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.     

Automated registration of sequential breath-hold dynamic contrast-enhanced MR images: a comparison of three techniques

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly in use as an investigational biomarker of response in cancer clinical studies. Proper registration of images acquired at different time points is essential for deriving diagnostic information from quantitative pharmacokinetic analysis of these data. Motion artifacts in the presence of time-varying intensity due to contrast enhancement make this registration problem challenging. DCE-MRI of chest and abdominal lesions is typically performed during sequential breath-holds, which introduces misregistration due to inconsistent diaphragm positions and also places constraints on temporal resolution vis-à-vis free-breathing. In this work, we have employed a computer-generated DCE-MRI phantom to compare the performance of two published methods, Progressive Principal Component Registration and Pharmacokinetic Model-Driven Registration, with Sequential Elastic Registration (SER) to register adjacent time-sample images using a published general-purpose elastic registration algorithm. In all three methods, a 3D rigid-body registration scheme with a mutual information similarity measure was used as a preprocessing step. The DCE-MRI phantom images were mathematically deformed to simulate misregistration, which was corrected using the three schemes. All three schemes were comparably successful in registering large regions of interest (ROIs) such as muscle, liver, and spleen. SER was superior in retaining tumor volume and shape, and in registering smaller but important ROIs such as tumor core and tumor rim. The performance of SER on clinical DCE-MRI data sets is also presented.     

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.     

用于压缩感知磁共振成像的分割字典学习算法

字典学习算法可以根据数据本身的特点构建稀疏域中的基,从而使数据的表示更加稀疏.该文在传统的字典学习算法基础上提出了分割字典学习算法,由于部分磁共振图像组织结构简单、可以进行图像分割,因此可根据此特点来优化字典中基函数的构建,使磁共振图像的表达更为稀疏,从而获得更高的重建图像质量.该文利用模拟数据和真实数据进行了重建实验,结果表明与传统的字典学习算法相比,分割字典学习算法能进一步改善重建图像质量.     

Low-field magnetic resonance imaging using multiplicative regularization

In this paper we present a magnetic resonance imaging (MRI) technique that is based on multiplicative regularization. Instead of adding a regularizing objective function to a data fidelity term, we multiply by such a regularizing function. By following this approach, no regularization parameter needs to be determined for each new data set that is acquired. Reconstructions are obtained by iteratively updating the images using short-term conjugate gradient-type update formulas and Polak-Ribière update directions. We show that the algorithm can be used as an image reconstruction algorithm and as a denoising algorithm. We illustrate the performance of the algorithm on two-dimensional simulated low-field MR data that is corrupted by noise and on three-dimensional measured data obtained from a low-field MR scanner. Our reconstruction results show that the algorithm effectively suppresses noise and produces accurate reconstructions even for low-field MR signals with a low signal-to-noise ratio.     

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.     

Referenceless PRFS MR Thermometry Using Partial Separability Model

Many areas of magnetic resonance (MR)-guided thermal therapy research would benefit from temperature maps with high spatial and temporal resolution. Conventional thermometry relies on the subtraction of baseline images, which makes it sensitive to tissue motion and frequency drift during the course of treatment. For another case is the limit of magnetic resonance imaging sampling speed, it is hard to accurately achieve MR thermometry with high spatiotemporal resolution especially for dynamic organs. To address these issues, a novel method for MR thermometry is presented by exploiting the data redundancy based on partial separability (PS) model and the referenceless thermometry. The PS model highly sparse sample two datasets in the (k–t) space for image reconstruction, which respectively determine the spatial and temporal resolutions. After the phase information is extracted from the images reconstructed by the PS model, the background phase outside the heated region from each acquired phase image through a polynomial fitting is estimated. Extrapolation of the polynomial to the heated region serves as the background phase estimate, which is then subtracted from the actual phase. The thermometry results showed that this method could accurately capture the dynamic change of MR thermometric images with 1.5 mm × 1.5 mm spatial resolution and 250 ms temporal resolution, respectively. The in vivo experiment of MR-guided high intensity focused ultrasound research and the cardiac dynamic MR thermometry are shown to demonstrate the benefits of the proposed method in high spatiotemporal resolution MR thermometry.     

Research and progress in magnetic resonance imaging of triple-negative breast cancer

Triple-negative breast cancer (TNBC), which characterized by distinct biological and clinical pathological features, has a worse prognosis because the lack of effective therapeutic targets. Breast MR is the most accurate imaging modality for diagnosis of breast cancer currently. MR imaging recognition could assist in diagnosis, pretreatment planning and prognosis evaluation of TNBC. MR findings of a larger solitary lesion, mass with smooth mass margin, high signal intensity on T₂-weighted images and rim enhancement are typical MRI features associated with TNBC. Further work is necessary about the clinical application of dynamic contrast-enhanced MR imaging (DCE-MRI), DWI and MRS.     

