A variational approach to image registration in dynamic contrast-enhanced MRI of the human kidney

Kidney function can be accessed by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measurements which yield spatially resolved maps of physiological parameters like perfusion or filtration. The motion of the kidneys during the scan is a dominant limitation of the measurement quality, and image registration is necessary for accurate quantification. We analyzed the feasibility of applying an algorithm, originally developed for multimodal registration, to kidney perfusion time series. The algorithm uses a variational calculation scheme to align the images. In four out of five data sets, kidney motion could be reduced to below the spatial resolution of the images of 1.6 mm while preserving the enhancement pattern of kidney perfusion. Fitting a pharmacokinetic model to the data showed an average reduction of the Akaike fit error of 10% for the registered data, suggesting more stable parameters. We conclude that this image registration algorithm is feasible for correcting kidney motion in renal DCE-MRI.     

Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results

PURPOSE: The objective of this study was to assess changes in the water apparent diffusion coefficient (ADC) and in pharmacokinetic parameters obtained from the fast-exchange regime (FXR) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) during neoadjuvant chemotherapy in breast cancer. MATERIALS AND METHODS: Eleven patients with locally advanced breast cancer underwent MRI examination prior to and after chemotherapy but prior to surgery. A 1.5-T scanner was used to obtain T1, ADC and DCE-MRI data. DCE-MRI data were analyzed by the FXR model returning estimates of K(trans) (volume transfer constant), v(e) (extravascular extracellular volume fraction) and tau(i) (average intracellular water lifetime). Histogram and correlation analyses assessed parameter changes post-treatment. RESULTS: Significant (P < .05) changes or trends towards significance (P < .10) were seen in all parameters except tau(i), although there was qualitative reduction in tau(i) values post-treatment. In particular, there was reduction (P < .035) in voxels with K(trans) values in the range 0.2-0.5 min(-1) and a decrease (P < .05) in voxels with ADC values in the range 0.99 x 10(-3) to 1.35 x 10(-3) mm2/s. ADC and v(e) were negatively correlated (r = -.60, P < .02). Parameters sensitive to water distribution and geometry (T(1), v(e), tau(i) and ADC) correlated with a multivariable linear regression model. CONCLUSION: The analysis presented here is sensitive to longitudinal changes in breast tumor status; K(trans) and ADC are most sensitive to these changes. Relationships between parameters provide information on water distribution and geometry in the tumor environment.     

Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: Preliminary results

Purpose

To evaluate whether semi-quantitative analysis of high temporal resolution dynamic contrast-enhanced MRI (DCE-MRI) acquired early in treatment can predict the response of locally advanced breast cancer (LABC) to neoadjuvant chemotherapy (NAC).

Materials and Methods

As part of an IRB-approved prospective study, 21 patients with LABC provided informed consent and underwent high temporal resolution 3 T DCE-MRI before and after 1 cycle of NAC. Using measurements performed by two radiologists, the following parameters were extracted for lesions at both examinations: lesion size (short and long axes, in both early and late phases of enhancement), radiologist's subjective assessment of lesion enhancement, and percentages of voxels within the lesion demonstrating progressive, plateau, or washout kinetics. The latter data were calculated using two filters, one selecting for voxels enhancing ≥ 50% over baseline and one for voxels enhancing ≥ 100% over baseline. Pretreatment imaging parameters and parameter changes following cycle 1 of NAC were evaluated for their ability to discriminate patients with an eventual pathological complete response (pCR).

Results

All 21 patients completed NAC followed by surgery, with 9 patients achieving a pCR. No pretreatment imaging parameters were predictive of pCR. However, change after cycle 1 of NAC in percentage of voxels demonstrating washout kinetics with a 100% enhancement filter discriminated patients with an eventual pCR with an area under the receiver operating characteristic curve (AUC) of 0.77. Changes in other parameters, including lesion size, did not predict pCR.

Conclusion

Semi-quantitative analysis of high temporal resolution DCE-MRI in patients with LABC can discriminate patients with an eventual pCR after one cycle of NAC.     

一种基于Canny边缘的红外与可见光图像配准算法

针对异源传感器在图像配准时如何精确地找到相应的匹配特征问题,结合红外图像与可见光图像的特征,利用2种传感器对同一场景成像时具有一定的边缘轮廓相似特性,提出了提取图像的Canny边缘,在边缘轮廓上根据边缘曲线上像素点的位置夹角的相似性寻找对应匹配点,通过仿真和实验数据与真实值的比较验证算法的有效性和配准的精度.     

