Quantitative rotating multisegment slant-hole SPECT mammography with attenuation and collimator-detector response compensation

Rotating multisegment slant-hole (RMSSH) single photon emission computed tomography (SPECT) is suitable for detecting small and low-contrast breast lesions since it has much higher detection efficiency than conventional SPECT with a parallel-hole collimator and can image the breast at a closer distance. Our RMSSH SPECT reconstruction extends a previous rotation-shear transformation-based method to include nonuniform attenuation and collimator-detector response (CDR) compensation. To evaluate our reconstruction method, we performed two phantom simulation studies with 1) an isolated breast and 2) a breast phantom attached to the body torso. The reconstructed RMSSH SPECT images with attenuation and CDR compensation showed improved quantitative accuracy and less image artifacts than without. To evaluate the clinical efficacy of RMSSH SPECT mammography, we used a simulation study to compare with planar scintimammography in terms of the signal-to-noise ratio (SNR) value of a breast lesion. The RMSSH SPECT reconstruction images showed higher SNR value than the planar scintimammography images and even more so as we applied compensation for attenuation and collimator detector response. We conclude that attenuation and CDR compensation provide RMSSH SPECT mammography images with improved quality and quantitative accuracy.     

A confocal microwave imaging algorithm for breast cancer detection

We present a computationally efficient and robust image reconstruction algorithm for breast cancer detection using an ultrawideband confocal microwave imaging system. To test the efficacy of this approach, we have developed a two-dimensional (2-D) anatomically realistic MRI-derived FDTD model of the cancerous breast. The image reconstruction algorithm is applied to FDTD-computed backscatter signals, resulting in a microwave image that clearly identifies the presence and location of the malignant lesion. These simulations demonstrate the feasibility of detecting and imaging small breast tumors using this novel approach     

Spatially variant apodization for image reconstruction from partialFourier data

Sidelobe artifacts are a common problem in image reconstruction from finite-extent Fourier data. Conventional shift-invariant windows reduce sidelobe artifacts only at the expense of worsened mainlobe resolution. Spatially variant apodization (SVA) was previously introduced as a means of reducing sidelobe artifacts, while preserving mainlobe resolution. Although the algorithm has been shown to be effective in synthetic aperture radar (SAR), it is heuristically motivated and it has received somewhat limited analysis. In this paper, we show that SVA is a version of minimum-variance spectral estimation (MVSE). We then present a complete development of the four types of two-dimensional SVA for image reconstruction from partial Fourier data. We provide simulation results for various real-valued and complex-valued targets and point out some of the limitations of SVA. Performance measures are presented to help further evaluate the effectiveness of SVA.     

Curvature preserving image super-resolution with gradient-consistency-anisotropic-regularization prior

Single image super-resolution (SR) often suffers from annoying interpolation artifacts such as blur, jagged edges, and ringing. In this paper, we aim to achieve artifact-free SR reconstruction from an input low resolution (LR) image using adaptive de-convolution and curvature refinement. To achieve this, we propose a curvature preserving image SR method based on a gradient-consistency-anisotropic-regularization (GCAR) prior. The gradient consistency term effectively suppresses visual artifacts such as ringing and preserves sharp edges in images while the anisotropic regularization term adaptively preserves the high frequency information according to the gradient magnitude. The complementary two terms are elaborately combined into the GCAR prior for the SR reconstruction. The GCAR prior is very effective in preserving image details and recovering high frequency information. Moreover, we use curvature refinement to remove jagged artifacts caused by aliasing due to decimation. The proposed method employs an effective feedback-control loop which contains adaptive de-convolution, re-convolution, pixel substitution, and curvature refinement. The GCAR prior is utilized in the adaptive de-convolution step. Extensive experiments on various test images demonstrate that the proposed method produces natural-looking and artifact-free SR results in terms of both visual quality and quantitative performance.     

Improved model-based magnetic resonance spectroscopic imaging

Model-based techniques have the potential to reduce the artifacts and improve resolution in magnetic resonance spectroscopic imaging, without sacrificing the signal-to-noise ratio. However, the current approaches have a few drawbacks that limit their performance in practical applications. Specifically, the classical schemes use less flexible image models that lead to model misfit, thus resulting in artifacts. Moreover, the performance of the current approaches is negatively affected by the magnetic field inhomogeneity and spatial mismatch between the anatomical references and spectroscopic imaging data. In this paper, we propose efficient solutions to overcome these problems. We introduce a more flexible image model that represents the signal as a linear combination of compartmental and local basis functions. The former set represents the signal variations within the compartments, while the latter captures the local perturbations resulting from lesions or segmentation errors. Since the combined set is redundant, we obtain the reconstructions using sparsity penalized optimization. To compensate for the artifacts resulting from field inhomogeneity, we estimate the field map using alternate scans and use it in the reconstruction. We model the spatial mismatch as an affine transformation, whose parameters are estimated from the spectroscopy data.     