Deriving new soft tissue contrasts from conventional MR images using deep learning

Versatile soft tissue contrast in magnetic resonance imaging is a unique advantage of the imaging modality. However, the versatility is not fully exploited. In this study, we propose a deep learning-based strategy to derive more soft tissue contrasts from conventional MR images obtained in standard clinical MRI. Two types of experiments are performed. First, MR images corresponding to different pulse sequences are predicted from one or more images already acquired. As an example, we predict T_1ρ weighted knee image from T₂ weighted image and/or T₁ weighted image. Furthermore, we estimate images corresponding to alternative imaging parameter values. In a representative case, variable flip angle images are predicted from a single T₁ weighted image, whose accuracy is further validated in quantitative T₁ map subsequently derived. To accomplish these tasks, images are retrospectively collected from 56 subjects, and self-attention convolutional neural network models are trained using 1104 knee images from 46 subjects and tested using 240 images from 10 other subjects. High accuracy has been achieved in resultant qualitative images as well as quantitative T₁ maps. The proposed deep learning method can be broadly applied to obtain more versatile soft tissue contrasts without additional scans or used to normalize MR data that were inconsistently acquired for quantitative analysis.     

A novel acquisition-reconstruction algorithm for surface magnetic resonance imaging

In U-shaped, hand-size magnetic resonance surface scanners, imaging is performed along only one spatial direction, with the application of just one gradient (one-dimensional imaging). Lateral spatial resolution can be obtained by magnet displacement, but, in this case, resolution is very poor (on the order of some millimeters) and cannot be useful for high-resolution imaging applications. In this article, an innovative technique for acquisition and reconstruction of images produced by U-shaped, hand-size MRI surface scanners is presented. The proposed method is based on the acquisition of overlapping strips and an analytical reconstruction technique; it is capable of arbitrarily improving spatial lateral resolution without either using a second magnetic field gradient or making any assumptions about the imaged sample extension. Numerical simulations on synthetic images are reported demonstrating the method functionalities. The presented method also makes it possible to use U-shaped, hand-size MRI surface scanners for high-resolution biomedical applications, such as the imaging of skin lesions.     

On the utility of spectroscopic imaging as a tool for generating geometrically accurate MR images and parameter maps in the presence of field inhomogeneities and chemical shift effects

Lack of spatial accuracy is a recognized problem in magnetic resonance imaging (MRI) which severely detracts from its value as a stand-alone modality for applications that put high demands on geometric fidelity, such as radiotherapy treatment planning and stereotactic neurosurgery. In this paper, we illustrate the potential and discuss the limitations of spectroscopic imaging as a tool for generating purely phase-encoded MR images and parameter maps that preserve the geometry of an object and allow localization of object features in world coordinates.     

Image-guided optical spectroscopy provides molecular-specific information in vivo: MRI-guided spectroscopy of breast cancer hemoglobin, water, and scatterer size

A multimodality instrument that integrated optical or near-infrared spectroscopy into a magnetic resonance imaging (MRI) breast coil was used to perform a pilot study of image-guided spectroscopy on cancerous breast tissue. These results are believed to be the first multiwavelength spectroscopic images of breast cancer using MRI-guided constraints, and they show the cancer tumor to have high hemoglobin and water values, decreased oxygen saturation, and increased subcellular granularity. The use of frequency-domain diffuse tomography methods at many wavelengths provides the spectroscopy required for recovering maps of absorbers and scattering spectra, but the integration with MRI allows these data to be recovered on an image field that preserves high resolution and fuses the two data sets together. Integration of molecular spectroscopy into standard clinical MRI can be achieved with this approach to spectral tomography.     

三维磁共振电阻抗成像的改进分层灵敏度算法

针对人体组织电导率的三维成像问题,提出一种改进的分层灵敏度磁共振电阻抗重建算法.利用单方向磁感应强度信息,对三维电导率图像实行分层重建,每层重建仅利用该层磁通密度分量测量数据,然后对单层重建结果进行修正以获得三维电导率重建图像.三介质长方体模型上的仿真实验证明,改进的分层重建算法改善了层间串扰现象,可以获得比一般分层算法甚至整体算法更高的图像分辨率,而且重建时间较整体算法显著减少;基于人体腿模型的仿真实验表明该算法对复杂模型三维重构的可行性;最后通过仿体实验验证算法的重建效果.改进的分层灵敏度重建算法降低了灵敏度矩阵法的计算机硬件需求,减少了重建时间,对MREIT的三维重建具有较高的成像精度和求解效率.     

Respiratory Motion Correction of Compressively Sampled Myocardial Perfusion Data by Using Robust Matrix Decomposition

Motion correction is a challenging problem in free breathing undersampled cardiac perfusion magnetic resonance images. It is due to aliasing artifacts in the reconstructed images and the rapid contrast changes in the perfusion images. In addition to the reconstruction limitations, many registration algorithms underperforms in the presence of the rapid intensity changes. In this paper, we propose a novel motion correction technique that reconstructs the motion-free images from the undersampled cardiac perfusion MR data. The technique utilizes the robust principal component analysis along with the periodic decomposition to separate the respiratory motion component that can be registered, from the unchanged contrast intensity variations. It was tested on synthetic data, simulated data, and the clinically acquired data. The performance of the method was qualitatively assessed and validated by comparing manually acquired time–intensity curves of the myocardial sectors to automatically generated curves before and after registration.     

