Detection of ingested cocaine-filled packets: comparison of filtered back projection CT with adaptive statistical iterative reconstructed images

Purpose: To evaluate the diagnostic value and image quality of CT with filtered back projection (FBP) compared with adaptive statistical iterative reconstructed images (ASIR) in body stuffers with ingested cocaine-filled packets.Methods and Materials: Twenty-nine body stuffers (mean age 31.9 years, 3 women) suspected for ingestion of cocaine-filled packets underwent routine-dose 64-row multidetector CT with FBP (120kV, pitch 1.375, 100-300 mA and automatic tube current modulation (auto mA), rotation time 0.7sec, collimation 2.5mm), secondarily reconstructed with 30 % and 60 % ASIR. In 13 (44.83%) out of the body stuffers cocaine-filled packets were detected, confirmed by exact analysis of the faecal content including verification of the number (range 1-25). Three radiologists independently and blindly evaluated anonymous CT examinations (29 FBP-CT and 68 ASIR-CT) for the presence and number of cocaine-filled packets indicating observers' confidence, and graded them for diagnostic quality, image noise, and sharpness. Sensitivity, specificity, area under the receiver operating curve (ROC) Az and interobserver agreement between the 3 radiologists for FBP-CT and ASIR-CT were calculated.Results: The increase of the percentage of ASIR significantly diminished the objective image noise (p<0.001). Overall sensitivity and specificity for the detection of the cocaine-filled packets were 87.72% and 76.15%, respectively. The difference of ROC area Az between the different reconstruction techniques was significant (p= 0.0101), that is 0.938 for FBP-CT, 0.916 for 30 % ASIR-CT, and 0.894 for 60 % ASIR-CT.Conclusion: Despite the evident image noise reduction obtained by ASIR, the diagnostic value for detecting cocaine-filled packets decreases, depending on the applied ASIR percentage.

An iterative Multiscale Finite-Volume algorithm converging to the exact solution

The multiscale finite volume (MsFV) method has been developed to efficiently solve large heterogeneous problems (elliptic or parabolic); it is usually employed for pressure equations and delivers conservative flux fields to be used in transport problems. The method essentially relies on the hypothesis that the (fine-scale) problem can be reasonably described by a set of local solutions coupled by a conservative global (coarse-scale) problem. In most cases, the boundary conditions assigned for the local problems are satisfactory and the approximate conservative fluxes provided by the method are accurate. In numerically challenging cases, however, a more accurate localization is required to obtain a good approximation of the fine-scale solution. In this paper we develop a procedure to iteratively improve the boundary conditions of the local problems. The algorithm relies on the data structure of the MsFV method and employs a Krylov-subspace projection method to obtain an unconditionally stable scheme and accelerate convergence. Two variants are considered: in the first, only the MsFV operator is used; in the second, the MsFV operator is combined in a two-step method with an operator derived from the problem solved to construct the conservative flux field. The resulting iterative MsFV algorithms allow arbitrary reduction of the solution error without compromising the construction of a conservative flux field, which is guaranteed at any iteration. Since it converges to the exact solution, the method can be regarded as a linear solver. In this context, the schemes proposed here can be viewed as preconditioned versions of the Generalized Minimal Residual method (GMRES), with a very peculiar characteristic that the residual on the coarse grid is zero at any iteration (thus conservative fluxes can be obtained).

Progression of auditory discrimination based on neural decoding predicts awakening from coma.

Auditory evoked potentials are informative of intact cortical functions of comatose patients. The integrity of auditory functions evaluated using mismatch negativity paradigms has been associated with their chances of survival. However, because auditory discrimination is assessed at various delays after coma onset, it is still unclear whether this impairment depends on the time of the recording. We hypothesized that impairment in auditory discrimination capabilities is indicative of coma progression, rather than of the comatose state itself and that rudimentary auditory discrimination remains intact during acute stages of coma. We studied 30 post-anoxic comatose patients resuscitated from cardiac arrest and five healthy, age-matched controls. Using a mismatch negativity paradigm, we performed two electroencephalography recordings with a standard 19-channel clinical montage: the first within 24 h after coma onset and under mild therapeutic hypothermia, and the second after 1 day and under normothermic conditions. We analysed electroencephalography responses based on a multivariate decoding algorithm that automatically quantifies neural discrimination at the single patient level. Results showed high average decoding accuracy in discriminating sounds both for control subjects and comatose patients. Importantly, accurate decoding was largely independent of patients' chance of survival. However, the progression of auditory discrimination between the first and second recordings was informative of a patient's chance of survival. A deterioration of auditory discrimination was observed in all non-survivors (equivalent to 100% positive predictive value for survivors). We show, for the first time, evidence of intact auditory processing even in comatose patients who do not survive and that progression of sound discrimination over time is informative of a patient's chance of survival. Tracking auditory discrimination in comatose patients could provide new insight to the chance of awakening in a quantitative and automatic fashion during early stages of coma.

