Two interpolating filters for scatter estimation

We have previously reported on a dual-measurement sample-and-estimate technique for scatter correction. In this paper, we present a scatter-correction technique that uses the previous sampling scheme but a different method of estimation. To provide samples of the scatter directly, an array of small, uniformly spaced lead disks is placed immediately before the object during only the first measurement. Interpolating from these samples we form an estimate of the scatter. We subtract this estimate from the second measurement to form a scatter-corrected image. Previously, we used least-squares interpolation to estimate the scatter. Because the samples are uniformly spaced, classical sampling theory motivated the investigation of interpolating filters for scatter estimation. To form the scatter image, we convolved the sample set with two different interpolating filters--a sinc function from classical sampling theory and a jinc function because the scatter function is radially symmetric. Using phantoms as objects, we applied both filters for scatter correction in vessel imaging and energy-subtraction imaging. Initial corrected images contained an artifact attributed to aliasing. We modified the filter widths to reduce the aliasing. Although improvements in image quality were measured and the artifact was less pronounced, the artifact was still present. We present the phantom results obtained with this class of filters and discuss methods for its improved performance.     

Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT

In this study, the super-resolution convolutional neural network (SRCNN) scheme, which is the emerging deep-learning-based super-resolution method for enhancing image resolution in chest CT images, was applied and evaluated using the post-processing approach. For evaluation, 89 chest CT cases were sampled from The Cancer Imaging Archive. The 89 CT cases were divided randomly into 45 training cases and 44 external test cases. The SRCNN was trained using the training dataset. With the trained SRCNN, a high-resolution image was reconstructed from a low-resolution image, which was down-sampled from an original test image. For quantitative evaluation, two image quality metrics were measured and compared to those of the conventional linear interpolation methods. The image restoration quality of the SRCNN scheme was significantly higher than that of the linear interpolation methods (p < 0.001 or p < 0.05). The high-resolution image reconstructed by the SRCNN scheme was highly restored and comparable to the original reference image, in particular, for a ×2 magnification. These results indicate that the SRCNN scheme significantly outperforms the linear interpolation methods for enhancing image resolution in chest CT images. The results also suggest that SRCNN may become a potential solution for generating high-resolution CT images from standard CT images.     

基于三次样条层间插值的多模态医学图像配准

我们从PET-CT多模态图像序列的特点出发,提出了一种全新的图像配准及融合方法,它采用三次样条插值法对PET-CT图像进行层间插值,然后再利用最大互信息法进行配准,最后应用改进的主成分分析(PCA)法融合PET-CT图像用以增强PET显像效果,从而得到满意的配准以及融合结果。用三次样条插值法进行层间插值并恢复层间缺失图像的信息,弥补了现有配准方法的不足,提高了配准精度,使融合后的图像更加接近实际的物理断层。该方法已经成功应用于三维适形放疗(3D-CRT)系统的开发中。     

Modifying constrained least-squares restoration for application to single photon emission computed tomography projection images

Image restoration methods have been shown to increase the contrast of nuclear medicine images by decreasing the effects of scatter and septal penetration. Image restoration can also reduce the high-frequency noise in the image. This study applies constrained least-squares (CLS) restoration to the projection images of single photon emission computed tomography (SPECT). In a previous study, it was noted that CLS restoration has the potential advantage of automatically adapting to the blurred object. This potential is confirmed using planar images. CLS restoration is then modified to improve its performance when applied to SPECT projection image sets. The modification was necessary because the Poisson noise in low count SPECT images causes considerable variation in the CLS filter. On phantom studies, count-dependent Metz restoration was slightly better than the modified CLS restoration method, according to measures of contrast and noise. However, CLS restoration was generally judged as yielding the best results when applied to clinical studies, apparently because of its ability to adapt to the image being restored.     

