Search

Background Purpose Methods Results Conclusion Forty to fifty percent of women and 13%–22% of men experience an osteoporosis‐related fragility fracture in their lifetimes. After the age of 50 years, the risk of hip fracture doubles in every 10 years. x‐Ray based DXA is currently clinically used to diagnose osteoporosis and predict fracture risk. However, it provides only 2‐D representation of bone and is associated with other technical limitations. Thus, alternative methods are needed.To develop and evaluate an ultra‐low dose (ULD) hip CT‐based automated method for assessment of volumetric bone mineral density (vBMD) at proximal femoral subregions.An automated method was developed to segment the proximal femur in ULD hip CT images and delineate femoral subregions. The computational pipeline consists of deep learning (DL)‐based computation of femur likelihood map followed by shape model‐based femur segmentation and finite element analysis‐based warping of a reference subregion labeling onto individual femur shapes. Finally, vBMD is computed over each subregion in the target image using a calibration phantom scan. A total of 100 participants (50 females) were recruited from the Genetic Epidemiology of COPD (COPDGene) study, and ULD hip CT imaging, equivalent to 18 days of background radiation received by U.S. residents, was performed on each participant. Additional hip CT imaging using a clinical protocol was performed on 12 participants and repeat ULD hip CT was acquired on another five participants. ULD CT images from 80 participants were used to train the DL network; ULD CT images of the remaining 20 participants as well as clinical and repeat ULD CT images were used to evaluate the accuracy, generalizability, and reproducibility of segmentation of femoral subregions. Finally, clinical CT and repeat ULD CT images were used to evaluate accuracy and reproducibility of ULD CT‐based automated measurements of femoral vBMD.Dice scores of accuracy (n = 20), reproducibility (n = 5), and generalizability (n = 12) of ULD CT‐based automated subregion segmentation were 0.990, 0.982, and 0.977, respectively, for the femoral head and 0.941, 0.970, and 0.960, respectively, for the femoral neck. ULD CT‐based regional vBMD showed Pearson and concordance correlation coefficients of 0.994 and 0.977, respectively, and a root‐mean‐square coefficient of variation (RMSCV) (%) of 1.39% with the clinical CT‐derived reference measure. After 3‐digit approximation, each of Pearson and concordance correlation coefficients as well as intraclass correlation coefficient (ICC) between baseline and repeat scans were 0.996 with RMSCV of 0.72%. Results of ULD CT‐based bone analysis on 100 participants (age (mean ± SD) 73.6 ± 6.6 years) show that males have significantly greater (p < 0.01) vBMD at the femoral head and trochanteric regions than females, while females have moderately greater vBMD (p = 0.05) at the medial half of the femoral neck than males.Deep learning, combined with shape model and finite element analysis, offers an accurate, reproducible, and generalizable algorithm for automated segmentation of the proximal femur and anatomic femoral subregions using ULD hip CT images. ULD CT‐based regional measures of femoral vBMD are accurate and reproducible and demonstrate regional differences between males and females. [ABSTRACT FROM AUTHOR]

