518 research outputs found
Assessing Reliability of Myocardial Blood Flow After Motion Correction With Dynamic PET Using a Bayesian Framework
The estimation of myocardial blood flow (MBF) in dynamic PET can be biased by many different processes. A major source of error, particularly in clinical applications, is patient motion. Patient motion, or gross motion, creates displacements between different PET frames as well as between the PET frames and the CT-derived attenuation map, leading to errors in MBF calculation from voxel time series. Motion correction techniques are challenging to evaluate quantitatively and the impact on MBF reliability is not fully understood. Most metrics, such as signal-to-noise ratio (SNR), are characteristic of static images, and are not specific to motion correction in dynamic data. This study presents a new approach of estimating motion correction quality in dynamic cardiac PET imaging. It relies on calculating a MBF surrogate, K 1 , along with the uncertainty on the parameter. This technique exploits a Bayesian framework, representing the kinetic parameters as a probability distribution, from which the uncertainty measures can be extracted. If the uncertainty extracted is high, the parameter studied is considered to have high variability - or low confidence - and vice versa. The robustness of the framework is evaluated on simulated time activity curves to ensure that the uncertainties are consistently estimated at the multiple levels of noise. Our framework is applied on 40 patient datasets, divided in 4 motion magnitude categories. Experienced observers manually realigned clinical datasets with 3D translations to correct for motion. K 1 uncertainties were compared before and after correction. A reduction of uncertainty after motion correction of up to 60% demonstrates the benefit of motion correction in dynamic PET and as well as provides evidence of the usefulness of the new method presented
Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [11C]PHNO brain PET-MR studies
INTRODUCTION: Q.Clear is a Bayesian penalised likelihood (BPL) reconstruction algorithm available on General Electric (GE) Positron Emission Tomography (PET)-Computed Tomography (CT) and PET-Magnetic Resonance (MR) scanners. This algorithm is regulated by a ÎČ value which acts as a noise penalisation factor and yields improvements in signal to noise ratio (SNR) in clinical scans, and in contrast recovery and spatial resolution in phantom studies. However, its performance in human brain imaging studies remains to be evaluated in depth. This pilot study aims to investigate the impact of Q.Clear reconstruction methods using different ÎČ value versus ordered subset expectation maximization (OSEM) on brain kinetic modelling analysis of low count brain images acquired in the PET-MR. METHODS: Six [(11)C]PHNO PET-MR brain datasets were reconstructed with Q.Clear with ÎČ100â1000 (in increments of 100) and OSEM. The binding potential relative to non-displaceable volume (BP(ND)) were obtained for the Substantia Nigra (SN), Striatum (St), Globus Pallidus (GP), Thalamus (Th), Caudate (Cd) and Putamen (Pt), using the MIAKATâą software. Intraclass correlation coefficients (ICC), repeatability coefficients (RC), coefficients of variation (CV) and bias from BlandâAltman plots were reported. Statistical analysis was conducted using a 2-way ANOVA model with correction for multiple comparisons. RESULTS: When comparing a standard OSEM reconstruction of 6 iterations/16 subsets and 5Â mm filter with Q.Clear with different ÎČ values under low counts, the bias and RC were lower for Q.Clear with ÎČ100 for the SN (RCâ=â2.17), Th (RCâ=â0.08) and GP (RCâ=â0.22) and with ÎČ200 for the St (RCâ=â0.14), Cd (RCâ=â0.18)and Pt (RCâ=â0.10). The p-values in the 2-way ANOVA model corroborate these findings. ICC values obtained for Th, St, GP, Pt and Cd demonstrate good reliability (0.87, 0.99, 0.96, 0.99 and 0.96, respectively). For the SN, ICC values demonstrate poor reliability (0.43). CONCLUSION: BP(ND) results obtained from quantitative low count brain PET studies using [(11)C]PHNO and reconstructed with Q.Clear with ÎČâ<â400, which is the value used for clinical [(18)F]FDG whole-body studies, demonstrate the lowest bias versus the typical iterative reconstruction method OSEM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13550-022-00883-1
Analysis of contrast-enhanced medical images.
Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejectionâthe immunological response of the human immune system to a foreign kidneyâis the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patientâs chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of todayâs CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images
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State of the Art of Level Set Methods in Segmentation and Registration of Medical Imaging Modalities
Segmentation of medical images is an important step in various applications such as visualization, quantitative analysis and image-guided surgery. Numerous segmentation methods have been developed in the past two decades for extraction of organ contours on medical images. Low-level segmentation methods, such as pixel-based clustering, region growing, and filter-based edge detection, require additional pre-processing and post-processing as well as considerable amounts of expert intervention or information of the objects of interest. Furthermore the subsequent analysis of segmented objects is hampered by the primitive, pixel or voxel level representations from those region-based segmentation. Deformable models, on the other hand, provide an explicit representation of the boundary and the shape of the object. They combine several desirable features such as inherent connectivity and smoothness, which counteract noise and boundary irregularities, as well as the ability to incorporate knowledge about the object of interest. However, parametric deformable models have two main limitations. First, in situations where the initial model and desired object boundary differ greatly in size and shape, the model must be re-parameterized dynamically to faithfully recover the object boundary. The second limitation is that it has difficulty dealing with topological adaptation such as splitting or merging model parts, a useful property for recovering either multiple objects or objects with unknown topology. This difficulty is caused by the fact that a new parameterization must be constructed whenever topology change occurs, which requires sophisticated schemes. Level set deformable models, also referred to as geometric deformable models, provide an elegant solution to address the primary limitations of parametric deformable models. These methods have drawn a great deal of attention since their introduction in 1988. Advantages of the contour implicit formulation of the deformable model over parametric formulation include: (1) no parameterization of the contour, (2) topological flexibility, (3) good numerical stability, (4) straightforward extension of the 2D formulation to n-D. Recent reviews on the subject include papers from Suri. In this chapter we give a general overview of the level set segmentation methods with emphasize on new frameworks recently introduced in the context of medical imaging problems. We then introduce novel approaches that aim at combining segmentation and registration in a level set formulation. Finally we review a selective set of clinical works with detailed validation of the level set methods for several clinical applications
QUANTITATIVE NUCLEAR MEDICINE IMAGING USING ADVANCED IMAGE RECONSTRUCTION AND RADIOMICS
Our aim is to help put nuclear medicine at the forefront of quantitation on the path to the realization of personalized medicine. We propose and evaluate (Part I) advanced image reconstruction and (Part II) robust radiomics (large-scale data-oriented study of radiological images). The goal is to attain significantly improved diagnostic, prognostic and treatment-response assessment capabilities.
Part I presents a new paradigm in point-spread function (PSF)-modeling, a partial volume correction method in PET imaging where resolution-degrading phenomena are modeled within the reconstruction framework. PSF-modeling improves resolution and enhances contrast, but significantly alters noise properties and induces edge-overshoots. Past efforts involve a dichotomy of PSF vs. no-PSF modeling; by contrast, we focus on a wide-spectrum of PSF models, including under- and over-estimation of the true PSF, for the potential of enhanced quantitation in standardized uptake values (SUVs).
We show for the standard range of iterations employed in clinic (not excessive), edge enhancement due to overestimation actually lower SUV bias in small regions, while inter-voxel correlations suppress image roughness and enhance uniformity. An overestimated PSF yields improved contrast and limited edge-overshoot effects at lower iterations, enabling enhanced SUV quantitation. Overall, our framework provides an effective venue for quantitative task-based optimization.
Part II proposes robust and reproducible radiomics methods. Radiomics workflows are complex, generating hundreds of features, which can lead to high variability and overfitting, and ultimately hampering performance. We developed and released a Standardized Environment for Radiomics Analysis (SERA) solution to enable robust radiomics analyses. We conduct studies on two unique imaging datasets â renal cell carcinoma SPECT and prostate cancer PET â identifying robust and reproducible radiomic features.
In addition, we evaluate a novel hypothesis that radiomic features extracted from clinically normal (non-ischemic) myocardial perfusion SPECT (MPS) can predict coronary artery calcification (CAC; as extracted from CT). This has important implications, since CAC assessment is not commonly-performed nor reimbursed in wide community settings. SERA-derived radiomic features were utilized in a multi-step feature selection framework, followed by the application of machine learning to radiomic features. Our results show the potential to predict CAC from normal MPS, suggesting added usage and value for routine standard MPS
Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging
Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET).
Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools â by tracking the heartâs kinetic activity using micro-sized MEMS sensors â and novel computational approaches â by deploying signal processing and machine learning techniquesâfor detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations.
Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes.
Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien kÀyttö sydÀnkardiografiassa sekÀ lÀÀketieteellisessÀ 4D-kuvantamisessa
Tausta: SydÀn- ja verisuonitaudit ovat yleisin kuolinsyy. NÀistÀ kuolemantapauksista lÀhes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron hÀiriöistÀ. Moniulotteiset mikroelektromekaaniset jÀrjestelmÀt (MEMS) mahdollistavat sydÀnlihaksen mekaanisen liikkeen mittaamisen, mikÀ puolestaan tarjoaa tÀysin uudenlaisen ja innovatiivisen ratkaisun sydÀmen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjÀrjestelmien kÀyttÀmisen sydÀmen toiminnan tutkimuksessa sekÀ lÀÀketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa.
MenetelmÀt: TÀmÀ vÀitöskirjatyö esittelee uuden sydÀmen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien kÀyttöön. Uudet laskennalliset lÀhestymistavat, jotka perustuvat signaalinkÀsittelyyn ja koneoppimiseen, mahdollistavat sydÀmen patologisten hÀiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. TÀssÀ tutkimuksessa keskitytÀÀn erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). NÀiden tekniikoiden avulla voidaan mitata kardiorespiratorisen jÀrjestelmÀn mekaanisia ominaisuuksia.
Tulokset: Kokeelliset analyysit osoittivat, ettÀ integroimalla usean sensorin dataa voidaan mitata syketiheyttÀ 99% (terveillÀ n=29) tarkkuudella, havaita sydÀmen rytmihÀiriöt (n=435) 95-97%, tarkkuudella, sekÀ havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). LisÀksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydÀmen 4D PET-kuvan laatua, kun liikeepÀtarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillÀ, n=9) osoitti lupaavia tuloksia sydÀnsykkeen ajoituksen ja intervallien sekÀ sydÀnlihasmuutosten mittaamisessa.
PÀÀtelmÀ: TÀmÀn tutkimuksen tulokset osoittavat, ettÀ kardiologisilla MEMS-liikeantureilla on kliinistÀ potentiaalia sydÀmen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistÀÀ eteisvÀrinÀn (AFib), sydÀninfarktin (MI) ja CAD:n havaitsemista. LisÀksi MEMS-liiketunnistus parantaa sydÀmen PET-kuvantamisen luotettavuutta ja laatua
Virtual clinical trials in medical imaging: a review
The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities
Extraction de la courbe d'entrée à partir des images TEP du coeur chez le petit animal pour la modélisation pharmacocinétique
Dans cette thĂšse, nous prĂ©sentons l'ensemble de nos contributions relatives Ă la mise en oeuvre et Ă la validation de techniques d'extraction d'une courbe de l'activitĂ© d'un traceur radioactif, dite courbe d'entrĂ©e (CE), Ă partir des images enregistrĂ©es par tomographie d'Ă©mission par positrons (TEP). Cette courbe est primordiale pour la quantification de paramĂštres physiologiques et mĂ©taboliques comme le mĂ©tabolisme du glucose au niveau du myocarde chez le petit animal. La modalitĂ© d'imagerie TEP sert Ă dĂ©celer, Ă des phases souvent prĂ©coces, le dysfonctionnement d'un organe par un examen mĂ©dical. L'examen consiste en une injection d'un Ă©lĂ©ment radioactif, Ă©metteur de positrons attachĂ©s Ă une molĂ©cule caractĂ©risĂ©e par les mĂȘmes propriĂ©tĂ©s chimiques et biologiques qu'une molĂ©cule naturelle, et de suivre son activitĂ© temporelle. La quantitĂ© du traceur mesurĂ©e dans le plasma sanguin en fonction du temps constitue la CE, tandis que la