The knee connectome: A novel tool for studying spatiotemporal change in cartilage thickness.

Cartilage thickness change is a well-documented biomarker of osteoarthritis pathogenesis. However, there is still much to learn about the spatial and temporal patterns of cartilage thickness change in health and disease. In this study, we develop a novel analysis method for elucidating such patterns using a functional connectivity approach.

A More Posterior Tibial Tubercle (Decreased Sagittal Tibial Tubercle-Trochlear Groove Distance) Is Significantly Associated With Patellofemoral Joint Degenerative Cartilage Change: A Deep Learning Analysis.

Purpose: To perform patellofemoral joint (PFJ) geometric measurements on knee magnetic resonance imaging scans and determine their relations with chondral lesions in a multicenter cohort using deep learning.

Methods: The sagittal tibial tubercle-trochlear groove (sTTTG) distance, tibial tubercle-trochlear groove distance, trochlear sulcus angle, trochlear depth, Caton-Deschamps Index (CDI), and flexion angle were measured by use of deep learning-generated segmentations on a subset of the Osteoarthritis Initiative study with radiologist-graded PFJ cartilage grades (n = 2,461). Kruskal-Wallis H tests were performed to compare differences in PFJ morphology between subjects without PFJ osteoarthritis (OA) and those with PFJ OA.

Surface spherical encoding and contrastive learning for virtual bone shape aging.

Bone shape changes are considered a relevant biomarker in understanding the onset and progression of knee osteoarthritis (OA). This study used a novel deep learning pipeline to predict longitudinal bone shape changes in the femur four years in advance, using bone surfaces that were extracted in knee MRIs from the OA initiative study, via a segmentation procedure and encoded as shape maps using spherical coordinates. Given a sequence of three consecutive shape maps (collected in a time window of 24 months), a fully convolutional network was trained to predict the whole bone surface 48 months after the last observed time point, and a classifier to diagnose OA in the predicted maps.

Learning osteoarthritis imaging biomarkers from bone surface spherical encoding.

Purpose: To learn bone shape features from spherical bone map of knee MRI images using established convolutional neural networks (CNN) and use these features to diagnose and predict osteoarthritis (OA).

Methods: A bone segmentation model was trained on 25 manually annotated 3D MRI volumes to segment the femur, tibia, and patella from 47 078 3D MRI volumes. Each bone segmentation was converted to a 3D point cloud and transformed into spherical coordinates.

Nano-printed miniature compound refractive lens for desktop hard x-ray microscopy.

Hard x-ray lenses are useful elements in x-ray microscopy and in creating focused illumination for analytical applications such as x-ray fluorescence imaging. Recently, polymer compound refractive lenses for focused illumination in the soft x-ray regime (< 10 keV) have been created with nano-printing. However, there are no such lenses yet for hard x-rays, particularly of short focal lengths for benchtop microscopy.

Correlative Detection of Isolated Single and Multi-Cellular Calcifications in the Internal Elastic Lamina of Human Coronary Artery Samples.

Histopathology protocols often require sectioning and processing of numerous microscopy slides to survey a sample. Trade-offs between workload and sampling density means that small features can be missed. Aiming to reduce the workload of routine histology protocols and the concern over missed pathology in skipped sections, we developed a prototype x-ray tomographic scanner dedicated to rapid scouting and identification of regions of interest in pathology specimens, thereby allowing targeted histopathology analysis to replace blanket searches.

Geometric calibration and correction for a lens-coupled detector in x-ray phase-contrast imaging.

A lens-coupled x-ray camera with a tilted phosphor collects light emission from the x-ray illuminated (front) side of phosphor. Experimentally, it has been shown to double x-ray photon capture efficiency and triple the spatial resolution along the phosphor tilt direction relative to the same detector at normal phosphor incidence. These characteristics benefit grating-based phase-contrast methods, where linear interference fringes need to be clearly resolved.