声折射对光声成像的影响

研究了光声信号声速失配时所起的声折射对光声成像的影响,提出了利用与组织声速匹配的耦合液进行光声成像,并配置了几种适合于组织声速的超声耦合液.实验结果表明,进行声速匹配后重建出的图像对比度有明显地提高,背景伪迹减少,分辨率由0.50 mm提高到0.15 mm,重建图像与实物十分吻合.     

Variable pitch reconstruction using John's equation

We present an algorithm to reconstruct helical cone beam computed tomography (CT) data acquired at variable pitch. The algorithm extracts a halfscan segment of projections using an extended version of the advanced single slice rebinning (ASSR) algorithm. ASSR rebins constant pitch cone beam data to fan beam projections that approximately lie on a plane that is tilted to optimally fit the source helix. For variable pitch, the error between the tilted plane chosen by ASSR and the source helix increases, resulting in increased image artifacts. To reduce the artifacts, we choose a reconstruction plane, which is tilted and shifted relative to the source trajectory. We then correct rebinned fan beam data using John's equation to virtually move the source into the tilted and shifted reconstruction plane. Results obtained from simulated phantom images and scanner images demonstrate the applicability of the proposed algorithm.      

Half-time image reconstruction in thermoacoustic tomography

Thermoacoustic tomography (TAT) is an emerging imaging technique with great potential for a wide range of biomedical imaging applications. In this paper, we propose and investigate reconstruction approaches for TAT that are based on the half-time reflectivity tomography paradigm. We reveal that half-time reconstruction approaches permit for the explicit control of statistically complementary information that can result in the optimal reduction of image variances. We also show that half-time reconstruction approaches can mitigate image artifacts due to heterogeneous acoustic properties of an object. Reconstructed images and numerical results produced from simulated and experimental TAT measurement data are employed to demonstrate these effects.     

Spatially adaptive high-resolution image reconstruction of DCT-based compressed images

The problem of recovering a high-resolution image from a sequence of low-resolution DCT-based compressed observations is considered in this paper. The introduction of compression complicates the recovery problem. We analyze the DCT quantization noise and propose to model it in the spatial domain as a colored Gaussian process. This allows us to estimate the quantization noise at low bit-rates without explicit knowledge of the original image frame, and we propose a method that simultaneously estimates the quantization noise along with the high-resolution data. We also incorporate a nonstationary image prior model to address blocking and ringing artifacts while still preserving edges. To facilitate the simultaneous estimate, we employ a regularization functional to determine the regularization parameter without any prior knowledge of the reconstruction procedure. The smoothing functional to be minimized is then formulated to have a global minimizer in spite of its nonlinearity by enforcing convergence and convexity requirements. Experiments illustrate the benefit of the proposed method when compared to traditional high-resolution image reconstruction methods. Quantitative and qualitative comparisons are provided.     

Image Reconstruction in a Wide Aspect Ratio HDTV System

One of the important characteristics of a high-definition television (HDTV) image is a wide aspect ratio. In some HDTV systems the image is segmented and different formats are used to transmit portions of the image. We address the problem of image reconstruction at the receiver and present an analysis of a wide aspect ratio HDTV system. Several image reconstruction techniques are described, including a channel-dependent design, and their effectiveness discussed. We also present a channel-independent system for image reconstruction that utilizes a vertical interval test line. Performance has been evaluated, via computer simulation, using still images. The results indicate an imperceptible amount of artifacts at the edge-center joints with an overlap as small as one picture element for the channel-independent reconstruction technique.     

