Mohammad Masarwy | Searcharxiv

arXiv2024

Value of Assistance for Grasping

In multiple realistic settings, a robot is tasked with grasping an object without knowing its exact pose and relies on a probabilistic estimation of the pose to decide how to attempt the grasp. We support settings in which it is possible to provide the robot with an observation of the object before a grasp is attempted but this possibility is limited and there is a need to decide which sensing action would be most beneficial. We support this decision by offering a novel Value of Assistance (VOA) measure for assessing the expected effect a specific observation will have on the robot's ability to complete its task. We evaluate our suggested measure in simulated and real-world collaborative grasping settings.

cs.RO↗

arXiv2021

Deep-Learning based Motion Correction for Myocardial T1 Mapping

Myocardial T1 mapping is a cardiac MRI technique, used to assess myocardial fibrosis. In this technique, a series of T1-weighted MRI images are acquired with different saturation or inversion times. These images are fitted to the T1 model to estimate the model parameters and construct the desired T1 maps. In the presence of motion, the different T1-weighted images are not aligned. This, in turn, will cause errors and inaccuracies in the final estimation of the T1 maps. Therefore, motion correction is a necessary process for myocardial T1 mapping. We present a deep-learning (DL) based system for cardiac T1-weighted MRI images motion correction. When applying our DL-based motion correction system we achieve a statistically significant improved performance by means of R2 of the model fitting regression, in compared to the model fitting regression without motion correction (0.52 vs 0.29, p<0.05).

eess.IV↗