Image distortion correction in fMRI: A quantitative evaluation

A well-recognized problem with the echo-planar imaging (EPI) technique most commonly used for functional magnetic resonance imaging (fMRI) studies is geometric distortion caused by magnetic field inhomogeneity. This makes it difficult to achieve an accurate registration between a functional activation map calculated from an EPI time series and an undistorted, high resolution anatomical image. A correction method based on mapping the spatial distribution of field inhomogeneities can be used to reduce these distortions. This approach is attractive in its simplicity but requires postprocessing to improve the robustness of the acquired field map and reduce any secondary artifacts. Furthermore, the distribution of the internal magnetic field throughout the head is position dependent resulting in an interaction between distortion and head motion. Therefore, a single field map may not be sufficient to correct for the distortions throughout a whole fMRI time series. In this paper we present a quantitative evaluation of image distortion correction for fMRI at 2T. We assess (i) methods for the acquisition and calculation of field maps, (ii) the effect of image distortion correction on the coregistration between anatomical and functional images, and (iii) the interaction between distortion and head motion, assessing the feasibility of using field maps to reduce this effect. We propose that field maps with acceptable noise levels can be generated easily using a dual echo-time EPI sequence and demonstrate the importance of distortion correction for anatomical coregistration, even for small distortions. Using a dual echo-time series to generate a unique field map at each time point, we characterize the interaction between head motion and geometric distortion. However, we suggest that the variance between successively measured field maps introduces additional unwanted variance in the voxel time-series and is therefore not adequate to correct for time-varying distortions.     

Fusion of autoradiographs with an MR volume using 2-D and 3-D linear transformations

In the past years, the development of 3-D medical imaging has enabled the 3-D imaging of in vivo tissues, from an anatomical (MR, CT) or even functional (fMRI, PET, SPECT) point of view. However, despite immense technological progress, the resolution of these images is still short of the level of anatomical or functional details that in vitro imaging (e.g., histology, autoradiography) permits. The motivation of this work is to compare fMRI activations to activations observed in autoradiographic images from the same animals. We aim to fuse post-mortem autoradiographic data with a pre-mortem anatomical MR image. We first reconstruct a 3-D volume from the 2-D autoradiographic sections, coherent both in geometry and intensity. Then, this volume is fused with the MR image. This way, we ensure that the reconstructed 3-D volume can be superimposed onto the MR image that represents the reference anatomy. We demonstrate that this fusion can be achieved by using only simple global transformations (rigid and/or affine, 2-D and 3-D), while yielding very satisfactory results.     

Single-shot compensation of image distortions and BOLD contrast optimization using multi-echo EPI for real-time fMRI

Functional magnetic resonance imaging (fMRI) is most commonly based on echo-planar imaging (EPI). With higher field strengths, gradient performance, and computational power, real-time fMRI has become feasible; that is, brain activation can be monitored during the ongoing scan. However, EPI suffers from geometric distortions due to inhomogeneities of the magnetic field, especially close to air-tissue interfaces. Thus, functional activations might be mislocalized and assigned to the wrong anatomical structures. Several techniques have been reported which reduce geometric distortions, for example, mapping of the static magnetic field B(0) or the point spread function for all voxels. Yet these techniques require additional reference scans and in some cases extensive computational time. Moreover, only static field inhomogeneities can be corrected, because the correction is based on a static reference scan. We present an approach which allows for simultaneous acquisition and distortion correction of a functional image without a reference scan. The technique is based on a modified multi-echo EPI data acquisition scheme using a phase-encoding (PE) gradient with alternating polarity. The images exhibit opposite distortions due to the inverted PE gradient. After adjusting the contrast of the images acquired at different echo times, this information is used for the distortion correction. We present the theory, implementation, and applications of this single-shot distortion correction. Significant reduction in geometric distortion is shown both for phantom images and human fMRI data. Moreover, sensitivity to the blood oxygen level-dependent (BOLD) effect is increased by weighted summation of the undistorted images.     

