MRI inter-slice reconstruction using super-resolution

MRI reconstruction using super-resolution is presented and shown to improve spatial resolution in cases when spatially-selective RF pulses are used for localization. In 2-D multislice MRI, the resolution in the slice direction is often lower than the in-plane resolution. For certain diagnostic imaging applications, isotropic resolution is necessary but true 3-D acquisition methods are not practical. In this case, if the imaging volume is acquired two or more times, with small spatial shifts between acquisitions, combination of the data sets using an iterative super-resolution algorithm gives improved resolution and better edge definition in the slice-select direction. Resolution augmentation in MRI is important for visualization and early diagnosis. The method also improves the signal-to-noise efficiency of the data acquisition.     

Fast calculation of pulsed photoacoustic fields in fluids using k-space methods

Two related numerical models that calculate the time-dependent pressure field radiated by an arbitrary photoacoustic source in a fluid, such as that generated by the absorption of a short laser pulse, are presented. Frequency-wavenumber (k-space) implementations have been used to produce fast and accurate predictions. Model I calculates the field everywhere at any instant of time, and is useful for visualizing the three-dimensional evolution of the wave field. Model II calculates pressure time series for points on a straight line or plane and is therefore useful for simulating array measurements. By mapping the vertical wavenumber spectrum directly to frequency, this model can calculate time series up to 50 times faster than current numerical models of photoacoustic propagation. As the propagating and evanescent parts of the field are calculated separately, model II can be used to calculate far- and near-field radiation patterns. Also, it can readily be adapted to calculate the velocity potential and thus particle velocity and acoustic intensity vectors. Both models exploit the efficiency of the fast Fourier transform, and can include the frequency-dependent directional response of an acoustic detector straightforwardly. The models were verified by comparison with a known analytic solution and a slower, but well-understood, numerical model.     

Structural similarity based single image super-resolution with nonlocal regularization

Recently, sparse coding based image super-resolution has attracted increasing interests. This paper proposes an improved image super-resolution method, by incorporating structural similarity (SSIM) index and nonlocal regularization into the framework of image super-resolution via sparse coding. Firstly, an algorithm of combining SSIM based sparse coding and K-SVD is proposed to train the high resolution (HR) and low resolution (LR) dictionary pairs. And then, the sparse representations of observed LR image are sought to reconstruct the HR image with the trained LR and HR dictionary pairs by exploiting nonlocal self-similarities. Experimental results demonstrate the effectiveness of the proposed method, both in its visual effects and in quantitative terms.     

Motion predicted online dynamic MRI reconstruction from partially sampled k-space data

In this work we address the problem of reconstructing dynamic MRI sequences in an online fashion, i.e. reconstructing the current frame given that the previous frames have been already reconstructed. The reconstruction consists of a prediction and a correction step. The prediction step is based on an Auto-Regressive AR(1) model. Assuming that the prediction is good, the difference between the predicted frame and the actual frame (to be reconstructed) will be sparse. In the correction step, the difference between the predicted frame and the actual frame is estimated from partially sampled K-space data via a sparsity promoting least squares minimization problem. We have compared the proposed method with state-of-the-art methods in online dynamic MRI reconstruction. The experiments have been carried out on 2D and 3D Dynamic Contrast Enhanced (DCE) MRI datasets. Results show that our method yields the least reconstruction error.     

MR image reconstruction of sparsely sampled 3D k-space data by projection-onto-convex sets

In many rapid three-dimensional (3D) magnetic resonance (MR) imaging applications, such as when following a contrast bolus in the vasculature using a moving table technique, the desired k-space data cannot be fully acquired due to scan time limitations. One solution to this problem is to sparsely sample the data space. Typically, the central zone of k-space is fully sampled, but the peripheral zone is partially sampled. We have experimentally evaluated the application of the projection-onto-convex sets (POCS) and zero-filling (ZF) algorithms for the reconstruction of sparsely sampled 3D k-space data. Both a subjective assessment (by direct image visualization) and an objective analysis [using standard image quality parameters such as global and local performance error and signal-to-noise ratio (SNR)] were employed. Compared to ZF, the POCS algorithm was found to be a powerful and robust method for reconstructing images from sparsely sampled 3D k-space data, a practical strategy for greatly reducing scan time. The POCS algorithm reconstructed a faithful representation of the true image and improved image quality with regard to global and local performance error, with respect to the ZF images. SNR, however, was superior to ZF only when more than 20% of the data were sparsely sampled. POCS-based methods show potential for reconstructing fast 3D MR images obtained by sparse sampling.     