Computerized breast lesions detection using kinetic and morphologic analysis for dynamic contrast-enhanced MRI

To facilitate rapid and accurate assessment, this study proposed a novel fully automatic method to detect and identify focal tumor breast lesions using both kinetic and morphologic features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). After motion registration of all phases of the DCE-MRI study, three automatically generated lines were used to segment the whole breast region of each slice. The kinetic features extracted from the pixel-based time-signal intensity curve (TIC) by a two-stage detection algorithm was first used, and then three-dimensional (3-D) morphologic characteristics of the detected regions were applied to differentiate between tumor and non-tumor regions. In this study, 95 biopsy-confirmed lesions (28 benign and 67 malignant lesions) in 54 women were used to evaluate the detection efficacy of the proposed system. The detection performance was analyzed using the free-response operating characteristics (FROC) curve and detection rate. The proposed computer-aided detection (CADe) system had a detection rate of 92.63% (88/95) of all tumor lesions, with 6.15 false positives per case. Based on the results, kinetic features extracted by TIC can be used to detect tumor lesions and 3-D morphology can effectively reduce the false positives.     

A robust graph-based segmentation method for breast tumors in ultrasound images

Objectives

This paper introduces a new graph-based method for segmenting breast tumors in US images.

Background and motivation

Segmentation for breast tumors in ultrasound (US) images is crucial for computer-aided diagnosis system, but it has always been a difficult task due to the defects inherent in the US images, such as speckles and low contrast.

Methods

The proposed segmentation algorithm constructed a graph using improved neighborhood models. In addition, taking advantages of local statistics, a new pair-wise region comparison predicate that was insensitive to noises was proposed to determine the mergence of any two of adjacent subregions.

Results and conclusion

Experimental results have shown that the proposed method could improve the segmentation accuracy by 1.5-5.6% in comparison with three often used segmentation methods, and should be capable of segmenting breast tumors in US images.     

针对图像序列的快速低存储开销配准算法

针对序列图像配准问题,提出一种快速低存储开销配准算法。首先,生成一系列与具体图像内容无关的特征点。而后,使用正向-反向跟踪来获取稳定的特征点对,其中,正向跟踪用于获得所有可能的特征点对,反向跟踪用来得到正向-反向误差,并且利用此误差来获取最终稳定的特征点对。最后,在稳定特征点对的基础上通过归一化直接线性变换计算得到可用于图像配准的单应矩阵。实验表明该算法能够提供与优秀的传统算法相当的配准性能。由于该算法对序列中图像之间的连续性进行了充分利用,不仅降低了存储开销,还提高了运算速度。对480360的序列而言,该算法需要的存储开销仅为421 kB,且运算速度达到32 帧/s。     

针对图像序列的快速低存储开销配准算法

针对序列图像配准问题,提出一种快速低存储开销配准算法。首先,生成一系列与具体图像内容无关的特征点。而后,使用正向-反向跟踪来获取稳定的特征点对,其中,正向跟踪用于获得所有可能的特征点对,反向跟踪用来得到正向-反向误差,并且利用此误差来获取最终稳定的特征点对。最后,在稳定特征点对的基础上通过归一化直接线性变换计算得到可用于图像配准的单应矩阵。实验表明该算法能够提供与优秀的传统算法相当的配准性能。由于该算法对序列中图像之间的连续性进行了充分利用,不仅降低了存储开销,还提高了运算速度。对480360的序列而言,该算法需要的存储开销仅为421 kB,且运算速度达到32 帧/s。     

Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis

Three-dimensional (3-D) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) consists of a large number of images in different enhancement phases which are used to identify and characterize breast lesions. The purpose of this study was to develop a computer-assisted algorithm for tumor segmentation and characterization using both kinetic information and morphological features of 3-D breast DCE-MRI. An integrated color map created by intersecting kinetic and area under the curve (AUC) color maps was used to detect potential breast lesions, followed by the application of a region growing algorithm to segment the tumor. Modified fuzzy c-means clustering was used to identify the most representative kinetic curve of the whole segmented tumor, which was then characterized by using conventional curve analysis or pharmacokinetic model. The 3-D morphological features including shape features (compactness, margin, and ellipsoid fitting) and texture features (based on the grey level co-occurrence matrix) of the segmented tumor were obtained to characterize the lesion. One hundred and thirty-two biopsy-proven lesions (63 benign and 69 malignant) were used to evaluate the performance of the proposed computer-aided system for breast MRI. Five combined features including rate constant (k_ep), volume of plasma (v_p), energy (G₁), entropy (G₂), and compactness (C₁), had the best performance with an accuracy of 91.67% (121/132), sensitivity of 91.30% (63/69), specificity of 92.06% (58/63), and A_z value of 0.9427. Combining the kinetic and morphological features of 3-D breast MRI is a potentially useful and robust algorithm when attempting to differentiate benign and malignant lesions.     

