Improving Automated Hemorrhage Detection in Sparse-view Computed Tomography via Deep Convolutional Neural Network based Artifact Reduction

Purpose: Sparse-view computed tomography (CT) is an effective way to reduce dose by lowering the total number of views acquired, albeit at the expense of image quality, which, in turn, can impact the ability to detect diseases. We explore deep learning-based artifact reduction in sparse-view cranial CT scans and its impact on automated hemorrhage detection. Methods: We trained a U-Net for artefact reduction on simulated sparse-view cranial CT scans from 3000 patients obtained from a public dataset and reconstructed with varying levels of sub-sampling. Additionally, we trained a convolutional neural network on fully sampled CT data from 17,545 patients for automated hemorrhage detection. We evaluated the classification performance using the area under the receiver operator characteristic curves (AUC-ROCs) with corresponding 95% confidence intervals (CIs) and the DeLong test, along with confusion matrices. The performance of the U-Net was compared to an analytical approach based on total variation (TV). Results: The U-Net performed superior compared to unprocessed and TV-processed images with respect to image quality and automated hemorrhage diagnosis. With U-Net post-processing, the number of views can be reduced from 4096 (AUC-ROC: 0.974; 95% CI: 0.972-0.976) views to 512 views (0.973; 0.971-0.975) with minimal decrease in hemorrhage detection (P<.001) and to 256 views (0.967; 0.964-0.969) with a slight performance decrease (P<.001). Conclusion: The results suggest that U-Net based artifact reduction substantially enhances automated hemorrhage detection in sparse-view cranial CTs. Our findings highlight that appropriate post-processing is crucial for optimal image quality and diagnostic accuracy while minimizing radiation dose.Comment: 11 pages, 6 figures, 1 tabl

Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging

Purpose: To optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. Methods: 7,194 CT images (3,597 with COPD; 3,597 healthy controls) from 78 subjects (43 with COPD; 35 healthy controls) were selected retrospectively (10.2018-12.2019) and preprocessed. For each image, intensity values were manually clipped to the emphysema window setting and a baseline 'full-range' window setting. Class-balanced train, validation, and test sets contained 3,392, 1,114, and 2,688 images. The network backbone was optimized by comparing various CNN architectures. Furthermore, automated WSO was implemented by adding a customized layer to the model. The image-level area under the Receiver Operating Characteristics curve (AUC) [lower, upper limit 95% confidence] and P-values calculated from one-sided Mann-Whitney U-test were utilized to compare model variations. Results: Repeated inference (n=7) on the test set showed that the DenseNet was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85] without WSO. Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89] (P=0.03). By adding a customized WSO layer to the DenseNet, an optimal window in the proximity of the emphysema window setting was learned automatically, and a mean AUC of 0.82 [0.78, 0.86] was achieved. Conclusion: Detection of COPD with DenseNet models was improved by WSO of CT data to the emphysema window setting range

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

Non-iterative Directional Dark-field Tomography

Dark-field imaging is a scattering-based X-ray imaging method that can be performed with laboratory X-ray tubes. The possibility to obtain information about unresolvable structures has already seen a lot of interest for both medical and material science applications. Unlike conventional X-ray attenuation, orientation dependent changes of the dark-field signal can be used to reveal microscopic structural orientation. To date, reconstruction of the three-dimensional dark-field signal requires dedicated, highly complex algorithms and specialized acquisition hardware. This severely hinders the possible application of orientation-dependent dark-field tomography. In this paper, we show that it is possible to perform this kind of dark-field tomography with common Talbot-Lau interferometer setups by reducing the reconstruction to several smaller independent problems. This allows for the reconstruction to be performed with commercially available software and our findings will therefore help pave the way for a straightforward implementation of orientation-dependent dark-field tomography

The compositional and nano-structural basis of fracture healing in healthy and osteoporotic bone

Osteoporosis, a prevalent metabolic bone disorder, predisposes individuals to increased susceptibility to fractures. It is also, somewhat controversially, thought to delay or impair the regenerative response. Using high-resolution Fourier-transform infrared spectroscopy and small/wide-angle X-ray scattering we sought to answer the following questions: Does the molecular composition and the nano-structure in the newly regenerated bone differ between healthy and osteoporotic environments? And how do pharmacological treatments, such as bone morphogenetic protein 7 (BMP-7) alone or synergistically combined with zoledronate (ZA), alter callus composition and nano-structure in such environments? Cumulatively, on the basis of compositional and nano-structural characterizations of newly formed bone in an open-osteotomy rat model, the healing response in untreated healthy and ovariectomy-induced osteoporotic environments was fundamentally the same. However, the BMP-7 induced osteogenic response resulted in greater heterogeneity in the nano-structural crystal dimensions and this effect was more pronounced with osteoporosis. ZA mitigated the effects of the upregulated catabolism induced by both BMP-7 and an osteoporotic bone environment. The findings contribute to our understanding of how the repair processes in healthy and osteoporotic bone differ in both untreated and treated contexts and the data presented represents the most comprehensive study of fracture healing at the nanoscale undertaken to date

Bone mineral crystal size and organization vary across mature rat bone cortex

The macro- and micro-features of bone can be assessed by using imaging methods. However, nano- and molecular features require more detailed characterization, such as use of e.g., vibrational spectroscopy and X-ray scattering. Nano- and molecular features also affect the mechanical competence of bone tissue. The aim of the present study was to reveal the effects of mineralization and its alterations on the mineral crystal scale, by investigating the spatial variation of molecular composition and mineral crystal structure across the cross-section of femur diaphyses in young rats, and healthy and osteoporotic mature rats (N = 5). Fourier transform infrared spectroscopy and scanning small- and wide-angle X-ray scattering (SAXS/WAXS) techniques with high spatial resolution were used at identical locations over the whole cross-section. This allowed quantification of point-by-point information about the spatial distribution of mineral crystal volume. All measured parameters (crystal dimensions, degree of orientation and predominant orientation) varied across the cortex. Specifically, the crystal dimensions were lower in the central cortex than in the endosteal and periosteal regions. Mineral crystal orientation followed the cortical circumference in the periosteal and endosteal regions, but was less well-oriented in the central regions. Central cortex is formed rapidly during development through endochondral ossification. Since rats possess no osteonal remodeling, this bone remains (until old age). Significant linear correlations were observed between the dimensional and organizational parameters, e.g., between crystal length and degree of orientation (R2 = 0.83, p < 0.001). Application of SAXS/WAXS provides valuable information on bone nanostructure and its constituents, effects of diseases and, prospectively, mechanical competence