Precise labeling of subcortical structures plays a key role in functional neurosurgical applications. Labels from an atlas image are propagated to a patient image using atlas-based segmentation. Atlas-based segmentation is highly dependent on the registration framework used to guide the atlas label propagation. This paper focuses on atlas-based segmentation of subcortical brain structures and the effect of different registration methods on the generated subcortical labels. A single-step and three two-step registration methods appearing in the literature based on affine and deformable registration algorithms in the ANTS and FSL algorithms are considered. Experiments are carried out with two atlas databases of IBSR and LPBA40. Six segmentation metrics consisting of Dice overlap, relative volume error, false positive, false negative, surface distance, and spatial extent are used for evaluation. Segmentation results are reported individually and as averages for nine subcortical brain structures. Based on two statistical tests, the results are ranked. In general, among four different registration strategies investigated in this paper, a two-step registration consisting of an initial affine registration followed by a deformable registration applied to subcortical structures provides superior segmentation outcomes. This method can be used to provide an improved labeling of the subcortical brain structures in MRIs for different applications.
The recent development of motion robust super-resolution fetal brain MRI holds out the potential for dramatic new advances in volumetric and morphometric analysis. Volumetric analysis based on volumetric and morphometric biomarkers of the developing fetal brain must include segmentation. Automatic segmentation of fetal brain MRI is challenging, however, due to the highly variable size and shape of the developing brain; possible structural abnormalities; and the relatively poor resolution of fetal MRI scans. To overcome these limitations, we present a novel, constrained, multi-atlas, multi-shape automatic segmentation method that specifically addresses the challenge of segmenting multiple structures with similar intensity values in subjects with strong anatomic variability. Accordingly, we have applied this method to shape segmentation of normal, dilated, or fused lateral ventricles for quantitative analysis of ventriculomegaly (VM), which is a pivotal finding in the earliest stages of fetal brain development, and warrants further investigation. Utilizing these innovative techniques, we introduce novel volumetric and morphometric biomarkers of VM comparing these values to those that are generated by standard methods of VM analysis, i.e., by measuring the ventricular atrial diameter (AD) on manually selected sections of 2D ultrasound or 2D MRI. To this end, we studied 25 normal and abnormal fetuses in the gestation age (GA) range of 19 to 39 weeks (mean=28.26, stdev=6.56). This heterogeneous dataset was essentially used to 1) validate our segmentation method for normal and abnormal ventricles; and 2) show that the proposed biomarkers may provide improved detection of VM as compared to the AD measurement.
Normal brain development is associated with expansion and folding of the cerebral cortex following a highly orchestrated sequence of gyral-sulcal formation. Although several studies have described the evolution of cerebral cortical development ex vivo or ex utero, to date, very few studies have characterized and quantified the gyrification process for the in vivo fetal brain. Recent advances in fetal magnetic resonance imaging and post-processing computational methods are providing new insights into fetal brain maturation in vivo. In this study, we investigate the in vivo fetal cortical folding pattern in healthy fetuses between 25 and 35 weeks gestational age using 3-D reconstructed fetal cortical surfaces. We describe the in vivo fetal gyrification process using a robust feature extraction algorithm applied directly on the cortical surface, providing an explicit delineation of the sulcal pattern during fetal brain development. We also delineate cortical surface measures, including surface area and gyrification index. Our data support an exuberant third trimester gyrification process and suggest a non-linear evolution of sulcal development. The availability of normative indices of cerebral cortical developing in the living fetus may provide critical insights on the timing and progression of impaired cerebral development in the high-risk fetus.
Increasing the spatial resolution in diffusion-weighted imaging (DWI) is challenging with a single-shot EPI acquisition because of the decreased SNR and T2* relaxation. Recently, acquisition of orthogonal anisotropic acquisitions and super-resolution reconstruction (SRR) of the underlying high-resolution image has been proposed to achieve higher resolution. Promising results have been shown with simulations. However, practical evidence that SRR enables resolution enhancement remains unclear. Particularly, real DWI scans acquired in orthogonal directions are subject to very different distortion. This makes the precise alignment of the images impossible and strongly perturbs the reconstruction. In this work we propose to combine distortion compensation and SRR. Distortion compensation is achieved by acquisition of a dual echo field map, providing an estimate of the field inhomogeneity. The SRR is formulated as a maximum a posteriori problem and relies on a realistic image generation model. We evaluate our approach with real anisotropic acquisitions. Importantly, we demonstrate that combining distortion compensation and SRR provides better results than acquisition of a single isotropic scan for the same acquisition duration time. The SRR provides more detailed structures and better tractography results. This work provides the first evidence that SRR, which employs conventional SS-EPI techniques, may enable resolution enhancement in DWI, and may dramatically impact the way to achieve DW imaging in both neuroscience and clinical applications.
