Collagen fibers in biological tissues have a complex 3D organization containing rich information linked to tissue mechanical properties and are affected by mutations that lead to diseases. Quantitative assessment of this 3D collagen fiber organization could help to develop reliable biomechanical models and understand tissue structure-function relationships, which impact diagnosis and treatment of diseases or injuries. While there are advanced techniques for imaging collagen fibers, published methods for quantifying 3D collagen fiber organization have been sparse and give limited structural information which cannot distinguish a wide range of 3D organizations. In this article, we demonstrate an algorithm for quantitative classification of 3D collagen fiber organization. The algorithm first simulates five groups, or classifications, of fiber organization unidirectional, crimped, disordered, two-fiber family, and helical. These five groups are widespread in natural tissues and are known to affect the tissue's mechanical properties. We use quantitative metrics based on features such as preferred 3D fiber orientation and spherical variance to differentiate each classification in a repeatable manner. We validate our algorithm by applying it to second-harmonic generation images of collagen fibers in tendon and cervix tissue that has been sectioned in specified orientations, and we find strong agreement between classification from simulated data and the physical fiber organization. Our approach provides insight for interpreting 3D fiber organization directly from volumetric images. This algorithm could be applied to other fiber-like structures that are not necessarily made of collagen.Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for second-trimester anomaly screening, for which ultrasound (US) is employed. Although expert sonographers are adept at reading US images, MR images which closely resemble anatomical images are much easier for non-experts to interpret. Thus in this article we propose to generate MR-like images directly from clinical US images. In medical image analysis such a capability is potentially useful as well, for instance for automatic US-MRI registration and fusion.  https://www.selleckchem.com/products/jnj-64264681.html  The proposed model is end-to-end trainable and self-supervised without any external annotations. Specifically, based on an assumption that the US and MRI data share a similar anatomical latent space, we first utilise a network to extract the shared latent features, which are then used for MRI synthesis. Since paired data is unavailable for our study (and rare in practice), pixel-level constraints are infeasible to apply. We instead propose to enforce the distributions to be statistically indistinguishable, by adversarial learning in both the image domain and feature space. To regularise the anatomical structures between US and MRI during synthesis, we further propose an adversarial structural constraint. A new cross-modal attention technique is proposed to utilise non-local spatial information, by encouraging multi-modal knowledge fusion and propagation. We extend the approach to consider the case where 3D auxiliary information (e.g., 3D neighbours and a 3D location index) from volumetric data is also available, and show that this improves image synthesis. The proposed approach is evaluated quantitatively and qualitatively with comparison to real fetal MR images and other approaches to synthesis, demonstrating its feasibility of synthesising realistic MR images.A novel approach of applying deep reinforcement learning to an RF pulse design is introduced. This method, which is referred to as DeepRFSLR, is designed to minimize the peak amplitude or, equivalently, minimize the pulse duration of a multiband refocusing pulse generated by the Shinar Le-Roux (SLR) algorithm. In the method, the root pattern of SLR polynomial, which determines the RF pulse shape, is optimized by iterative applications of deep reinforcement learning and greedy tree search. When tested for the designs of the multiband pulses with three and seven slices, DeepRFSLR demonstrated improved performance compared to conventional methods, generating shorter duration RF pulses in shorter computational time. In the experiments, the RF pulse from DeepRFSLR produced a slice profile similar to the minimum-phase SLR RF pulse and the profiles matched to that of the computer simulation. Our approach suggests a new way of designing an RF by applying a machine learning algorithm, demonstrating a "machine-designed" MRI sequence.We present an approach to estimate 3D human pose from a monocular image. The method consists of two steps it first estimates a 2D pose from an image and then recovers the corresponding 3D pose. This work focuses on the second step. The Graph Convolutional Network (GCN) has recently become the de facto standard for human pose related tasks such as action recognition. However, in this work, we show that it has critical limitations when used for 3D pose estimation due to the inherent weight sharing scheme. The limitations are clearly exposed through a more generic reformulation of GCN, in which both GCN and Fully Connected Network (FCN) are its special cases. In addition, on top of the formulation, we propose Locally Connected Network (LCN) to overcome the limitations of GCN by allocating dedicated rather than shared filters for different joints. We train the LCN network together with the 2D pose estimator such that LCN can be trained to handle inaccurate 2D poses. We evaluate our approach on two benchmarks, and observe that LCN outperforms GCN, FCN and the state-of-the-arts by a large margin. More importantly, it demonstrates stronger cross-dataset generalization ability because of the sparse connections among joints.We asked Dietmar Offenhuber for an interview upon investigating his recent works, Staubmarke (Dust mark) and Ozone Tattoo, which together suggest an artist with an insightful eye for injecting particulate data engagingly into art pieces that spur awareness in an audience. We were impressed by his deep considerations on the potential for scientific and informatics data sets to offer the potential for, and foster, wider discussions and collaborations.