Low-dose multidetector computed tomography of the cervical spine: optimization of iterative reconstruction strength levels.

BACKGROUND: Iterative reconstruction (IR) techniques reduce image noise in multidetector computed tomography (MDCT) imaging. They can therefore be used to reduce radiation dose while maintaining diagnostic image quality nearly constant. However, CT manufacturers offer several strength levels of IR to choose from. PURPOSE: To determine the optimal strength level of IR in low-dose MDCT of the cervical spine. MATERIAL AND METHODS: Thirty consecutive patients investigated by low-dose cervical spine MDCT were prospectively studied. Raw data were reconstructed using filtered back-projection and sinogram-affirmed IR (SAFIRE, strength levels 1 to 5) techniques. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured at C3-C4 and C6-C7 levels. Two radiologists independently and blindly evaluated various anatomical structures (both dense and soft tissues) using a 4-point scale. They also rated the overall diagnostic image quality using a 10-point scale. RESULTS: As IR strength levels increased, image noise decreased linearly, while SNR and CNR both increased linearly at C3-C4 and C6-C7 levels (P < 0.001). For the intervertebral discs, the content of neural foramina and dural sac, and for the ligaments, subjective image quality scores increased linearly with increasing IR strength level (P ≤ 0.03). Conversely, for the soft tissues and trabecular bone, the scores decreased linearly with increasing IR strength level (P < 0.001). Finally, the overall diagnostic image quality scores increased linearly with increasing IR strength level (P < 0.001). CONCLUSION: The optimal strength level of IR in low-dose cervical spine MDCT depends on the anatomical structure to be analyzed. For the intervertebral discs and the content of neural foramina, high strength levels of IR are recommended.

MDCT Arthrography of the Hip: Value of the Adaptive Statistical Iterative Reconstruction Technique and Potential for Radiation Dose Reduction

Au cours des deux dernières décennies, la technique d'imagerie arthro-scanner a bénéficié de nombreux progrès technologiques et représente aujourd'hui une excellente alternative à l'imagerie par résonance magnétique (IRM) et / ou arthro-IRM dans l'évaluation des pathologies de la hanche. Cependant, elle reste limitée par l'exposition aux rayonnements ionisants importante. Les techniques de reconstruction itérative (IR) ont récemment été mis en oeuvre avec succès en imagerie ; la littérature montre que l'utilisation ces dernières contribue à réduire la dose d'environ 40 à 55%, comparativement aux protocoles courants utilisant la rétroprojection filtrée (FBP), en scanner de rachis. A notre connaissance, l'utilisation de techniques IR en arthro-scanner de hanche n'a pas été évaluée jusqu'à présent. Le but de notre étude était d'évaluer l'impact de la technique ASIR (GE Healthcare) sur la qualité de l'image objective et subjective en arthro-scanner de hanche, et d'évaluer son potentiel en terme de réduction de dose. Pour cela, trente sept patients examinés par arthro-scanner de hanche ont été randomisés en trois groupes : dose standard (CTDIvol = 38,4 mGy) et deux groupes de dose réduite (CTDIvol = 24,6 ou 15,4 mGy). Les images ont été reconstruites en rétroprojection filtrée (FBP) puis en appliquant différents pourcentages croissants d'ASIR (30, 50, 70 et 90%). Le bruit et le rapport contraste sur bruit (CNR) ont été mesurés. Deux radiologues spécialisés en imagerie musculo-squelettique ont évalué de manière indépendante la qualité de l'image au niveau de plusieurs structures anatomiques en utilisant une échelle de quatre grades. Ils ont également évalué les lésions labrales et du cartilage articulaire. Les résultats révèlent que le bruit augmente (p = 0,0009) et le CNR diminue (p = 0,001) de manière significative lorsque la dose diminue. A l'inverse, le bruit diminue (p = 0,0001) et le contraste sur bruit augmente (p < 0,003) de manière significative lorsque le pourcentage d'ASIR augmente ; on trouve également une augmentation significative des scores de la qualité de l'image pour le labrum, le cartilage, l'os sous-chondral, la qualité de l'image globale (au delà de ASIR 50%), ainsi que le bruit (p < 0,04), et une réduction significative pour l'os trabuculaire et les muscles (p < 0,03). Indépendamment du niveau de dose, il n'y a pas de différence significative pour la détection et la caractérisation des lésions labrales (n=24, p = 1) et des lésions cartilagineuses (n=40, p > 0,89) en fonction du pourcentage d'ASIR. Notre travail a permis de montrer que l'utilisation de plus de 50% d'ASIR permet de reduire de manière significative la dose d'irradiation reçue par le patient lors d'un arthro-scanner de hanche tout en maintenant une qualité d'image diagnostique comparable par rapport à un protocole de dose standard utilisant la rétroprojection filtrée.