Diffusion-weighted imaging (DWI) enables non-invasive investigation and characterization of the white matter but suffers from a relatively poor spatial resolution. Increasing the spatial resolution in DWI is challenging with a single-shot EPI acquisition due to the decreased signal-to-noise ratio and T2(∗) relaxation effect amplified with increased echo time. In this work we propose a super-resolution reconstruction (SRR) technique based on the acquisition of multiple anisotropic orthogonal DWI scans. DWI scans acquired in different planes are not typically closely aligned due to the geometric distortion introduced by magnetic susceptibility differences in each phase-encoding direction. We compensate each scan for geometric distortion by acquisition of a dual echo gradient echo field map, providing an estimate of the field inhomogeneity. We address the problem of patient motion by aligning the volumes in both space and q-space. The SRR is formulated as a maximum a posteriori problem. It relies on a volume acquisition model which describes how the acquired scans are observations of an unknown high-resolution image which we aim to recover. Our model enables the introduction of image priors that exploit spatial homogeneity and enables regularized solutions. We detail our SRR optimization procedure and report experiments including numerical simulations, synthetic SRR and real world SRR. In particular, we demonstrate that combining distortion compensation and SRR provides better results than acquisition of a single isotropic scan for the same acquisition duration time. Importantly, SRR enables DWI with resolution beyond the scanner hardware limitations. This work provides the first evidence that SRR, which employs conventional single shot EPI techniques, enables resolution enhancement in DWI, and may dramatically impact the role of DWI in both neuroscience and clinical applications.
Rapid and efficient imaging of the brain to monitor brain activity and neural connectivity is performed through functional MRI and diffusion tensor imaging (DTI) using the Echo-planar imaging (EPI) sequence. An entire volume of the brain is imaged by EPI in a few seconds through the measurement of all k-space lines within one repetition time. However, this makes the sequence extremely sensitive to imperfections of magnetic field. In particular, the error caused by susceptibility induced magnetic field inhomogeneity accumulates over the duration of phase encoding, which in turn results in severe geometric distortion (warping) in EPI scans. EPI distortion correction through unwarping can be performed by field map based or image based techniques. However, due to the lack of ground truth it has been difficult to compare and validate different approaches. In this paper we propose a hybrid field map guided constrained deformable registration approach and compare it to field map based and image based unwarping approaches through a novel in-vivo validation framework which is based on the acquisition and alignment of EPI scans with different phase encoding directions. The quantitative evaluation results show that our hybrid approach of field map guided deformable registration to an undistorted T2-weighted image outperforms the other approaches.
PURPOSE: Fetal MRI volumetry is a useful technique but it is limited by a dependency upon motion-free scans, tedious manual segmentation, and spatial inaccuracy due to thick-slice scans. An image processing pipeline that addresses these limitations was developed and tested.
MATERIALS AND METHODS: The principal sequences acquired in fetal MRI clinical practice are multiple orthogonal single-shot fast spin echo scans. State-of-the-art image processing techniques were used for inter-slice motion correction and super-resolution reconstruction of high-resolution volumetric images from these scans. The reconstructed volume images were processed with intensity non-uniformity correction and the fetal brain extracted by using supervised automated segmentation.
RESULTS: Reconstruction, segmentation and volumetry of the fetal brains for a cohort of twenty-five clinically acquired fetal MRI scans was done. Performance metrics for volume reconstruction, segmentation and volumetry were determined by comparing to manual tracings in five randomly chosen cases. Finally, analysis of the fetal brain and parenchymal volumes was performed based on the gestational age of the fetuses.
CONCLUSION: The image processing pipeline developed in this study enables volume rendering and accurate fetal brain volumetry by addressing the limitations of current volumetry techniques, which include dependency on motion-free scans, manual segmentation, and inaccurate thick-slice interpolation.
Magnetic Resonance Imaging (MRI) is highly sensitive to motion; hence current practice is based on the prevention of motion during scan. In newborns, young children, and patients with limited cooperation, this commonly requires full sedation or general anesthesia, which is time consuming, costly, and is associated with significant risks. Despite progress in prospective motion correction in MRI, the use of motion compensation techniques is limited by the type and amount of motion that can be compensated for, the dependency on the scanner platform, the need for pulse sequence modifications, and/or difficult setup. In this paper we introduce a novel platform-independent motion-robust MRI technique based on prospective real-time motion tracking through a miniature magnetic field sensor and retrospective super-resolution volume reconstruction. The technique is based on fast 2D scans that maintain high-quality of slices in the presence of motion but are degraded in 3D due to inter-slice motion artifacts. The sensor, conveniently attached to the subject forehead, provides real-time estimation of the motion, which in turn gives the relative location of the slice acquisitions. These location parameters are used to compensate the inter-slice motion to reconstruct an isotropic high-resolution volumetric image from slices in a super-resolution reconstruction framework. The quantitative results obtained for phantom and volunteer subject experiments in this study show the efficacy of the developed technique, which is particularly useful for motion-robust high-resolution T2-weighted imaging of newborns and pediatric subjects.
