114 research outputs found
Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis
The purposes of this study were to investigate: 1) the effect of placement of
region-of-interest (ROI) for texture analysis of subchondral bone in knee
radiographs, and 2) the ability of several texture descriptors to distinguish
between the knees with and without radiographic osteoarthritis (OA). Bilateral
posterior-anterior knee radiographs were analyzed from the baseline of OAI and
MOST datasets. A fully automatic method to locate the most informative region
from subchondral bone using adaptive segmentation was developed. We used an
oversegmentation strategy for partitioning knee images into the compact regions
that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick
features, Shannon entropy, and HOG methods were computed within the standard
ROI and within the proposed adaptive ROIs. Subsequently, we built logistic
regression models to identify and compare the performances of each texture
descriptor and each ROI placement method using 5-fold cross validation setting.
Importantly, we also investigated the generalizability of our approach by
training the models on OAI and testing them on MOST dataset.We used area under
the receiver operating characteristic (ROC) curve (AUC) and average precision
(AP) obtained from the precision-recall (PR) curve to compare the results. We
found that the adaptive ROI improves the classification performance (OA vs.
non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC).
We also observed that, from all texture parameters, LBP yielded the best
performance in all settings with the best AUC of 0.840 [0.825, 0.852] and
associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art
approaches, our results suggest that the proposed adaptive ROI approach in
texture analysis of subchondral bone can increase the diagnostic performance
for detecting the presence of radiographic OA
Quantitative Magnetic Resonance Imaging of Articular Cartilage: Structural, Compositional and Functional Characterization of Normal, Degraded and Engineered Tissue
Genetic and environmental effects affecting the variation in birth date and birth weight of reindeer calves
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Unsupervised denoising for sparse multi-spectral computed tomography
Multi-energy computed tomography (CT) with photon counting detectors (PCDs)
enables spectral imaging as PCDs can assign the incoming photons to specific
energy channels. However, PCDs with many spectral channels drastically increase
the computational complexity of the CT reconstruction, and bespoke
reconstruction algorithms need fine-tuning to varying noise statistics.
\rev{Especially if many projections are taken, a large amount of data has to be
collected and stored. Sparse view CT is one solution for data reduction.
However, these issues are especially exacerbated when sparse imaging scenarios
are encountered due to a significant reduction in photon counts.} In this work,
we investigate the suitability of learning-based improvements to the
challenging task of obtaining high-quality reconstructions from sparse
measurements for a 64-channel PCD-CT. In particular, to overcome missing
reference data for the training procedure, we propose an unsupervised denoising
and artefact removal approach by exploiting different filter functions in the
reconstruction and an explicit coupling of spectral channels with the nuclear
norm. Performance is assessed on both simulated synthetic data and the openly
available experimental Multi-Spectral Imaging via Computed Tomography (MUSIC)
dataset. We compared the quality of our unsupervised method to iterative total
nuclear variation regularized reconstructions and a supervised denoiser trained
with reference data. We show that improved reconstruction quality can be
achieved with flexibility on noise statistics and effective suppression of
streaking artefacts when using unsupervised denoising with spectral coupling
Impaired WNT signaling and the spine-Heterozygous WNT1 mutation causes severe age-related spinal pathology
Background: WNT signaling plays a major role in bone and cartilage metabolism. Impaired WNT/beta-catenin signaling leads to early-onset osteoporosis, but specific features in bone and other tissues remain inadequately characterized. We have identified two large Finnish families with early-onset osteoporosis due to a heterozygous WNT1 mutation c.652T>G, p.C218G. This study evaluated the impact of impaired WNT/beta-catenin signaling on spinal structures. Methods: Altogether 18 WNT1 mutation-positive (age range 11-76 years, median 49 years) and 14 mutation negative subjects (10-77 years, median 43 years) underwent magnetic resonance imaging (MRI) of the spine. The images were reviewed for spinal alignment, vertebral compression fractures, intervertebral disc changes and possible endplate deterioration. The findings were correlated with clinical data. Results: Vertebral compression fractures were present in 78% (7/9) of those aged over 50 years but were not seen in younger mutation-positive subjects. All those with fractures had several severely compressed vertebrae. Altogether spinal compression fractures were present in 39% of those with a WNT1 mutation. Only 14% (2/14) mutation -negative subjects had one mild compressed vertebra each. The mutation-positive subjects had a higher mean spinal deformity index (4.0 +/- 7.3 vs 0.0 +/- 0.4) and more often increased thoracic kyphosis (Z-score > + 2.0 in 33% vs 0%). Further, they had more often Schmorl nodes (61% vs 36%), already in adolescence, and their intervertebral discs were enlarged. Conclusion: Compromised WNT signaling introduces severe and progressive changes to the spinal structures. Schmorl nodes are prevalent even at an early age and increased thoracic kyphosis and compression fractures become evident after the age of 50 years. Therapies targeting the WNT pathway may be an effective way to prevent spinal pathology not only in those harboring a mutation but also in the general population with similar pathology. (C) 2017 Elsevier Inc. All rights reserved.Peer reviewe
