OBJECTIVE: To extend the application of Gibbs artifact reduction method that exploits local subvoxel- shifts (LSS) to zero- padded k-space magnetic resonance imaging (MRI) data. METHODS: We investigated two approaches to extending the application of LSS-based method to under-sampled data. The first approach, namely LSS+ interpolation, utilized the original LSS-based method to minimize the local variation on nonzero-padding reconstructed images, followed by image interpolation to obtain the final images. The second approach, interlaced local variation, used zero-padded Fourier transformation followed by elimination of Gibbs artifacts by minimizing a novel interlaced local variations (iLV) term. We compared the two methods with the original LSS and Hamming window filter algorithms, and verified their feasibility and robustness in phantom and data. RESULTS: The two methods proposed showed better performance than the original LSS and Hamming window filters and effectively eliminated Gibbs artifacts while preserving the image details. Compared to LSS + interpolation method, iLV method better preserved the details of the images. CONCLUSIONS The iLV and LSS+interpolation methods proposed herein both extend the application of the original LSS method and can eliminate Gibbs artifacts in zero-filled k-space data reconstruction images, and iLV method shows a more prominent advantage in retaining the image details.
Algorithms ; Artifacts ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Phantoms, Imaging

Objective To explore the sensitivity of volumetric modulated arc therapy(VMAT)plan to setup errors in radiotherapy for brain metastases of various sizes.Methods Five spheres were set to simulate multiple brain metastases in the CT image,with the volumes of 0.5,1.7,4.0,8.1,and 14.0 cm3,respectively.A single isocenter 5-arc non-coplanar VMAT plan was generated,and the distance from isocenter to each target was 5 cm.The rotational setup errorθ around the Z axis was simulated by rotating the treatment couch(clockwise from-3.0°to 3.0°,with a step length of 0.5°);and the translational setup error(from-3.0 mm to 3.0 mm,with a step length of 0.5 mm)was simulated by changing the position of the isocenter on the three-dimensional axis(X/Y/Z).The dose distribution was recalculated in the optimization conditions remaining unchanged.The target coverage after rotation and translation was recorded and normalized to the relative coverage without setup error.The correlation between the target coverage of brain metastases of various sizes and the setup error was analyzed with linear regression method.Results The relative coverage decreased gradually as rotational setup error increased.With the same rotational setup error,the relative coverage was in linear correlation with target volume(θ=0.5°,P=0.006;θ=1.0°,P=0.024;θ=1.5°,P=0.028;θ=2.0°,P=0.019;θ=2.5°,P=0.014;θ=3.0°,P=0.007),and the relative coverage increased with increasing target volume.When the rotational setup error was less than 0.5°,the relative coverage was less than 2%regardless of whether the target area was large(14.0 cm3)or small(0.5 cm3).The relative coverage decreased gradually as translational setup error increased.Compared with large target area,small target area was more sensitive to translational setup error.When the translational setup error was less than 0.5 mm,the relative coverage of the large target area varied less than 2%,while the relative coverage of the small target area(0.5 cm3)decreased by nearly 5%.Conclusion When the rotational setup error is constant,the relative coverage of the target has linear relationship with target volume.Small target area is more sensitive to translational setup error than large target area.The rotational setup error within 0.5°and the translational setup error within 0.5 mm are recommended when implementing SRS VMAT plan for small target area.

Objective To improve the image reconstruction quality of diffusion-weighted imaging with high acceleration factor for accelerating the acquisition.Methods Image block matching method was used to extract similar image blocks in diffusion-weighted images for low-rank constraint and sparseness constraint,and it was integrated into the traditional sensitivity encoding(SENSE)parallel reconstruction algorithm to improve image reconstruction quality and reduce image noise.Two sets of human data were collected in the experiments.The reconstructed results using traditional SENSE reconstruction,total variation constraint-based SENSE(SENSE-TV)reconstruction and the proposed method were compared at 3×and 4×accelerations.The errors of the diffusion images and fractional anisotropy(FA)maps with the reference images from fully sampled data were quantitatively calculated.Results Compared with traditional SENSE and SENSE-TV methods,the proposed method resulted in the reconstructed diffusion images with higher image quality and lower image errors in the 3×and 4×acceleration experiments.The quantitative analysis showed that the FA calculated by the proposed method was more accurate and had lower errors.Conclusion By constraining low-rank and sparseness of similar image blocks from images in reconstruction,it is expected to achieve high image quality under high acceleration factor.

The bioelectrical impedance measurement is a detection technique that uses the electrical characteristics and changes in human tissues and organs to detect biomedical information related to human physiological and pathological conditions. This article makes a comprehensive introduction from the aspects of impedance cardiography, electrical impedance respiratory monitoring, electrical impedance tomography, electrical impedance gastric dynamics detection technique, contact impedance and so on, as well as comprehensively introduces the progress and application status of bioelectrical impedance measurement methods.
Electric Impedance ; Humans ; Monitoring, Physiologic ; Tomography

This study describes the development of a wireless and wearable ECG monitoring system with ultra-low power consumption. The system is mainly composed of a connection part of an ECG electrode sticker, an electrocardiogram collecting part, a data storage part, a Bluetooth main control unit, a charging module, a voltage regulator and a lithium battery. The low-power ECG acquisition chip ADS1292R and the ultra-low-power Bluetooth microcontroller nRF51822 together constitute the ECG signal acquisition and wireless data communication part. The collected ECG signals can be sent to the mobile APP through the Bluetooth function provided by the MCU, and can completly display and analysis to achieve low power system. After testing, the system power consumption is only (3.7 V×2.87 mA)10.619 mW, and if it is optimized, it can further reduce power consumption, therefore, the system design can have good applicability.
Electric Power Supplies ; Electrocardiography ; Equipment Design ; Monitoring, Physiologic/instrumentation* ; Signal Processing, Computer-Assisted ; Wearable Electronic Devices ; Wireless Technology