Diffusion-weighted imaging (DWI) enables non-invasive investigation and characterization of the white-matter but suffers from a relatively poor resolution. In this work we propose a super-resolution reconstruction (SRR) technique based on the acquisition of multiple anisotropic orthogonal DWI scans. We address the problem of patient motions by aligning the volumes both in space and in q-space. The SRR is formulated as a maximum a posteriori (MAP) problem. It relies on a volume acquisition model which describes the generation of the acquired scans from the unknown high-resolution image. It enables the introduction of image priors that exploit spatial homogeneity and enables regularized solutions. We detail our resulting SRR optimization procedure and report various experiments including numerical simulations, synthetic SRR scenario and real world SRR scenario. Super-resolution reconstruction in DWI may enable DWI to be performed with unprecedented resolution.
Thick-slice image acquisitions are sometimes inevitable in magnetic resonance imaging due to limitations posed by pulse sequence timing and signal-to-noise-ratio. The estimation of an isotropic high-resolution volume from thick-slice MRI scans is desired for improved image analysis and evaluation. In this article we formulate a maximum a posteriori (MAP) estimation algorithm for high-resolution volumetric MRI reconstruction. As compared to the previous techniques, this probabilistic formulation relies on a slice acquisition model and allows the incorporation of image priors. We focus on image priors based on image gradients and compare the developed MAP estimation approach to scattered data interpolation (SDI) and maximum likelihood reconstruction. The results indicate that the developed MAP estimation approach outperforms the SDI techniques and appropriate image priors may improve the volume estimation when the acquired thick-slice scans do not sufficiently sample the imaged volume. We also report applications in pediatric and fetal imaging.
Fast magnetic resonance imaging slice acquisition techniques such as single shot fast spin echo are routinely used in the presence of uncontrollable motion. These techniques are widely used for fetal magnetic resonance imaging (MRI) and MRI of moving subjects and organs. Although high-quality slices are frequently acquired by these techniques, inter-slice motion leads to severe motion artifacts that are apparent in out-of-plane views. Slice sequential acquisitions do not enable 3-D volume representation. In this study, we have developed a novel technique based on a slice acquisition model, which enables the reconstruction of a volumetric image from multiple-scan slice acquisitions. The super-resolution volume reconstruction is formulated as an inverse problem of finding the underlying structure generating the acquired slices. We have developed a robust M-estimation solution which minimizes a robust error norm function between the model-generated slices and the acquired slices. The accuracy and robustness of this novel technique has been quantitatively assessed through simulations with digital brain phantom images as well as high-resolution newborn images. We also report here successful application of our new technique for the reconstruction of volumetric fetal brain MRI from clinically acquired data.
Magnetic resonance field map images are normally used in characterizing the magnetic field inhomogeneity for distortion correction in Echo-Planar Imaging (EPI) and accurate localization in functional MRI (fMRI). In this paper, the computation and applications of an average field map image template is investigated based on real field maps. The introduced methodology and the obtained field map image templates may be used in EPI and fMRI image analysis, distortion correction, registration, and functional localization when high-resolution field map images are not available for individual datasets. The introduced methodology involves three stages of pre-processing, registration, and spatial normalization. The analysis and results presented in this paper show the impact and usefulness of the investigated methodology in several applications.
Accurate segmentation of different brain tissues is of much importance in magnetic resonance imaging. This paper presents a comparison of the existing segmentation algorithms that are deployed in the neuroimaging community as part of two widely used software packages. The results obtained in this comparison can be used to select the appropriate segmentation algorithm for the neuroimaging application of interest. In addition to the entire brain area, a comparison is carried out for the subcortical region of the brain in terms of its gray matter composition.
This paper presents a set of validation procedures for nonrigid registration of functional EPI to anatomical MRI brain images. Although various registration techniques have been developed and validated for high-resolution anatomical MRI images, due to a lack of quantitative and qualitative validation procedures, the use of nonrigid registration between functional EPI and anatomical MRI images has not yet been deployed in neuroimaging studies. In this paper, the performance of a robust formulation of a nonrigid registration technique is evaluated in a quantitative manner based on simulated data and is further evaluated in a quantitative and qualitative manner based on in vivo data as compared to the commonly used rigid and affine registration techniques in the neuroimaging software packages. The nonrigid registration technique is formulated as a second-order constrained optimization problem using a free-form deformation model and mutual information similarity measure. Bound constraints, resolution level and cross-validation issues have been discussed to show the degree of accuracy and effectiveness of the nonrigid registration technique. The analyses performed reveal that the nonrigid approach provides a more accurate registration, in particular when the functional regions of interest lie in regions distorted by susceptibility artifacts.
Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.