Time-resolved interventional cardiac C-arm cone-beam CT: an application of the PICCS algorithm

Time-resolved cardiac imaging is particularly interesting in the interventional setting since it would provide both image guidance for accurate procedural planning and cardiac functional evaluations directly in the operating room. Imaging the heart in vivo using a slowly rotating C-arm system is extremely challenging due to the limitations of the data acquisition system and the high temporal resolution required to avoid motion artifacts. In this paper, a data acquisition scheme and an image reconstruction method are proposed to achieve time-resolved cardiac cone-beam computed tomography imaging with isotropic spatial resolution and high temporal resolution using a slowly rotating C-arm system. The data are acquired within 14 s using a single gantry rotation with a short scan angular range. The enabling image reconstruction method is the prior image constrained compressed sensing (PICCS) algorithm. The prior image is reconstructed from data acquired over all cardiac phases. Each cardiac phase is then reconstructed from the retrospectively gated cardiac data using the PICCS algorithm. To validate the method, several studies were performed. Both numerical simulations using a hybrid motion phantom with static background anatomy as well as physical phantom studies have been used to demonstrate that the proposed method enables accurate reconstruction of image objects with a high isotropic spatial resolution. A canine animal model scanned in vivo was used to further validate the method.     

Suppression of Metal Artifacts in CT Using a Reconstruction Procedure That Combines MAP and Projection Completion

Metal implants such as hip prostheses and dental fillings produce streak and star artifacts in the reconstructed computed tomography (CT) images. Due to these artifacts, the CT image may not be diagnostically usable. A new reconstruction procedure is proposed that reduces the streak artifacts and that might improve the diagnostic value of the CT images. The procedure starts with a maximum a posteriori (MAP) reconstruction using an iterative reconstruction algorithm and a multimodal prior. This produces an artifact-free constrained image. This constrained image is the basis for an image-based projection completion procedure. The algorithm was validated on simulations, phantom and patient data, and compared with other metal artifact reduction algorithms.      

抑制寄生波纹的超分辨率图像复原

分析了超分辨率图像复原中寄生波纹产生的原因及其对图像不同区域的影响情况,提出了一种能够抑制寄生波纹、自适应的正则化参数计算方法,并在此基础上构建了自适应的正则化函数,形成了一种抑制寄生波纹的新方法.实验结果表明,本文方法在抑制寄生波纹的效果上明显优于Lee方法.     

Aperture collimation correction and maximum-likelihood image reconstruction for near-field coded aperture imaging of single photon emission computerized tomography

Coded aperture (CA) imaging originally developed in X-ray astronomy has not been widely used in nuclear medicine due to the decoding complexity of near-field CA images. In this paper, we present a near-field CA imaging technique and image reconstruction method for high sensitivity and high resolution single photon emission computerized tomography (SPECT). Our approach makes three contributions. First, a correction method for the aperture collimation effect is used to eliminate the near-field artifacts without dual acquisitions of mask and anti-mask images. Second, a maximum-likelihood expectation-maximization (MLEM) deconvolution method is used to restore CA images. Finally, a new MLEM-based algorithm is used to partially reconstruct three-dimensional (3-D) objects from a single projection of CA images. Experiments conducted using a dual-head SPECT system equipped with a parallel-hole collimator and a CA module show a tenfold increase in count sensitivity and significant improvement in image resolution with CA collimation as compared to parallel-hole collimation. Experiments conducted using the same dual-head SPECT system equipped with a pinhole collimator show that when the object is closer to the pinhole collimator the CA image resolution is only slightly inferior to the pinhole collimated image. We found that the MLEM deconvolution method provides an inherent nonnegativity constraint on pixel values and remarkably reduces background activities of CA images. The MLEM reconstruction algorithm for CA images is capable of reconstructing 3-D objects from a single projection and can be potentially extended to full 3-D SPECT reconstruction for CA images.     

Fluorescence-enhanced optical tomography using referenced measurements of heterogeneous media

A three-dimensional image reconstruction for fluorescence-enhanced frequency-domain photon migration (FDPM) measurements in turbid media is developed and investigated for three different simulated measurement types: 1) absolute emission measurement, or emission measurements of phase and amplitude attenuation made for a given incident point source of excitation light; 2) referenced emission measurements made relative to an excitation measurement conducted at a single reference point away from the incident source; and 3) referenced emission measurements made relative to the excitation measurement conducted at identical points of detection. The image reconstruction algorithm employs a gradient-based constrained truncated Newton (CONTN) method which implements a bounding parameter, which can be used to govern the level of contrast used to discriminate tissue volumes from heterogeneous background tissues. Reverse differentiation technique is used to calculate the gradients. Using simulated data with superimposed noise to achieve a signal-to-noise ratio of 55 and 35 dB to mimic experimental excitation and emission FDPM measurements, respectively, we show the robustness of emission measurements referenced to excitation light. We investigate the performance of algorithm CONTN using these measurement techniques and show that the absorption coefficients due to fluorophore are reconstructed by CONTN accurately and efficiently. Furthermore, we demonstrate the performance of the bounding parameter for rejection of background artifacts owing to background tissue heterogeneity.     