Assessment of statistical iterative reconstructions to optimize CT examination protocols for children and young adults

La tomodensitométrie (CT) est une technique d'imagerie dont l'intérêt n'a cessé de croître depuis son apparition dans le début des années 70. Dans le domaine médical, son utilisation est incontournable à tel point que ce système d'imagerie pourrait être amené à devenir victime de son succès si son impact au niveau de l'exposition de la population ne fait pas l'objet d'une attention particulière. Bien évidemment, l'augmentation du nombre d'examens CT a permis d'améliorer la prise en charge des patients ou a rendu certaines procédures moins invasives. Toutefois, pour assurer que le compromis risque - bénéfice soit toujours en faveur du patient, il est nécessaire d'éviter de délivrer des doses non utiles au diagnostic.¦Si cette action est importante chez l'adulte elle doit être une priorité lorsque les examens se font chez l'enfant, en particulier lorsque l'on suit des pathologies qui nécessitent plusieurs examens CT au cours de la vie du patient. En effet, les enfants et jeunes adultes sont plus radiosensibles. De plus, leur espérance de vie étant supérieure à celle de l'adulte, ils présentent un risque accru de développer un cancer radio-induit dont la phase de latence peut être supérieure à vingt ans. Partant du principe que chaque examen radiologique est justifié, il devient dès lors nécessaire d'optimiser les protocoles d'acquisitions pour s'assurer que le patient ne soit pas irradié inutilement. L'avancée technologique au niveau du CT est très rapide et depuis 2009, de nouvelles techniques de reconstructions d'images, dites itératives, ont été introduites afin de réduire la dose et améliorer la qualité d'image.¦Le présent travail a pour objectif de déterminer le potentiel des reconstructions itératives statistiques pour réduire au minimum les doses délivrées lors d'examens CT chez l'enfant et le jeune adulte tout en conservant une qualité d'image permettant le diagnostic, ceci afin de proposer des protocoles optimisés.¦L'optimisation d'un protocole d'examen CT nécessite de pouvoir évaluer la dose délivrée et la qualité d'image utile au diagnostic. Alors que la dose est estimée au moyen d'indices CT (CTDIV0| et DLP), ce travail a la particularité d'utiliser deux approches radicalement différentes pour évaluer la qualité d'image. La première approche dite « physique », se base sur le calcul de métriques physiques (SD, MTF, NPS, etc.) mesurées dans des conditions bien définies, le plus souvent sur fantômes. Bien que cette démarche soit limitée car elle n'intègre pas la perception des radiologues, elle permet de caractériser de manière rapide et simple certaines propriétés d'une image. La seconde approche, dite « clinique », est basée sur l'évaluation de structures anatomiques (critères diagnostiques) présentes sur les images de patients. Des radiologues, impliqués dans l'étape d'évaluation, doivent qualifier la qualité des structures d'un point de vue diagnostique en utilisant une échelle de notation simple. Cette approche, lourde à mettre en place, a l'avantage d'être proche du travail du radiologue et peut être considérée comme méthode de référence.¦Parmi les principaux résultats de ce travail, il a été montré que les algorithmes itératifs statistiques étudiés en clinique (ASIR?, VEO?) ont un important potentiel pour réduire la dose au CT (jusqu'à-90%). Cependant, par leur fonctionnement, ils modifient l'apparence de l'image en entraînant un changement de texture qui pourrait affecter la qualité du diagnostic. En comparant les résultats fournis par les approches « clinique » et « physique », il a été montré que ce changement de texture se traduit par une modification du spectre fréquentiel du bruit dont l'analyse permet d'anticiper ou d'éviter une perte diagnostique. Ce travail montre également que l'intégration de ces nouvelles techniques de reconstruction en clinique ne peut se faire de manière simple sur la base de protocoles utilisant des reconstructions classiques. Les conclusions de ce travail ainsi que les outils développés pourront également guider de futures études dans le domaine de la qualité d'image, comme par exemple, l'analyse de textures ou la modélisation d'observateurs pour le CT.