An MRI denoising method using image data redundancy and local SNR estimation

This paper presents an LMMSE-based method for the three-dimensional (3D) denoising of MR images assuming a Rician noise model. Conventionally, the LMMSE method estimates the noise-less signal values using the observed MR data samples within local neighborhoods. This is not an efficient procedure to deal with this issue while the 3D MR data intrinsically includes many similar samples that can be used to improve the estimation results. To overcome this problem, we model MR data as random fields and establish a principled way which is capable of choosing the samples not only from a local neighborhood but also from a large portion of the given data. To follow the similar samples within the MR data, an effective similarity measure based on the local statistical moments of images is presented. The parameters of the proposed filter are automatically chosen from the estimated local signal-to-noise ratio. To further enhance the denoising performance, a recursive version of the introduced approach is also addressed. The proposed filter is compared with related state-of-the-art filters using both synthetic and real MR datasets. The experimental results demonstrate the superior performance of our proposal in removing the noise and preserving the anatomical structures of MR images.     

Elimination of k-space spikes in fMRI data

The subtle signal changes in functional magnetic resonance imaging (fMRI) can be easily overwhelmed by noise of various origins. Spikes in the collected fMRI raw data often arise from high-duty usage of the scanner hardware and can introduce significant noise in the image and thereby in the image time series. Consequently, the spikes will corrupt the functional data and degrade the result of functional mapping. In this work, a simple method based on processing the time course of the k-space data are introduced and implemented to remove the spikes in the acquired data. Application of the method to experimental data shows that the methods are robust and effective for eliminating of spike-related noise in fMRI time series.     

基于半像素错位的多幅图像重建高分辨率图像技术研究

介绍了一种基于半像素错位的多幅图像重建高分辨率图像技术。分析了半像素错位的多幅图像与高分辨率图像各像素灰度值的对应关系 ,并从CCD数字化采样的角度进行了论证。同时 ,结合实际摄像机CCD结构 ,求出了高分辨率图像重建的计算公式 ,并通过实验进行了验证和完善。重建的本质是以原高分辨率图像的 4邻域平均图像为基础 ,增加一定比例的边缘细节信息 ,去接近原高分辨率图像。CCD的动态范围越大 ,图像的灰度级越多 ,那么计算误差就越小 ,图像的边缘细节信息就可以利用更多 ,重建的图像就越接近原高分辨率图像。通过实验和分析表明 ,利用半像素错位的多幅低分辨率图像重建高分辨率图像的原理是正确的 ,方案是可行的     

Fast and quiet MRI using a swept radiofrequency

A novel fast and quiet method of magnetic resonance imaging (MRI) is introduced which creates new opportunities for imaging in medicine and materials science. The method is called SWIFT, sweep imaging with Fourier transformation. In SWIFT, time-domain signals are acquired in a time-shared manner during a swept radiofrequency excitation of the nuclear spins. With negligible time between excitation and signal acquisition, new possibilities exist for imaging objects consisting of spins with extremely fast transverse relaxation rates, such as macromolecules, semi-solids, and quadrupolar nuclei. The field gradient used for spatial-encoding is not pulsed on and off, but rather is stepped in orientation in an incremental manner, which results in low acoustic noise. This unique acquisition method is expected to be relatively insensitive to sample motion, which is important for imaging live objects. Additionally, the frequency-swept excitation distributes the signal energy in time and thus dynamic range requirements for proper signal digitization are reduced compared with conventional MRI. For demonstration, images of a plastic object and cortical bone are shown.     