功能磁共振成像/弥散张量成像图像融合:脑肿瘤治疗术前计划新方法(英文)

Objective To develop and test an image fusion software(fDf,fMRI/DTI fusion) which can overlap the anatomical(T1WI images),functional MRI(fMRI) and diffusion tensor imaging(DTI) simultaneously for preoperational evaluation of patients′ tumor.Methods fMRI with bilateral hands grasp movement and DTI were performed using GE 1.5 T magnetic resonance system on 10 subjects(5 healthy volunteers and 5 patients with brain tumor,of which 3 were metastases,1 was cavernous hemangioma and 1 was glioma).All data were input to the personal computer and off line postprocessing of fMRI and DTI data was performed using SPM5 and Volume One software package to visualize the activated functional cortex areas and corticospinal tracts.fDf was used to import the fMRI and structural images to Volume One to show them simultaneously.Results Brain functional activation maps and diffusion tensor fiber tracking images were obtained in all five healthy volunteers and four patients except one who suffered from left hemiplegia.The functional activation maps and the fiber tracking images were successfully fused by the fDf software,where the activations areas and the white matter fiber were displayed together.The fusion images of healthy volunteers showed the hand motion areas and corresponding corticospinal tracts,while that of the patients display the relationship of the eloquent cortex and peritumoral fiber tracts,which were useful in guiding the treatments for the surgeons and radiotherapists.It only took hours to present the fused images to the neurosurgeons or the radiotherapists.Conclusions All the results confirm that the image fusion software work well for all the data.It is an easy,free and fast way to realize the image fusion.The neurosurgeon and radiotherapist consider the software were very helpful for preoperative planning.     

Multimodal image synthesis based on disentanglement representations of anatomical and modality specific features,learned using uncooperative relativistic GAN

Growing number of methods for attenuation-coefficient map estimation from magnetic resonance (MR) images have recently been proposed because of the increasing interest in MR-guided radiotherapy and the introduction of positron emission tomography (PET) MR hybrid systems. We propose a deep-network ensemble incorporating stochastic-binary-anatomical encoders and imaging-modality variational autoencoders, to disentangle image-latent spaces into a space of modality-invariant anatomical features and spaces of modality attributes. The ensemble integrates modality-modulated decoders to normalize features and image intensities based on imaging modality. Besides promoting disentanglement, the architecture fosters uncooperative learning, offering ability to maintain anatomical structure in a cross-modality reconstruction. Introduction of a modality-invariant structural consistency constraint further enforces faithful embedding of anatomy. To improve training stability and fidelity of synthesized modalities, the ensemble is trained in a relativistic generative adversarial framework incorporating multiscale discriminators. Analyses of priors and network architectures as well as performance validation were performed on computed tomography (CT) and MR pelvis datasets. The proposed method demonstrated robustness against intensity inhomogeneity, improved tissue-class differentiation, and offered synthetic CT in Hounsfield units with intensities consistent and smooth across slices compared to the state-of-the-art approaches, offering median normalized mutual information of 1.28, normalized cross correlation of 0.97, and gradient cross correlation of 0.59 over 324 images.     

A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping

Functional neuroimaging commands a dominant role in current neuroscience research. However its use in bedside clinical and certain neuro-scientific studies has been limited because the current tools lack the combination of being non-invasive, non-ionizing and portable while maintaining moderate resolution and localization accuracy. Optical neuroimaging satisfies many of these requirements, but, until recent advances in high-density diffuse optical tomography (HD-DOT), has been hampered by limited resolution. While early results of HD-DOT have been promising, a quantitative voxel-wise comparison and validation of HD-DOT against the gold standard of functional magnetic resonance imaging (fMRI) has been lacking. Herein, we provide such an analysis within the visual cortex using matched visual stimulation protocols in a single group of subjects (n=5) during separate HD-DOT and fMRI scanning sessions. To attain the needed voxel-to-voxel co-registration between HD-DOT and fMRI image spaces, we implemented subject-specific head modeling that incorporated MRI anatomy, detailed segmentation, and alignment of source and detector positions. Comparisons of the visual responses found an average localization error between HD-DOT and fMRI of 4.4+/-1mm, significantly less than the average distance between cortical gyri. This specificity demonstrates that HD-DOT has sufficient image quality to be useful as a surrogate for fMRI.     

Generating accurate finite element meshes for the forward model of the human head in EIT

The use of realistic anatomy in the model used for image reconstruction in EIT of brain function appears to confer significant improvements compared to geometric shapes such as a sphere. Accurate model geometry may be achieved by numerical models based on magnetic resonance images (MRIs) of the head, and this group has elected to use finite element meshing (FEM) as it enables detailed internal anatomy to be modelled and has the capability to incorporate information about tissue anisotropy. In this paper a method for generating accurate FEMs of the human head is presented where MRI images are manually segmented using custom adaptation of industry standard commercial design software packages. This is illustrated with example surface models and meshes from adult epilepsy patients, a neonatal baby and a phantom latex tank incorporating a real skull. Mesh quality is assessed in terms of element stretch and hence distortion.     