基于运动估计的肺4D-CT图像冠矢状面超分辨率重建

肺4D-CT在肺癌放射治疗中发挥着重要的作用,但肺4D-CT数据层间的分辨率低,导致每个相位3D数据的肺冠矢状面均为低分辨率图像。本研究提出一种基于运动估计的超分辨率重建技术,以提高3D数据的冠矢状面图像分辨率。首先,分析图像退化模型;然后,采用基于完全搜索块匹配的运动估计法,估计出不同“帧”肺冠矢状面图像之间的运动场;最后,以此运动场为基础,采用迭代反投影法（IBP）,重建高分辨率的肺部冠矢状面图像。使用一个公共可用的数据集来评价所提出的算法,该数据集由10组肺4D-CT数据组成,每组数据包含10个相位。在每组图像中,选取不同相位的冠矢状面图像进行实验。结果表明,与传统的插值方法（如最近邻插值、双线性插值法）相比,图像边缘宽度均显著降低（最近邻插值9.93±0.59,双线性插值8.04±0.69,新算法5.41±0.60, P<0.001）;较双线性插值,图像平均梯度显著提高（5.41±0.59 vs 7.49±0.75, P<0.001）,新方法不仅能获得视觉上清晰的图像,而且量化评价指标也有明显提高。主观和客观实验结果表明,所提出的新方法能有效提高肺4D-CT冠矢状面图像的分辨率。     

Development of a fully automatic scheme for detection of masses in whole breast ultrasound images

Ultrasonography has been used for breast cancer screening in Japan. Screening using a conventional hand-held probe is operator dependent and thus it is possible that some areas of the breast may not be scanned. To overcome such problems, a mechanical whole breast ultrasound (US) scanner has been proposed and developed for screening purposes. However, another issue is that radiologists might tire while interpreting all images in a large-volume screening; this increases the likelihood that masses may remain undetected. Therefore, the aim of this study is to develop a fully automatic scheme for the detection of masses in whole breast US images in order to assist the interpretations of radiologists and potentially improve the screening accuracy. The authors database comprised 109 whole breast US imagoes, which include 36 masses (16 malignant masses, 5 fibroadenomas, and 15 cysts). A whole breast US image with 84 slice images (interval between two slice images: 2 mm) was obtained by the ASU-1004 US scanner (ALOKA Co., Ltd., Japan). The feature based on the edge directions in each slice and a method for subtracting between the slice images were used for the detection of masses in the authors proposed scheme. The Canny edge detector was applied to detect edges in US images; these edges were classified as near-vertical edges or near-horizontal edges using a morphological method. The positions of mass candidates were located using the near-vertical edges as a cue. Then, the located positions were segmented by the watershed algorithm and mass candidate regions were detected using the segmented regions and the low-density regions extracted by the slice subtraction method. For the removal of false positives (FPs), rule-based schemes and a quadratic discriminant analysis were applied for the distribution between masses and FPs. As a result, the sensitivity of the authors scheme for the detection of masses was 80.6% (29/36) with 3.8 FPs per whole breast image. The authors scheme for a computer-aided detection may be useful in improving the screening performance and efficiency.     

Accurate calibration of a stereo-vision system in image-guided radiotherapy

Image-guided radiotherapy using a three-dimensional (3D) camera as the on-board surface imaging system requires precise and accurate registration of the 3D surface images in the treatment machine coordinate system. Two simple calibration methods, an analytical solution as three-point matching and a least-squares estimation method as multipoint registration, were introduced to correlate the stereo-vision surface imaging frame with the machine coordinate system. Both types of calibrations utilized 3D surface images of a calibration template placed on the top of the treatment couch. Image transformational parameters were derived from corresponding 3D marked points on the surface images to their given coordinates in the treatment room coordinate system. Our experimental results demonstrated that both methods had provided the desired calibration accuracy of 0.5 mm. The multipoint registration method is more robust particularly for noisy 3D surface images. Both calibration methods have been used as our weekly QA tools for a 3D image-guided radiotherapy system.     

