A Prospective Study on Algorithms Adapted to the Spatial Frequency in Tomography

The use of iterative algorithms in tomographic reconstruction always leads to a frequency adapted rate of convergence in that low frequencies are accurately reconstructed after a few iterations, while high frequencies sometimes require many more computations. In this paper, we propose to build frequency adapted (FA) algorithms based on a condition of incomplete backprojection and propose an FA simultaneous algebraic reconstruction technique (FA-SART) algorithm as an example. The results obtained with the FA-SART algorithm demonstrate a very fast convergence on a highly detailed phantom when compared to the original SART algorithm. Though the use of such an FA algorithm may seem difficult, we specify in which case it is relevant and propose several ways to improve the reconstruction process with FA algorithms

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Data processing in observation

Although the observational status of data produced by instruments has been widely discussed among philosophers of science, those who defend it (e.g. Shapere (1982), Hacking (1983), Humphreys (2004) and many others) still do not completely account for contemporary practices of observation. Indeed, these data are very often computationally processed before they are examined by scientists and tend to be more and more so, as most detectors now produce data in a digital form. Hence, the raw data (the data detected and not yet modified) are stored as matrices or vectors that can easily be mathematically processed. In addition, while computational data processing shares important features with simulations, as both practices are based on the solving of equations associated to models, philosophical analyses of simulations (e.g. Humphreys (1994 and 2004), Hartman (1996)) are unable to account for data processing in observation, for in these different studies of simulations, the model aims to describe the phenomenon which is precisely at the center of scientific investigation. On the contrary, in data processing for observation, scientists make use of two types of models which are both neutral regarding the studied object or phenomenon. The first type of models concerns the different steps of data acquisition, and permits the scientist to predict the data corresponding to a given phenomenon. When used the other way around in an inverse problem, this type of treatments allows to specify the original phenomenon from the data, in a greater purity or in a spatial representation that can be grasped more easily by the observer. Hence, one can "deblur" (or deconvolve) images that are blurry due to a detector that is not accurate enough (e.g. in microscopy) or give a 3D representation of a phenomenon for which we originally could only produce 2D images (e.g. in CAT-scan imaging.) The second type of models, which deals more specifically with images, aims to describe some mechanism of vision such as the demarcation (or segmentation) of objects or the simplification of images, for example by making homogeneous some regions which are not so, but that we would tend to see as such. This permits to facilitate the reading of images and to obtain a better correlation between what two different observers see. While the inferential nature of the treatments applied to data is not dubious (see Delehanty (2005) for positron emission tomography (PET) images), the fundamental distinction in the context of observation is not between inferential and non-inferential, but rather between inferences which concern the very object of the scientific inquiry, and those which concern data acquisition and perception processes, since only the two latter types of inferences can be compatible with observation. More specifically, I shall argue that computer treatments which involve models of data acquisition do not bring any additional difficulty regarding the observational status of data, compared to the raw data produced by the same instrument, since the treatments only make explicit use of the knowledge of processes that the observer already adheres to (explicitly or implicitly). By implementing this knowledge in a systematic and reliable way, they also permit one to reduce the gap between (raw) data and phenomena. However, the role of treatments that make use of models of perception in observation is much harder to defend, since the resulting images often lack many of the original features of the raw data

Optimisation de la reconstruction en tomographie d'émission monophotonique avec collimateur sténopé