¦-¦Computed tomography (CT) is an imaging technique in which interest has been growing since it first began to be used in the early 1970s. In the clinical environment, this imaging system has emerged as the gold standard modality because of its high sensitivity in producing accurate diagnostic images. However, even if a direct benefit to patient healthcare is attributed to CT, the dramatic increase of the number of CT examinations performed has raised concerns about the potential negative effects of ionizing radiation on the population. To insure a benefit - risk that works in favor of a patient, it is important to balance image quality and dose in order to avoid unnecessary patient exposure.¦If this balance is important for adults, it should be an absolute priority for children undergoing CT examinations, especially for patients suffering from diseases requiring several follow-up examinations over the patient's lifetime. Indeed, children and young adults are more sensitive to ionizing radiation and have an extended life span in comparison to adults. For this population, the risk of developing cancer, whose latency period exceeds 20 years, is significantly higher than for adults. Assuming that each patient examination is justified, it then becomes a priority to optimize CT acquisition protocols in order to minimize the delivered dose to the patient. Over the past few years, CT advances have been developing at a rapid pace. Since 2009, new iterative image reconstruction techniques, called statistical iterative reconstructions, have been introduced in order to decrease patient exposure and improve image quality.¦The goal of the present work was to determine the potential of statistical iterative reconstructions to reduce dose as much as possible without compromising image quality and maintain diagnosis of children and young adult examinations.¦The optimization step requires the evaluation of the delivered dose and image quality useful to perform diagnosis. While the dose is estimated using CT indices (CTDIV0| and DLP), the particularity of this research was to use two radically different approaches to evaluate image quality. The first approach, called the "physical approach", computed physical metrics (SD, MTF, NPS, etc.) measured on phantoms in well-known conditions. Although this technique has some limitations because it does not take radiologist perspective into account, it enables the physical characterization of image properties in a simple and timely way. The second approach, called the "clinical approach", was based on the evaluation of anatomical structures (diagnostic criteria) present on patient images. Radiologists, involved in the assessment step, were asked to score image quality of structures for diagnostic purposes using a simple rating scale. This approach is relatively complicated to implement and also time-consuming. Nevertheless, it has the advantage of being very close to the practice of radiologists and is considered as a reference method.¦Primarily, this work revealed that the statistical iterative reconstructions studied in clinic (ASIR? and VECO have a strong potential to reduce CT dose (up to -90%). However, by their mechanisms, they lead to a modification of the image appearance with a change in image texture which may then effect the quality of the diagnosis. By comparing the results of the "clinical" and "physical" approach, it was showed that a change in texture is related to a modification of the noise spectrum bandwidth. The NPS analysis makes possible to anticipate or avoid a decrease in image quality. This project demonstrated that integrating these new statistical iterative reconstruction techniques can be complex and cannot be made on the basis of protocols using conventional reconstructions. The conclusions of this work and the image quality tools developed will be able to guide future studies in the field of image quality as texture analysis or model observers dedicated to CT.