Robust linearized image reconstruction for multifrequency EIT of the breast

Electrical impedance tomography (EIT) is a developing imaging modality that is beginning to show promise for detecting and characterizing tumors in the breast. At Rensselaer Polytechnic Institute, we have developed a combined EIT-tomosynthesis system that allows for the coregistered and simultaneous analysis of the breast using EIT and X-ray imaging. A significant challenge in EIT is the design of computationally efficient image reconstruction algorithms which are robust to various forms of model mismatch. Specifically, we have implemented a scaling procedure that is robust to the presence of a thin highly-resistive layer of skin at the boundary of the breast and we have developed an algorithm to detect and exclude from the image reconstruction electrodes that are in poor contact with the breast. In our initial clinical studies, it has been difficult to ensure that all electrodes make adequate contact with the breast, and thus procedures for the use of data sets containing poorly contacting electrodes are particularly important. We also present a novel, efficient method to compute the Jacobian matrix for our linearized image reconstruction algorithm by reducing the computation of the sensitivity for each voxel to a quadratic form. Initial clinical results are presented, showing the potential of our algorithms to detect and localize breast tumors.      

Single image reflection removal based on structure-texture layering

In computer vision, the reflections impair the overall perception of images and introduce undesired interference to high-level image understanding. To date, reflection removal approaches have been developed with advantage of visual quality enhancement. However, the reflection removal for single image is still a challenging task with its performance to be further boosted. During this process, additional visual artifacts such as blocking may also be introduced that degrades subjective quality of images. In this paper, we make the first attempt to eliminate image reflections and artifacts simultaneously within a unified framework. A structure-texture strategy is utilized to decompose the image into structure and texture layers. The proposed model handles reflection removal in both layers and further suppress blocking artifacts in the texture layer. Finally, a fusion process is employed to obtain the result image. Experiments with objective and subjective evaluations validate the superiority of the proposed method, which outperforms several state-of-the-art algorithms.     

采用超分辨率重建提升压缩图像质量的方法

针对JPEG的中低码率压缩图像即高压缩率图像存在较严重的块效应以及量化噪声,提出了一种对JPEG标准压缩图像进行优化的重建-采样方法.该方法对JPEG压缩图像采用三维块匹配算法(BM3D)进行去噪,去除图像中存在的块效应和量化噪声,进而提高超分辨率重建的映射准确性,再使用外部库对去噪后图像进行基于稀疏表示的超分辨率重建,补充一定的高频信息,最后对重建后的高分辨率图进行双三次下采样,得到与原始图像大小一致的图像作为最终优化图像.实验结果表明,该方法在中低码率情况下能够有效地提高JPEG压缩图像的质量,对高码率压缩图像也有一定效果.     

多值压缩编码孔径超分辨率成像方法

针对近期提出的基于压缩感知(CS,compressed sensing)理论的压缩编码成像方法在重建后引入较多类似于噪声的伪影(artitacts)问题,为了使压缩编码成像方法获得更好成像质量的图像,本文提出一种改进的压缩编码成像方法。本文方法将多值模板(MVM)代替二值模板来增强编码质量,并利用自适应全变分(TV,total variation)去噪方法去除重建后的高分辨率图像的伪影。实验结果表明,这种方法很好地改进了压缩编码孔径(CCA)的成像质量,并且大幅提高了图像的信噪比(SNR)。     

A Document Image Model and Estimation Algorithm for Optimized JPEG Decompression

The JPEG standard is one of the most prevalent image compression schemes in use today. While JPEG was designed for use with natural images, it is also widely used for the encoding of raster documents. Unfortunately, JPEG's characteristic blocking and ringing artifacts can severely degrade the quality of text and graphics in complex documents. We propose a JPEG decompression algorithm which is designed to produce substantially higher quality images from the same standard JPEG encodings. The method works by incorporating a document image model into the decoding process which accounts for the wide variety of content in modern complex color documents. The method works by first segmenting the JPEG encoded document into regions corresponding to background, text, and picture content. The regions corresponding to text and background are then decoded using maximum a posteriori (MAP) estimation. Most importantly, the MAP reconstruction of the text regions uses a model which accounts for the spatial characteristics of text and graphics. Our experimental comparisons to the baseline JPEG decoding as well as to three other decoding schemes, demonstrate that our method substantially improves the quality of decoded images, both visually and as measured by PSNR.