Segmentation of MRI brain scans using non-uniform partial volume densities

We present an algorithm that provides a partial volume segmentation of a T1-weighted image of the brain into gray matter, white matter and cerebrospinal fluid. The algorithm incorporates a non-uniform partial volume density that takes the curved nature of the cortex into account. The pure gray and white matter intensities are estimated from the image, using scanner noise and cortical partial volume effects. Expected tissue fractions are subsequently computed in each voxel. The algorithm has been tested for reliability, correct estimation of the pure tissue intensities on both real (repeated) MRI data and on simulated (brain) images. Intra-class correlation coefficients (ICCs) were above 0.93 for all volumes of the three tissue types for repeated scans from the same scanner, as well as for scans with different voxel sizes from different scanners with different field strengths. The implementation of our non-uniform partial volume density provided more reliable volumes and tissue fractions, compared to a uniform partial volume density. Applying the algorithm to simulated images showed that the pure tissue intensities were estimated accurately. Variations in cortical thickness did not influence the accuracy of the volume estimates, which is a valuable property when studying (possible) group differences. In conclusion, we have presented a new partial volume segmentation algorithm that allows for comparisons over scanners and voxel sizes.     

Fast numerical algorithms for fitting multiresolution hybrid shape models to brain MRI

In this paper, we present new and fast numerical algorithms for shape recovery from brain MRI using multiresolution hybrid shape models. In this modeling framework, shapes are represented by a core rigid shape characterized by a superquadric function and a superimposed displacement function which is characterized by a membrane spline discretized using the finite-element method. Fitting the model to brain MRI data is cast as an energy minimization problem which is solved numerically. We present three new computational methods for model fitting to data. These methods involve novel mathematical derivations that lead to efficient numerical solutions of the model fitting problem. The first method involves using the nonlinear conjugate gradient technique with a diagonal Hessian preconditioner. The second method involves the nonlinear conjugate gradient in the outer loop for solving global parameters of the model and a preconditioned conjugate gradient scheme for solving the local parameters of the model. The third method involves the nonlinear conjugate gradient in the outer loop for solving the global parameters and a combination of the Schur complement formula and the alternating direction-implicit method for solving the local parameters of the model. We demonstrate the efficiency of our model fitting methods via experiments on several MR brain scans.     

Estimation of the partial volume effect in MRI

The partial volume effect (PVE) arises in volumetric images when more than one tissue type occurs in a voxel. In such cases, the voxel intensity depends not only on the imaging sequence and tissue properties, but also on the proportions of each tissue type present in the voxel. We have demonstrated in previous work that ignoring this effect by establishing binary voxel-based segmentations introduces significant errors in quantitative measurements, such as estimations of the volumes of brain structures. In this paper, we provide a statistical estimation framework to quantify PVE and to propagate voxel-based estimates in order to compute global magnitudes, such as volume, with associated estimates of uncertainty. Validation is performed on ground truth synthetic images and MRI phantoms, and a clinical study is reported. Results show that the method allows for robust morphometric studies and provides resolution unattainable to date.     

Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET

Magnetic resonance imaging (MRI)-guided partial volume effect correction (PVC) in brain positron emission tomography (PET) is now a well-established approach to compensate the large bias in the estimate of regional radioactivity concentration, especially for small structures. The accuracy of the algorithms developed so far is, however, largely dependent on the performance of segmentation methods partitioning MRI brain data into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of three brain MRI segmentation algorithms using simulated and clinical brain MR data was performed, and subsequently their impact on PVC in 18F-FDG and 18F-DOPA brain PET imaging was assessed. Two algorithms, the first is bundled in the Statistical Parametric Mapping (SPM2) package while the other is the Expectation Maximization Segmentation (EMS) algorithm, incorporate a priori probability images derived from MR images of a large number of subjects. The third, here referred to as the HBSA algorithm, is a histogram-based segmentation algorithm incorporating an Expectation Maximization approach to model a four-Gaussian mixture for both global and local histograms. Simulated under different combinations of noise and intensity non-uniformity, MR brain phantoms with known true volumes for the different brain classes were generated. The algorithms' performance was checked by calculating the kappa index assessing similarities with the "ground truth" as well as multiclass type I and type II errors including misclassification rates. The impact of image segmentation algorithms on PVC was then quantified using clinical data. The segmented tissues of patients' brain MRI were given as input to the region of interest (RoI)-based geometric transfer matrix (GTM) PVC algorithm, and quantitative comparisons were made. The results of digital MRI phantom studies suggest that the use of HBSA produces the best performance for WM classification. For GM classification, it is suggested to use the EMS. Segmentation performed on clinical MRI data show quite substantial differences, especially when lesions are present. For the particular case of PVC, SPM2 and EMS algorithms show very similar results and may be used interchangeably. The use of HBSA is not recommended for PVC. The partial volume corrected activities in some regions of the brain show quite large relative differences when performing paired analysis on 2 algorithms, implying a careful choice of the segmentation algorithm for GTM-based PVC.     

