Free of speckle ultrasound images of small tissue structures

By using the Born approximation deconvolved inverse scattering method instead of the traditional pulse-echo method for analyzing ultrasound pulse reflections from plastic phantoms and soft tissue specimens, improvement in image resolution is shown to be possible provided these targets are fair approximations to layered media. These images are free of speckle and are more vivid than the usual pulse-echo images.     

Automated human frontal lobe identification in MR images based on fuzzy-logic encoded expert anatomic knowledge

Identification of human brain structures in MR images comprises an area of increasing interest, which also presents numerous methodological challenges. Here we describe a new knowledge-based automated method designed to identify several major brain sulci and then to define the frontal lobes by using the identified sulci as landmarks. To identify brain sulci, sulcal images were generated by morphologic operations and then separated into different components based on connectivity analysis. Subsequently, the individual anatomic features were evaluated by using fuzzy membership functions. The crisp decisions, i.e., the identification of sulci, were made by taking the maximum of the summation of all the membership functions. The identification was designed in a hierarchical order. The longitudinal fissure was extracted first. The left and right central sulci were then identified based on the left and right hemispheres. Next, the lateral sulci were identified based on the central sulci and hemispheres. Finally, the left and right frontal lobes were defined from the two hemispheres. The method was evaluated by visual inspection, comparison with manual segmentation, and comparison with manually volumetric results in references. The average Jaccard similarities of left and right frontal lobes between the automated and manual segmentation were 0.89 and 0.91, respectively. The average Kappa indices of left and right frontal lobes between the automated and manual segmentation were 0.94 and 0.95, respectively. These results show relatively high accuracy of using this novel method for human frontal lobe identification and segmentation.     

Three-dimensional segmentation of anatomical structures in MR images on large data bases

In this paper an image-based method founded on mathematical morphology is presented in order to facilitate the segmentation of cerebral structures over large data bases of 3D magnetic resonance images (MRIs). The segmentation is described as an immersion simulation, applied to the modified gradient image, modeled by a generated 3D-region adjacency graph (RAG). The segmentation relies on two main processes: homotopy modification and contour decision. The first one is achieved by a marker extraction stage where homogeneous 3D-regions are identified. This stage uses contrasted regions from morphological reconstruction and labeled flat regions constrained by the RAG. Then, the decision stage intends to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a 3D extension of the watershed transform. The method has been applied on a data base of 3D brain MRIs composed of fifty patients. Results are illustrated by segmenting the ventricles, corpus callosum, cerebellum, hippocampus, pons, medulla and midbrain on our data base and the approach is validated on two phantom 3D MRIs.     

The registration of MR images using multiscale robust methods

Acquisition of MR images involves their registration against some prechosen reference image. Motion artifacts and misregistration can seriously flaw their interpretation and analysis. This article provides a global registration method that is robust in the presence of noise and local distortions between pairs of images. It uses a two-stage approach, comprising an optional Fourier phase-matching method to carry out preregistration, followed by an iterative procedure. The iterative stage uses a prescribed set of registration points, defined on the reference image, at which a robust nonlinear regression is computed from the squared residuals at these points. The method can readily accommodate general linear, or even nonlinear, registration transformations on the images. The algorithm was tested by recovering the registration transformation parameters when a 256 × 256 pixel T₂¹-weighted human brain image was scaled, rotated, and translated by prescribed amounts, and to which different amounts of Gaussian noise had been added. The results show subpixel accuracy of recovery when no noise is present, and graceful degradation of accuracy as noise is added. When 40% noise is added to images undergoing small shifts, the recovery errors are less than 3 pixels. The same tests applied to the Woods algorithm gave slightly inferior accuracy for these images, but failed to converge to the correct parameters in some cases of large-scale-shifted images with 10% added noise.     

