Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers
Mathieu Boudreau
Agah Karakuzu
Arnaud Boré
Basile Pinsard
Kiril Zelenkovski
Eva Alonso‐Ortiz
Julie Boyle
Lune Bellec
Julien Cohen-Adad
Abstract Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used q… (see more)ualitative MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes 3 years of data collection and up to 10 sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) all exhibited high longitudinal reproducibility (intraclass correlation coefficient – ICC ≃ 1 and within-subject coefficient of variations – wCV 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsat exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.
2025-01-01
Imaging Neuroscience (published)
doi.org
Machine-learning-assisted Preoperative Prediction of Pediatric Appendicitis Severity.
Aylin Erman
Julia Ferreira
Waseem Abu Ashour
Elena Guadagno
Etienne St-Louis
Sherif Emil
Jackie Cheung
Dan Poenaru
2025-01-01
Journal of Pediatric Surgery (published)
doi.org
Machine-learning-assisted preoperative prediction of pediatric appendicitis severity
Aylin Erman
Julia Ferreira
Waseem Abu Ashour
Elena Guadagno
Etienne St-Louis
Sherif Emil
Jackie Cheung
Dan Poenaru
2025-01-01
Journal of Pediatric Surgery (published)
doi.org
MLOps, LLMOps, FMOps, and Beyond
Chakkrit Kla Tantithamthavorn
Fabio Palomba
Foutse Khomh
Joselito Joey Chua
2025-01-01
IEEE Software (published)
doi.org
MLOps, LLMOps, FMOps, and Beyond
Chakkrit Tantithamthavorn
Fabio Palomba
Foutse Khomh
Joselito Joey Chua
2025-01-01
IEEE Software (published)
doi.org
MLOps, LLMOps, FMOps, and Beyond
Chakkrit Tantithamthavorn
Fabio Palomba
Foutse Khomh
Joselito Joey Chua
2025-01-01
IEEE Software (published)
doi.org
Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor
Trevor Ablett
Oliver Limoyo
Adam Sigal
Affan Jilani
Jonathan Kelly
Kaleem Siddiqi
Francois Hogan
Gregory Dudek
Kinesthetic Teaching is a popular approach to collecting expert robotic demonstrations of contact-rich tasks for imitation learning (IL), bu… (see more)t it typically only measures motion, ignoring the force placed on the environment by the robot. Furthermore, contact-rich tasks require accurate sensing of both reaching and touching, which can be difficult to provide with conventional sensing modalities. We address these challenges with a See-Through-your-Skin (STS) visuotactile sensor, using the sensor both (i) as a measurement tool to improve kinesthetic teaching, and (ii) as a policy input in contact-rich door manipulation tasks. An STS sensor can be switched between visual and tactile modes by leveraging a semi-transparent surface and controllable lighting, allowing for both pre-contact visual sensing and during-contact tactile sensing with a single sensor. First, we propose tactile force matching, a methodology that enables a robot to match forces read during kinesthetic teaching using tactile signals. Second, we develop a policy that controls STS mode switching, allowing a policy to learn the appropriate moment to switch an STS from its visual to its tactile mode. Finally, we study multiple observation configurations to compare and contrast the value of visual and tactile data from an STS with visual data from a wrist-mounted eye-in-hand camera. With over 3,000 test episodes from real-world manipulation experiments, we find that the inclusion of force matching raises average policy success rates by 62.5%, STS mode switching by 30.3%, and STS data as a policy input by 42.5%. Our results highlight the utility of see-through tactile sensing for IL, both for data collection to allow force matching, and for policy execution to allow accurate task feedback.
2025-01-01
IEEE Transactions on Robotics (published)
doi.org
arxiv.org
A Multi-Robot Exploration Planner for Space Applications
Vivek Shankar Vardharajan
Giovanni Beltrame
We propose a distributed multi-robot exploration planning method designed for complex, unconstrained environments featuring steep elevation … (see more)changes. The method employs a two-tiered approach: a local exploration planner that constructs a grid graph to maximize exploration gain and a global planner that maintains a sparse navigational graph to track visited locations and frontier information. The global graphs are periodically synchronized among robots within communication range to maintain an updated representation of the environment. Our approach integrates localization loop closure estimates to correct global graph drift. In simulation and field tests, the proposed method achieves 50% lower computational runtime compared to state-of-the-art methods while demonstrating superior exploration coverage. We evaluate its performance in two simulated subterranean environments and in field experiments at a Mars-analog terrain.
2025-01-01
IEEE Robotics and Automation Letters (published)
doi.org
A Multi-Robot Exploration Planner for Space Applications
Vivek Shankar Vardharajan
Giovanni Beltrame
2025-01-01
IEEE Robotics and Automation Letters (published)
doi.org
Ex Post Conditions for the Exactness of Optimal Power Flow Conic Relaxations
Jean-Luc Lupien
Antoine Lesage-Landry
Convex relaxations of the optimal power flow (OPF) problem provide an efficient alternative to solving the intractable alternating current (… (see more)AC) optimal power flow. The conic subset of OPF convex relaxations, in particular, greatly accelerate resolution while leading to high-quality approximations that are exact in several scenarios. However, the sufficient conditions guaranteeing exactness are stringent, e.g., requiring radial topologies. In this short communication, we present two equivalent ex post conditions for the exactness of any conic relaxation of the OPF. These rely on obtaining either a rank-1 voltage matrix or self-coherent cycles. Instead of relying on sufficient conditions a priori, satisfying one of the presented ex post conditions acts as an exactness certificate for the computed solution. The operator can therefore obtain an optimality guarantee when solving a conic relaxation even when a priori exactness requirements are not met. Finally, we present numerical examples from the MATPOWER library where the ex post conditions hold even though the exactness sufficient conditions do not, thereby illustrating the use of the conditions.
2025-01-01
Electric Power Systems Research (published)
doi.org
arxiv.org
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy
Brice Rauby
Paul Xing
Jonathan Porée
Maxime Gasse
Jean Provost
2025-01-01
IEEE Transactions on Image Processing (published)
doi.org
arxiv.org
Recovering Dantzig–Wolfe Bounds by Cutting Planes
Rui Chen
Oktay Günlük
Andrea Lodi
Leveraging Dantzig–Wolfe Decomposition in the Original Variable Space for Mixed-Integer Programming Dantzig–Wolfe decomposition has been… (see more) extensively applied to solve large-scale mixed-integer programs with decomposable structures, leading to exact solution approaches, such as branch and price. However, these approaches would require solving the problem in an extended variable space and are not readily present in off-the-shelf solvers. In “Recovering Dantzig–Wolfe Bounds by Cutting Planes,” Chen, Günlük, and Lodi propose a computational effective approach for generating cutting planes from Dantzig–Wolfe decomposition to enhance branch and cut in the space of original variables. The proposed approach requires a relatively small number of cutting planes to recover the strength of the Dantzig–Wolfe dual bound and should be easy to implement in general-purpose mixed-integer programming solvers. The authors show that these cutting planes typically lead to a formulation with lower dual degeneracy and hence, a better computational performance than naïve approaches, such as the objective function cut.
2025-01-01
Oper. Res. (published)
doi.org
arxiv.org