Fast and robust multi-atlas segmentation of brain magnetic resonance images

We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity modelling based on segmentation using expectation maximisation (EM) and optimisation via graph cuts. The segmentation pipeline is evaluated with two data cohorts: IBSR data (N = 18, six subcortial structures: thalamus, caudate, putamen, pallidum, hippocampus, amygdala) and ADNI data (N =   60, hippocampus). The average similarity index between automatically and manually generated volumes was 0.849 (IBSR, six subcortical structures) and 0.880 (ADNI, hippocampus). The correlation coefficient for hippocampal volumes was 0.95 with the ADNI data. The computation time using a standard multicore PC computer was about 3–4 min. Our results compare favourably with other recently published results.     

A fully automatic and robust brain MRI tissue classification method

A novel, fully automatic, adaptive, robust procedure for brain tissue classification from 3D magnetic resonance head images (MRI) is described in this paper. The procedure is adaptive in that it customizes a training set, by using a 'pruning' strategy, such that the classification is robust against anatomical variability and pathology. Starting from a set of samples generated from prior tissue probability maps (a 'model') in a standard, brain-based coordinate system ('stereotaxic space'), the method first reduces the fraction of incorrectly labeled samples in this set by using a minimum spanning tree graph-theoretic approach. Then, the corrected set of samples is used by a supervised kNN classifier for classifying the entire 3D image. The classification procedure is robust against variability in the image quality through a non-parametric implementation: no assumptions are made about the tissue intensity distributions. The performance of this brain tissue classification procedure is demonstrated through quantitative and qualitative validation experiments on both simulated MRI data (10 subjects) and real MRI data (43 subjects). A significant improvement in output quality was observed on subjects who exhibit morphological deviations from the model due to aging and pathology.     

Fast surface and volume estimation from non-parallel cross-sections, for freehand three-dimensional ultrasound

Volume measurements from ultrasound B-scans are useful in many clinical areas. It has been demonstrated previously that using three-dimensional (3-D) ultrasound can greatly increase the accuracy of these measurements. Freehand 3-D ultrasound allows freedom of movement in scanning, but the processing is complicated by having non-parallel scan planes. Two techniques are proposed for volume measurement from such data, which also improve surface and volume estimation from data acquired on parallel planes. Cubic planimetry is a more accurate extension of a volume measurement technique involving vector areas and centroids of cross-sections. Maximal-disc shape-based interpolation is an extension of shape-based interpolation which uses maximal disc representations to adjust the interpolation direction locally and hence improve the quality of the surface generated. Both methods are tested in simulation and in vivo. Volumes estimated using cubic planimetry are more accurate than step-section planimetry, and require fewer cross-sections, even for complex objects. Maximal-disc shape-based interpolation provides a reliable means of reconstructing surfaces from a handful of cross-sections, and can therefore be used to give confidence in the segmentation and hence also the cubic planimetry volume.     

Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI

We present a framework for the analysis of short axis cardiac MRI, using statistical models of shape and appearance. The framework integrates temporal and structural constraints and avoids common optimization problems inherent in such high dimensional models. The first contribution is the introduction of an algorithm for fitting 3D active appearance models (AAMs) on short axis cardiac MRI. We observe a 44-fold increase in fitting speed and a segmentation accuracy that is on par with Gauss-Newton optimization, one of the most widely used optimization algorithms for such problems. The second contribution involves an investigation on hierarchical 2D+time active shape models (ASMs), that integrate temporal constraints and simultaneously improve the 3D AAM based segmentation. We obtain encouraging results (endocardial/epicardial error 1.43+/-0.49 mm/1.51+/-0.48 mm) on 7980 short axis cardiac MR images acquired from 33 subjects. We have placed our dataset online, for the community to use and build upon.     

