X-ray CT metal artifact reduction using wavelets: an application for imaging total hip prostheses

Traditional computed tomography (CT) reconstructions of total joint prostheses are limited by metal artifacts from corrupted projection data. Published metal artifact reduction methods are based on the assumption that severe attenuation of X-rays by prostheses renders corresponding portions of projection data unavailable, hence the "missing" data are either avoided (in iterative reconstruction) or interpolated (in filtered backprojection with data completion; typically, with filling data "gaps" via linear functions). In this paper, we propose a wavelet-based multiresolution analysis method for metal artifact reduction, in which information is extracted from corrupted projection data. The wavelet method improves image quality by a successive interpolation in the wavelet domain. Theoretical analysis and experimental results demonstrate that the metal artifacts due to both photon starving and beam hardening can be effectively suppressed using our method. As compared to the filtered backprojection after linear interpolation, the wavelet-based reconstruction is significantly more accurate for depiction of anatomical structures, especially in the immediate neighborhood of the prostheses. This superior imaging precision is highly advantageous in geometric modeling for fitting hip prostheses.     

Fourier-based reconstruction for fully 3-D PET: optimization of interpolation parameters

Fourier-based approaches for three-dimensional (3-D) reconstruction are based on the relationship between the 3-D Fourier transform (FT) of the volume and the two-dimensional (2-D) FT of a parallel-ray projection of the volume. The critical step in the Fourier-based methods is the estimation of the samples of the 3-D transform of the image from the samples of the 2-D transforms of the projections on the planes through the origin of Fourier space, and vice versa for forward-projection (reprojection). The Fourier-based approaches have the potential for very fast reconstruction, but their straightforward implementation might lead to unsatisfactory results if careful attention is not paid to interpolation and weighting functions. In our previous work, we have investigated optimal interpolation parameters for the Fourier-based forward and back-projectors for iterative image reconstruction. The optimized interpolation kernels were shown to provide excellent quality comparable to the ideal sinc interpolator. This work presents an optimization of interpolation parameters of the 3-D direct Fourier method with Fourier reprojection (3D-FRP) for fully 3-D positron emission tomography (PET) data with incomplete oblique projections. The reprojection step is needed for the estimation (from an initial image) of the missing portions of the oblique data. In the 3D-FRP implementation, we use the gridding interpolation strategy, combined with proper weighting approaches in the transform and image domains. We have found that while the 3-D reprojection step requires similar optimal interpolation parameters as found in our previous studies on Fourier-based iterative approaches, the optimal interpolation parameters for the main 3D-FRP reconstruction stage are quite different. Our experimental results confirm that for the optimal interpolation parameters a very good image accuracy can be achieved even without any extra spectral oversampling, which is a common practice to decrease errors caused by interpolation in Fourier reconstruction.     

Constrained Fourier space method for compensation of missing data in emission computed tomography

A method is introduced to compensate for missing projection data that can result from gas between detectors or from malfunctioning detectors. This method uses constraints in the Fourier domain to estimate the missing data, thus completing the data set so that the filtered backprojection algorithm can be used to reconstruct artifact-free images. The image reconstructed from estimates using this technique and a data set with gaps is nearly indistinguishable from an image reconstructed from a complete data set without gaps, using a simulated brain phantom.     

Noniterative interpolation-based super-resolution minimizing aliasing in the reconstructed image

Super-resolution (SR) techniques produce a high-resolution image from a set of low-resolution undersampled images. In this paper, we propose a new method for super-resolution that uses sampling theory concepts to derive a noniterative SR algorithm. We first raise the issue of the validity of the data model usually assumed in SR, pointing out that it imposes a band-limited reconstructed image plus a certain type of noise. We propose a sampling theory framework with a prefiltering step that allows us to work with more general data models and also a specific new method for SR that uses Delaunay triangulation and B-splines to build the super-resolved image. The proposed method is noniterative and well posed. We prove its effectiveness against traditional iterative and noniterative SR methods on synthetic and real data. Additionally, we also prove that we can first solve the interpolation problem and then make the deblurring not only when the motion is translational but also when there are rotations and shifts and the imaging system Point Spread Function (PSF) is rotationally symmetric.     