Simultaneous 3-T fMRI and high-density recording of human auditory evoked potentials

We acquired simultaneous high-field (3 T) functional magnetic resonance imaging (fMRI) and high-density (64- and 128-channel) EEG using a sparse sampling technique to measure auditory cortical activity generated by right ear stimulus presentation. Using dipole source localization, we showed that the anatomical location of the grand mean equivalent dipole of auditory evoked potentials (AEPs) and the center of gravity of fMRI activity were in good agreement in the horizontal plane. However, the grand mean equivalent dipole was located significantly superior in the cortex compared to fMRI activity. Interhemispheric asymmetry was exhibited by fMRI, whereas neither the AEP dipole moments nor the mean global field power (MGFP) of the AEPs showed significant asymmetry. Increasing the number of recording electrodes from 64 to 128 improved the accuracy of the equivalent dipole source localization but decreased the signal-to-noise ratio (SNR) of MR images. This suggests that 64 electrodes may be optimal for use in simultaneous recording of EEG and fMRI.     

Diffusion tensor studies dissociated two fronto-temporal pathways in the human memory system

Recent functional neuroimaging studies have shown that multiple cortical areas are involved in memory encoding and retrieval. However, the underlying anatomical connections among these memory-related areas in humans remain elusive due to methodological limitations. Diffusion tensor imaging (DTI) is a technique based on detecting the diffusion of water molecules from magnetic resonance images. DTI allows non-invasive mapping of anatomical connections and gives a comprehensive picture of connectivity throughout the entire brain. By combining functional magnetic resonance imaging (fMRI) and DTI, we show that memory-related areas in the left dorsolateral prefrontal cortex (DLPFC) and the left ventrolateral prefrontal cortex (VLPFC) each connect with memory-related areas in the left temporal cortex. This result suggests there are two pathways between prefrontal cortex and temporal cortex related to the human memory system.     

A stereotactic method for image-guided transcranial magnetic stimulation validated with fMRI and motor-evoked potentials

Transcranial Magnetic Stimulation (TMS) delivers short magnetic pulses that penetrate the skull unattenuated, disrupting neural processing in a noninvasive, reversible way. To disrupt specific neural processes, coil placement over the proper site is critical. Therefore, a neural navigator (NeNa) was developed. NeNa is a frameless stereotactic device using structural and functional magnetic resonance imaging (fMRI) data to guide TMS coil placement. To coregister the participant's head to his MRI, 3D cursors are moved to anatomical landmarks on a skin rendering of the participants MRI on a screen, and measured at the head with a position measurement device. A method is proposed to calculate a rigid body transformation that can coregister both sets of coordinates under realistic noise conditions. After coregistration, NeNa visualizes in real time where the device is located with respect to the head, brain structures, and activated areas, enabling precise placement of the TMS coil over a predefined target region. NeNa was validated by stimulating 5 x 5 positions around the 'motor hotspot' (thumb movement area), which was marked on the scalp guided by individual fMRI data, while recording motor-evoked potentials (MEPs) from the abductor pollicis brevis (APB). The distance between the center of gravity (CoG) of MEP responses and the location marked on the scalp overlying maximum fMRI activation was on average less then 5 mm. The present results demonstrate that NeNa is a reliable method for image-guided TMS coil placement.     

Registration of 3D fetal neurosonography and MRI

We propose a method for registration of 3D fetal brain ultrasound with a reconstructed magnetic resonance fetal brain volume. This method, for the first time, allows the alignment of models of the fetal brain built from magnetic resonance images with 3D fetal brain ultrasound, opening possibilities to develop new, prior information based image analysis methods for 3D fetal neurosonography. The reconstructed magnetic resonance volume is first segmented using a probabilistic atlas and a pseudo ultrasound image volume is simulated from the segmentation. This pseudo ultrasound image is then affinely aligned with clinical ultrasound fetal brain volumes using a robust block-matching approach that can deal with intensity artefacts and missing features in the ultrasound images. A qualitative and quantitative evaluation demonstrates good performance of the method for our application, in comparison with other tested approaches. The intensity average of 27 ultrasound images co-aligned with the pseudo ultrasound template shows good correlation with anatomy of the fetal brain as seen in the reconstructed magnetic resonance image.     

