Aerospace Medicine and Biology. A continuing bibliography with indexes

This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

CNN-based Lung CT Registration with Multiple Anatomical Constraints

Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to small deformation or they fail to handle a superposition of large and small deformations without producing implausible deformation fields with foldings inside. In this paper, we identify important strategies of conventional registration methods for lung registration and successfully developed the deep-learning counterpart. We employ a Gaussian-pyramid-based multilevel framework that can solve the image registration optimization in a coarse-to-fine fashion. Furthermore, we prevent foldings of the deformation field and restrict the determinant of the Jacobian to physiologically meaningful values by combining a volume change penalty with a curvature regularizer in the loss function. Keypoint correspondences are integrated to focus on the alignment of smaller structures. We perform an extensive evaluation to assess the accuracy, the robustness, the plausibility of the estimated deformation fields, and the transferability of our registration approach. We show that it achieves state-of-the-art results on the COPDGene dataset compared to conventional registration method with much shorter execution time. In our experiments on the DIRLab exhale to inhale lung registration, we demonstrate substantial improvements (TRE below $1.2$ mm) over other deep learning methods. Our algorithm is publicly available at https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/

A Survey on Deep Learning in Medical Image Registration: New Technologies, Uncertainty, Evaluation Metrics, and Beyond

Over the past decade, deep learning technologies have greatly advanced the field of medical image registration. The initial developments, such as ResNet-based and U-Net-based networks, laid the groundwork for deep learning-driven image registration. Subsequent progress has been made in various aspects of deep learning-based registration, including similarity measures, deformation regularizations, and uncertainty estimation. These advancements have not only enriched the field of deformable image registration but have also facilitated its application in a wide range of tasks, including atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D registration. In this paper, we present a comprehensive overview of the most recent advancements in deep learning-based image registration. We begin with a concise introduction to the core concepts of deep learning-based image registration. Then, we delve into innovative network architectures, loss functions specific to registration, and methods for estimating registration uncertainty. Additionally, this paper explores appropriate evaluation metrics for assessing the performance of deep learning models in registration tasks. Finally, we highlight the practical applications of these novel techniques in medical imaging and discuss the future prospects of deep learning-based image registration

Aerospace medicine and biology: A cumulative index to the continuing bibliography of the 1973 issues

A cumulative index to the abstracts contained in Supplements 112 through 123 of Aerospace Medicine and Biology A Continuing Bibliography is presented. It includes three indexes: subject, personal author, and corporate source

Aerospace Medicine and Biology: A cumulative index to the 1974 issues of a continuing bibliography

This publication is a cumulative index to the abstracts contained in supplements 125 through 136 of Aerospace Medicine and Biology: A Continuing Bibliography. It includes three indexes--subject, personal author, and corporate source

Le recalage robuste d’images médicales et la modélisation du mouvement basée sur l’apprentissage profond