An edge-guided image interpolation algorithm via directional filtering and data fusion.

Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high-resolution image from a low-resolution counterpart. We propose a new edge-guided nonlinear interpolation technique through directional filtering and data fusion. For a pixel to be interpolated, two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts.     

Adaptive homogeneity-directed demosaicing algorithm.

A cost-effective digital camera uses a single-image sensor, applying alternating patterns of red, green, and blue color filters to each pixel location. A way to reconstruct a full three-color representation of color images by estimating the missing pixel components in each color plane is called a demosaicing algorithm. This paper presents three inherent problems often associated with demosaicing algorithms that incorporate two-dimensional (2-D) directional interpolation: misguidance color artifacts, interpolation color artifacts, and aliasing. The level of misguidance color artifacts present in two images can be compared using metric neighborhood modeling. The proposed demosaicing algorithm estimates missing pixels by interpolating in the direction with fewer color artifacts. The aliasing problem is addressed by applying filterbank techniques to 2-D directional interpolation. The interpolation artifacts are reduced using a nonlinear iterative procedure. Experimental results using digital images confirm the effectiveness of this approach.     

Real-time artifact-free image upscaling

The problem of creating artifact-free upscaled images appearing sharp and natural to the human observer is probably more interesting and less trivial than it may appear. The solution to the problem, often referred to also as "single-image super-resolution," is related both to the statistical relationship between low-resolution and high-resolution image sampling and to the human perception of image quality. In many practical applications, simple linear or cubic interpolation algorithms are applied for this task, but the results obtained are not really satisfactory, being affected by relevant artifacts like blurring and jaggies. Several methods have been proposed to obtain better results, involving simple heuristics, edge modeling, or statistical learning. The most powerful ones, however, present a high computational complexity and are not suitable for real-time applications, while fast methods, even if edge adaptive, are not able to provide artifacts-free images. In this paper, we describe a new upscaling method (iterative curvature-based interpolation) based on a two-step grid filling and an iterative correction of the interpolated pixels obtained by minimizing an objective function depending on the second-order directional derivatives of the image intensity. We show that the constraints used to derive the function are related with those applied in another well-known interpolation method, providing good results but computationally heavy (i.e., new edge-directed interpolation (NEDI). The high quality of the images enlarged with the new method is demonstrated with objective and subjective tests, while the computation time is reduced of one to two orders of magnitude with respect to NEDI so that we were able, using a graphics processing unit implementation based on the nVidia Compute Unified Device Architecture technology, to obtain real-time performances.     

An optimal rotator for iterative reconstruction

For implementations of iterative reconstruction algorithms that rotate the image matrix, the characteristics of the rotator may affect the reconstruction quality. Desirable qualities for the rotator include: (1) preservation of global and local image counts; (2) accurate count positioning; (3) a uniform and predictable amount of blurring due to the rotation. A new rotation method for iterative reconstruction is proposed which employs Gaussian interpolation. This method was compared to standard rotation techniques and is shown to be superior to standard techniques when measured by these qualities. The computational cost was demonstrated to be only slightly more than bilinear interpolation     

Errors in reprojection methods in computenzed tomography

Iterative tomographic reconstruction methods have been developed which can enforce various physical constraints on the reconstructed image. An integral part of most of these methods is the repro. jection of the reconstructed image. These estimated projections are compared to the original projection data and modified according to some criteria based on a priori constraints. In this paper, the errors generated by such reprojection schemes are investigated. Bounds for these errors are derived under simple signal energy assumptions and using probabilistic assumptions on the distribution of discontinuities. These bounds can be used in the enforcement of constraints, in the determination of convergence of the iterative methods, and in the detection of artifacts.     

Study on infrared image super-resolution reconstruction based on an improved POCS algorithm

Aiming at the disadvantages of the traditional projection onto convex sets of blurry edges and lack of image details, this paper proposes an improved projection onto convex sets (POCS) method to enhance the quality of image super-resolution reconstruction (SRR). In traditional POCS method, bilinear interpolation easily blurs the image. In order to improve the initial estimation of high-resolution image (HRI) during reconstruction of POCS algorithm, the initial estimation of HRI is obtained through iterative curvature-based interpolation (ICBI) instead of bilinear interpolation. Compared with the traditional POCS algorithm, the experimental results in subjective evaluation and objective evaluation demonstrate the effectiveness of the proposed method. The visual effect is improved significantly and image detail information is preserved better.     