Reducing inter-subject anatomical variation: Effect of normalization method on sensitivity of functional magnetic resonance imaging data analysis in auditory cortex and the superior temporal region

Conventional group analysis of functional MRI (fMRI) data usually involves spatial alignment of anatomy across participants by registering every brain image to an anatomical reference image. Due to the high degree of inter-subject anatomical variability, a low-resolution average anatomical model is typically used as the target template, and/or smoothing kernels are applied to the fMRI data to increase the overlap among subjects' image data. However, such smoothing can make it difficult to resolve small regions such as subregions of auditory cortex when anatomical morphology varies among subjects. Here, we use data from an auditory fMRI study to show that using a high-dimensional registration technique (HAMMER) results in an enhanced functional signal-to-noise ratio (fSNR) for functional data analysis within auditory regions, with more localized activation patterns. The technique is validated against DARTEL, a high-dimensional diffeomorphic registration, as well as against commonly used low-dimensional normalization techniques such as the techniques provided with SPM2 (cosine basis functions) and SPM5 (unified segmentation) software packages. We also systematically examine how spatial resolution of the template image and spatial smoothing of the functional data affect the results. Only the high-dimensional technique (HAMMER) appears to be able to capitalize on the excellent anatomical resolution of a single-subject reference template, and, as expected, smoothing increased fSNR, but at the cost of spatial resolution. In general, results demonstrate significant improvement in fSNR using HAMMER compared to analysis after normalization using DARTEL, or conventional normalization such as cosine basis function and unified segmentation in SPM, with more precisely localized activation foci, at least for activation in the region of auditory cortex.     

Electromyography as a recording system for eyeblink conditioning with functional magnetic resonance imaging

This study was designed to develop a suitable method of recording eyeblink responses while conducting functional magnetic resonance imaging (fMRI). Given the complexity of this behavioral setup outside of the magnet, this study sought to adapt and further optimize an approach to eyeblink conditioning that would be suitable for conducting event-related fMRI experiments. This method involved the acquisition of electromyographic (EMG) signals from the orbicularis oculi of the right eye, which were subsequently amplified and converted into an optical signal outside of the head coil. This optical signal was converted back into an electrical signal once outside the magnet room. Electromyography (EMG)-detected eyeblinks were used to measure responses in a delay eyeblink conditioning paradigm. Our results indicate that: (1) electromyography is a sensitive method for the detection of eyeblinks during fMRI; (2) minimal interactions or artifacts of the EMG signal were created from the magnetic resonance pulse sequence; and (3) no electromyography-related artifacts were detected in the magnetic resonance images. Furthermore, an analysis of the functional data showed areas of activation that have previously been shown in positron emission tomography studies of human eyeblink conditioning. Our results support the strength of this behavioral setup as a suitable method to be used in association with fMRI.     

脑功能磁共振图像中Nyquist伪影的处理方法研究

Nyquist伪影是功能磁共振成像过程中产生的一种特有的伪影,使图像信噪比下降,并对图像的可视化及功能定位产生不利影响.尽可能地消除Nyquist伪影,无论对于改善图像的视觉效果,还是对后续的功能定位分析和可视化都具有重要意义.目前对Nyquist伪影还没有有效的处理方法.本文根据功能磁共振图像本身的信息,首次提出一种基于模糊聚类技术的Nyquist伪影的处理方法,能比较有效地消除Nyquist伪影.     

Variable precision registration via wavelets: optimal spatial scales for inter-subject registration of functional MRI

The detection of significantly activated brain regions in multi-subject functional magnetic resonance imaging (fMRI) studies almost invariably entails the coregistration of individual subjects' data in a standard space. Here, we investigate how sensitivity to detect loci of generic activation in such studies may be conditioned by the precision of anatomical registration. We describe a novel algorithm, implemented in the wavelet domain, for inhomogeneous deformation of individual images to match a template. The algorithm matches anatomical features in a coarse-to-fine fashion, first minimising a cost function in terms of relatively coarse spatial features and then proceeding iteratively to match the images in terms of progressively more detailed anatomical features. Applying the method to data acquired from two groups of 12 healthy volunteers (with mean age 27 and 70 years, respectively), during performance of a paired associate learning task, we show that geometrical overlap between template and individual images is monotonically improved, compared to an affine transform, by additional inhomogeneous deformations informed by more detailed features. Likewise, sensitivity to detect activated voxels can be substantially improved, by a factor of 4 or more, if wavelet-mediated deformations informed by medium-sized anatomical features are applied in addition to a preliminary affine transform. However, sensitivity to detect activated voxels was reduced by "over-registering" data or matching anatomical features at the finest scales of the wavelet transform. The benefits of variable precision registration are particularly salient for data acquired in older subjects, which showed evidence of greater inter-subject anatomic variability and generally required more extensive local deformation to achieve a satisfactory match to the template image. We conclude that major benefits in sensitivity to detect functional activation in multi-subject fMRI studies can be attained with an inhomogeneous deformation applied over appropriate spatial scales.     