Fast image enhancement using multi-scale saliency extraction in infrared imagery

We propose a multi-scale saliency extraction based fast infrared image enhancement approach. A local frequency-tuned based saliency extraction technique is designed for highlighting the salient regions, firstly. Then, multi-scale saliency extraction is demonstrated, introducing multi-scale local windows with different sizes to extract regions of interest at different scales. Finally, the original image is enhanced with combining multi-scale salient image regions into one image. The experimental results prove the proposed approach is robust and efficient for infrared image enhancement.     

Sparse MRI reconstruction using multi-contrast image guided graph representation

Accelerating the imaging speed without sacrificing image structures plays an important role in magnetic resonance imaging. Under-sampling the k-space data and reconstructing the image with sparsity constraint is one efficient way to reduce the data acquisition time. However, achieving high acceleration factor is challenging since image structures may be lost or blurred when the acquired information is not sufficient. Therefore, incorporating extra knowledge to improve image reconstruction is expected for highly accelerated imaging. Fortunately, multi-contrast images in the same region of interest are usually acquired in magnetic resonance imaging protocols. In this work, we propose a new approach to reconstruct magnetic resonance images by learning the prior knowledge from these multi-contrast images with graph-based wavelet representations. We further formulate the reconstruction as a bi-level optimization problem to allow misalignment between these images. Experiments on realistic imaging datasets demonstrate that the proposed approach improves the image reconstruction significantly and is practical for real world application since patients are unnecessarily to stay still during successive reference image scans.     

Effects of k-space filtering and image interpolation on image fidelity in (1)H MRSI.

2D MRSI suffers from the effect of the spatial response function due to the truncation of the sampling of k-space. Filtering of the k-space data-set is often used to suppress the side lobes caused by the effects of the SRF, where the sampled data-set is multiplied with a weighting function before inverse FT. Commonly used filters in MRSI are the cosine, Hanning and Hamming filters. The data-set is often interpolated into a larger image matrix size for analysis, where "Fourier interpolation" (FoI) and "cubic spline interpolation" (CSpI) are two common methods. In this work, the effects of k-space filtering in practical usage was examined, and the image representations of the object for the two interpolation methods were compared.This study showed that application of filtering improves the image representation of the structures in the object and should be used in MRSI. FoI correctly visualizes the information inherent in the data-set, while the features of the object were dependent on the position of the object relative the original matrix in the CSpI interpolated images. FoI should therefore be used for quantitative evaluation of MRSI images.     

Steganographic data hiding in image processing using predictive differencing

In this paper, we propose a stenography scheme based on predictive differencing to embed data in a grey-image. In order to promote the embedding capacity of pixel-value differencing (PVD), we use differencing between a predictive value and an input pixel as the predictive differencing to embed the message where a predictive value is calculated by using various predictors. If the predictive differencing is large, then it means that the input pixel is located in the edge area and, thus, has a larger embedding capacity than the pixel in a smooth area. The experimental result shows that our proposed scheme is capable of providing greater embedding capacity and high quality of stego-images then previous works. Furthermore, we have also applied various predictors to evaluate our proposed scheme.     

Fast 3D modeling in complex environments using a single Kinect sensor

Three-dimensional (3D) modeling technology has been widely used in inverse engineering, urban planning, robot navigation, and many other applications. How to build a dense model of the environment with limited processing resources is still a challenging topic. A fast 3D modeling algorithm that only uses a single Kinect sensor is proposed in this paper. For every color image captured by Kinect, corner feature extraction is carried out first. Then a spiral search strategy is utilized to select the region of interest (ROI) that contains enough feature corners. Next, the iterative closest point (ICP) method is applied to the points in the ROI to align consecutive data frames. Finally, the analysis of which areas can be walked through by human beings is presented. Comparative experiments with the well-known KinectFusion algorithm have been done and the results demonstrate that the accuracy of the proposed algorithm is the same as KinectFusion but the computing speed is nearly twice of KinectFusion. 3D modeling of two scenes of a public garden and traversable areas analysis in these regions further verified the feasibility of our algorithm.