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The human creativity is now being supported, or challenged, by an easy access to generative tools utilising Artificial Intelligence (AI). Within the last few years, Generative Adversarial Network-based image generation tools have become available for the public. In this paper, we explore how one such tool, called Midjourney, could be used for generating moodboards. Our work focuses on exploring moodboard image generation via text-based prompts. We take note of the active online discussions, where examples and ethical concerns have strongly emerged during the late 2022. As a result, the AI produced imagery that the authors could not have produced themselves in any reasonable amount of time, if at all. The GAN-generated images are of high quality, and visually useable for design purposes. However, there are ethical concerns on the sources of the images used for training the AI systems, and copyright concerns on the outcomes.

The SESAR-funded Modern ATM via Human / Automation Learning Optimisation (MAHALO) project recently completed two years of technical work exploring the human performance impacts of AI and Machine Learning (ML), as applied to enroute ATC conflict detection and resolution (CD&R). It first developed a hybrid ML CD&R capability, along with a realtime simulation platform and experimental User Interface. After a series of development trials, the project culminated in a pair of field studies (i.e., human-in-the-loop trials) across two EU countries, with a total of 35 operational air traffic controllers. In each of these two field studies, controller behaviour was first captured in a pre-test phase, and used to train the ML system. Subsequent main experiment trials then experimentally manipulated within controllers both Conformance (as either a personalised-, group average-, or optimized model) and Transparency (as ether a baseline vector depiction, an enhanced graphical diagram, or a diagram-plus-text presentation). The proposed paper presents guidelines on the design and implementation of ML systems in Air Traffic Control, derived from the results and lesson learned from the Simulations, as well as the qualitative feedback received from the controllers themselves., Control & Simulation

We propose a novel probabilistic framework to merge information from diffusion weighted imaging tractography and resting-state functional magnetic resonance imaging correlations to identify connectivity patterns in the brain. In particular, we model the interaction between latent anatomical and functional connectivity and present an intuitive extension to population studies. We employ the EM algorithm to estimate the model parameters by maximizing the data likelihood. The method simultaneously infers the templates of latent connectivity for each population and the differences in connectivity between the groups. We demonstrate our method on a schizophrenia study. Our model identifies significant increases in functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior cingulate region and the temporal lobe in schizophrenia. We further establish that our model learns predictive differences between the control and clinical populations, and that combining the two modalities yields better results than considering each one in isolation. © 2011 IEEE.

One of the approaches in diffusion tensor imaging is to consider a Riemannian metric given by the inverse diffusion tensor. Such a metric is used for geodesic tractography and connectivity analysis in white matter. We propose a metric tensor given by the adjugate rather than the previously proposed inverse diffusion tensor. The adjugate metric can also be employed in the sharpening framework. Tractography experiments on synthetic and real brain diffusion data show improvement for high-curvature tracts and in the vicinity of isotropic diffusion regions relative to most results for inverse (sharpened) diffusion tensors, and especially on real data. In addition, adjugate tensors are shown to be more robust to noise. Keywords: Riemannian geometry; Geodesic tractography; Diffusion tensor imaging; Brownian motion; Diffusion generator; Sharpened diffusion tensor

One of the approaches in diffusion tensor imaging is to consider a Riemannian metric given by the inverse diffusion tensor. Such a metric is used for geodesic tractography and connectivity analysis in white matter. We propose a metric tensor given by the adjugate rather than the previously proposed inverse diffusion tensor. The adjugate metric can also be employed in the sharpening framework. Tractography experiments on synthetic and real brain diffusion data show improvement for high-curvature tracts and in the vicinity of isotropic diffusion regions relative to most results for inverse (sharpened) diffusion tensors, and especially on real data. In addition, adjugate tensors are shown to be more robust to noise. Keywords: Riemannian geometry; Geodesic tractography; Diffusion tensor imaging; Brownian motion; Diffusion generator; Sharpened diffusion tensor

One of the approaches in diffusion tensor imaging is to consider a Riemannian metric given by the inverse diffusion tensor. Such a metric is used for geodesic tractography and connectivity analysis in white matter. We propose a metric tensor given by the adjugate rather than the previously proposed inverse diffusion tensor. The adjugate metric can also be employed in the sharpening framework. Tractography experiments on synthetic and real brain diffusion data show improvement for high-curvature tracts and in the vicinity of isotropic diffusion regions relative to most results for inverse (sharpened) diffusion tensors, and especially on real data. In addition, adjugate tensors are shown to be more robust to noise. Keywords: Riemannian geometry; Geodesic tractography; Diffusion tensor imaging; Brownian motion; Diffusion generator; Sharpened diffusion tensor

