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. Descriptive statistics are reported for 1186 knees that did not develop osteoarthritis during the 8 years of observation, which we present as a model of cartilage thickness change related to healthy aging. Within the control population, patterns vary greatly between male and female subjects, while body mass index (BMI) has a more moderate impact. Finally, several differences are shown between knees that did and did not develop osteoarthritis. Some but not all significance appears to be accounted for by differences in sex, BMI, and knee alignment. With this work, we present the connectome as a novel tool for studying spatiotemporal dynamics of tissue change.

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. For this, a novel multi-term loss function, based on contrastive learning was designed. Experimental results show that the model predicted shape changes with an L1 error comparable to the MRI slice thickness (0.7mm). Next, an ablation study demonstrated that the introduction of a contrastive term in the loss improved sensitivity of the OA classifier, increasing sensitivity from 0.537 to 0.709, just shy of the upper bound of 0.740 computed on the ground truth bone shape maps. Our approach provides a promising tool, suitable for patient specific OA trajectory analysis.

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. Different fusion strategies were performed to merge spherical maps obtained by each bone. A total of 41 822 merged spherical maps with corresponding Kellgren-Lawrence grades for radiographic OA were used to train a CNN classifier model to diagnose OA using bone shape learned features. Several OA Diagnosis models were tested and the weights for each trained model were transferred to the OA Incidence models. The OA incidence task consisted of predicting OA from a healthy scan within a range of eight time points, from 1 y to 8 y. The validation performance was compared and the test set performance was reported. RESULTS: The OA Diagnosis model had an area-under-the-curve (AUC) of 0.905 on the test set with a sensitivity and specificity of 0.815 and 0.839. The OA Incidence models had an AUC ranging from 0.841 to 0.646 on the test set for the range from 1 y to 8 y. CONCLUSION: Bone shape was successfully used as a predictive imaging biomarker for OA. This approach is novel in the field of deep learning applications for musculoskeletal imaging and can be expanded to other OA biomarkers.

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. We report the first instance of a nano-printed lens for hard x-ray microscopy, and evaluate its imaging performance. The lens consists of a spherically focusing compound refractive lens designed for 22 keV photon energy, with a tightly packed structure to provide a short total length of 1.8 mm and a focal length of 21.5 mm. The resulting lens technology was found to enable benchtop microscopy at 74x magnification and 1.1 µm de-magnified image pixel size at the object plane. It was used to image and evaluate the focal spots of tungsten-anode micro-focus x-ray sources. The overall system resolution with broadband illumination from a tungsten-anode x-ray tube at 30 kV and 10 mm focal distance was measured to be 2.30±0.22 µm.

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. In coronary artery samples of a deceased HIV patient, the scanner, called Tomopath, obtained depth-resolved cross-sectional images at 15 µm resolution in a 15-minute scan, which guided the subsequent histological sectioning and microscopy. When compared to a commercial tabletop micro-CT scanner, the prototype provided several-fold contrast-to-noise ratio in 1/11th the scan time. Correlated tomographic and histological images revealed two types of micro calcifications: scattered loose calcifications typically found in atherosclerotic lesions; isolated focal calcifications in one or several cells in the internal elastic lamina and occasionally in the tunica media, which we speculate were the initiation of medial calcification linked to kidney disease, but rarely detected at this early stage due to their similarity to particle contaminants introduced during histological processing, if not for the evidence from the tomography scan prior to sectioning. Thus, in addition to its utility as a scouting tool, in this study it provided complementary information to histological microscopy. Overall, the prototype scanner represents a step toward a dedicated scouting and complementary imaging tool for routine use in pathology labs.

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. However, both the shallow incident angle on the phosphor and lens aberrations of the camera cause geometric distortions. When tiling multiple images of limited vertical view into a full-field image, geometric distortion causes blurring due to image misregistration. Here, we report a procedure of geometric correction based on global polynomial transformation of image coordinates. The corrected image is equivalent to one obtained with a single full-field flat panel detector placed at the sample plane. In a separate evaluation scan, the position deviations in the horizontal and vertical directions were reduced from 0.76 and 0.028 mm, respectively, to 0.006 and 0.009 mm, respectively, by the correction procedure, which were below the 0.028-mm pixel size of the imaging system. In a demonstration of a phase-contrast imaging experiment, the correction reduced blurring of small structures.