Joint reconstruction of multiecho MR images using correlated sparsity

This works addresses the problem of reconstructing multiple T1- or T2-weighted images of the same anatomical cross section from partially sampled K-space data. Previous studies in reconstructing magnetic resonance (MR) images from partial samples of the K-space used compressed sensing (CS) techniques to exploit the spatial correlation of the images (leading to sparsity in wavelet domain). Such techniques can be employed to reconstruct the individual T1- or T2-weighted images. However, in the current context, the different images are not really independent; they are images of the same cross section and, hence, are highly correlated. We exploit the correlation between the images, along with the spatial correlation within the images to achieve better reconstruction results than exploiting spatial correlation only.For individual MR images, CS-based techniques lead to a sparsity-promoting optimization problem in the wavelet domain. In this article, we show that the same framework can be extended to incorporate correlation between images leading to group/row sparsity-promoting optimization. Algorithms for solving such optimization problems have already been developed in the CS literature. We show that significant improvement in reconstruction accuracy can be achieved by considering the correlation between different T1- and T2-weighted images. If the reconstruction accuracy is considered to be constant, our proposed group sparse formulation can yield the same result with 33% less K-space samples compared with simple sparsity-promoting reconstruction. Moreover, the reconstruction time by our proposed method is about two to four times less than the previous method.     

Reliability of brain structure morphometry in hydrocephalic children using MR images

To assess the ability of human operators to make decisions about region boundaries in significantly malformed brains, we performed a study of the reliability of morphometric measurements of specific brain structures from MRI in children with hydrocephalus and controls. Cross-sectional area measures of the corpus callosum, internal capsules and centrum semiovale, and volumes of the lateral ventricles were made in 50 children. Independent measurements were made by two raters on T₁ and T₂-weighted MR images. Pearson's correlation coefficients (r) and intraclass correlation coefficients (ICC) between the two rater's sets of measures were computed for each structure across all subjects. ICCs ranged from a low of 0.7502 to a high of 0.9895. All ICCs were significant at the p < .0001 level and were generally less than or equal to the corresponding Pearson's r value in every case. Therefore, the Pearson's r may overestimate the reliability. The results of this study support the claim that the ICC should be used rather than the Pearson's r when assessing interater reliability in situations where large between-group differences are present. In addition, the results show that brains malformed by disorders, such as hydrocephalus, can be reliably assessed using morphometric measures of MR images.     

Longitudinal assessment of mouse renal injury using high-resolution anatomic and magnetization transfer MR imaging

The purpose of this study is to evaluate the utility of high-resolution non-invasive endogenous high-field MRI methods for the longitudinal structural and quantitative assessments of mouse kidney disease using the model of unilateral ureter obstruction (UUO). T₁-weighted, T₂-weighted and magnetization transfer (MT) imaging protocols were optimized to improve the regional contrast in mouse kidney. Conventional T₁ and T₂ weighted images were collected in UUO mice on day 0 (~ 3 h), day 1, day 3 and day 6 after injury, on a 7 T small animal MRI system. Cortical and medullary thickness, corticomedullary contrast and Magnetization Transfer Ratio (MTR) were assessed longitudinally. Masson trichrome staining was used to histologically assess changes in tissue microstructure. Over the course of UUO progression there were significant (p < 0.05) changes in thickness of cortex and outer medulla, and regional changes in T₂ signal intensity and MTR values. Histological changes included tubular cell death, tubular dilation, urine retention, and interstitial fibrosis, assessed by histology. The MRI measures of renal cortical and medullary atrophy, cortical–medullary differentiation and MTR changes provide an endogenous, non-invasive and quantitative evaluation of renal morphology and tissue composition during UUO progression.     

Computational integral imaging reconstruction method of 3D images using pixel-to-pixel mapping and image interpolation

In this paper, we propose a novel computational integral imaging reconstruction (CIIR) method to improve the visual quality of the reconstructed images using a pixel-to-pixel mapping and an interpolation technique. Since an elemental image is magnified inversely through the corresponding pinhole and mapped on the reconstruction output plane based on pinhole-array model in the conventional CIIR method, the visual quality of reconstructed output image (ROI) degrades due to the interference problem between adjacent pixels during the superposition of the magnified elemental images. To avoid this problem, the proposed CIIR method generates dot-pattern ROIs using a pixel-to-pixel mapping and substitutes interpolated values for the empty pixels within the dot-pattern ROIs using an interpolation technique. The interpolated ROIs provides a much improved visual quality compared with the conventional method because of the exact regeneration of pixel positions sampled in the pickup process without interference between pixels. Moreover, it can enable us to reduce a computational cost by eliminating the magnification process used in the conventional CIIR. To confirm the feasibility of the proposed system, some experiments are carried out and the results are presented.     

Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images

PurposeSegmentation of the whole breast and fibroglandular tissue (FGT) is important for quantitatively analyzing the breast cancer risk in the dynamic contrast-enhanced magnetic resonance (DCE-MR) images. The purpose of this study is to improve the accuracy and efficiency of the segmentation of the whole breast and FGT in 3-D fat-suppressed DCE-MR images with a versatile deep learning (DL) framework.MethodsWe randomly collected 100 breast DCE-MR scans from Shanghai Cancer Hospital of Fudan University. The MR scans in the dataset were different in both the spatial resolution and the MR scanners employed. Furthermore, four breast density categories were assessed by radiologists based on Breast Imaging Reporting and Data System (BI-RADS) of American College of Radiology. The dataset was separated into the training and the testing sets, while keeping a balanced distribution of scans with different imaging parameters and density categories. The nnU-Net has been recently proposed to automatically adapt preprocessing strategies and network architectures for a given medical image dataset, thus showing a great potential in the systematic adaptation of DL methods to different datasets. In this study, we applied the nnU-Net to segment the whole breast and FGT in 3-D fat-suppressed DCE-MR images. Five-fold cross validation was employed to train and validate the segmentation method.ResultsThe segmentation performance was evaluated with the volume and surface agreement metrics between the DL-based automatic and the manually delineated masks, as quantified with the following measures: the average Dice volume overlap (0.968 ± 0.017 and 0.877 ± 0.081), the average surface distances (0.201 ± 0.080 mm and 0.310 ± 0.043 mm), and the Pearson correlation coefficient of masks (0.995 and 0.972) between the automatic and the manually delineated masks, as calculated for the whole breast and the FGT segmentation, respectively. The correlation coefficient between the breast densities obtained with the DL-based segmentation and the manual delineation was 0.981. There was a positive bias of 0.8% (DL-based relative to manual) in breast density measurement with the Bland-Altman plot. The execution time of the DL-based segmentation was approximately 20 s for the whole breast segmentation and 15 s for the FGT segmentation.ConclusionsOur DL-based segmentation framework using nnU-Net could robustly achieve high accuracy and efficiency across variable MR imaging settings without extra pre- or post-processing procedures. It would be useful for developing DCE-MR-based CAD systems to quantify breast cancer risk and to be integrated into the clinical workflow.     

Multi-exponential analysis of magnitude MR images using a quantitative multispectral edge-preserving filter

A solution for discrete multi-exponential analysis of T(2) relaxation decay curves obtained in current multi-echo imaging protocol conditions is described. We propose a preprocessing step to improve the signal-to-noise ratio and thus lower the signal-to-noise ratio threshold from which a high percentage of true multi-exponential detection is detected. It consists of a multispectral nonlinear edge-preserving filter that takes into account the signal-dependent Rician distribution of noise affecting magnitude MR images. Discrete multi-exponential decomposition, which requires no a priori knowledge, is performed by a non-linear least-squares procedure initialized with estimates obtained from a total least-squares linear prediction algorithm. This approach was validated and optimized experimentally on simulated data sets of normal human brains.     

Eliminating distortions of nonperiodic structures in images

Methods for reconstructing distorted images of microstructures are proposed. The spatial-frequency filtering of a distorted image, as a result of which the distribution of the Fourier transform phases of this image is adjusted according to the distribution of zeros of the visualization system??s frequency characteristic, and the amplitude distribution is transformed so that the Fourier spectrum??s integrated frequency characteristic (IFC) is modified into a function that decays according to the power law.     

Implementation and evaluation of data compression of MR images

A full-frame bit-allocation technique of data compression has been implemented using the existing computer facilities of an MR imager. Images reconstructed from compressed data files have been compared with the original image and changes in noise and pixel value measured to evaluate any image degradation introduced by the compression process. A 256 x 256 x 8 bit brain image can be compressed in 20 seconds with a compression ratio of greater than 4:1 without significant loss of information.     

