Model-Independent Lens Distortion Correction Based on Sub-Pixel Phase Encoding

Lens distortion can introduce deviations in visual measurement and positioning. The distortion can be minimized by optimizing the lens and selecting high-quality optical glass, but it cannot be completely eliminated. Most existing correction methods are based on accurate distortion models and stable image characteristics. However, the distortion is usually a mixture of the radial distortion and the tangential distortion of the lens group, which makes it difficult for the mathematical model to accurately fit the non-uniform distortion. This paper proposes a new model-independent lens complex distortion correction method. Taking the horizontal and vertical stripe pattern as the calibration target, the sub-pixel value distribution visualizes the image distortion, and the correction parameters are directly obtained from the pixel distribution. A quantitative evaluation method suitable for model-independent methods is proposed. The method only calculates the error based on the characteristic points of the corrected picture itself. Experiments show that this method can accurately correct distortion with only 8 pictures, with an error of 0.39 pixels, which provides a simple method for complex lens distortion correction.

MRI-Based Image Signal-to-Noise Ratio Enhancement with Different Receiving Gains in K-Space

Echo signals in different regions in the k-space of magnetic resonance imaging (MRI) data possess different amplitudes. The signal-to-noise ratio (SNR) of a received signal can be improved by differentially setting the receiving gain (RG) parameter in different areas of the k-space. Previously, the k-space data splicing method and the gain normalization implementation method were not specifically investigated; however, this study focuses on this aspect. Specifically, to improve the SNR, three RGs and MRI scans are herein designed for each gain parameter using the gradient echo sequence to obtain one group of k-space data. Subsequently, the three groups of experimental k-space data obtained using MRI scans are spliced into one group of k-space data. For the splicing process, a method for gain and phase correction and compensation is developed that normalizes different RG parameters in the k-space. The experimental results indicate that the developed methods improve the SNR by 5–13%. When the RGs are set to other combinations, the k-space data splicing and gain normalization methods presented in this paper are still applicable.

Dynamic Optimization Method for Broadband ADCP Waveform with Environment Constraints

Broadband acoustic Doppler current profiler (ADCP) is widely used in agricultural water resource explorations, such as river discharge monitoring and flood warning. Improving the velocity estimation accuracy of broadband ADCP by adjusting the waveform parameters of a phase-encoded signal will reduce the velocity measurement range and water stratification accuracy, while the promotion of stratification accuracy will degrade the velocity estimation accuracy. In order to minimize the impact of these two problems on the measurement results, the ADCP waveform optimization problem that satisfies the environment constraints while keeping high velocity estimation accuracy or stratification accuracy is studied. Firstly, the relationship between velocity or distance estimation accuracy and signal waveform parameters is studied by using an ambiguity function. Secondly, the constraints of current velocity range, velocity distribution and other environmental characteristics on the waveform parameters are studied. For two common measurement applications, two dynamic configuration methods of waveform parameters with environmental adaptability and optimal velocity estimation accuracy or stratification accuracy are proposed based on the nonlinear programming principle. Experimental results show that compared with the existing methods, the velocity estimation accuracy of the proposed method is improved by more than 50%, and the stratification accuracy is improved by more than 22%.

Phase-encoded single-voxel magnetic resonance spectroscopy for suppressing outer volume signals at 7 Tesla

BACKGROUND

Due to imperfect slice profiles, unwanted signals from outside the selected voxel may significantly contaminate metabolite signals acquired using in vivo magnetic resonance spectroscopy (MRS). The use of outer volume suppression may exceed the SAR threshold, especially at high field.

OBJECTIVE

We propose using phase-encoding gradients after radiofrequency (RF) excitation to spatially encode unwanted signals originating from outside of the selected single voxel.

METHODS

Phase-encoding gradients were added to a standard single voxel point-resolved spectroscopy (PRESS) sequence which selects a 2 × 2 × 2 cm³ voxel. Subsequent spatial Fourier transform was used to encode outer volume signals. Phantom and in vivo experiments were performed using both phase-encoded PRESS and standard PRESS at 7 Tesla. Quantification was performed using fitting software developed in-house.

RESULTS

Both phantom and in vivo studies showed that spectra from the phase-encoded PRESS sequence were relatively immune from contamination by oil signals and have more accurate quantification results than spectra from standard PRESS spectra of the same voxel.

CONCLUSION

The proposed phase-encoded single-voxel PRESS method can significantly suppress outer volume signals that may appear in the spectra of standard PRESS without increasing RF power deposition.