Decoding decisions in healthy subjects and auditory discrimination in comatose patients through single-trial topographic EEG analyses

Neuroimaging studies analyzing neurophysiological signals are typically based on comparing averages of peri-stimulus epochs across experimental conditions. This approach can however be problematic in the case of high-level cognitive tasks, where response variability across trials is expected to be high and in cases where subjects cannot be considered part of a group. The main goal of this thesis has been to address this issue by developing a novel approach for analyzing electroencephalography (EEG) responses at the single-trial level. This approach takes advantage of the spatial distribution of the electric field on the scalp (topography) and exploits repetitions across trials for quantifying the degree of discrimination between experimental conditions through a classification scheme. In the first part of this thesis, I developed and validated this new method (Tzovara et al., 2012a,b). Its general applicability was demonstrated with three separate datasets, two in the visual modality and one in the auditory. This development allowed then to target two new lines of research, one in basic and one in clinical neuroscience, which represent the second and third part of this thesis respectively. For the second part of this thesis (Tzovara et al., 2012c), I employed the developed method for assessing the timing of exploratory decision-making. Using single-trial topographic EEG activity during presentation of a choice's payoff, I could predict the subjects' subsequent decisions. This prediction was due to a topographic difference which appeared on average at ~516ms after the presentation of payoff and was subject-specific. These results exploit for the first time the temporal correlates of individual subjects' decisions and additionally show that the underlying neural generators start differentiating their responses already ~880ms before the button press. Finally, in the third part of this project, I focused on a clinical study with the goal of assessing the degree of intact neural functions in comatose patients. Auditory EEG responses were assessed through a classical mismatch negativity paradigm, during the very early phase of coma, which is currently under-investigated. By taking advantage of the decoding method developed in the first part of the thesis, I could quantify the degree of auditory discrimination at the single patient level (Tzovara et al., in press). Our results showed for the first time that even patients who do not survive the coma can discriminate sounds at the neural level, during the first hours after coma onset. Importantly, an improvement in auditory discrimination during the first 48hours of coma was predictive of awakening and survival, with 100% positive predictive value. - L'analyse des signaux électrophysiologiques en neuroimagerie se base typiquement sur la comparaison des réponses neurophysiologiques à différentes conditions expérimentales qui sont moyennées après plusieurs répétitions d'une tâche. Pourtant, cette approche peut être problématique dans le cas des fonctions cognitives de haut niveau, où la variabilité des réponses entre les essais peut être très élevéeou dans le cas où des sujets individuels ne peuvent pas être considérés comme partie d'un groupe. Le but principal de cette thèse est d'investiguer cette problématique en développant une nouvelle approche pour l'analyse des réponses d'électroencephalographie (EEG) au niveau de chaque essai. Cette approche se base sur la modélisation de la distribution du champ électrique sur le crâne (topographie) et profite des répétitions parmi les essais afin de quantifier, à l'aide d'un schéma de classification, le degré de discrimination entre des conditions expérimentales. Dans la première partie de cette thèse, j'ai développé et validé cette nouvelle méthode (Tzovara et al., 2012a,b). Son applicabilité générale a été démontrée avec trois ensembles de données, deux dans le domaine visuel et un dans l'auditif. Ce développement a permis de cibler deux nouvelles lignes de recherche, la première dans le domaine des neurosciences cognitives et l'autre dans le domaine des neurosciences cliniques, représentant respectivement la deuxième et troisième partie de ce projet. En particulier, pour la partie cognitive, j'ai appliqué cette méthode pour évaluer l'information temporelle de la prise des décisions (Tzovara et al., 2012c). En se basant sur l'activité topographique de l'EEG au niveau de chaque essai pendant la présentation de la récompense liée à un choix, on a pu prédire les décisions suivantes des sujets (en termes d'exploration/exploitation). Cette prédiction s'appuie sur une différence topographique qui apparaît en moyenne ~516ms après la présentation de la récompense. Ces résultats exploitent pour la première fois, les corrélés temporels des décisions au niveau de chaque sujet séparément et montrent que les générateurs neuronaux de ces décisions commencent à différentier leurs réponses déjà depuis ~880ms avant que les sujets appuient sur le bouton. Finalement, pour la dernière partie de ce projet, je me suis focalisée sur une étude Clinique afin d'évaluer le degré des fonctions neuronales intactes chez les patients comateux. Des réponses EEG auditives ont été examinées avec un paradigme classique de mismatch negativity, pendant la phase précoce du coma qui est actuellement sous-investiguée. En utilisant la méthode de décodage développée dans la première partie de la thèse, j'ai pu quantifier le degré de discrimination auditive au niveau de chaque patient (Tzovara et al., in press). Nos résultats montrent pour la première fois que même des patients comateux qui ne vont pas survivre peuvent discriminer des sons au niveau neuronal, lors de la phase aigue du coma. De plus, une amélioration dans la discrimination auditive pendant les premières 48heures du coma a été observée seulement chez des patients qui se sont réveillés par la suite (100% de valeur prédictive pour un réveil).

Iterative image reconstruction techniques: cardiothoracic computed tomography applications.

Iterative image reconstruction algorithms provide significant improvements over traditional filtered back projection in computed tomography (CT). Clinically available through recent advances in modern CT technology, iterative reconstruction enhances image quality through cyclical image calculation, suppressing image noise and artifacts, particularly blooming artifacts. The advantages of iterative reconstruction are apparent in traditionally challenging cases-for example, in obese patients, those with significant artery calcification, or those with coronary artery stents. In addition, as clinical use of CT has grown, so have concerns over ionizing radiation associated with CT examinations. Through noise reduction, iterative reconstruction has been shown to permit radiation dose reduction while preserving diagnostic image quality. This approach is becoming increasingly attractive as the routine use of CT for pediatric and repeated follow-up evaluation grows ever more common. Cardiovascular CT in particular, with its focus on detailed structural and functional analyses, stands to benefit greatly from the promising iterative solutions that are readily available.

