Mcclearypalm3605

The sensitivity of the fluid temperature and the polymer stresses to increases in shear-thinning characteristics as well as to increases in polymeric properties is investigated. In general, it is observed that, at comparative parameter values, the viscoelastic fluids give the best resistance to temperature increases, followed by the generalized viscoelastic fluids, followed in turn by the Newtonian fluids, and with the generalized Newtonian fluids recording the highest temperature increases.Purpose Radiogenomics offers a potential virtual and noninvasive biopsy. However, radiogenomics models often suffer from generalizability issues, which cause a performance degradation on unseen data. In MRI, differences in the sequence parameters, manufacturers, and scanners make this generalizability issue worse. Such image acquisition information may be used to define different environments and select robust and invariant radiomic features associated with the clinical outcome that should be included in radiomics/radiogenomics models. Approach We assessed 77 low-grade gliomas and glioblastomas multiform patients publicly available in TCGA and TCIA. Radiomics features were extracted from multiparametric MRI images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and fluid-attenuated inversion recovery) and different regions-of-interest (enhancing tumor, nonenhancing tumor/necrosis, and edema). A method developed to find variables that are part of causal structures was used for feature selection and ceters.Purpose Automating fiducial detection and localization in the patient's pre-operative images can lead to better registration accuracy, reduced human errors, and shorter intervention time. Most current approaches are optimized for a single marker type, mainly spherical adhesive markers. A fully automated algorithm is proposed and evaluated for screw and spherical titanium fiducials, typically used in high-accurate frameless surgical navigation. Approach The algorithm builds on previous approaches with morphological functions and pose estimation algorithms. A 3D convolutional neural network (CNN) is proposed for the fiducial classification task and evaluated for both traditional closed-set and emerging open-set classifiers. A proposed digital ground-truth experiment, with cone-beam computed tomography (CBCT) imaging software, is performed to determine the localization accuracy of the algorithm. The localized fiducial positions in the CBCT images by the presented algorithm were compared to the actual known positions in the virtual phantom models. The difference represents the fiducial localization error (FLE). Results A total of 241 screws, 151 spherical fiducials, and 1550 other structures are identified with the best true positive rate 95.9% for screw and 99.3% for spherical fiducials at 8.7% and 3.4% false positive rate, respectively. The best achieved FLE mean and its standard deviation for a screw and spherical marker are 58 (14) and 14   ( 6 )    μ m , respectively. Conclusions Accurate marker detection and localization were achieved, with spherical fiducials being superior to screws. Large marker volume and smaller voxel size yield significantly smaller FLEs. Attenuating noise by mesh smoothing has a minor effect on FLE. Future work will focus on expanding the CNN for image segmentation.Purpose Develop and validate algorithms that can enable a novice user to quantitatively measure the head shape parameters associated with deformational plagiocephaly and brachycephaly (DPB) using 2D rendered images. Approach First, the head contour is extracted semi-automatically using the intelligent scissors method. We then automatically compute two indices used in the clinical determination of the DPB from the head shape parameters the cranial index (CI) and the cranial vault asymmetry index (CVAI). We also present methods to quantify and compensate for the user variability, including camera angle and distance from the head using 2D rendered images. learn more We compared the results of our technology with ground-truth (GT) measurements from 53 infants with DPB and normal cranial parameters. Results The Spearman correlation coefficient between the new 2D rendered method and the 3D GT was 0.94 ( p less then 0.001 ) and 0.96 ( p less then 0.001 ) for CI and CVAI, respectively. Different simulated camera angles and distances from the head resulted in variation in CI and CVAI in the range of [ - 2.0 , 6.0 ] and [ - 4.0 , 4.0 ] units, respectively. The limits of agreement of the Bland-Altman test were reduced from [ - 3.6 , 5.3 ] and [ - 3.6 , 4.2 ] to [ - 0.5 , 3.0 ] and [ - 1.3 , 1.6 ] for CI and CVAI, respectively, by combining results from different camera angles and positions in our method. The overall accuracy of the proposed technology for DPB detection was 100%. Conclusions The 2D rendered images of the head can be accurately analyzed to assess DPB. Further study on 2D photos taken from human subjects is warranted.Purpose Detection of Alzheimer's disease (AD) on magnetic resonance imaging (MRI) using convolutional neural networks (CNNs), which is useful for detecting AD in its preliminary states. Approach Our study implements and compares several deep models and configurations, including two-dimensional (2D) and three-dimensional (3D) CNNs and recurrent neural networks (RNNs). To use a 2D CNN on 3D MRI volumes, each MRI scan is split into 2D slices, neglecting the connection among 2D image slices in an MRI volume. Instead, a CNN model could be followed by an RNN in a way that the model of 2D CNN + RNN can understand the connection among sequences of 2D image slices for an MRI. The issue is that the feature extraction step in the 2D CNN is independent of classification in the RNN. To tackle this, 3D CNNs can be employed instead of 2D CNNs