In SPECT small animal imaging, it is highly recommended to accurately model the response of the detector in order to improve the low spatial resolution. The volume to reconstruct is thus obtained both by backprojecting and deconvolving the projections. We chose iterative methods, which permit one to solve the inverse problem independently from the model's complexity.We describe in this work a gaussian model of point spread function (PSF) whose position, width and maximum are computed according to physical and geometrical parameters. Then we use the rotation symmetry to replace the computation of P projection operators, each one corresponding to one position of the detector around the object, by the computation of only one of them. This is achieved by choosing an appropriate polar discretization, for which we control the angular density of voxels to avoid oversampling the center of the field of view.Finally, we propose a new family of algorithms, the so-called frequency adapted algorithms, which enable to optimize the reconstruction of a given band in the frequency domain on both the speed of convergence and the quality of the image.En imagerie du petit animal par tomographie d'émission monophotonique (TEMP), la modélisation de la réponse physique du détecteur demande beaucoup de soin pour en contrebalancer la faible résolution intrinsèque. Les coupes du volume à reconstruire s'obtiennent ainsi à partir des projections, à la fois par une opération de rétroprojection et une déconvolution de la réponse impulsionnelle du détecteur. Nous optons dès lors pour des méthodes itératives de résolution d'un grand système linéaire qui fonctionnent indépendamment de la complexité du modèle.Pour parvenir à des résultats exploitables, tant en terme de résolution spatiale que de temps de calcul chez le rat ou la souris, nous décrivons dans ce travail les choix de notre modélisation par une réponse impulsionnelle gaussienne, ajustée suivant des paramètres physiques et géométriques. Nous utilisons ensuite la symétrie de rotation inhérente au dispositif pour ramener le calcul de P opérateurs de projections au calcul d'un seul d'entre eux, par une discrétisation de l'espace compatible avec cette symétrie, tout en contrôlant la densité angulaire de voxels pour éviter un suréchantillonnage au centre du volume.Enfin, nous proposons une nouvelle classe d'algorithmes adaptés à la fréquence qui permettent d'optimiser la reconstruction d'une gamme de fréquence spatiale donnée, évitant ainsi d'avoir à calculer de nombreuses itérations lorsque le spectre à reconstruire se retrouve surtout dans les hautes fréquences

Optimization of pinhole single photon emission computed tomography (pinhole SPECT) reconstruction

En imagerie du petit animal par tomographie d'émission monophotonique (TEMP), la modélisation de la réponse physique du détecteur demande beaucoup de soin pour en contrebalancer la faible résolution intrinsèque. Les coupes du volume à reconstruire s'obtieIn SPECT small animal imaging, it is highly recommended to accurately model the response of the detector in order to improve the low spatial resolution. The volume to reconstruct is thus obtained both by backprojecting and deconvolving the projections. W

Réponse à Youna Tonnerre : Simulations, stabilité et observation

Dans sa recension de mon ouvrage consacré à l’observation, Youna Tonnerre propose un résumé qui synthétise parfaitement les principaux arguments que j’avais souhaité y exposer. Ma réponse ne va donc porter que sur un point qu’elle soulève avec raison : le statut des simulations numériques, observationnel ou non. Dans mon ouvrage, je défends la thèse selon laquelle les simulations ne relèveraient pas de l’observation mais Youna Tonnerre m’invite à préciser cette position et les arguments par lesquels elle peut, et doit à mon avis, être défendue