Consistency and consensus in conflict solution performance was investigated in two real-time simulations involving experienced air traffic controllers and trainees. The simulations consisted of participants repeatedly playing a specific en route traffic scenario. Conflict solution performance and consistency was measured by analysing participant’s solutions to a specifically designed conflict. Consensus was determined by comparing participant’s consistent conflict solution behaviour. Four different clusters of consistent behaviour were identified. All participants could be classified as consistent according to one or more of these clusters. Consensus in solving the designed conflict could not be determined between participants. There were, however, smaller groups of participants who solved the designed conflict similarly. Participants varied considerably in terms if when to interact and solve the conflict. This is a potential issue when it comes to determining thresholds for the timing of decision-aid interaction., Control & Operations, Aerospace Engineering

Consistency and consensus in conflict solution performance was investigated in two real-time simulations involving experienced air traffic controllers and trainees. The simulations consisted of participants repeatedly playing a specific en route traffic scenario. Conflict solution performance and consistency was measured by analysing participant’s solutions to a specifically designed conflict. Consensus was determined by comparing participant’s consistent conflict solution behaviour. Four different clusters of consistent behaviour were identified. All participants could be classified as consistent according to one or more of these clusters. Consensus in solving the designed conflict could not be determined between participants. There were, however, smaller groups of participants who solved the designed conflict similarly. Participants varied considerably in terms if when to interact and solve the conflict. This is a potential issue when it comes to determining thresholds for the timing of decision-aid interaction., Control & Operations, Aerospace Engineering

Large multisite efforts (e.g., the ENIGMA Consortium), have shown that neuroimaging traits including tract integrity (from DTI fractional anisotropy, FA) and subcortical volumes (from T1-weighted scans) are highly heritable and promising phenotypes for discovering genetic variants associated with brain structure. However, genetic correlations (rg) among measures from these different modalities for mapping the human genome to the brain remain unknown. Discovering these correlations can help map genetic and neuroanatomical pathways implicated in development and inherited risk for disease. We use structural equation models and a twin design to find rg between pairs of phenotypes extracted from DTI and MRI scans. When controlling for intracranial volume, the caudate as well as related measures from the limbic system - hippocampal volume - showed high rg with the cingulum FA. Using an unrelated sample and a Seemingly Unrelated Regression model for bivariate analysis of this connection, we show that a multivariate GWAS approach may be more promising for genetic discovery than a univariate approach applied to each trait separately.

Brain connectivity analyses are increasingly popular for investigating organization. Many connectivity measures including path lengths are generally defined as the number of nodes traversed to connect a node in a graph to the others. Despite its name, path length is purely topological, and does not take into account the physical length of the connections. The distance of the trajectory may also be highly relevant, but is typically overlooked in connectivity analyses. Here we combined genotyping, anatomical MRI and HARDI to understand how our genes influence the cortical connections, using whole-brain tractography. We defined a new measure, based on Dijkstra's algorithm, to compute path lengths for tracts connecting pairs of cortical regions. We compiled these measures into matrices where elements represent the physical distance traveled along tracts. We then analyzed a large cohort of healthy twins and show that our path length measure is reliable, heritable, and influenced even in young adults by the Alzheimer's risk gene, CLU.

Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our "label fusion" method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults. © 2012 Springer-Verlag.

(e-Poster), CMIV, CADICS

Introduction: MR diffusion tensor imaging (DTI) is the most powerful and currentlythe only way to visualize the organization of white matter fiber tracts in vivo. As this is a relatively newimaging technique, new tools are developed for quantifying fiber tracts, andrequire evaluation. We examined scalar indices of the diffusion tensor with two different tractography methods. We compared a novel clustering approach with a multiple region of interest (MROI) approach in a healthy and disease (schizophrenia) population. Methods: DTI images were acquired in 12 participants (n=6 patients withschizophrenia: 58 ± 12 years; n=6 controls: 57 ± 21 years) on a 1.5 Tesla GE system with diffusion gradients applied in 23 non-collinear directions, repeated three times. Tractography andfiber tract creation was performed using 3D Slicer software. Interraterreliability of the clustering approach and its similarity to the MROI methodwere evaluated. Results: The clustering approach was reliable both quantitatively and spatially (k > 0.8 for all tracts). There was high spatial(voxel-based) agreement between the clustering and MROI methods. Fractionalan isotropy and trace values were highly correlated between the clustering and MROI methods (p < 0.001 for all tracts). Discussion: Our clustering method has excellent interrater reliability and thereis a high level of agreement between our clustering method and the MROI method, both quantitatively and spatially. The clustering method is less susceptible touser bias. Moreover, not limited by a priori predictions, our clustering method may be a more robust and efficient way to identify and measure fiber tracts of interest. (colour figure available in PDF version)

