Notes

Molecular characteristics: an effective instrument for checking out the therapeutic hormone balance involving ion route modulators.
This study aimed to explore the diagnostic accuracy of cardiac magnetic resonance tissue tracking (CMR-TT) technology in the quantitative evaluation of left myocardial infarction for differentiating between acute and chronic myocardial infarction.

A total of 104 human subjects were enrolled in this prospective study. Among them, 64 healthy subjects and 40 patients with left ventricular myocardial infarction and 7 days and 6 months' follow-up CMR studies, including steady-state free precession (SSFP) sequence and late gadolinium enhancement MR imaging, were enrolled. The strain parameters of the infarcted myocardium, its corresponding remote segments, and global right ventricular strain were analyzed using tissue tracking technology, and CMR-TT 3D strain parameters in radial, circumferential, and longitudinal directions were obtained. RMI14514 Receiver operating characteristic (ROC) analysis was used to determine the diagnostic accuracy of the CMR-TT strain parameters for discriminating between acute and chronic myocardial infarction.

Peak radial strain (RS) of infarcted myocardium increased from 12.99±7.28 to 18.57±6.66 at 6 months (P<0.001), whereas peak circumferential strain (CS) increased from -8.82±4.71 to -12.78±3.55 (P<0.001). CS yielded the best areas under the ROC curve (AUC) of 0.751 in showing differentiation between acute and chronic myocardial infarction of all the strain parameters obtained. The highest significant differences between acute myocardial infarction and normal myocardium, both in the left and right ventricles, were also found in the RS (P<0.001) and CS (P<0.001).

RS and CS obtained by CMR-TT have high sensitivity and specificity in the differential diagnosis of acute versus chronic myocardial infarction, and their use is thus worth popularizing in clinical application.
RS and CS obtained by CMR-TT have high sensitivity and specificity in the differential diagnosis of acute versus chronic myocardial infarction, and their use is thus worth popularizing in clinical application.
Detecting discomfort in infants is an important topic for their well-being and development. In this paper, we present an automatic and continuous video-based system for monitoring and detecting discomfort in infants.

The proposed system employs a novel and efficient 3D convolutional neural network (CNN), which achieves an end-to-end solution without the conventional face detection and tracking steps. In the scheme of this study, we thoroughly investigate the video characteristics (e.g., intensity images and motion images) and CNN architectures (e.g., 2D and 3D) for infant discomfort detection. The realized improvements of the 3D-CNN are based on capturing both the motion and the facial expression information of the infants.

The performance of the system is assessed using videos recorded from 24 hospitalized infants by visualizing receiver operating characteristic (ROC) curves and measuring the values of area under the ROC curve (AUC). Additional performance metrics (labeling accuracy) are also calculated. Experimental results show that the proposed system achieves an AUC of 0.99, while the overall labeling accuracy is 0.98.

These results confirms the robustness by using the 3D-CNN for infant discomfort monitoring and capturing both motion and facial expressions simultaneously.
These results confirms the robustness by using the 3D-CNN for infant discomfort monitoring and capturing both motion and facial expressions simultaneously.
Chest CT angiography (CTA) is a common clinical examination technique for children. Iterative reconstruction algorithms are often used to reduce image noise but encounter limitations under low dose conditions. Deep learning-based image reconstruction algorithms have been developed to overcome these limitations. We assessed the quantitative and qualitative image quality of thin-slice chest CTA in children acquired with low radiation dose and contrast volume by using a deep learning image reconstruction (DLIR) algorithm.

A total of 33 children underwent chest CTA with 70 kVp and automatic tube current modulation for noise indices of 11-15 based on their age and contrast volume of 0.8-1.2 mL/kg. Images were reconstructed with 50% and 100% adaptive statistical iterative reconstruction-V (ASIR-V) and high-setting DLIR (DLIR-H) at 0.625 mm slice thickness. Two radiologists evaluated images in consensus for overall image noise, artery margin, and artery contrast separately on a 5-point scale (5, excellent; 4, good; 3, acceptable; 2, sub-acceptable, and 1, not acceptable). The CT value and image noise of the descending aorta and back muscle were measured. Radiation dose and contrast volume was recorded.

The volume CT dose index, dose length product, and contrast volume were 1.37±0.29 mGy, 35.43±10.59 mGy·cm, and 25.43±13.32 mL, respectively. The image noises (in HU) of the aorta with DLIR-H (19.24±5.77) and 100% ASIR-V (20.45±6.93) were not significantly different (P>0.05) and were substantially lower than 50% ASIR-V (29.45±7.59) (P<0.001). The 100% ASIR-V images had over-smoothed artery margins, but only the DLIR-H images provided acceptable scores on all 3 aspects of the qualitative image quality evaluation.

It is feasible to improve the image quality of a low radiation dose and contrast volume chest CTA in children using the high-setting DLIR algorithm.
It is feasible to improve the image quality of a low radiation dose and contrast volume chest CTA in children using the high-setting DLIR algorithm.
Wasting disease entities like cachexia or sarcopenia are associated with a decreasing muscle mass and changing muscle composition. For valid and reliable disease detection and monitoring diagnostic techniques offering quantitative musculature assessment are needed. Multi-detector computed tomography (MDCT) is a broadly available imaging modality allowing for muscle composition analysis. A major disadvantage of using MDCT for muscle composition assessment is the radiation exposure. In this study we evaluated the performance of different methods of radiation dose reduction for paravertebral muscle composition assessment.

MDCT scans of eighteen subjects (6 males, age 71.5±15.9 years, and 12 females, age 71.0±8.9 years) were retrospectively simulated as if they were acquired at 50%, 10%, 5%, and 3% of the original X-ray tube current or number of projections (i.e., sparse sampling). Images were reconstructed with a statistical iterative reconstruction (SIR) algorithm. Paraspinal muscles (psoas and erector spinae muscles) at the level of L4 were segmented in the original-dose images.
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