一种基于改进频域图像配准技术的超分辨力图像处理方法

为提高光电成像系统的空间分辨力,提出了一种基于改进的频率域图像配准技术的超分辨力图像处理方法。首先利用改进的频域图像配准方法估算出低分辨力图像之间的微位移量,然后采用Papoulis-Gerchberg超分辨力处理方法完成图像复原。利用不同重构方法进行了仿真及实验研究,给出了评价参数。模拟和实际显微热图像的处理结果表明:该算法可使图像质量得到改善,分辨的细节更多,可有效地提高光电成像系统的空间分辨力;处理算法简单,计算量小,可实现快速处理。该算法还可应用于其他不可控光学微扫描成像系统中,具有广泛的应用前景。     

Application of three-dimensional magnetic resonance image registration for monitoring hip joint diseases

The purpose of this study was to estimate the accuracy of a method in which three-dimensional (3D) magnetic resonance (MR) volume registration is used for monitoring hip joint disease. Data were analyzed using a normalized cross-correlation (NCC) algorithm involving a user-selected 3D box including the proximal femur. Most of the femoral head was not included in the 3D box because it can become deformed during the course of disease. The accuracy of registration around the femoral head was evaluated using five phantoms and clinical MR data of 17 patients with hip joint disease. In the phantom experiment, registration accuracy was evaluated using four fiducial markers attached to the femoral head. In the experiment using clinical data, registration accuracy was evaluated using a landmark in the femoral head. The registration accuracy in the phantom and clinical experiment was 0.43+/-0.18 mm (S.D.) and 1.12+/-0.46 mm (S.D.), respectively. The former is a value less than half the minimum dimension of a voxel (1.25 x 1.25 x 1.0 mm). Although the latter is slightly larger than the minimum dimension of a voxel, actual errors would be smaller because of the uncertainty in landmark localization. In conclusion, the present method based on an NCC algorithm can be used to accurately register serial MR images of the femoral heads with an error on the order of a voxel. We believe that this method is sufficiently accurate for monitoring hip joint diseases.     

High-resolution imaging using Hadamard encoding

High-resolution imaging techniques using noninvasive modalities such as magnetic resonance (MR) imaging are being pursued as in vivo cancer screening techniques in an attempt to eliminate the invasive nature of surgical biopsy. When acquiring high-resolution MR images for tissue screening, image fields of view have in the past been limited by the matrix sizes available in conventional MR scanners. We present here a technique that uses aliasing to produce high resolution images with larger matrix sizes than are currently available. The image is allowed to alias in both the frequency encoding and phase encoding dimensions, and the individual, aliased fields of view are recovered by Hadamard encoding methods. These fields may then be tiled to obtain a composite image with high spatial resolution and a large field of view. The technique is demonstrated using two-dimensional and three-dimensional in vivo imaging of the human brain and breast.     

Adaptive k-space sampling design for edge-enhanced DCE-MRI using compressed sensing

The critical challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the trade-off between spatial and temporal resolution due to the limited availability of acquisition time. To address this, it is imperative to under-sample k-space and to develop specific reconstruction techniques. Our proposed method reconstructs high-quality images from under-sampled dynamic k-space data by proposing two main improvements; i) design of an adaptive k-space sampling lattice and ii) edge-enhanced reconstruction technique. A high-resolution data set obtained before the start of the dynamic phase is utilized. The sampling pattern is designed to adapt to the nature of k-space energy distribution obtained from the static high-resolution data. For image reconstruction, the well-known compressed sensing-based total variation (TV) minimization constrained reconstruction scheme is utilized by incorporating the gradient information obtained from the static high-resolution data. The proposed method is tested on seven real dynamic time series consisting of 2 breast data sets and 5 abdomen data sets spanning 1196 images in all. For data availability of only 10%, performance improvement is seen across various quality metrics. Average improvements in Universal Image Quality Index and Structural Similarity Index Metric of up to 28% and 24% on breast data and about 17% and 9% on abdomen data, respectively, are obtained for the proposed method as against the baseline TV reconstruction with variable density random sampling pattern.     

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).     

基于大脑模板的MRI扫描自动定位方法

在大脑磁共振成像(MRI)影像学的数据采集中,通常先扫描一幅定位图像,并根据解剖学先验知识手动调整合适的扫描定位参数,再进行后续的正式扫描.该文实现了一种直接以大脑模板为参照的自动定位的方法:首先采集一幅中等分辨率的快速三维定位图像,然后通过与模板的配准确定定位参数,并应用到后续序列的扫描,以保证不同被试在图像采集时采用与模板一致的空间定位.该方法一方面便于不同被试的图像数据之间进行系统性比较与参照,帮助诊断者快速定位病灶,也可在后续常用的基于体素分析过程最大化数据的利用效率.另一方面,针对单个体多次扫描之间的自动定位,该文进一步使用迭代方法,通过多次"扫描、配准、自动定位"步骤,逐步减小图像配准算法的误差.实验证明,该文基于大脑模板的自动定位方法能够确保不同被试之间和同一被试之内在图像数据采集时的空间定位高度一致性,其中同一被试内多次扫描的空间定位误差1.0 mm和1.0o.