An inherent drawback of the traditional diffusion tensor model is its limited ability to provide detailed information about multidirectional fiber architecture within a voxel. This leads to erroneous fiber tractography results in locations where fiber bundles cross each other. In this paper, we present a deterministic two-tensor extended Streamline Tractography (XST) technique, which successfully traces through regions of crossing fibers. The method has been evaluated on simulated and in-vivo human brain data, and compared with the traditional single tensor, and a probabilistic tractography technique. By tracing the corticospinal tract we demonstrate that when compared to the two methods, our technique can accurately identify fiber bundles known to be consistent with anatomy. When compared to the dense connectivity maps generated by probabilistic tractography, the method is computationally efficient and generates discrete geometric pathways that are simple to visualize and clinically useful.

An inherent drawback of the traditional diffusion tensor model is its limited ability to provide detailed information about multidirectional fiber architecture within a voxel. This leads to erroneous fiber tractography results in locations where fiber bundles cross each other. In this paper, we present a deterministic two-tensor extended Streamline Tractography (XST) technique, which successfully traces through regions of crossing fibers. The method has been evaluated on simulated and in-vivo human brain data, and compared with the traditional single tensor, and a probabilistic tractography technique. By tracing the corticospinal tract we demonstrate that when compared to the two methods, our technique can accurately identify fiber bundles known to be consistent with anatomy. When compared to the dense connectivity maps generated by probabilistic tractography, the method is computationally efficient and generates discrete geometric pathways that are simple to visualize and clinically useful.

The visualization of diffusion tensor fields remains a challenging topic. A representation based on volume rendering of a scalar field is presented. The method uses the tensor to correlate a noise field in the direction of greater diffusivity while preserving the high frequency components of the noise field in transversal diffusion directions.

The visualization of diffusion tensor fields remains a challenging topic. A representation based on volume rendering of a scalar field is presented. The method uses the tensor to correlate a noise field in the direction of greater diffusivity while preserving the high frequency components of the noise field in transversal diffusion directions.

The visualization of diffusion tensor fields remains a challenging topic. A representation based on volume rendering of a scalar field is presented. The method uses the tensor to correlate a noise field in the direction of greater diffusivity while preserving the high frequency components of the noise field in transversal diffusion directions.

The visualization of diffusion tensor fields remains a challenging topic. A representation based on volume rendering of a scalar field is presented. The method uses the tensor to correlate a noise field in the direction of greater diffusivity while preserving the high frequency components of the noise field in transversal diffusion directions.

The visualization of diffusion tensor fields remains a challenging topic. A representation based on volume rendering of a scalar field is presented. The method uses the tensor to correlate a noise field in the direction of greater diffusivity while preserving the high frequency components of the noise field in transversal diffusion directions.

Publicerad som CD på Neuroimage

To a great extent the success of advanced image-guided medical procedures hinges on non-rigid volume registration. For example non-rigid registration must be applied in interventional approaches where intra-operative information is used to update high-quality preoperative data; in follow-up studies in order to assess time-evolution of development; aging pathology or treatment; and in many other applications including intersubject variability and population-based atlas construction. In this paper we examine several computational schemes that warps one volumetric dataset onto another. We also explore the inevitable trade-off between the computational load and the incorporation of sophisticated similarity measures necessary for multimodal volumes. Estimated deformation fields are based on the variational formulation of Partial Derivative Equations (PDEs) which includes a similarity, and a regularization term. We compare numerical solutions to this problem using the Euler-Lagrange equations (EL) the Finite Elements discretization (FE) and Decoupled Optimization over the possible deformations (DO)., Papers from the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, October 25-28, 2001, held in Istanbul, Turkey. See also ADM001351 for entire conference on cd-rom. The original document contains color images.

Accurate localization of minimally invasive devices is critical to the success of interventional procedures. Device orientation and tip position are two of the most important pieces of information needed to define device location for magnetic resonance imaging (MRI)-guided interventional procedures. While a single one-element micro coil incorporated into an interventional device has proven to be effective in some applications, it can only supply tip position information. However, multiple positions on the device are necessary to also determine its orientation. For this purpose, a novel single micro coil design with three separate winding elements that provides both the device orientation and tip position is described in this study. Definition of MR scan planes, by using the device orientation and the target tissue location, permits automatic tracking of the insertion of the device. Furthermore, devices that include this coil design are permitted to bend to a limited extent. This makes the micro coil design appropriate for many flexible interventional devices. Reliable near-real-time tracking of three points on an interventional device is demonstrated on a 0.2T MRI system with modest gradient performance. Phantom and in vivo animal experiments are used to demonstrate the utility of this new coil design. © 2001 Wiley-Liss, Inc.