Evaluation of anatomy based reconstruction for partial volume correction in brain FDG-PET

FDG-PET contributes to the diagnosis and management of neurological diseases. In some of these diseases, pathological gray matter (GM) areas may have a reduced FDG uptake. Detection of these regions can be difficult and some remain undiscovered using visual assessment. The main reason for this detection problem is the relatively small thickness of GM compared to the spatial resolution of PET, known as the partial volume effect. We have developed an anatomy-based maximum-a-posteriori reconstruction algorithm (A-MAP) which corrects for this effect during the reconstruction using segmented magnetic resonance (MR) data. Monte-Carlo based 3-D brain software phantom simulations were used to investigate the influence of the strength of anatomy-based smoothing in GM, the influence of misaligned MR data, and the effect of local segmentation errors. A human observer study was designed to assess the detection performance of A-MAP versus post-smoothed maximum-likelihood (ML) reconstruction. We demonstrated the applicability of A-MAP using real patient data. The results for A-MAP showed improved recovery values and robustness for local segmentation errors. Misaligned MR data reduced the recovery values towards those obtained by post-smoothed ML, for small registration errors. In the human observer study, detection accuracy of hypometabolic regions was significantly improved using A-MAP, compared to post-smoothed ML (P < 0.004). The patient study confirmed the applicability of A-MAP in clinical practice. Conclusion: A-MAP is a promising technique for voxel-based partial volume correction of FDG-PET of the human brain.     

Functional and structural synergy for resolution recovery and partial volume correction in brain PET

Purpose: Positron Emission Tomography (PET) has the unique capability of measuring brain function but its clinical potential is affected by low resolution and lack of morphological detail. Here we propose and evaluate a wavelet synergistic approach that combines functional and structural information from a number of sources (CT, MRI and anatomical probabilistic atlases) for the accurate quantitative recovery of radioactivity concentration in PET images. When the method is combined with anatomical probabilistic atlases, the outcome is a functional volume corrected for partial volume effects.Methods: The proposed method is based on the multiresolution property of the wavelet transform. First, the target PET image and the corresponding anatomical image (CT/MRI/atlas-based segmented MRI) are decomposed into several resolution elements. Secondly, high-resolution components of the PET image are replaced, in part, with those of the anatomical image after appropriate scaling. The amount of structural input is weighted by the relative high frequency signal content of the two modalities. The method was validated on a digital Zubal phantom and clinical data to evaluate its quantitative potential.Results: Simulation studies showed the expected relationship between functional recovery and the amount of correct structural detail provided, with perfect recovery achieved when true images were used as anatomical reference. The use of T1-MRI images brought significant improvements in PET image resolution. However improvements were maximized when atlas-based segmented images as anatomical references were used; these results were replicated in clinical data sets.Conclusion: The synergistic use of functional and structural data, and the incorporation of anatomical probabilistic information in particular, generates morphologically corrected PET images of exquisite quality.     

Fast and robust registration of PET and MR images of human brain

In recent years, mutual information has proved to be an excellent criterion for registration of intra-individual images from different modalities. Multi-resolution coarse-to-fine optimization was proposed for speeding-up of the registration process. The aim of our work was to further improve registration speed without compromising robustness or accuracy. We present and evaluate two procedures for co-registration of positron emission tomography (PET) and magnetic resonance (MR) images of human brain that combine a multi-resolution approach with an automatic segmentation of input image volumes into areas of interest and background. We show that an acceleration factor of 10 can be achieved for clinical data and that a suitable preprocessing can improve robustness of registration. Emphasis was laid on creation of an automatic registration system that could be used routinely in a clinical environment. For this purpose, an easy-to-use graphical user interface has been developed. It allows physicians with no special knowledge of the registration algorithm to perform a fast and reliable alignment of images. Registration progress is presented on the fly on a fusion of images and enables visual checking during a registration.     

MRI定量评估胎儿脑体积

目的观察MRI定量脑体积评估胎儿大脑发育的价值。方法对60例排除胎儿中枢神经系统异常或发育不良、孕周(GA)21～37周的单胎妊娠孕妇行胎儿MR检查,采用单次激发快速自旋回波(SSTSE)序列采集胎儿MRI,经后处理后手动分割大脑,测量胎儿三维脑体积指标颅腔内体积(ICV)、脑总容积(TBV)和脑脊液容积(CFV),计算大脑二维径线指标,包括大脑双顶径(BPD)、骨性双顶径(SBD)、枕额径(SOD)及头围(HC)。分析二维径线指标及三维脑体积指标与GA的相关性,并进行回归分析;观察三维脑体积指标与二维径线指标间的关系。结果 TBV(r=0.98)、ICV(r=0.97)、CFV(r=0.89)、BPD(r=0.96)、SBD(r=0.94)、SOD(r=0.96)、HC(r=0.96)均与GA呈高度正相关(P均<0.01)。TBV及ICV与二维径线各指标间均高度正相关(P均<0.01),HC与CFV间亦呈高度相关(P<0.01)。结论 MRI三维定量脑体积可较好评估GA 21～37周胎儿颅脑生长发育,有望为产前诊断胎儿脑疾病及研究其发病机制提供新的影像学手段。     