An Improved Algorithm for Reprojecting Rays through Pixel Images

It is often desired to calculate line integrals through a field of reconstructed CT density pixels for the purpose of improving CT image quality. Two algorithms widely published and discussed in the past are known to either degrade spatial resolution or generate errors in the results due to the discontinuous "square pixel" modeling of the reconstructed image. An algorithm is described, based on linear interpolation between pixels, which provides superior accuracy without unnecessary loss of resolution. It was tested on simulated data for a head section and on a narrow Gaussian density distribution. The experimental results demonstrated improved performance. The method is expected to prove useful for many types of post-reconstruction processing, including beam hardening, missing data, and noise supression algorithms.     

H.26L中基于双线性和立方卷积的混合插值算法

本文根据对H.26L视频编码算法的研究,提出一种基于双线性和立方卷积的混合插值算法.通过分析比较H.26L中的双线性插值和立方卷积插值,指出单一插值算法的局限性,并在H.26L软件编解码中实现了混合插值算法.实验表明,该算法可以较大幅度改善主客观图像质量,提高图像压缩编码效率.     

小波域多角度轮廓模板变分模型的单幅图像超分辨率重建

近年来图像超分辨率重建技术因其可以提高图像的识别精度和识别能力而受到重视,其中一个难点问题是如何保证图像边缘纹理区域的重建质量.本文提出一种基于小波域的单幅图像超分辨率重建方法,首先对输入图像进行非下采样小波变换,根据小波变换的多方向性提出三类多角度模板,并采用TV模型估计各子带轮廓,确定其所属的最优方向,然后利用多角度模板来对各个子带进行双三次B样条插值,最后进行非下采样小波反变换.该方法使重建后图像的边缘、纹理信息更加精细,克服了诸如双线性插值法与双三次插值法等传统插值重建所产生的边缘模糊与边缘锯齿化,以及纹理区域失真等不足,在一定程度上提高了重建图像的质量.该方法可用于图像监控、遥感影像分析和医学图像处理等领域.大量的仿真实验验证了所提出方法的有效性.     

Heart sound cancellation based on multiscale products and linear prediction

This paper presents a novel method for Heart Sound (HS) cancellation from Lung Sound (LS) records. The method uses the multiscale product of the wavelet coefficients of the original signal to detect HS-included segments. Once the HS segments are identified, the method removes them from the wavelet coefficients at every level and estimates the created gaps by using a set of linear prediction filters. It is shown that if the segment to be predicted is stationary, a final record with no audible artifacts such as clicks can be reconstructed using this approach. The results were promising for HS removal from LS records and showed no hampering of the main components of the LS. The results were confirmed both qualitatively by listening to the reconstructed signal and quantitatively by spectral analysis.     

Preliminary study on helical CT algorithms for patient motion estimation and compensation

Helical computed tomography (helical/spiral CT) has replaced conventional CT in many clinical applications. In current helical CT, a patient is assumed to be rigid and motionless during scanning and planar projection sets are produced from raw data via longitudinal interpolation. However, rigid patient: motion is a problem in some cases (such as in the skull base and temporal bone imaging). Motion artifacts thus generated in reconstructed images can prevent accurate diagnosis. Modeling a uniform translational movement, the authors address how patient motion is ascertained and how it may be compensated. First, mismatch between adjacent fan-beam projections of the same orientation is determined via classical correlation, which is approximately proportional to the patient displacement projected onto an axis orthogonal to the central ray of the involved fan-beam. Then, the patient motion vector (the patient displacement per gantry rotation) is estimated from its projections using a least-square-root method. To suppress motion artifacts, adaptive interpolation algorithms are developed that synthesize full-scan and half-scan planar projection data sets, respectively. In the adaptive scheme, the interpolation is performed along inclined paths dependent upon the patient motion vector. The simulation results show that the patient motion vector can be accurately and reliably estimated using the authors' correlation and least-square-root algorithm, patient motion artifacts can be effectively suppressed via adaptive interpolation, and adaptive half-scan interpolation is advantageous compared with its full-scan counterpart in terms of high contrast image resolution.     

