Serial Computed Tomography Imaging Research Articles

Skeletal Muscle Segmentation at the Level of the Third Lumbar Vertebra (L3) in Low-Dose Computed Tomography: A Lightweight Algorithm.

The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) measured from computed tomography (CT) images is an established imaging biomarker used to assess patients' nutritional status. With the increasing prevalence of low-dose CT scans in clinical practice, accurate and automated skeletal muscle segmentation at the L3 level in low-dose CT images has become an issue to address. This study proposed a lightweight algorithm for automated segmentation of skeletal muscles at the L3 level in low-dose CT images. This study included 57 patients with rectal cancer, with both low-dose plain and contrast-enhanced pelvic CT image series acquired using a radiotherapy CT scanner. A training set of 30 randomly selected patients was used to develop a lightweight segmentation algorithm, and the other 27 patients were used as the test set. A radiologist selected the most representative axial CT image at the L3 level for both the image series for all the patients, and three groups of observers manually annotated the skeletal muscles in the 54 CT images of the test set as the gold standard. The performance of the proposed algorithm was evaluated in terms of the Dice similarity coefficient (DSC), precision, recall, 95th percentile of the Hausdorff distance (HD95), and average surface distance (ASD). The running time of the proposed algorithm was recorded. An open source deep learning-based AutoMATICA algorithm was compared with the proposed algorithm. The inter-observer variations were also used as the reference. The DSC, precision, recall, HD95, ASD, and running time were 93.2 ± 1.9% (mean ± standard deviation), 96.7 ± 2.9%, 90.0 ± 2.9%, 4.8 ± 1.3 mm, 0.8 ± 0.2 mm, and 303 ± 43 ms (on CPU) for the proposed algorithm, and 94.1 ± 4.1%, 92.7 ± 5.5%, 95.7 ± 4.0%, 7.4 ± 5.7 mm, 0.9 ± 0.6 mm, and 448 ± 40 ms (on GPU) for AutoMATICA, respectively. The differences between the proposed algorithm and the inter-observer reference were 4.7%, 1.2%, 7.9%, 3.2 mm, and 0.6 mm, respectively, for the averaged DSC, precision, recall, HD95, and ASD. The proposed algorithm can be used to segment skeletal muscles at the L3 level in either the plain or enhanced low-dose CT images.

High-resolution computed tomographic (HRCT) image series from 413 canid and 18 felid skulls

Computed tomography (CT) is a non-invasive, three-dimensional imaging tool used in medical imaging, forensic science, industry and engineering, anthropology, and archaeology. The current study used high-resolution medical CT scanning of 431 animal skulls, including 399 dog skulls from 152 breeds, 14 cat skulls from 9 breeds, 14 skulls from 8 wild canid species (gray wolf, golden jackal, coyote, maned wolf, bush dog, red fox, Fennec fox, bat-eared fox), and 4 skulls from 4 wild felid species (wildcat, leopard, serval, caracal). This comprehensive and unique collection of CT image series of skulls can provide a solid foundation not only for comparative anatomical and evolutionary studies but also for the advancement of veterinary education, virtual surgery planning, and the facilitation of training in sophisticated machine learning methodologies.

Minimal Detectable Bone Fracture Gaps in CT Images and Digital Three-Dimensional (3D) Radii Models.

Knowledge of the minimal detectable bone fracture gap is essential in three-dimensional (3D) models, particularly in pre-operative planning of osteosynthesis to avoid overlooking gaps. In this study, defined incisions and bony displacements ranging from 100 to 400 µm were created in diaphyseal radii in 20 paired forearm specimens and verified with light microscopy. The specimens were scanned utilizing different computed tomography (CT) technologies/scanners, specimen positionings, scan protocols, image segmentations, and processing protocols. Inter- and intra-operator variabilities were reported as coefficient kappa. In CT images, fracture gaps of 100 µm and bone lamellae of 300 µm and 400 µm width were identified at a rate of 80 to 100%, respectively, independent of the investigated settings. In contrast, only 400µm incisions and bony displacements were visible in digital 3D models, with detection rates dependent on CT technology, image segmentation, and post-processing algorithm. 3D bone models based on state-of-the-art CT imaging can reliably visualize clinically relevant bone fracture gap sizes. However, verification of fractures to be surgically addressed should be verified with the original CT image series.