基于互信息的人脑图像配准研究

近来利用互信息进行多模医学图像配准已成为医学图像处理领域的热点,人脑多模医学图像配准对研究神经组织的结构功能关系和引导神经外科手术有着重要的指导意义,本文描述了一种基于互信息的人脑图像配准方法,我们将这种方法应用于图像的几何对准并给出了初步的评估结果,同时,我们还就归一化互信息、多分辨率策略,多种插值和优化算法对配准速度和精度的影响作了讨论,由于不需要对不同成像模式下的图像灰度间的关系作任何假设,互信息法是一种稳健性强、可广泛应用于基于体素的多模医学图像的配准方法。     

Hiding clinical information in medical images: A new high capacity and reversible data hiding technique

A new high capacity and reversible data hiding scheme for e-healthcare applications has been presented in this paper. Pixel to Block (PTB) conversion technique has been used as an effective and computationally efficient alternative to interpolation for the cover image generation to ensure reversibility of medical images. A fragile watermark and Block Checksum (computed for each 4 × 4 block) have been embedded in the cover image for facilitating tamper detection and tamper localization, and hence content authentication at receiver. The EPR, watermark data and checksum data has been embedded using Intermediate Significant Bit Substitution (ISBS) to avoid commonly used LSB removal/replacement attack. Non-linear dynamics of chaos have been put to use for encrypting the Electronic Patient Record (EPR)/clinical data and watermark data for improving the security of data embedded. The scheme has been evaluated for perceptual imperceptibility and tamper detection capability by subjecting it to various image processing and geometric attacks. Experimental results reveal that the proposed system besides being completely reversible is capable of providing high quality watermarked images for fairly high payload. Further, it has been observed that the proposed technique is able to detect and localise the tamper. A comparison of the observed results with that of some state-of-art schemes show that our scheme performs better.     

Subunity coordinate translation with Fourier transform to achieve efficient and quality three-dimensional medical image interpolation.

A new approach to the interpolation of three-dimensional (3D) medical images is presented. Instead of going through the conventional interpolation scheme where the continuous function is first reconstructed from the discrete data set and then resampled, the interpolation is achieved with a subunity coordinate translation technique. The original image is first transformed into the spatial-frequency domain. The phase of the transform is then modified with n-1 linear phase terms in the axial direction to achieve n-1 subunity coordinate translations with a distance 1/n, where n is an interpolation ratio, following the phase shift theorem of Fourier transformation. All the translated images after inverse Fourier transformation are then interspersed in turn into the original image. Since windowing plays an important role in the process, different window functions have been studied and a proper recommendation is provided. The interpolation quality produced with the present method is as good as that with the sampling (sinc) function, while the efficiency, thanks to the fast Fourier transformation, is very much improved. The approach has been validated with both computed tomography (CT) and magnetic resonance (MR) images. The interpolations of 3D CT and MR images are demonstrated.     

空间域增强方法在CBCT中的应用研究

CBCT具有空间分辨率高,射线剂量少等许多优点,但是其散射伪影较大,图像对比度较差。为了使CBCT技术可以更好地应用于临床,应该致力于提高改善CBCT图像质量。该研究主要针对空间域的图像增强展开探索,把传统的、效果明显的图像增强法结合到CBCT的图像中,弥补CBCT图像的不足,提高图像的质量。并就各种图像处理的方法总结出各自的优缺点,提出今后改善CBCT图像质量,需要结合多种方法的优势融合,同时使用多种数学工具,再加经典传统的算法对图像进行增强处理。     

Enhancing the performance of model-based elastography by incorporating additional a priori information in the modulus image reconstruction process

Model-based elastography is fraught with problems owing to the ill-posed nature of the inverse elasticity problem. To overcome this limitation, we have recently developed a novel inversion scheme that incorporates a priori information concerning the mechanical properties of the underlying tissue structures, and the variance incurred during displacement estimation in the modulus image reconstruction process. The information was procured by employing standard strain imaging methodology, and introduced in the reconstruction process through the generalized Tikhonov approach. In this paper, we report the results of experiments conducted on gelatin phantoms to evaluate the performance of modulus elastograms computed with the generalized Tikhonov (GTK) estimation criterion relative to those computed by employing the un-weighted least-squares estimation criterion, the weighted least-squares estimation criterion and the standard Tikhonov method (i.e., the generalized Tikhonov method with no modulus prior). The results indicate that modulus elastograms computed with the generalized Tikhonov approach had superior elastographic contrast discrimination and contrast recovery. In addition, image reconstruction was more resilient to structural decorrelation noise when additional constraints were imposed on the reconstruction process through the GTK method.     

