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In this work we proposed a new respiratory and cardiac motion correction method for cardiac PET/MR imaging by using 2D-MRI image navigator technique and novel dynamic MRI reconstruction method. In the PET/MR acquisition, ECG trigger is used. After trigger starts, 2D-MRI image navigator and golden angle cardiac imaging sequence are applied. After acquisition, cardiac MRI data and PET data are binned to different motion phases according to 2D-MRI image navigator and ECG trigger. We use the high down sampling acquisition and novel reconstruction method based on Low-rank and Sparsity decomposition to reduce MRI acquisition time and improve the time resolution. Finally, we estimate the motion field with both cardiac and respiratory motion based on the reconstructed MRI image series. And PET images are reconstructed by OSEM algorithm based on the binned data. After reconstruction, the PET images are deformed to the same motion phase by accurate motion field from MRI. Finally, sum up all the deformed images to get corrected PET image. PET data is simulated via analytical projection method using segmented MRI images. The results of the proposed method are compared to methods with only respiratory and without motion correction. The simulation shows that the proposed method could improve the image quality and the accuracy of lesion estimation especially on the ventricular wall with cardiac motion. In conclusion, the simulation result demonstrated the feasibility and improvement of the new method.

A high-precision programmable gain amplifier (PGA) for ultra high frequency radio frequency identification (UHF RFID) readers is designed in standard 0.18µm CMOS technology. For the purpose of a good tradeoff between linearity and power consumption, a close-loop resistive-feedback technique is employed in the former to achieve a fine gain control and improve the linearity and an open-loop source negative-feedback technique is adopted in the latter to realize a coarse gain control and a power reduction. Sophisticated biasing techniques are implemented to prevent the gain resolution of PGA being affected from pressure-volume-temperature (PVT) variations. Simulations results exhibit that the PGA operates reliably at all process corners and can be tuned from 0 dB to 48 dB with 1 dB step, achieves a gain error of less than 0.016dB and consumes 4.65mW from a single 1.8V power supply.

System matrix works as an association between image and detected data. Accurate expression of the system matrix is a key component during PET image reconstruction. However, with numerous Line-of-Responses (LORs) detected in modern 3D PET scanner, the huge storage size of a full system matrix increases computational burden. To solve this essential issue, we designed a rotationally symmetric polygonal pixel grid with fan-shaped segments in the image domain to efficiently compress the matrix with geometrical symmetries. This idea mainly came from the polygonal geometry of detector rings in most PET system.

Dual-ended readout detector is a promising approach to extract DOI information in PET detector. To guide DOI detector design, a typical dual-ended readout DOI detector module was simulated using GATE. Four different crystal surface treatments including diffuse crystal surface combined with diffuse reflecting films (DD), diffuse crystal surface combined with specular reflecting films (DS), specular crystal surface combined with diffuse reflecting films (SD) and specular crystal surface combined with specular reflecting films (SS) was evaluated, and two different DOI calibration methods, a linear calibration method and logarithm calibration method to extract DOI positions from the simulation data were investigated. DOI calibration error and DOI resolution were calculated. Signal amplitude uniformity (SAU) was defined and calculated to evaluate the non-uniform effect of total output signal at different DOI positions under different crystal surface treatments. The results show that a DOI resolution superior to 2 mm is achievable with DD, DS and SD using either of the calibration method and a very poor DOI resolution is observed with SS. The logarithm calibration method works much better in term of reducing DOI calibration error, and the achieved DOI resolution is comparable with that using linear calibration method. The best SAU is achieved with SS while degradations in SAU are observed with DD, DS and SD. In general, a diffuse crystal surface or reflecting film leads to an improved DOI resolution but a degraded SAU. The degraded SAU will in turn deteriorate DOI resolution as well as other detector performance such as energy resolution as the reduced system SNR (signal-to-noise ratio). This work studies the impact of PET crystal surface treatment and calibration method on DOI positioning accuracy. It can provide guidance in optimizing crystal surface treatments and DOI extraction methods to facilitate dual-ended readout DOI detector design.

Due to special imaging mechanism, shadow contour of SAR image is not as clear as that of optical image. Aiming at target recognition, this paper presents a SAR image segmentation method based on Partial Differential De-noising and Marker-Controlled, and carries out automatic target recognition on result of image segmentation. Experimental results show that, using the method of this paper, segmentation result of SAR image can classify target effectively.

For its strategic importance, this article is directed at a special core decision group in the enterprise called Top Management Team (TMT) which plays a vital role in the improvement of the enterprise's core competitiveness and performance. This article abstracts diverse factors which have an impact on TMT strategic decision-making into a force from the perspective of mechanics, which will analyze the impact of various factors more intuitively and effectively. The aim is to depict a picture of comprehensive, dynamic and external competitive environment and internal operating environment for TMT, which can provide reliable information to enable the TMT to fully and accurately assess their own competitive ability and various opportunities and threats hiding in the external environment, consequently to lay the foundation of developing and implementing the right strategy, creating and maintaining a sustainable competitive advantage for enterprise.

The parallel magnetic resonance imaging (parallel imaging) technique reduces the MR data acquisition time by using multiple receiver coils. Because of its ill-conditioned system matrix, the reconstruction suffers from noise amplification at high reduction factors, such as in the standard SENSE reconstruction. Total variation (TV) regularization is a popular technique for solving this problem. However, TV regularized images are vulnerable to staircase artifacts and texture loss. In this paper, we proposed a similarity-based regularization technique which enforces the consistence and similarity of pixel values within the image. The phantom simulation and in vivo experimental results demonstrate that this method can effectively suppress noise amplification in SENSE reconstruction while preserving image details. Compared with TV regularized images, images reconstructed by the new method are free of staircase artifacts and suffer less from structure loss.

This paper gives a new image feature extraction technique coined two-dimensional clustering-based discriminant analysis (2DCDA), which is based on 2D image matrices for constructing the scatter matrices and assumes that the data obeys the multimodal distribution. The detailed procedure of 2DCDA is to first partition each class of the data into multiple clusters via fast 2D global k-means clustering algorithm, and then try to find some directions such that the projections of every pair of clusters from different classes are well separated while the within-cluster scatter is minimized. Therefore, it fully exploits the cluster information and alleviates the linearly unseparable problem to some extent. According to the projection fashion, we divide 2DCDA into two versions, the right 2DCDA (R-2DCDA) and the left 2DCDA (L-2DCDA). They compress the image row or column only, so they need more features. To solve this problem, two-directional 2DCDA ((2D)2CDA) is developed, which compresses the image row and column simultaneously. Experiments have been carried out for recognition of three types of ground vehicles in the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database to evaluate and compare the performances of the proposed algorithms with other methods. Results demonstrate that 2DCDA and (2D)2CDA are effective. And, the highest recognition rate is up to 97.89%, which is the best ever reported in the literatures.