Effect of footwear type on biomechanical risk factors for knee osteoarthritis
Background: Regular walking in different types of footwear may increase the mediolateral shear force, knee adduction moment, or vertical ground-reaction forces that could increase the risk of early development of knee osteoarthritis (OA). Purpose: To compare kinematic and kinetic parameters that could affect the development of knee OA in 3 footwear conditions. Study design: Controlled laboratory study. Methods: A total of 40 asymptomatic participants performed walking trials in the laboratory at self-selected walking speeds under barefoot (BF), minimalistic (MF), and neutral (NF) footwear conditions. Knee joint parameters were described using discrete point values, and continuous curves were evaluated using statistical parametric mapping. A 3 × 1 repeated-measures analysis of variance was used to determine the main effect of footwear for both discrete and continuous data. To compare differences between footwear conditions, a post hoc paired t test was used. Results: Discrete point analyses showed a significantly greater knee power in NF compared with MF and BF in the weight absorption phase (P < .001 for both). Statistical parametric mapping analysis indicated a significantly greater knee angle in the sagittal plane at the end of the propulsive phase in BF compared with NF and MF (P = .043). Knee joint moment was significantly greater in the propulsive phase for the sagittal (P = .038) and frontal planes (P = .035) in BF compared with NF and MF and in the absorption phase in the sagittal plane (P = .034) in BF compared with MF and NF. A significant main effect of footwear was found for anteroposterior (propulsion, ↑MF, NF, ↓BF [P = .008]; absorption, ↑BF, MF, ↓NF [P = .001]), mediolateral (propulsion, ↑MF, NF, ↓BF [P = .005]; absorption, ↑NF, MF, ↓BF [P = .044]), and vertical (propulsion, ↑NF, BF, ↓MF [P = .001]; absorption, ↑MF, BF, ↓NF [P < .001]) ground-reaction forces. Knee power showed a significant main effect of footwear (absorption, ↑NF, MF, ↓BF [P = .015]; propulsion, ↑MF, NF, ↓BF [P = .039]). Conclusion: Walking in MF without sufficient accommodation affected kinetic and kinematic parameters and could increase the risk of early development of knee OA
Local edge computing for radiological image reconstruction and computer-assisted detection: A feasibility study
Computational requirements for data processing at different stages of the radiology value chain are increasing. Cone beam computed tomography (CBCT) is a diagnostic imaging technique used in dental and extremity imaging, involving a highly demanding image reconstruction task. In turn, artificial intelligence (AI) assisted diagnostics are becoming increasingly popular, thus increasing the use of computation resources. Furthermore, the need for fully independent imaging units outside radiology departments and with remotely performed diagnostics emphasize the need for wireless connectivity between the imaging unit and hospital infrastructure. In this feasibility study, we propose an approach based on a distributed edge-cloud computing platform, consisting of small-scale local edge nodes, edge servers with traditional cloud resources to perform data processing tasks in radiology. We are interested in the use of local computing resources with Graphics Processing Units (GPUs), in our case Jetson Xavier NX, for hosting the algorithms for two use-cases, namely image reconstruction in cone beam computed tomography and AI-assisted cancer detection from mammographic images. Particularly, we wanted to determine the technical requirements for local edge computing platform for these two tasks and whether CBCT image reconstruction and breast cancer detection tasks are possible in a diagnostically acceptable time frame. We validated the use-cases and the proposed edge computing platform in two stages. First, the algorithms were validated use-case-wise by comparing the computing performance of the edge nodes against a reference setup (regular workstation). Second, we performed qualitative evaluation on the edge computing platform by running the algorithms as nanoservices. Our results, obtained through real-life prototyping, indicate that it is possible and technically feasible to run both reconstruction and AI-assisted image analysis functions in a diagnostically acceptable computing time. Furthermore, based on the qualitative evaluation, we confirmed that the local edge computing capacity can be scaled up and down during runtime by adding or removing edge devices without the need for manual reconfigurations. We also found all previously implemented software components to be transferable as such. Overall, the results are promising and help in developing future applications, e.g., in mobile imaging scenarios, where such a platform is beneficial
An Automatic Regularization Method : An Application for 3-D X-Ray Micro-CT Reconstruction Using Sparse Data
X-ray tomography is a reliable tool for determining the inner structure of 3-D object with penetrating X-rays. However, traditional reconstruction methods, such as Feldkamp-Davis-Kress (FDK), require dense angular sampling in the data acquisition phase leading to long measurement times, especially in X-ray micro-tomography to obtain high-resolution scans. Acquiring less data using greater angular steps is an obvious way for speeding up the process and avoiding the need to save huge data sets. However, computing 3-D reconstruction from such a sparsely sampled data set is difficult because the measurement data are usually contaminated by errors, and linear measurement models do not contain sufficient information to solve the problem in practice. An automatic regularization method is proposed for robust reconstruction, based on enforcing sparsity in the 3-D shearlet transform domain. The inputs of the algorithm are the projection data and a priori known expected degree of sparsity, denoted as 0 <C-prPeer reviewe
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