基于调幅-调频与熵图多模态医学图像的配准

背景：基于传统互信息量的多模态医学图像配准方法配准时需要利用二维直方图或者Parzen窗函数的方法估计概率密度分布,进而计算互信息量,这种方式计算速度慢,而且只考虑了图像的灰度信息,容易出现误配。 目的：针对目前主流的配准方法鲁棒性差、耗时的缺点,提出了一种新的基于调幅-调频(AM-FM)特征互信息量的快速配准方法。 方法：该方法考虑了图像的空间和结构信息;首先通过AM-FM模型对图像进行分解,得到图像的AM-FM特征,与图像的灰度特征一起组成高维特征;然后利用熵图和最小生成树加快AM-FM特征互信息量的计算,从而实现了医学图像的快速配准。 结果与结论：对20组磁共振T1-T2加权图像、CT/正电子发射计算机断层成像图像进行了实验,结果表明该方法在图像空间分辨率较低,有噪声影响等情况下均可以达到较好的结果,且配准精度优于国际上的主流方法,具有计算速度快,精度高,鲁棒性强的特点,适于临床应用。     

Shape-based grey-level image interpolation.

The three-dimensional (3D) object data obtained from a CT scanner usually have unequal sampling frequencies in the x-, y- and z-directions. Generally, the 3D data are first interpolated between slices to obtain isotropic resolution, reconstructed, then operated on using object extraction and display algorithms. The traditional grey-level interpolation introduces a layer of intermediate substance and is not suitable for objects that are very different from the opposite background. The shape-based interpolation method transfers a pixel location to a parameter related to the object shape and the interpolation is performed on that parameter. This process is able to achieve a better interpolation but its application is limited to binary images only. In this paper, we present an improved shape-based interpolation method for grey-level images. The new method uses a polygon to approximate the object shape and performs the interpolation using polygon vertices as references. The binary images representing the shape of the object were first generated via image segmentation on the source images. The target object binary image was then created using regular shape-based interpolation. The polygon enclosing the object for each slice can be generated from the shape of that slice. We determined the relative location in the source slices of each pixel inside the target polygon using the vertices of a polygon as the reference. The target slice grey-level was interpolated from the corresponding source image pixels. The image quality of this interpolation method is better and the mean squared difference is smaller than with traditional grey-level interpolation.     

Weight-matrix structured regularization provides optimal generalized least-squares estimate in diffuse optical tomography

Diffuse optical tomography (DOT) involves estimation of tissue optical properties using noninvasive boundary measurements. The image reconstruction procedure is a nonlinear, ill-posed, and ill-determined problem, so overcoming these difficulties requires regularization of the solution. While the methods developed for solving the DOT image reconstruction procedure have a long history, there is less direct evidence on the optimal regularization methods, or exploring a common theoretical framework for techniques which uses least-squares (LS) minimization. A generalized least-squares (GLS) method is discussed here, which takes into account the variances and covariances among the individual data points and optical properties in the image into a structured weight matrix. It is shown that most of the least-squares techniques applied in DOT can be considered as special cases of this more generalized LS approach. The performance of three minimization techniques using the same implementation scheme is compared using test problems with increasing noise level and increasing complexity within the imaging field. Techniques that use spatial-prior information as constraints can be also incorporated into the GLS formalism. It is also illustrated that inclusion of spatial priors reduces the image error by at least a factor of 2. The improvement of GLS minimization is even more apparent when the noise level in the data is high (as high as 10%), indicating that the benefits of this approach are important for reconstruction of data in a routine setting where the data variance can be known based upon the signal to noise properties of the instruments.     