This thesis presents new computational tools for quantifying deformations and motion of anatomical structures from medical images as required by a large variety of clinical applications. Generic deformable registration tools are presented that enable deformation analysis useful for improving diagnosis, prognosis and therapy guidance. These tools were built by combining state-of-the-art medical image analysis methods with cutting-edge machine learning methods.First, we focus on difficult inter-subject registration problems. By learning from given deformation examples, we propose a novel agent-based optimization scheme inspired by deep reinforcement learning where a statistical deformation model is explored in a trial-and-error fashion showing improved registration accuracy. Second, we develop a diffeomorphic deformation model that allows for accurate multiscale registration and deformation analysis by learning a low-dimensional representation of intra-subject deformations. The unsupervised method uses a latent variable model in form of a conditional variational autoencoder (CVAE) for learning a probabilistic deformation encoding that is useful for the simulation, classification and comparison of deformations.Third, we propose a probabilistic motion model derived from image sequences of moving organs. This generative model embeds motion in a structured latent space, the motion matrix, which enables the consistent tracking of structures and various analysis tasks. For instance, it leads to the simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation.Finally, we demonstrate the importance of the developed tools in a clinical application where the motion model is used for disease prognosis and therapy planning. It is shown that the survival risk for heart failure patients can be predicted from the discriminative motion matrix with a higher accuracy compared to classical image-derived risk factors.Cette thèse présente de nouveaux outils informatiques pour quantifier les déformations et le mouvement de structures anatomiques à partir d’images médicales dans le cadre d’une grande variété d’applications cliniques. Des outils génériques de recalage déformable sont présentés qui permettent l’analyse de la déformation de tissus anatomiques pour améliorer le diagnostic, le pronostic et la thérapie. Ces outils combinent des méthodes avancées d’analyse d’images médicales avec des méthodes d’apprentissage automatique performantes.Dans un premier temps, nous nous concentrons sur les problèmes de recalages inter-sujets difficiles. En apprenant à partir d’exemples de déformation donnés, nous proposons un nouveau schéma d’optimisation basé sur un agent inspiré de l’apprentissage par renforcement profond dans lequel un modèle de déformation statistique est exploré de manière itérative montrant une précision améliorée de recalage. Dans un second temps, nous développons un modèle de déformation difféomorphe qui permet un recalage multi-échelle précis et une analyse de déformation en apprenant une représentation de faible dimension des déformations intra-sujet. La méthode non supervisée utilise un modèle de variable latente sous la forme d’un autoencodeur variationnel conditionnel (CVAE) pour apprendre une représentation probabiliste des déformations qui est utile pour la simulation, la classification et la comparaison des déformations. Troisièmement, nous proposons un modèle de mouvement probabiliste dérivé de séquences d’images d’organes en mouvement. Ce modèle génératif décrit le mouvement dans un espace latent structuré, la matrice de mouvement, qui permet le suivi cohérent des structures ainsi que l’analyse du mouvement. Ainsi cette approche permet la simulation et l’interpolation de modèles de mouvement réalistes conduisant à une acquisition et une augmentation des données plus rapides.Enfin, nous démontrons l’intérêt des outils développés dans une application clinique où le modèle de mouvement est utilisé pour le pronostic de maladies et la planification de thérapies. Il est démontré que le risque de survie des patients souffrant d’insuffisance cardiaque peut être prédit à partir de la matrice de mouvement discriminant avec une précision supérieure par rapport aux facteurs de risque classiques dérivés de l’image

MRI Artefact Augmentation: Robust Deep Learning Systems and Automated Quality Control

Quality control (QC) of magnetic resonance imaging (MRI) is essential to establish whether a scan or dataset meets a required set of standards. In MRI, many potential artefacts must be identified so that problematic images can either be excluded or accounted for in further image processing or analysis. To date, the gold standard for the identification of these issues is visual inspection by experts. A primary source of MRI artefacts is caused by patient movement, which can affect clinical diagnosis and impact the accuracy of Deep Learning systems. In this thesis, I present a method to simulate motion artefacts from artefact-free images to augment convolutional neural networks (CNNs), increasing training appearance variability and robustness to motion artefacts. I show that models trained with artefact augmentation generalise better and are more robust to real-world artefacts, with negligible cost to performance on clean data. I argue that it is often better to optimise frameworks end-to-end with artefact augmentation rather than learning to retrospectively remove artefacts, thus enforcing robustness to artefacts at the feature level representation of the data. The labour-intensive and subjective nature of QC has increased interest in automated methods. To address this, I approach MRI quality estimation as the uncertainty in performing a downstream task, using probabilistic CNNs to predict segmentation uncertainty as a function of the input data. Extending this framework, I introduce a novel decoupled uncertainty model, enabling separate uncertainty predictions for different types of image degradation. Training with an extended k-space artefact augmentation pipeline, the model provides informative measures of uncertainty on problematic real-world scans classified by QC raters and enables sources of segmentation uncertainty to be identified. Suitable quality for algorithmic processing may differ from an image's perceptual quality. Exploring this, I pose MRI visual quality assessment as an image restoration task. Using Bayesian CNNs to recover clean images from noisy data, I show that the uncertainty indicates the possible recoverability of an image. A multi-task network combining uncertainty-aware artefact recovery with tissue segmentation highlights the distinction between visual and algorithmic quality, which has the impact that, depending on the downstream task, less data should be discarded for purely visual quality reasons

Aerospace Medicine and Biology: A cumulative index to the 1982 issues

This publication is a cumulative index to the abstracts contained in the Supplements 229 through 240 of Aerospace Medicine and Biology: A continuing Bibliography. It includes three indexes: subject, personal author, and corporate source