Analyse des images scientifiques par le concept d'observation

National audienceMany of the scientific images are produced in order to observe entities or phenomena, that is, to obtain a reliable knowledge about them, acquired by the most direct means. This use of images - and of instruments that produce them - has been discussed by philosophers, especially since the early 1980s, in order to understand whether the term "observe" is appropriate when a complex chain of instruments is put together to produce images. Indeed, in this case, images can be contaminated both by artifacts, when some part of the instrument does not work as expected, and by the theories that are used to explain the functioning of the imaging device. I do not bring a conclusion in this work regarding the validity of instruments for observation but I describe instead what makes the philosophical analyses on this question unsatisfactory to give an account of the actual use of images by scientists. Specifically, I make two points in the course of my analysis: first, that it is required to consider an image within a precise scientific context that gives the goal of the experimental setting and, second, that phenomena of the greatest diversity can be represented on an image. This second aspect contradicts the idea largely shared by philosophers, that only spatial properties of entities can be explored through images. Although I acknowledge the importance of such aspects as shape, scale and position, one must also consider other properties, starting with detectable physical properties such as luminosity, radioactivity etc. and pursuing with properties that are specific to the different scientific disciplines, that we call high-level properties. I thus open the question of whether or not we can observe such phenomena as cardiac perfusion, energy production in the solar core, the region of emotions in a cerebral MRI, etc.Une partie importante des images scientifiques sont produites dans le but d'observer des entités ou des phénomènes, c'est-à-dire d'obtenir à leur sujet une connaissance sûre, acquise par des moyens aussi directs que possible. Ce rôle des images et des instruments qui les produisent a été évalué par les philosophes, en particulier depuis le début des années 1980, pour tenter de comprendre si l'emploi du terme " observer " est justifié dans le cas où une chaîne complexe d'instrumentation est mise en place pour produire des images. Dans ce cas, en effet, on peut craindre une contamination des images à la fois par des artefacts, en cas de défaut du dispositif expérimental, et par les théories sur lesquelles repose ce même dispositif. Sans se prononcer sur la validité des instruments pour l'observation dans le présent travail, nous décrivons en quoi les analyses philosophiques relatives à cette question sont insuffisantes pour rendre compte de l'utilisation réelle qui est faite des images par les scientifiques. Nous tirons deux éléments importants de cette analyse : d'une part, la nécessité de considérer une image à la lumière d'un contexte scientifique spécifique qui donne l'objectif de la démarche expérimentale et précise ce que l'on tente d'observer et, d'autre part, le fait que des phénomènes de la plus grande variété peuvent être représentés sur une image. Ce deuxième aspect s'oppose à l'idée très généralement partagée par les philosophes que seules les propriétés spatiales des entités pourraient être explorées par l'image. Pour importants que soient ces aspects de forme, d'échelle et de localisation, il faut les compléter d'abord par la possibilité d'explorer des propriétés physiques détectables, telles que la luminosité, la radioactivité, etc., et ensuite, en introduisant la notion de propriétés de haut niveau, propres à chaque domaine des sciences. Nous ouvrons ainsi la question de savoir si l'on peut observer des phénomènes aussi divers que la perfusion cardiaque, la production d'énergie dans le noyau solaire, la région des émotions sur des images cérébrales de résonance magnétique fonctionnelle, etc

L'observation scientifique : aspects philosophiques et pratiques

Th èse pour l'obtention du grade de docteur en Philosophie de l'Universit é de Paris 1 Panth éon-Sorbonne, pr ésent ée et soutenue publiquement le 9 d écembre 2011 sous la Direction de J. Gayon et A. BarberousseDuring the past century, great transformations have occurred in the way empirical investigations are carried out. Instruments detecting all sorts of electromagnetic and mechanical waves are now in use in most areas of empirical science and the data they produce (lists of numbers) can be numerically processed. Therefore, observation as it is traditionally conceived by philosophers, that is, based on unassisted perception and often quali fied as being direct, a-theoretical and neutral, has become irrelevant. In this dissertation, I claim that scienti fic observation can be reformed by taking into account the essential role that background knowledge plays in observational practices, in both the production and the interpretation of data. By de fining a scientifi c investigation as a dynamic and corrective process, during which a material, conceptual and epistemic framework evolves, I show that this framework can be stabilized, thus defining the observational phase of an investigation. That this conception of observation fits contemporary practices is demonstrated through numerous examples, especially from medical imaging.Au cours du dernier siècle, les pratiques d'investigations empiriques ont été bouleversées par le développement d'instruments qui détectent toutes sortes d'ondes électromagnétiques et mécaniques et par la possibilité de traiter numériquement les données qu'ils produisent. Ainsi, l'observation telle qu'elle est conçue par les philosophes, qui repose traditionnellement sur la perception non assistée, et qui est souvent qualifiée de directe, a-théorique et neutre, est devenue inadaptée. Dans cette thèse, je propose de redéfinir l'observation scientifique en prenant en compte le rôle essentiel que joue la connaissance déjà acquise dans les pratiques observationnelles, autant dans la production des données que dans leur interprétation. En définissant l'investigation scientifique comme un processus dynamique et correctif, au cours duquel un cadre matériel, conceptuel et épistémique est en évolution, je montre que ce cadre peut être stabilisé, définissant ainsi une phase observationnelle de l'investigation. L'adéquation de cette conception de l'observation scientifique à la pratique est démontrée par de nombreux exemples, tirés notamment de l'imagerie médicale