使用方向参数的双三次图像内插方法

传统的双三次内插方法仅在水平和垂直方向估计丢失的像素,易在边缘或纹理区域产生抖动、振铃等现象。为了克服这种现象,提出一种新的使用方向参数的双三次图像内插方法。对待内插的像素,首先在其邻域计算水平、垂直、45°和135°四个方向的梯度,以提取图像局部边缘的强度和方向。对强边缘上的像素,直接沿边缘方向采用对应方向的双三次内插模型估计像素值;否则,先分别沿梯度较大的两个方向采用相应方向的双三次内插模型估计像素,然后采用适当的权系数对所得结果加权平均。此外,为减小计算复杂度,方法中还根据图像局部方差的大小,动态使用双线性内插方法。相比于双三次内插,提出的方法能有效地保存图像边缘和细节;同时,提出的方法还能够实现任意倍数的放大。实验结果表明,与现有的边缘导向的图像内插方法相比,提出的方法具有更好的主观和客观效果,同时计算复杂度并不高。      

基于协方差特征的图像内插方法的研究

本文提出了一种改进图像插值的方法,该方法通过提取低分辨率图像的协方差矩阵和协方差向量估算出高分辨率图像的相应参数,然后经过内插得到高分辨率图像。其突出优点是它的边缘自适应特性。经过实验证明,用这种方法获得的高分辨率图像和其他方法相比,图像的峰值信噪比(ＰＳＮＲ)有显著提高。在图像边缘模糊和阶梯形失真不仅比传统的线性内插方法有所降低,即使与基于凸面投影(ＰＯＣＳ)等方法相比图像也更加清晰。同时由于算法避免了复杂的迭代计算,因而运算的速度更快。     

多线阵CCD亚像元成像超分辨率重构技术研究

为实现以多片线阵CCD亚像元成像为基础,提出一种超分辨率重构算法。首先,在高分辨率网格上建立插值模型;然后,辨识插值重构图像在线阵列方向和扫描方向的模糊核,得到整幅图像的模糊核;最后,采用带有Neumman边界条件(BCs)的梯度平滑Richard-Lucy(GSRL)滤波复原算法去除模糊,抑制了振铃效应。实验结果表明,用本文算法重构超分辨率图像的灰度平均梯度(GMG)值较双线性插值法提高了7.63,主观目视清晰、细节丰富;可以实现对多片线阵CCD亚像元成像的超分辨率重构,获取更高的系统分辨率。     

Color reproduction from noisy CFA data of single sensor digital cameras.

Single sensor digital color still/video cameras capture images using a color filter array (CFA) and require color interpolation (demosaicking) to reconstruct full color images. The color reproduction has to combat sensor noises which are channel dependent. If untreated in demosaicking, sensor noises can cause color artifacts that are hard to remove later by a separate denoising process, because the demosaicking process complicates the noise characteristics by blending noises of different color channels. This paper presents a joint demosaicking-denoising approach to overcome this difficulty. The color image is restored from noisy mosaic data in two steps. First, the difference signals of color channels are estimated by linear minimum mean square-error estimation. This process exploits both spectral and spatial correlations to simultaneously suppress sensor noise and interpolation error. With the estimated difference signals, the full resolution green channel is recovered. The second step involves in a wavelet-based denoising process to remove the CFA channel-dependent noises from the reconstructed green channel. The red and blue channels are subsequently recovered. Simulated and real CFA mosaic data are used to evaluate the performance of the proposed joint demosaicking-denoising scheme and compare it with many recently developed sophisticated demosaicking and denoising schemes.     

一种边缘定向平滑图像插值算法

针对传统图像放大算法边缘处理效果较差,自适应图像插值方法存在高计算复杂度的问题,该文提出一种有效增强图像边缘轮廓的插值放大算法。该算法结合边缘定向平滑滤波器和双线性插值的特点,使得图像在平坦和非平坦区域均能取得理想效果。仿真测试结果表明,与基于统计特征的自适应插值算法相比,该文提出算法能显著提高插值速度,平均运行时间降低8.33 s;与双三次插值算法相比,图像峰值信噪比平均增加0.30 dB。