Accuracy of image registration between MRI and light microscopy in the ex vivo brain

A multistep procedure was developed to register magnetic resonance imaging (MRI) and histological data from the same sample in the light microscopy image space, with the ultimate goal of allowing quantitative comparisons of the two datasets. The fixed brain of an owl monkey was used to develop and test the procedure. In addition to the MRI and histological data, photographic images of the brain tissue block acquired during sectioning were assembled into a blockface volume to provide an intermediate step for the overall registration process. The MR volume was first registered to the blockface volume using a combination of linear and nonlinear registration, and two dimensional (2D) blockface sections were registered to corresponding myelin-stained sections using a combination of linear and nonlinear registration. Before this 2D registration, two major types of tissue distortions were corrected: tissue tearing and independent movement of different parts of the brain, both introduced during histological processing of the sections. The correction procedure utilized a 2D method to close tissue tears and a multiple iterative closest point (ICP) algorithm to reposition separate pieces of tissue in the image. The accuracy of the overall MR to micrograph registration procedure was assessed by measuring the distance between registered landmarks chosen in the MR image space and the corresponding landmarks chosen in the micrograph space. The average error distance of the MR data registered to micrograph data was 0.324±0.277 mm, only 8% larger than the width of the MRI voxel (0.3 mm).     

Blood oxygen level-dependent MRI for the monitoring of neoadjuvant chemotherapy in breast carcinoma: initial experience

Purpose

To determine the feasibility of using R2? map MRI for pretreatment diagnosis and monitoring of tumor response to neoadjuvant chemotherapy (NAC) in patients with breast cancer.

Material and Methods

Twenty-eight women with breast cancer, as evidenced by pathology, underwent MR imaging prior to and after chemotherapy. All patients were examined by conventional MRI and R2? map imaging. Subjects were divided into major histological response (MHR) and non-major histological response (NMHR) groups. Mean R2? values of cancerous and normal glandular tissues were measured before and following NAC. Differences in R2? and ΔR2?% values between these two groups were compared with paired or independent t tests. The relationship between ΔR2?% and histological response was examined using Spearman's correlation test.

Results

Before NAC, the average R2? values in carcinoma were lower than in normal glandular tissue (P<.05). After two to four cycles of NAC, the R2? values in carcinoma were increased (P<.05 ), but this change was not significant in normal glandular tissue. After NAC, ΔR2?% was significantly higher in MHR as compared to NMHR (P<.05). The ΔR2?% correlated with the histological response (r=0.581, P<.01).

Conclusion

In women undergoing NAC for breast cancer treatment, R2? and ΔR2?% appear to provide predictive information of tumor response which is probably associated with changes in tumor angiogenesis and tissue oxygenation. R2? map imaging of breasts may therefore be useful in monitoring tumor response to NAC.     

Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering

The purpose of this study is to evaluate the diagnostic efficacy of the representative characteristic kinetic curve of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) extracted by fuzzy c-means (FCM) clustering for the discrimination of benign and malignant breast tumors using a novel computer-aided diagnosis (CAD) system. About the research data set, DCE-MRIs of 132 solid breast masses with definite histopathologic diagnosis (63 benign and 69 malignant) were used in this study. At first, the tumor region was automatically segmented using the region growing method based on the integrated color map formed by the combination of kinetic and area under curve color map. Then, the FCM clustering was used to identify the time-signal curve with the larger initial enhancement inside the segmented region as the representative kinetic curve, and then the parameters of the Tofts pharmacokinetic model for the representative kinetic curve were compared with conventional curve analysis (maximal enhancement, time to peak, uptake rate and washout rate) for each mass. The results were analyzed with a receiver operating characteristic curve and Student's t test to evaluate the classification performance. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the combined model-based parameters of the extracted kinetic curve from FCM clustering were 86.36% (114/132), 85.51% (59/69), 87.30% (55/63), 88.06% (59/67) and 84.62% (55/65), better than those from a conventional curve analysis. The A(Z) value was 0.9154 for Tofts model-based parametric features, better than that for conventional curve analysis (0.8673), for discriminating malignant and benign lesions. In conclusion, model-based analysis of the characteristic kinetic curve of breast mass derived from FCM clustering provides effective lesion classification. This approach has potential in the development of a CAD system for DCE breast MRI.     

图像配准中几种特征点提取方法的分析与实验

研究了图像配准中常用的几种特征点提取方法。基于图像配准对特征提取方法的特殊要求,提出了一组量化的特征提取方法评价因子,能用于合理评价各种方法的性能、优点与缺点,为这些方法的改进提供指导性意见和比较依据。通过大量的实验,利用评价因子比较和分析了上述的几种常用特征提取方法的优缺点、适用环境,并提出改进建议。     

Background parenchymal enhancement in the contralateral normal breast of patients undergoing neoadjuvant chemotherapy measured by DCE-MRI

The purpose of this study was to analyze background parenchymal enhancement (BPE) in the contralateral normal breast of cancer patients during the course of neoadjuvant chemotherapy (NAC).     

