Goats decrease hindlimb stiffness when walking over compliant surfaces.

Leg stiffness, commonly estimated as the 'compression' of a defined leg element in response to a load, has long been used to characterize terrestrial locomotion. This study investigated how goats adjust the stiffness of their hindlimbs to accommodate surfaces of different stiffness. Goats provide a compelling animal model for studying leg stiffness modulation, because they skillfully ambulate over a range of substrates that vary in compliance. To investigate the adjustments that goats make when walking over such substrates, ground reaction forces and three-dimensional trajectories of hindlimb markers were recorded as goats walked on rigid, rubber and foam surfaces. Net joint moments, power and work at the hip, knee, ankle and metatarsophalangeal joints were estimated throughout stance via inverse dynamics. Hindlimb stiffness was estimated from plots of total leg force versus total leg length, and individual joint stiffness was estimated from plots of joint moment versus joint angle. Our results support the hypothesis that goats modulate hindlimb stiffness in response to surface stiffness; specifically, hindlimb stiffness decreased on the more compliant surfaces (P<0.002). Estimates of joint stiffness identified hip and ankle muscles as the primary drivers of these adjustments. When humans run on compliant surfaces, they generally increase leg stiffness to preserve their center-of-mass mechanics. We did not estimate center-of-mass mechanics in this study; nevertheless, our estimates of hindlimb stiffness suggest that goats exhibit a different behavior. This study offers new insight into mechanisms that allow quadrupeds to modulate their gait mechanics when walking on surfaces of variable compliance.

Can we predict motion artifacts in clinical MRI before the scan completes?

Subject motion can cause artifacts in clinical MRI, frequently necessitating repeat scans. We propose to alleviate this inefficiency by predicting artifact scores from partial multi-shot multi-slice acquisitions, which may guide the operator in aborting corrupted scans early.

Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis.

We propose neural network layers that explicitly combine frequency and image feature representations and show that they can be used as a versatile building block for reconstruction from frequency space data. Our work is motivated by the challenges arising in MRI acquisition where the signal is a corrupted Fourier transform of the desired image. The proposed joint learning schemes enable both correction of artifacts native to the frequency space and manipulation of image space representations to reconstruct coherent image structures at every layer of the network. This is in contrast to most current deep learning approaches for image reconstruction that treat frequency and image space features separately and often operate exclusively in one of the two spaces. We demonstrate the advantages of joint convolutional learning for a variety of tasks, including motion correction, denoising, reconstruction from undersampled acquisitions, and combined undersampling and motion correction on simulated and real world multicoil MRI data. The joint models produce consistently high quality output images across all tasks and datasets. When integrated into a state of the art unrolled optimization network with physics-inspired data consistency constraints for undersampled reconstruction, the proposed architectures significantly improve the optimization landscape, which yields an order of magnitude reduction of training time. This result suggests that joint representations are particularly well suited for MRI signals in deep learning networks. Our code and pretrained models are publicly available at https://github.com/nalinimsingh/interlacer.

How Machine Learning is Powering Neuroimaging to Improve Brain Health.

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.