Background: Osteoporosis is a bone disease related to increased bone loss and fracture‐risk. The variability in bone strength is partially explained by bone mineral density (BMD), and the remainder is contributed by bone microstructure. Recently, clinical CT has emerged as a viable option for in vivo bone microstructural imaging. Wide variations in spatial‐resolution and other imaging features among different CT scanners add inconsistency to derived bone microstructural metrics, urging the need for harmonization of image data from different scanners. Purpose: This paper presents a new deep learning (DL) method for the harmonization of bone microstructural images derived from low‐ and high‐resolution CT scanners and evaluates the method's performance at the levels of image data as well as derived microstructural metrics. Methods: We generalized a three‐dimensional (3D) version of GAN‐CIRCLE that applies two generative adversarial networks (GANs) constrained by the identical, residual, and cycle learning ensemble (CIRCLE). Two GAN modules simultaneously learn to map low‐resolution CT (LRCT) to high‐resolution CT (HRCT) and vice versa. Twenty volunteers were recruited. LRCT and HRCT scans of the distal tibia of their left legs were acquired. Five‐hundred pairs of LRCT and HRCT image blocks of 64×64×64$64 \times 64 \times 64 $ voxels were sampled for each of the twelve volunteers and used for training in supervised as well as unsupervised setups. LRCT and HRCT images of the remaining eight volunteers were used for evaluation. LRCT blocks were sampled at 32 voxel intervals in each coordinate direction and predicted HRCT blocks were stitched to generate a predicted HRCT image. Results: Mean ± standard deviation of structural similarity (SSIM) values between predicted and true HRCT using both 3DGAN‐CIRCLE‐based supervised (0.84 ± 0.03) and unsupervised (0.83 ± 0.04) methods were significantly (p < 0.001) higher than the mean SSIM value between LRCT and true HRCT (0.75 ± 0.03). All Tb measures derived from predicted HRCT by the supervised 3DGAN‐CIRCLE showed higher agreement (CCC ∈$ \in $ [0.956 0.991]) with the reference values from true HRCT as compared to LRCT‐derived values (CCC ∈$ \in $ [0.732 0.989]). For all Tb measures, except Tb plate‐width (CCC = 0.866), the unsupervised 3DGAN‐CIRCLE showed high agreement (CCC ∈$ \in $ [0.920 0.964]) with the true HRCT‐derived reference measures. Moreover, Bland‐Altman plots showed that supervised 3DGAN‐CIRCLE predicted HRCT reduces bias and variability in residual values of different Tb measures as compared to LRCT and unsupervised 3DGAN‐CIRCLE predicted HRCT. The supervised 3DGAN‐CIRCLE method produced significantly improved performance (p < 0.001) for all Tb measures as compared to the two DL‐based supervised methods available in the literature. Conclusions: 3DGAN‐CIRCLE, trained in either unsupervised or supervised fashion, generates HRCT images with high structural similarity to the reference true HRCT images. The supervised 3DGAN‐CIRCLE improves agreements of computed Tb microstructural measures with their reference values and outperforms the unsupervised 3DGAN‐CIRCLE. 3DGAN‐CIRCLE offers a viable DL solution to retrospectively improve image resolution, which may aid in data harmonization in multi‐site longitudinal studies where scanner mismatch is unavoidable. [ABSTRACT FROM AUTHOR]

The description of frequency fluctuations for highly coupled vibrational transitions has been a challenging problem in physical chemistry. In particular, the complexity of their vibrational Hamiltonian does not allow us to directly derive the time evolution of vibrational frequencies for these systems. In this paper, we present a new approach to this problem by exploiting the artificial neural network to describe the vibrational frequencies without relying on the deconstruction of the vibrational Hamiltonian. To this end, we first explored the use of the methodology to predict the frequency fluctuations of the amide I mode of N-methylacetamide in water. The results show good performance compared with the previous experimental and theoretical results. In the second part, the neural network approach is used to investigate the frequency fluctuations of the highly coupled carbonyl stretch modes for the organic carbonates in the solvation shell of the lithium ion. In this case, the frequency fluctuation predicted by the neural networks shows a good agreement with the experimental results, which suggests that this model can be used to describe the dynamics of the frequency in highly coupled transitions. [ABSTRACT FROM AUTHOR]

Carbonate-based lithium-ion electrolytes are of great importance due to their close relationship with the resulting battery efficiency and safety. Modifying the organic electrolyte has been paramount for achieving more efficient and safer lithium-ion batteries. However, the molecular picture of the electrolyte is still under scrutiny. Lately, ultrafast infrared spectroscopic studies have investigated the solvation structure and dynamics of the lithium ion (Li+) in both linear and cyclic carbonates. However, theoretical studies describing the molecular arrangements and transformation occurring in such time scales are scarce. In this study, ab initio molecular dynamics simulations were used to obtain the molecular structure and dynamics of the Li+ solvation shell in cyclic and linear carbonates. The theoretical results showed that molecular arrangement of the carbonates directly coordinating Li+ is not significantly altered by the carbonate chemical nature. However, the cyclic and linear carbonates showed significant different pictures of the overall solvation shell due to the intercalation phenomenon observed for cyclic carbonates, which significantly alters the motions of coordinated solvent. In addition, the intercalation appears to affect the propensity of ion pair formation and/or solvent exchange. Finally, the dynamics of the geometrical changes of the carbonates solvating Li+ is found to occur with characteristic times of tenths of picoseconds, while ion pair and solvent chemical exchange appear to happen in time scales which are at least an order of magnitude larger. Our study provides a comprehensive picture of the structure and dynamics of the molecular components in different carbonate-based lithium-ion electrolytes occurring in picosecond time scales. [ABSTRACT FROM AUTHOR]