A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm

Considering the difficulties in image segmentation caused by the complexity of diverse ecological environments and various artificial targets in high resolution remote sensing images, especially in city scene, and in order to overcome the limitations existing in the traditional segmentation algorithm, JSEG (J-Segmentation), for high resolution remote sensing image segmentation and to further improve the segmentation accuracy, WJSEG (Wavelet-JSEG), a novel multi-scale segmentation algorithm based on wavelet transform, is proposed, which is an improved JSEG algorithm. WJSEG is an improved form of JSEG in relation to three aspects, including color quantization, multi-scale segmentation and region merging by introducing the multi-scale analysis tool based on wavelet transform. Experiments have been conducted on high resolution SPOT 5 pan-sharpened multispectral image and IKONOS panchromatic image. These experimental results were compared with those gained by the traditional JSEG algorithm and the famous commercial software named eCognition, which validated the effectiveness and reliability of the proposed WJSEG algorithm.     

A fusion algorithm for infrared and visible images based on RDU-PCNN and ICA-bases in NSST domain

The aim of infrared and visible image fusion is to enhance the feature in infrared image and preserve abundant detail information in visible image. Based on the fact that the human sense system accepts external stimulation only when the stimulus intensity is greater than a certain value and the reaction of neuronal cells have obvious regional characters, an image fusion algorithm based on region dual-channel unit-linking pulse coupled neural networks (RDU-PCNN) and independent component analysis (ICA) bases in non-subsampled shearlet transform (NSST) domain for infrared and visible images is proposed. RDU-PCNN we constructed has obvious regional characters and much lower computational costs. We trained ICA-bases using a number of images that the content and statistical properties are similar with the fusion images but applied it as low-frequency ICA-bases, which can reduce calculation complexity. Experimental results demonstrate that the proposed method can significantly improved the fusion quality and need less computational costs.     

Face and eyes localization algorithm in thermal images for temperature measurement of the inner canthus of the eyes

In this paper, a novel algorithm for the detection and localization of the face and eyes in thermal images is presented, particularly the temperature measurement of the human body by measuring the eye corner (inner canthus) temperature. The algorithm uses a combination of the template-matching, knowledge-based and morphological methods, particularly the modified Randomized Hough Transform (RHT) in the localization process, also growing segmentation to increase accuracy of the localization algorithm. In many solutions, the localization of the face and/or eyes is made by manual selection of the regions of the face and eyes and then the average temperature in the region is measured. The paper also discusses experimental studies and the results, which allowed the evaluation of the effectiveness of the developed algorithm. The standardization of measurement, necessary for proper temperature measurement with the use of infrared thermal imaging, are also presented.     

Enhancement of histological volumes through averaging and their use for the analysis of magnetic resonance images

Magnetic resonance imaging (MRI) of small animals is routinely performed in research centers. But despite its many advantages, MR still suffers from limited spatial resolution which makes the interpretation and quantitative analysis of the images difficult, particularly for small structures of interest within areas of significant heterogeneity. One possibility to address this issue is to complement the MR images with histological data, which requires reconstructing 3D volumes from a series of 2D images. A number of methods have been proposed recently in the literature to address this issue, but deformation or tearing during the slicing process often produces reconstructed volumes with visible artifacts and imperfections. In this paper, we show that a possible solution to this problem is to work with several histological volumes, reconstruct each of these separately and then compute an average. The resulting histological atlas shows structures and substructures more clearly than any individual volume. We also propose an original approach to normalize intensity values across slices, a required preprocessing step when reconstructing histological volumes. We show that the histological atlas we have created can be used to localize structures and substructures, which cannot be seen easily in MR images. We also create an MR atlas that is associated with the histological atlas. We show that using the histological volumes to create the MR atlas is better than using the MR volumes only. Finally, we validate our approach quantitatively on MR image volumes by comparing volumetric measurements obtained manually and obtained automatically with our atlases.     

一种新的截断线及标志点处缺失相位插补方法

在基于相位信息的三维面形测量中,为了得到正确的相位展开而引入的截断线和为了便于三维拼接而附加在物体表面的标志点都会造成其覆盖区域的相位缺失,需要进行人为地插补修复。一种新的缺失相位插补方法——八方向梯度估算法,通过计算待插补点周围八方向梯度和其八邻域内的相位平均值,可估算出缺失的相位。与其它常用缺失相位插补方法相比,该方法运算速度快、插补效果好,运算过程无需人为参与,便于计算机自动实现,尤其适合于插补倾斜相位面上的较大标志点,有助于提高相位重建质量和在此基础上的三维面形重建质量。