographie, à savoir l'échantillonnage angulaire. La théorie du compressed sensing identifie les conditions sous lesquelles des données sous-échantillonnées peuvent être reconstruites en minimisant une fonctionnelle qui combine un terme de fidélité quadratique et une contrainte parcimonieuse. S'appuyant sur cette théorie, un formalisme original de reconstruction est proposé : il repose sur la rétroprojection filtrée itérative, les algorithmes proximaux, la minimisation de normes L1 et l'homotopie. Ce formalisme est ensuite dérivé pour intégrer différentes contraintes spatiales et temporelles. Une telle stratégie s'avère plus performante que la rétroprojection filtrée analytique utilisée dans la pratique clinique, permettant la réduction d'artefacts de mouvement et d'échantillonnage dans des cas cliniques bien identifiés de l'imagerie cérébrale et abdominale. Les résultats obtenus soulignent l'une des principales contributions de ce travail, à savoir : l'importance de l'homotopie, en supplément de la régularisation, pour améliorer la qualité image, un gain indispensable dans le domaine d'applicabilitéMedical imaging has known great advances over the past decades to become a powerful tool for the clinical practice. It has led to the tremendous growth of interventional radiology, in which medical devices are inserted and manipulated under image guidance through the vascular system to the pathology location and then used to deliver the therapy. In these minimally-invasive procedures, X-ray guidance is carried out with C-arm systems through two-dimensional real-time projective low-dose images. More recently, three-dimensional visualization via tomographic acquisition has also become available. This work tackles tomographic reconstruction in the aforementioned context. More specifically, it deals with the correction of motion artifacts that originate from the temporal variations of the contrast-enhanced vessels and thus tackles a central aspect of tomography: data (angular) sampling. The compressed sensing theory identifies conditions under which subsampled data can be recovered through the minimization of a least-square data fidelity term combined with sparse constraints. Relying on this theory, an original reconstruction framework is proposed based on iterative filtered backprojection, proximal splitting, 1-minimization and homotopy. This framework is derived for integrating several spatial and temporal penalties. Such a strategy is shown to outperform the analytical filtered backprojection algorithm that is used in the current clinical practice by reducing motion and sampling artifacts in well-identified clinical cases, with focus on cerebral and abdominal imaging. The obtained results emphasize one of the key contributions of this work that is the importance of homotopy in addition to regularization, to provide much needed image quality improvement in the suggested domain of applicability.CHATENAY MALABRY-Ecole centrale (920192301) / SudocSudocFranceF

Segmentation of knee injuries swelling on infrared images

International audienceInterpretation of medical infrared images is complex due to thermal noise, absence of texture, and small temperature differences in pathological zones. Acute inflammatory response is a characteristic symptom of some knee injuries like anterior cruciate ligament sprains, muscle or tendons strains, and meniscus tear. Whereas artificial coloring of the original grey level images may allow to visually assess the extent inflammation in the area, their automated segmentation remains a challenging problem. This paper presents a hybrid segmentation algorithm to evaluate the extent of inflammation after knee injury, in terms of temperature variations and surface shape. It is based on the intersection of rapid color segmentation and homogeneous region segmentation, to which a Laplacian of a Gaussian filter is applied. While rapid color segmentation enables to properly detect the observed core of swollen area, homogeneous region segmentation identifies possible inflammation zones, combining homogeneous grey level and hue area segmentation. The hybrid segmentation algorithm compares the potential inflammation regions partially detected by each method to identify overlapping areas. Noise filtering and edge segmentation are then applied to common zones in order to segment the swelling surfaces of the injury. Experimental results on images of a patient with anterior cruciate ligament sprain show the improved performance of the hybrid algorithm with respect to its separated components. The main contribution of this work is a meaningful automatic segmentation of abnormal skin temperature variations on infrared thermography images of knee injury swelling

Sparsity constraints and dedicated acquisition protocols for improved Digital Subtraction Rotational Angiography

Digital Subtraction Rotational Angiography (DSRA) allows reconstruction of three-dimensional vascular structures from two spins: the contrast is acquired after injecting vessels with a contrast medium, whereas the mask is acquired in the absence of injection. The vessels are then detected by subtraction of the mask from the contrast. Standard DSRA protocol samples the same set of equiangular-spaced positions for both spins. Due to technical limitations of C-arm systems, streak artifacts degrade the quality of all three reconstructed volumes. Recent developments of compressed sensing have demonstrated that it is possible to recover a signal that is sparse in some basis under limited sampling conditions. In this paper, we propose to improve the reconstruction quality of non-sparse volumes when there exists a sparse combination of these volumes. To this purpose, we develop an extension of iterative filtered backprojection that jointly reconstructs the mask and contrast volumes via '1-minimization of sparse priors. A dedicated protocol based upon interleaving both spins is shown to further benefit from the sparsity assumptions, while using the same total number of measurements. Our approach is evaluated in parallel geometry on simulated phantom data

A Monte Carlo tree search approach to learning decision trees

Comunicació presentada a: 17th IEEE International Conference on Machine Learning and Applications (ICMLA) celebrada del 17 al 20 de 2018 a Orlando, Estats Units.Decision trees (DTs) are a widely used prediction tool, owing to their interpretability. Standard learning methods follow a locally-optimal approach that trades off prediction performance for computational efficiency. Such methods can however be far from optimal, and it may pay off to spend more computational resources to increase performance. Monte Carlo tree search (MCTS) is an approach to approximate optimal choices in exponentially large search spaces. Since exploring the space of all possible DTs is computationally intractable, we propose a DT learning approach based on MCTS. To bound the branching factor of MCTS, we limit the number of decisions at each level of the search tree, and introduce mechanisms to balance exploration, DT size and the statistical significance of the predictions. To mitigate the computational cost of our method, we employ a move pruning strategy that discards some branches of the search tree, leading to improved performance. The experiments show that our approach outperformed locally optimal search in 20 out of 31 datasets, with a reduction in DT size in most of the cases.This work is supported by the European Union Horizon 2020 Programme for Research and Innovation, under grant agreement No. 642676 (CardioFunXion)

Characterizing patterns of response during mild stress-testing in continuous echocardiography recordings using a multiview dimensionality reduction technique

Comunicació presentada a: 9th international conference on Functional Imaging and Modeling of the Heart (FIMH 2017), celebrada de l'11 al 13 de juny de 2017 a Toronto, Canada.In this paper, we capture patterns of response to cardiac stress-testing using a multiview dimensionality reduction technique that allows the compact representation of patient response to stress, regarding multiple features over consecutive cycles, as a low-dimensional trajectory. In this low-dimensional space, patients can be compared and clustered in distinct healthy and pathological responses, and the patterns that characterize each of them can be reconstructed. Experiments were performed on (a) synthetic data simulating different types of response and (b) a real acquisition during a cold pressor test. Results show that the proposed approach allows the clustering of healthy and pathological responses, as well as the reconstruction of characteristic patterns of such responses, in terms of multiple features of interest.This work is supported by the European Union Horizon 2020 Programme for Research and Innovation, under grant agreement No. 642676 (CardioFunXion)