PET image reconstruction with anatomical edge guided level set prior

Acquiring both anatomical and functional images during one scan, PET/CT systems improve the ability to detect and localize abnormal uptakes. In addition, CT images provide anatomical boundary information that can be used to regularize positron emission tomography (PET) images. Here we propose a new approach to maximum a posteriori reconstruction of PET images with a level set prior guided by anatomical edges. The image prior models both the smoothness of PET images and the similarity between functional boundaries in PET and anatomical boundaries in CT. Level set functions (LSFs) are used to represent smooth and closed functional boundaries. The proposed method does not assume an exact match between PET and CT boundaries. Instead, it encourages similarity between the two boundaries, while allowing different region definition in PET images to accommodate possible signal and position mismatch between functional and anatomical images. While the functional boundaries are guaranteed to be closed by the LSFs, the proposed method does not require closed anatomical boundaries and can utilize incomplete edges obtained from an automatic edge detection algorithm. We conducted computer simulations to evaluate the performance of the proposed method. Two digital phantoms were constructed based on the Digimouse data and a human CT image, respectively. Anatomical edges were extracted automatically from the CT images. Tumors were simulated in the PET phantoms with different mismatched anatomical boundaries. Compared with existing methods, the new method achieved better bias-variance performance. The proposed method was also applied to real mouse data and achieved higher contrast than other methods.     

Automatic segmentation of lung fields in chest radiographs

The delineation of important structures in chest radiographs is an essential preprocessing step in order to automatically analyze these images, e.g., for tuberculosis screening support or in computer assisted diagnosis. We present algorithms for the automatic segmentation of lung fields in chest radiographs. We compare several segmentation techniques: a matching approach; pixel classifiers based on several combinations of features; a new rule-based scheme that detects lung contours using a general framework for the detection of oriented edges and ridges in images; and a hybrid scheme. Each approach is discussed and the performance of nine systems is compared with interobserver variability and results available from the literature. The best performance is obtained by the hybrid scheme that combines the rule-based segmentation algorithm with a pixel classification approach. The combinations of two complementary techniques leads to robust performance; the accuracy is above 94% for all 115 images in the test set. The average accuracy of the scheme is 0.969 +/- 0.0080, which is close to the interobserver variability of 0.984 +/- 0.0048. The methods are fast, and implemented on a standard PC platform.     

A fast image reconstruction algorithm based on penalized-likelihood estimate

Statistical iterative methods for image reconstruction like maximum likelihood expectation maximization (ML-EM) are more robust and flexible than analytical inversion methods and allow for accurately modeling the counting statistics and the photon transport during acquisition. They are rapidly becoming the standard for image reconstruction in emission computed tomography. The maximum likelihood approach provides images with superior noise characteristics compared to the conventional filtered back projection algorithm. But a major drawback of the statistical iterative image reconstruction is its high computational cost. In this paper, a fast algorithm is proposed as a modified OS-EM (MOS-EM) using a penalized function, which is applied to the least squares merit function to accelerate image reconstruction and to achieve better convergence. The experimental results show that the algorithm can provide high quality reconstructed images with a small number of iterations.     

医学图像多维重建中的插值问题研究进展

插值广泛应用于医学成像和图像多维重建中。本文首先对传统插值方法中基于形状的插值和弹性匹配插值作了比较深入的介绍，然后分析了旋转扫描超声心脏图像插值方法的特点、难点和研究现状，论述了插值和匹配的关系，分析表明准确的旋转扫描插值方法应该是基于匹配的方法，最后讨论了图像插值的几种评价方法。     

Maximum likelihood estimation-based denoising of magnetic resonance images using restricted local neighborhoods

In this paper, we propose a method to denoise magnitude magnetic resonance (MR) images, which are Rician distributed. Conventionally, maximum likelihood methods incorporate the Rice distribution to estimate the true, underlying signal from a local neighborhood within which the signal is assumed to be constant. However, if this assumption is not met, such filtering will lead to blurred edges and loss of fine structures. As a solution to this problem, we put forward the concept of restricted local neighborhoods where the true intensity for each noisy pixel is estimated from a set of preselected neighboring pixels. To this end, a reference image is created from the noisy image using a recently proposed nonlocal means algorithm. This reference image is used as a prior for further noise reduction. A scheme is developed to locally select an appropriate subset of pixels from which the underlying signal is estimated. Experimental results based on the peak signal to noise ratio, structural similarity index matrix, Bhattacharyya coefficient and mean absolute difference from synthetic and real MR images demonstrate the superior performance of the proposed method over other state-of-the-art methods.