Factors Associated with Procedure-related Tumor Seeding in Advanced Stage Lung Cancer Patients with Malignant Pleural Effusions

Objective: Pleural procedure-related tumor seeding detected by computed tomography (CT) is common in lung cancer patients with malignant pleural effusion. This study aimed to identify the incidence of tumor seeding and the associated factors among lung cancer patients with malignant pleural effusions. Material and Methods: This retrospective cohort study was conducted on 146 lung cancer patients with malignant pleural effusions, diagnosed between 2010 and 2017, who underwent at least 1 pleural procedure and had at least 2 series of CT images. The potential factors were categorized into clinical characteristics, pleural characteristics, treatment factors, and pleural procedures. Incidence rate ratios (IRR) were analyzed by Poisson regression to identify factors that were independently associated with tumor seeding. Results: The incidence of procedure-related tumor seeding was 26%. Significantly increased IRRs of tumor seeding were found in relation to 1 time (IRR 5.653, 95% confidence interval [CI] 2.549 to 12.538) and ≥2 times of conventional intercostal chest drainage (ICD) insertion (IRR 5.837, 95% CI 1.768 to 19.266), 1 time (IRR 8.924, 95% CI 3.181 to 25.033) and ≥2 times of pleural biopsy (IRR 6.485, 95% CI 1.372 to 30.660), adenocarcinoma (IRR 8.329, 95% CI 2.804 to 24.747), and pleural thickening (IRR 12.458, 95% CI 1.360 to 114.152). Conclusion: Patients who had at least one pleural biopsy or ICD insertion, pleural fluid cytology positive or suspicious for malignancy, adenocarcinoma, or pleural thickening were found to be significantly at risk for tumor seeding.

“KAIZEN” method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals

Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this study, all development processes of the deep-learning models are performed based on strict criteria of the “KAIZEN checklist”, which is proposed based on previous AI development guidelines to overcome the deficiencies mentioned above. We develop and evaluate two binary-classification deep-learning models to triage COVID-19: a slice model examining a Computed Tomography (CT) slice to find COVID-19 lesions; a series model examining a series of CT images to find an infected patient. We collected 2,400,200 CT slices from twelve emergency centers in Japan. Area Under Curve (AUC) and accuracy were calculated for classification performance. The inference time of the system that includes these two models were measured. For validation data, the slice and series models recognized COVID-19 with AUCs and accuracies of 0.989 and 0.982, 95.9% and 93.0% respectively. For test data, the models’ AUCs and accuracies were 0.958 and 0.953, 90.0% and 91.4% respectively. The average inference time per case was 2.83 s. Our deep-learning system realizes accuracy and inference speed high enough for practical use. The systems have already been implemented in four hospitals and eight are under progression. We released an application software and implementation code for free in a highly usable state to allow its use in Japan and globally.

Filament Structure Analysis of Multifilament MgB2 Wires by Using X-ray CT

We have investigated the filament structure of 18-filament MgB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> wires by using an X-ray computed tomography (CT). For the structural analysis of MgB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> filaments and Nb barriers, we adopted two image processing, the adoptive binarization method and the closing technique, to the series of cross-sectional CT images. After the image processing, we successfully eliminated the effect of several artifacts, such as metal artifacts and beam hardening, and extrapolated the area of MgB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> filaments and Nb barriers. From the images, we made a three-dimensional model of MgB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> filaments and confirmed whether the breakout of the filaments and sausaging phenomena exist or not. Although the broken filaments were not observed, the Nb barrier's break was confirmed in all six inner filaments of the MgB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> wire after heat treatment. Moreover, from the statistical distribution analysis, sausaging is more significant in the inner filament than in the outer filament. These results indicate that the X-ray CT observation and advanced image processing is effective in understanding the filament structure in non-destructive ways.

Trade-off between the radiation parameters and image quality using iterative reconstruction techniques in head computed tomography: a phantom study.

Iterative reconstruction techniques (IRTs) are commonly used in computed tomography (CT) and help to reduce image noise. To determine the minimum radiation dose while preserving image quality in head CT using IRTs. The anthropomorphic phantom was used to scan nine head CT image series with varied radiation parameters. CT dose parameters, including volume CT dose index (CTDIvol [in mGy]) and dose length product (DLP [in mGy/cm]), were recorded for each scan series. Different noise levels (iDoseL1-6) were used in IRT reconstructions for soft and bone tissues. In total, 15 measurements were taken from five regions of interest (ROI) with an area of 10 mm2. The signal-to-noise ratio (SNR) and noise values obtained at different ROIs were compared among various reconstruction methods with repeated measures of statistical analysis. In the head CT scan, applying IRT iDoseL5 had the lowest noise and highest SNR for soft tissue (P < 0.05), and increased iDose can decrease CT dose by 54.6% without compromising image quality. While for bone tissue reconstruction, no clear association was found between the level of iDose and noise. However, when CTDIvol is >20 mGy, iDoseL4 is slightly superior to other reconstruction methods (P < 0.065). Using IRTs in head CTs reduces radiation dose while maintaining image quality. IDoseL5 provided optimal balance for soft tissue.