Decoding sequence learning from single-trial intracranial EEG in humans.

We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

Full diversity product codes for block erasure and block fading channels

We show how to build full-diversity product codes under both iterative encoding and decoding over non-ergodic channels, in presence of block erasure and block fading. The concept of a rootcheck or a root subcode is introduced by generalizing the same principle recently invented for low-density parity-check codes. We also describe some channel related graphical properties of the new family of product codes, a familyreferred to as root product codes.

On iterative performance of LDPC and Root-LDPC codes over block-fading channels

This paper presents our investigation on iterativedecoding performances of some sparse-graph codes on block-fading Rayleigh channels. The considered code ensembles are standard LDPC codes and Root-LDPC codes, first proposed in and shown to be able to attain the full transmission diversity. We study the iterative threshold performance of those codes as a function of fading gains of the transmission channel and propose a numerical approximation of the iterative threshold versus fading gains, both both LDPC and Root-LDPC codes.Also, we show analytically that, in the case of 2 fading blocks,the iterative threshold root of Root-LDPC codes is proportional to (α1 α2)1, where α1 and α2 are corresponding fading gains.From this result, the full diversity property of Root-LDPC codes immediately follows.

Paediatric cardiac CT examinations: impact of the iterative reconstruction method ASIR on image quality--preliminary findings.

BACKGROUND: Radiation dose exposure is of particular concern in children due to the possible harmful effects of ionizing radiation. The adaptive statistical iterative reconstruction (ASIR) method is a promising new technique that reduces image noise and produces better overall image quality compared with routine-dose contrast-enhanced methods. OBJECTIVE: To assess the benefits of ASIR on the diagnostic image quality in paediatric cardiac CT examinations. MATERIALS AND METHODS: Four paediatric radiologists based at two major hospitals evaluated ten low-dose paediatric cardiac examinations (80 kVp, CTDI(vol) 4.8-7.9 mGy, DLP 37.1-178.9 mGy·cm). The average age of the cohort studied was 2.6 years (range 1 day to 7 years). Acquisitions were performed on a 64-MDCT scanner. All images were reconstructed at various ASIR percentages (0-100%). For each examination, radiologists scored 19 anatomical structures using the relative visual grading analysis method. To estimate the potential for dose reduction, acquisitions were also performed on a Catphan phantom and a paediatric phantom. RESULTS: The best image quality for all clinical images was obtained with 20% and 40% ASIR (p < 0.001) whereas with ASIR above 50%, image quality significantly decreased (p < 0.001). With 100% ASIR, a strong noise-free appearance of the structures reduced image conspicuity. A potential for dose reduction of about 36% is predicted for a 2- to 3-year-old child when using 40% ASIR rather than the standard filtered back-projection method. CONCLUSION: Reconstruction including 20% to 40% ASIR slightly improved the conspicuity of various paediatric cardiac structures in newborns and children with respect to conventional reconstruction (filtered back-projection) alone.

Decoding stimulus-related information from single-trial EEG responses based on voltage topographies

Neuroimaging studies typically compare experimental conditions using average brain responses, thereby overlooking the stimulus-related information conveyed by distributed spatio-temporal patterns of single-trial responses. Here, we take advantage of this rich information at a single-trial level to decode stimulus-related signals in two event-related potential (ERP) studies. Our method models the statistical distribution of the voltage topographies with a Gaussian Mixture Model (GMM), which reduces the dataset to a number of representative voltage topographies. The degree of presence of these topographies across trials at specific latencies is then used to classify experimental conditions. We tested the algorithm using a cross-validation procedure in two independent EEG datasets. In the first ERP study, we classified left- versus right-hemifield checkerboard stimuli for upper and lower visual hemifields. In a second ERP study, when functional differences cannot be assumed, we classified initial versus repeated presentations of visual objects. With minimal a priori information, the GMM model provides neurophysiologically interpretable features - vis à vis voltage topographies - as well as dynamic information about brain function. This method can in principle be applied to any ERP dataset testing the functional relevance of specific time periods for stimulus processing, the predictability of subject's behavior and cognitive states, and the discrimination between healthy and clinical populations.