Realignment of myocardial first-pass MR perfusion images using an automatic detection of the heart-lung interface

Magnetic resonance first-pass imaging of a bolus of contrast agent is well adapted to distinguish normal and hypoperfused areas of the myocardium. In most cases, the signal intensity-time curves in user defined regions of interest (ROI) are studied. As image acquisition is ECG-gated, the images are acquired at the same moment in the cardiac cycle, and the basic shape of the heart is similar from one view to the next. However, superficial respiratory motion can displace the heart in the short-axis plane. The aim of this study is to correct for the respiratory motion of the heart by performing an automatic realignment of the myocardial ROI based on a method tracking the movement of the lung-myocardium interface. Visual and quantitative analyses performed on 120 curves show a very good concordance between two automatic methods and the manual one.     

Automated classification of multispectral MR images using unsupervised constrained energy minimization based on fuzzy logic

Constrained energy minimization (CEM) has proven highly effective for hyperspectral (or multispectral) target detection and classification. It requires a complete knowledge of the desired target signature in images. This work presents “Unsupervised CEM (UCEM),” a novel approach to automatically target detection and classification in multispectral magnetic resonance (MR) images. The UCEM involves two processes, namely, target generation process (TGP) and CEM. The TGP is a fuzzy-set process that generates a set of potential targets from unknown information and then applies these targets to be desired targets in CEM. Finally, two sets of images, namely, computer-generated phantom images and real MR images, are used in the experiments to evaluate the effectiveness of UCEM. Experimental results demonstrate that UCEM segments a multispectral MR image much more effectively than either Functional MRI of the Brain's (FMRIB's) automated segmentation tool or fuzzy C-means does.     

Evaluation of cystic ovarian lesions using apparent diffusion coefficient calculated from reordered turboflash MR images.

Reordered snapshot fast low-angle shot images with, and without, diffusion-perfusion gradients were used for the evaluation of contents of cystic ovarian lesions. Sonographically detected 51 cystic ovarian lesions (13 endometrial cysts, 17 ovarian cysts, 7 serous cystadenomas, 6 mucinous cystadenomas, 8 malignant cystic ovarian tumors) were studied. T1- and T2-weighted images, reordered snapshot fast low-angle shot images with and without diffusion-perfusion gradients (b = 106 and 0 s/mm2, respectively) were obtained. Using these images, apparent diffusion coefficients (ADCs) were calculated in the cystic contents of these lesions. Endometrial cysts and malignant cystic ovarian tumors showed lower ADC values than ovarian cysts, serous cystadenomas and mucinous cystadenomas (p < 0.02). There was no distinct ADC difference among ovarian cysts, serous cystadenomas, mucinous cystadenomas (p > 0.2). In conclusion, diffusion-weighted magnetic resonance imaging is possible to be useful to evaluate cystic contents of ovarian lesions.     

Localization of ischemia in canine hearts using tagged rotated long axis MR images,endocardial surface stretch and wall thickening

Tagged magnetic resonance imaging allows the noninvasive measurement of regional systolic myocardial deformations and helps localize ischemic regions in the left ventricle (LV). The objective of this study was to evaluate the potential accuracy of localizing ischemic regions in the LV using endocardial and epicardial data obtained from tagged rotated long axis images. Nine canine hearts with acute ischemia induced by coronary artery ligation were imaged along four long axis planes rotated around the LV long axis, at end diastole and end systole. Each plane was tagged by four parallel lines perpendicular to the LV long axis. Tracing the endocardial and epicardial intersection points of the tag lines, 24 myocardial cuboids were reconstructed for each LV at end diastole and end systole. Endocardial surface stretch and transmural systolic thickening were calculated for each cuboid. The functional data were compared to perfusion data obtained from postmortem monastral blue staining of the heart. The ability of each functional index to discriminate between ischemic and non-ischemic regions was assessed using the “t”-statistic. The potential accuracy in localizing ischemia was evaluated by studying the corresponding sensitivity-specificity curves. The results demonstrate that adequate discrimination and localization can be obtained with both functional indices. However, endocardial surface stretch is advantageous as it uses only endocardial data and can save 50% of the post-processing time.