Anatomical Variations between Sciatic Nerve and Piriformis Muscle on Computed Tomography Images from Radiotherapy Patients

Abstract Background: The sciatic nerve has several topographic variations, some of them in relation to the piriformis muscle. Such variations provide meaning insight while addressing pathologies related to sciatic nerve. Hence, this study was undertaken to check the anatomical variations of sciatic nerve using computed tomography (CT) images. Methodology: Thirty-three CT image series from pelvic radiotherapy patients were reviewed, with a DICOM viewer to assess the relation between the sciatic nerve and piriformis muscles, according to Beaton and Aston’s classification. Results: Thirty-three CT image sets and 66 gluteal regions were assessed. Type A of Beaton and Aston was found in 84.85% of the samples and Type B was found in 15.15%. No other types were found. Sixty-six percentage of the Type B classifications were bilateral. Conclusion: There is a 15.15% of topographical variation of sciatic nerve consistent with other studies. The use of medical images for this kind of study will allow more reliable results.

Comparison between single and serial computed tomography images in classification of acute appendicitis, acute right-sided diverticulitis, and normal appendix using EfficientNet.

This study aimed to develop a convolutional neural network (CNN) using the EfficientNet algorithm for the automated classification of acute appendicitis, acute diverticulitis, and normal appendix and to evaluate its diagnostic performance. We retrospectively enrolled 715 patients who underwent contrast-enhanced abdominopelvic computed tomography (CT). Of these, 246 patients had acute appendicitis, 254 had acute diverticulitis, and 215 had normal appendix. Training, validation, and test data were obtained from 4,078 CT images (1,959 acute appendicitis, 823 acute diverticulitis, and 1,296 normal appendix cases) using both single and serial (RGB [red, green, blue]) image methods. We augmented the training dataset to avoid training disturbances caused by unbalanced CT datasets. For classification of the normal appendix, the RGB serial image method showed a slightly higher sensitivity (89.66 vs. 87.89%; p = 0.244), accuracy (93.62% vs. 92.35%), and specificity (95.47% vs. 94.43%) than did the single image method. For the classification of acute diverticulitis, the RGB serial image method also yielded a slightly higher sensitivity (83.35 vs. 80.44%; p = 0.019), accuracy (93.48% vs. 92.15%), and specificity (96.04% vs. 95.12%) than the single image method. Moreover, the mean areas under the receiver operating characteristic curve (AUCs) were significantly higher for acute appendicitis (0.951 vs. 0.937; p < 0.0001), acute diverticulitis (0.972 vs. 0.963; p = 0.0025), and normal appendix (0.979 vs. 0.972; p = 0.0101) with the RGB serial image method than those obtained by the single method for each condition. Thus, acute appendicitis, acute diverticulitis, and normal appendix could be accurately distinguished on CT images by our model, particularly when using the RGB serial image method.

Understanding and Improving Risk Assessment of Abdominal Aortic Aneurysm Growth Using Statistical Shape Modeling of the Infrarenal Aorta