Robust statistical shape models for MRI bone segmentation in presence of small field of view

Accurate bone modeling from medical images is essential in the diagnosis and treatment of patients because it supports the detection of abnormal bone morphology, which is often responsible for many musculoskeletal diseases (MSDs) of human articulations. In a clinical setting, images of the suspected joints are acquired in a high resolution but with a small field of view (FOV) in order to maximize the image quality while reducing acquisition time. However bones are only partially visible in such small FOVs. This presents difficult challenges in automated bone segmentation and thus limits the application of sophisticated algorithms such as statistical shape models (SSM), which have been generally proven to be an efficient technique for bone segmentation. Indeed, the reduced image information affects the initialization and evolution of these deformable model-based approaches. In this paper, we present a robust multi-resolution SSM algorithm with an adapted initialization to address the segmentation of MRI bone images acquired in small FOVs for modeling and computer-aided diagnosis. Our innovation stems from the derivation of a robust SSM based on complete and corrupted shapes, as well as from a simultaneous optimization of transformation and shape parameters to yield an efficient initialization technique. We demonstrate our segmentation algorithm using 86 clinical MRI images of the femur and hip bones. These images have a varied resolution and limited FOVs. The results of our segmentation (e.g., average distance error of 1.12 ± 0.46 mm) are within the needs of image-based clinical diagnosis.     

Detecting the brain surface in sparse MRI using boundary models

We introduce a framework for the detection of the brain boundary (arachnoid) within sparse MRI. We use the term sparse to describe volumetric images in which the sampling resolution within the imaging plane is far higher than that of the perpendicular direction. Generic boundary detection schemes do not provide good results for such data. In the scheme we propose, the boundary is extracted using a constrained mesh surface which iteratively approximates a 3D point set consisting of detected boundary points. Boundary detection is based on a database of piecewise constant models, which represent the idealised MR intensity profile of the underlying boundary anatomy. A non-linear matching scheme is introduced to estimate the location of the boundary points using only the intensity data within each image plane. Results are shown for a number of images and are discussed in detail.     

乳腺MRI检查最佳序列选择及扫描参数优化

乳腺MRI已成为乳腺疾病诊断不可或缺的非侵入性手段.高质量的MR图像是乳腺MRI检查的关键.本文简要介绍乳腺MRI检查的基本技术要求、乳腺MRI主要参数和序列优化及乳腺MRI常用序列,最后介绍了乳腺MRI检查的图像后处理技术.     

A novel and robust method for rapid strain estimation in elastography

In elastography, change in signal shape from tissue deformation and nonaxial tissue motion reduce correlation between the pre- and postcompression echo signals. Appropriate global temporal stretching of postcompression signals can reduce the decorrelation. Adaptive stretching performs a search for the stretch factor that maximizes the correlation between the pre- and postcompression echo signal segments at each data window location. Adaptive stretching is robust but computation intensive. In contrast, global stretching is fast but performs well only in areas where local strains are close to the applied strain. We developed a method that strikes a balance between the speed of global stretching and the performance of adaptive stretching. In this method, several strain maps are computed by performing global stretching with a range of different stretch factors. The area in each computed strain image with strain values closely corresponding to the uniform stretch factor will contain 'good quality' strain estimates. To produce a single elastogram at the end, we identify the strain map with the maximum correlation at each location and the strain value in that strain map at that location is chosen for the combined map. Results from data generated by finite-element simulation and phantom experiments demonstrate that the described strain estimator is significantly less susceptible to signal degradation than conventional strain estimators.     

Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator

Respiratory motion models have potential application for estimating and correcting the effects of motion in a wide range of applications, for example in PET-MR imaging. Given that motion cycles caused by breathing are only approximately repeatable, an important quality of such models is their ability to capture and estimate the intra- and inter-cycle variability of the motion. In this paper we propose and describe a technique for free-form nonrigid respiratory motion correction in the thorax. Our model is based on a principal component analysis of the motion states encountered during different breathing patterns, and is formed from motion estimates made from dynamic 3-D MRI data. We apply our model using a data-driven technique based on a 2-D MRI image navigator. Unlike most previously reported work in the literature, our approach is able to capture both intra- and inter-cycle motion variability. In addition, the 2-D image navigator can be used to estimate how applicable the current motion model is, and hence report when more imaging data is required to update the model. We also use the motion model to decide on the best positioning for the image navigator. We validate our approach using MRI data acquired from 10 volunteers and demonstrate improvements of up to 40.5% over other reported motion modelling approaches, which corresponds to 61% of the overall respiratory motion present. Finally we demonstrate one potential application of our technique: MRI-based motion correction of real-time PET data for simultaneous PET-MRI acquisition.