radioactivitĂ© mesurĂ©e dans les tissus par la TEP constitue la rĂ©ponse des tissus. La CE et la rĂ©ponse des tissus sont les fonctions fondamentales d'un modĂšle mathĂ©matique appelĂ© "le modĂšle pharmacocinĂ©tique" qui estime les paramĂštres physiologiques et mĂ©taboliques. Habituellement la CE est obtenue d'une maniĂšre invasive par un prĂ©lĂšvement sanguin qui se fait parallĂšlement Ă l'acquisition des donnĂ©es. En plus, elle nĂ©cessite une chaĂźne de prĂ©paration pour enregistrer la concentration du traceur radioactif dans le plasma et une frĂ©quence d'Ă©chantillonnage corrĂ©lĂ©e avec le dĂ©coupage de la sĂ©quence d'images. Dans le cadre de nos recherches, nous avons dĂ©veloppĂ© des techniques d'extraction de la CE directement Ă partir d'une sĂ©quence d'images TEP. Cette approche prĂ©sente l'avantage d'ĂȘtre non-invasive et permet un contrĂŽle sur la frĂ©quence d'Ă©chantillonnage temporel. NĂ©anmoins, la rĂ©solution spatiale, les limites physiques, les limites physiologiques et les limites mĂ©thodologiques reliĂ©es Ă la reconstruction d'images sont des facteurs qui dĂ©tĂ©riorent la qualitĂ© de la courbe. Dans un premier temps, nous avons appliquĂ© un concept probabiliste Ă l'intĂ©rieur de deux rĂ©gions d'intĂ©rĂȘts (Ris) tracĂ©es sur la sĂ©quence d'images dĂ©limitant le ventricule gauche et le myocarde. La mĂ©thode estime la fraction du sang dans les deux rĂ©gions pour dĂ©terminer une CE non dĂ©gradĂ©e par les effets mentionnĂ©s prĂ©cĂ©demment. Cette approche a permis de corriger la courbe en tenant compte des effets causĂ©s par la contamination spatiale. Dans un deuxiĂšme temps, nous avons travaillĂ© sur la rĂ©duction de l'effet du mouvement du coeur et des poumons sur la qualitĂ© de la CE. Pour cela, nous avons utilisĂ© une acquisition de donnĂ©es synchronisĂ©e par rapport Ă l'Ă©lectrocardiogramme (ECG). Cette acquisition nĂ©cessite un suivi automatique des RIs sur les diffĂ©rents cadres synchronisĂ©s. Pour remĂ©dier aux effets de la faible rĂ©solution spatiale des images, nous avons dĂ©veloppĂ© un modĂšle particulier d'un contour dĂ©formable qui rĂ©pond aux faiblesses des images TEP. Notre modĂšle est capable de dĂ©limiter le ventricule gauche et le myocarde sur les images d'une façon quasi-automatique. Finalement, nous avons gĂ©nĂ©ralisĂ© l'idĂ©e de l'extraction de la CE pour diffĂ©rents traceurs tels que le glucose marquĂ© au fluor ([indice supĂ©rieur 18]F-FDG), l'ammoniaque marquĂ© Ă l'azote ([indice supĂ©rieur 13]N-ammoniaque), le [indice supĂ©rieur 82] rubidium ([indice supĂ©rieur 82]Rb) et l'acĂ©tate marquĂ© au carbone ([indice supĂ©rieur 11]C-acĂ©tate). Le modĂšle que nous avons dĂ©veloppĂ© est basĂ© sur l'estimation de la CE par l'analyse en composante indĂ©pendante (ACI) et la distribution gaussienne gĂ©nĂ©ralisĂ©e (DGG). Tous nos rĂ©sultats pour le traceur [indice supĂ©rieur 18]F-FDG sont comparĂ©s Ă la mĂ©thode de rĂ©fĂ©rence classique, Ă savoir le prĂ©lĂšvement sanguin. Les rĂ©sultats de l'extraction de la CE par l'ACI ont Ă©tĂ© comparĂ©s Ă ceux extraits par la mĂ©thode de rĂ©fĂ©rence et par la moyenne de l'activitĂ© d'une RI segmentĂ©e manuellement sur les images. Les rĂ©sultats montrent l'apport de la mĂ©thode sur l'amĂ©lioration de la courbe lorsque celle-ci est dĂ©gradĂ©e par la contamination croisĂ©e. Le travail accompli dans cette thĂšse montre la possibilitĂ© de contourner les limites de l'imagerie TEP par l'utilisation d'approches statistiques dans le but d'extraire une CE fiable. Les mĂ©thodes dĂ©veloppĂ©es reprĂ©sentent une alternative Ă la mĂ©thode invasive d'Ă©chantillonnage sanguin
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