Maximum diameter of the aneurysmal sac remains the primary method for defining surveillance intervals and the timing for surgical intervention. Recent studies have attempted to expand on this one-dimensional metric by incorporating additional descriptors of the aneurysmal sac such as undulation, curvature and tortuosity. However, these metrics are often biased and may overlook certain anatomical abnormalities, limiting predictive potential. This study aimed to identify novel three-dimensional shape features of the aneurysmal flow lumen and outer wall structure (OWS, with or without intraluminal thrombus) to predict aneurysmal growth. A cohort of 192 patients (median age, 72 [range, 62-79]; 99% male) with infrarenal abdominal aortic aneurysms (AAAs) (initial max diameter, 50.3 ± 4.2 mm) and serial computed tomography images obtained during AAA surveillance (median follow-up, 2.0 years [range, 1.0-5.3 years]) were retrospectively considered for this study. Estimated annual AAA growth was the clinical end point and was used to classify patients into slow (first quintile, <2.5 mm/year) and fast (fifth quintile, >5.0 mm/year) cohorts. A previously validated pipeline was implemented to automatically segment computed tomography images. Three-dimensional statistical shape models of the aneurysmal flow lumen and OWS were used to identify three-dimensional patterns involved in AAA growth. Undulation index and radius of curvature of the AAA, previously suggested to be predictive of AAA growth, were also calculated. Model training with derived shape features was performed using iterated 10-fold optimization with shuffling (100 iterations). C-statistics for each feature combination was used to evaluate performance. Integrating the lumen and OWS as unique components within the three-dimensional statistical shape model captured the lumen-thrombus interface. These features proved to be superior to max diameter, undulation index, and radius of curvature in prediction of AAA growth phenotype (P < .001). Corresponding median C-statistics for predicting slow and fast growth are highlighted in the Figure. Integrated models outperformed individual components in prediction of both slow and fast AAA growth (P < .001). Standardized feature extraction from both the aneurysmal flow lumen and the OWS, enabled by a fully automated pipeline, can characterize the evolving lumen-thrombus interface, and improves AAA growth prediction. Future work is focused on validating this statistical shape-based model on larger cohorts that also include smaller AAAs.

Computer-Aided Detection of Subsolid Nodules on Chest Computed Tomography: Assessment of Visualization on Vessel-Suppressed Images.

This study aimed to clarify the performance of automatic detection of subsolid nodules by commercially available software on computed tomography (CT) images of various slice thicknesses and compare it with visualization on the accompanying vessel-suppression CT (VS-CT) images. A total of 95 subsolid nodules from 84 CT examinations of 84 patients were included. The reconstructed CT image series of each case with 3-, 2-, and 1-mm slice thicknesses were loaded into a commercially available software application (ClearRead CT) for automatic detection of subsolid nodules and generation of VS-CT images. Automatic nodule detection sensitivity was assessed for 95 nodules on each series of images acquired at 3 slice thicknesses. Four radiologists subjectively evaluated visual assessment of the nodules on VS-CT. ClearRead CT automatically detected 69.5% (66/95 nodules), 68.4% (65/95 nodules), and 70.5% (67/95 nodules) of all subsolid nodules in 3-, 2-, and 1-mm slices, respectively. The detection rate was higher for part-solid nodules than for pure ground-glass nodules at all slice thicknesses. In the visualization assessment on VS-CT, 3 nodules at each slice thickness (3.2%) were judged as invisible, while 26 of 29 (89.7%), 27 of 30 (90.0%), and 25 of 28 (89.3%) nodules, which were missed by computer-aided detection, were judged as visible in 3-, 2-, and 1-mm slices, respectively. The automatic detection rate of subsolid nodules by ClearRead CT was approximately 70% at all slice thicknesses. More than 95% of subsolid nodules were visualized on VS-CT, including nodules undetected by the automated software. Computed tomography acquisition at slices thinner than 3 mm did not confer any benefits.

Pore scale investigation of unsaturated granular soil behaviour by means ofin situCT experiments

With continuing evolution of imaging techniques from medical applications and materials science, non-destructive imaging experiments have also become an important method to investigate soil specimens. Amongst other methods, computed tomography (CT) has developed to a tool to visualise and better understand the microstructure of different soils based on 3D image data. Furthermore, the acquisition of a temporal series of CT images allows to study processes in soils on the microscale, e. g., during mechanical loading. In order to study the hydraulic and mechanical behaviour of unsaturated granular soils, we combine different custom-built miniaturised experimental set-ups with geomechanics background with computed tomography, yielding so-calledin situCT experiments. By means of image reconstruction and further image analysis based on segmented CT images acquired during different hydraulic and mechanical experiments, we study the drainage and imbibition process as well as the shear process of unsaturated sand and glass bead specimens on the pore or grain scale. The analysis of data on the microscopic level, including the phase distribution, interfacial areas, contact lines as well as radii of curvature of capillary menisci, allows to obtain insights into the macroscopic water retention behaviour and shear behaviour of granular soils.

Generating High-Resolution CT Slices from Two Image Series Using Deep-Learning-Based Resolution Enhancement Methods.