to make voxel-based decisions. Our study's main contribution is to introduce transfer learning from a dataset of 2D images to 3D CNNs. Results The results on our MRI dataset indicate that sequence-based decisions improve the accuracy of slice-based decisions by 2% in classifying AD patients from healthy subjects. Also the 3D voxel-based method with transfer learning outperforms the other methods with 96.88% accuracy, 100% sensitivity, and 94.12% specificity. Conclusions Several implementations and experiments using CNNs on MRI scans for AD detection demonstrated that the voxel-based method with transfer learning from ImageNet to MRI datasets using 3D CNNs considerably improved the results compared with the others.Purpose To evaluate six cerebral arterial segmentation algorithms in a set of patients with a wide range of hemodynamic characteristics to determine real-world performance. Approach Time-of-flight magnetic resonance angiograms were acquired from 33 subjects normal controls ( N = 11 ), sickle cell disease ( N = 11 ), and non-sickle anemia ( N = 11 ) using a 3 Tesla Philips Achieva scanner. Six segmentation algorithms were tested (1) Otsu's method, (2) K-means, (3) region growing, (4) active contours, (5) minimum cost path, and (6) U-net machine learning. Segmentation algorithms were tested with two region-selection methods global, which selects the entire volume; and local, which iteratively tracks the arteries. Five slices were manually segmented from each patient by two readers. Agreement between manual and automatic segmentation was measured using Matthew's correlation coefficient (MCC). Results Median algorithm segmentation times ranged from 0.1 to 172.9 s for a single angiogram versus 10 h for manual segmentation. Algorithms had inferior performance to inter-observer vessel-based ( p less then 0.0001 , MCC = 0.65 ) and voxel-based ( p less then 0.0001 , MCC = 0.73 ) measurements. There were significant differences between algorithms ( p less then 0.0001 ) and between patients ( p less then 0.0042 ). Post-hoc analyses indicated (1) local minimum cost path performed best with vessel-based ( p = 0.0261 , MCC = 0.50 ) and voxel-based ( p = 0.0131 , MCC = 0.66 ) analyses; and (2) higher vessel-based performance in non-sickle anemia ( p = 0.0002 ) and lower voxel-based performance in sickle cell ( p = 0.0422 ) compared with normal controls. All reported MCCs are medians. Conclusions The best-performing algorithm (local minimum cost path, voxel-based) had 9.59% worse performance than inter-observer agreement but was 3 orders of magnitude faster. Automatic segmentation was non-inferior in patients with sickle cell disease and superior in non-sickle anemia.Purpose The relevance of presampling modulation transfer function (MTF) measurements in digital mammography (DM) quality control (QC) is examined. Two studies are presented a case study on the impact of a reduction in MTF on the technical image quality score and analysis of the robustness of routine QC MTF measurements. Approach In the first study, two needle computed radiography (CR) plates with identical sensitivities were used with differences in the 50% point of the MTF ( f MTF 0.5 ) larger than the limiting value in the European guidelines ( > 10 % change between successive measurements). Technical image quality was assessed via threshold gold thickness of the CDMAM phantom and threshold microcalcification diameter of the L1 structured phantom. For the second study, presampling MTF results from 595 half-yearly QC tests of 55 DM systems (16 types, six manufacturers) were analyzed for changes from the baseline value and changes in f MTF 0.5 between successive tests. Results A reduction of 20% in f MTF 0.5 of the two CR plates was observed. There was a tendency to a lower score for task-based metrics, but none were significant. Averaging over 55 systems, the absolute relative change in f MTF 0.5 between consecutive tests (with 95% confidence interval) was 3% (2.5% to 3.4%). Analysis of the maximum relative change from baseline revealed changes of up to - 10 % for one a-Se based system and - 15 % for a group of CsI-based systems. Conclusions A limit of 10% is a relevant action level for investigation. If exceeded, then the impact on performance has to be verified with extra metrics.Among 472 patients with human immunodeficiency virus-associated cryptococcal meningitis, 16% had severe visual loss at presentation, and 46% of these were 4-week survivors and remained severely impaired. Baseline cerebrospinal fluid opening pressure ≥40 cmH2O (adjusted odds ratio [aOR], 2.56; 95% confidence interval [CI], 1.36-4.83; P = .02) and fungal burden >6.0 log10 colonies/mL (aOR, 3.01; 95% CI, 1.58-5.7; P = .003) were independently associated with severe visual loss.

We compared all-cause mortality between individuals in South Korea with and without coronavirus disease 2019 (COVID-19) using propensity score (PS) matching.

This population-based cohort study used data from the National Health Insurance Service COVID-19 cohort database. In the database, we included individuals (COVID-19 patients, control population, and test-negative individuals) aged 20 years or older, regardless of hospitalization. The primary end point was all-cause mortality between January 1, 2020, and August 27, 2020.

A total of 328 374 adults were included in the study 7713 and 320 660 in the COVID-19 group and the control group. After PS matching, a total of 15 426 individuals (7713 per group) were included in the analysis. All-cause mortality was 3.2% (248/7713) and 1.6% (126/7713) in the COVID-19 group and the control group, respectively. In Cox regression analysis after PS matching, the risk of death in the COVID-19 group was twice as high (hazard ratio, 2.00; 95% CI, 1.61-2.48;

 < .001) as that in the control group.