Medical image super-resolution (SR) has mainly been developed for a single image in the literature. However, there is a growing demand for high-resolution, thin-slice medical images. We hypothesized that fusing the two planes of a computed tomography (CT) study and applying the SR model to the third plane could yield high-quality thin-slice SR images. From the same CT study, we collected axial planes of 1 mm and 5 mm in thickness and coronal planes of 5 mm in thickness. Four SR algorithms were then used for SR reconstruction. Quantitative measurements were performed for image quality testing. We also tested the effects of different regions of interest (ROIs). Based on quantitative comparisons, the image quality obtained when the SR models were applied to the sagittal plane was better than that when applying the models to the other planes. The results were statistically significant according to the Wilcoxon signed-rank test. The overall effect of the enhanced deep residual network (EDSR) model was superior to those of the other three resolution-enhancement methods. A maximal ROI containing minimal blank areas was the most appropriate for quantitative measurements. Fusing two series of thick-slice CT images and applying SR models to the third plane can yield high-resolution thin-slice CT images. EDSR provides superior SR performance across all ROI conditions.

CORONARY VASOSPASM FROM COMBINATION CAPECITABINE AND OXALIPLATIN THERAPY

CORONARY VASOSPASM FROM COMBINATION CAPECITABINE AND OXALIPLATIN THERAPY

Deterministic small-scale undulations of image-based risk predictions from the deep learning of lung tumors in motion.

Deep learning (DL) models that use medical images to predict clinical outcomes are poised for clinical translation. For tumors that reside in organs that move, however, the impact of motion (i.e., degenerated object appearance or blur) on DL model accuracy remains unclear. We examine the impact of tumor motion on an image-based DL framework that predicts local failure risk after lung stereotactic body radiotherapy (SBRT). We input pre-therapy free breathing (FB) computed tomography (CT) images from 849 patients treated with lung SBRT into a multitask deep neural network to generate an image fingerprint signature (or DL score) that predicts time-to-event local failure outcomes. The network includes a convolutional neural network encoder for extracting imaging features and building a task-specific fingerprint, a decoder for estimating handcrafted radiomic features, and a task-specific network for generating image signature for radiotherapy outcome prediction. The impact of tumor motion on the DL scores was then examined for a holdout set of 468 images from 39 patients comprising: (1) FB CT, (2) four-dimensional (4D) CT, and (3) maximum-intensity projection (MIP) images. Tumor motion was estimated using a 3D vector of the maximum distance traveled, and its association with DL score variance was assessed by linear regression. The variance and amplitude in 4D CT image-derived DL scores were associated with tumor motion (R2 =0.48 and 0.46, respectively). Specifically, DL score variance was deterministic and represented by sinusoidal undulations in phase with the respiratory cycle. DL scores, but not tumor volumes, peaked near end-exhalation. The mean of the scores derived from 4D CT images and the score obtained from FB CT images were highly associated (Pearson r=0.99). MIP-derived DL scores were significantly higher than 4D- or FB-derived risk scores (p<0.0001). An image-based DL risk score derived from a series of 4D CT images varies in a deterministic, sinusoidal trajectory in a phase with the respiratory cycle. These results indicate that DL models of tumors in motion can be robust to fluctuations in object appearance due to movement and can guide standardization processes in the clinical translation of DL models for patients with lung cancer.

Bone Densities Assessed by Hounsfield Units at L5 in Computed Tomography Image Independently Predict Hepatocellular Carcinoma Development in Cirrhotic Patients.

A previous study identified that bone density (BD) assessed by Hounsfield unit (HU) at T12 in computed tomography (CT) image was a predictor for hepatocellular carcinoma (HCC) development in cirrhotic patients. Here, we conducted a verification study, where clinical variables together with BDs (assessed from three different bone areas: T12, L5, and femur trochanter) were assessed for their predictive values for time-to-HCC development in cirrhotic patients. Univariate Cox proportional hazard analysis showed that age (p = 0.017), T12 BD (p = 0.013) and L5 BD (p = 0.005), but not femur BD, were significant predictors. Multivariate analysis revealed that L5 BD was the only independent factor associated with time-to-HCC development (adjusted p = 0.007). Kaplan-Meier analysis confirmed that BD which was lower than median HU was associated with a shorter time-to-HCC development for both T12 BD and L5 BD (p = 0.001 each). Longitudinal follow-ups for BDs in HCC patients having received serial CT imaging studies unveiled a significantly rapid reduction in BD, right before HCC was diagnosed (p = 0.025 when compared with the average BD reduction rate). In conclusion, BD assessed by HU at L5 was an independent predictor for HCC development in cirrhotic patients. Rapid BD reduction during CT scan follow-ups could serve as a warning sign for HCC development.

Imaging analysis of dural exposure in cholesteatomas with a skull base defect.

Dural exposure during cholesteatoma surgery can pose a risk of cerebrospinal fluid leakage or residual disease. Therefore, delicate handling of the area surrounding the bone defect in the cranial fossa is required. However, in small-sized defects, preoperative prediction of dural exposure can be challenging. This study aimed to evaluate the diagnostic value of computed tomography (CT) for preoperative prediction of cholesteatoma-related dural exposure in bone discontinuities in the skull base. We evaluated serial high-resolution CT images showing bone density discontinuities in the middle cranial fossa (MCF) requiring mastoidectomy for cholesteatoma. The CT and intraoperative findings were analyzed retrospectively. We evaluated the length between the superior margins of the bone density discontinuities using coronal CT planes. Receiveroperatingcharacteristic (ROC) curves were constructed to determine the optimal cut-off values. We extracted data from 107 bone density discontinuities, among which 54 (50.5%) showed dural exposure intraoperatively. Discontinuities with dural exposure (n = 54) had significantly greater lengths than did those without (n = 53) (p < 0.001, Wilcoxon rank-sum test). The area under the curve was 0.9780 according to the ROC analysis, and the optimal cut-off value was determined to be 2.99mm (sensitivity 92.59%; specificity 94.34%). A bone density discontinuity length of > 2.99mm in the MCF on coronal CT plane is a reliable diagnostic marker for cholesteatoma-related dural exposure. Thus, preoperative high-resolution CT analysis can inform optimal surgical preparation and planning before manipulating the area surrounding the osteolytic lesion in the MCF.

Evidence of Linear Bone Flap Resorption in Patients Undergoing Autologous Cranioplasty Following Decompressive Craniectomy: A 3D Slicer Segmented Analysis of Serial Computed Tomography Images

Autologous cranioplasty (CP) following decompressive craniectomy (DC) carries a risk of bone flap resorption (BFR). The current literature offers limited information regarding the natural progression of BFR and the rate at which it occurs. We aim to characterize the progression of BFR over time and elucidate risk factors for accelerated BFR. A retrospective analysis was conducted on patients who underwent DC and autologous CP. Serial computed tomography (CT) images were used to quantify the degree of BFR over time. Risk factors included age, diabetes, smoking status, flap fragmentation, defect size, and DC-CP time interval. χ2 analyses and Student's t-tests were performed to examine differences between patients who experienced BFR and those who did not. Overall, 82% of patients demonstrated evidence of clinically relevant resorption on CT. On average, the bone flap decreased in volume by 36.7% within the first year, with a linear loss in volume after multiple years of follow-up. Individuals who developed greater BFR were significantly younger (43±17 vs. 56±12, P= 0.022), had a lower incidence of diabetes (5.9% vs. 43%, P= 0.037), and had more bone flap fragments (1.4±0.67 vs. 1.00±0, P<0.001) than those who did not. Resorption following CP with cryopreserved bone appears to progress in a fairly linear and continuous fashion over time. Using serial CT images, we found a resorption rate of 82% at our institution. We identified several possible risk factors for resorption, including flap fragmentation, younger age, and absence of diabetes.

On the effect of freeze-thaw on fracture and instability of hole-fissure-contained granite under uniaxial variable frequency- amplitude cyclic loads

This work aims at investigating the fracture and instability behaviors of Xinjiang granite containing hole and fissures under freeze-thaw (FT) and cyclic loads using acoustic emission (AE) monitoring and X-ray computed tomography (CT) imaging. The stress path is sinusoidal load with decreasing frequency and increasing amplitude pattern. The experimental results reveal a sudden increase of volumetric strain and a sudden decrease of AE-b value. Their increasing rate becomes quick as cycle increases reflecting the rapid propagation of damage. The frequency of AE activities displays different proportions that is impacted by the FT cycle. The percentage of the low-frequency and high-frequency signals is relatively high and low respectively for a rock experiencing more FT cycles. The theoretical description of F-T influence coupled with mechanical performance are performed. Based on acoustic emission energy, a damage evolution model considering the coupling effect of freezing-thawing cycle and cyclic load is proposed, which can fit the experimental data well. A series of CT images reveal the influence of freeze-thaw on rock failure modes. Typical failure modes of double shear coalescence, single shear coalescence and single tensile coalescence failure modes were identified from the reconstructured CT images.

COVID-19 and atherosclerosis: looking beyond the acute crisis

COVID-19 and atherosclerosis: looking beyond the acute crisis