Experimental model for functional magnetic resonance imaging of somatic sensory cortex in the unanesthetized rat

Functional magnetic resonance imaging (fMRI) has evolved into a method widely used to map neural activation in the human brain. fMRI is a method for recording blood oxygen level-dependent (BOLD) signals. These signals change with local cerebral blood flow coupled to neural activity. However, the relationship between BOLD signals and neural function is poorly understood and requires the development of animal models. Here we use an unanesthetized rat preparation to study BOLD responses to whisker stimulation in somatic sensory barrel cortex. Five rats were trained to tolerate restraint in a holder and fMRI noise with positive reinforcement. For maximal immobilization, the head was fastened to the holder with nuts screwed on threaded bolts attached to the head. On scanning day, residual stress was alleviated with injections of diazepam, and the rats were restrained in the holder and transferred into the scanner. After >75 min to allow the tranquilization to abate, structural images were acquired from three coronal brain slices. Subsequently, functional images were taken utilizing 4-min epochs without stimulation alternated with equivalent epochs during which the right caudal whiskers were stimulated with three air puffs/s. After 4 weeks, fMRI could be repeated in four rats. In seven of the nine functional runs, head motion was minimal and whisker stimulation resulted in a statistically significant (P 相似文献   

Graph-partitioned spatial priors for functional magnetic resonance images

Spatial models of functional magnetic resonance imaging (fMRI) data allow one to estimate the spatial smoothness of general linear model (GLM) parameters and eschew pre-process smoothing of data entailed by conventional mass-univariate analyses. Recently diffusion-based spatial priors [Harrison, L.M., Penny, W., Daunizeau, J., and Friston, K.J. (2008). Diffusion-based spatial priors for functional magnetic resonance images. NeuroImage.] were proposed, which provide a way to formulate an adaptive spatial basis, where the diffusion kernel of a weighted graph-Laplacian (WGL) is used as the prior covariance matrix over GLM parameters. An advantage of these is that they can be used to relax the assumption of isotropy and stationarity implicit in smoothing data with a fixed Gaussian kernel. The limitation of diffusion-based models is purely computational, due to the large number of voxels in a brain volume. One solution is to partition a brain volume into slices, using a spatial model for each slice. This reduces computational burden by approximating the full WGL with a block diagonal form, where each block can be analysed separately. While fMRI data are collected in slices, the functional structures exhibiting spatial coherence and continuity are generally three-dimensional, calling for a more informed partition. We address this using the graph-Laplacian to divide a brain volume into sub-graphs, whose shape can be arbitrary. Their shape depends crucially on edge weights of the graph, which can be based on the Euclidean distance between voxels (isotropic) or on GLM parameters (anisotropic) encoding functional responses. The result is an approximation the full WGL that retains its 3D form and also has potential for parallelism. We applied the method to high-resolution (1 mm³) fMRI data and compared models where a volume was divided into either slices or graph-partitions. Models were optimized using Expectation-Maximization and the approximate log-evidence computed to compare these different ways to partition a spatial prior. The high-resolution fMRI data presented here had greatest evidence for the graph partitioned anisotropic model, which was best able to preserve fine functional detail.     

Functional MRI in the brain tumor patient

Functional magnetic resonance imaging (fMRI) has been adopted almost universally by disciplines that endeavor to understand how the brain works. As basic scientists tune the technique, clinicians are increasingly able to apply brain mapping with fMRI to their clinical practice. We present here a guide to using fMRI in a clinical setting. We discuss the basic considerations of functional brain mapping in patients with brain tumors including: patient screening and training, paradigm design, data analysis and interpretation of the fMRI scans.     

Subtraction ictal SPECT coregistered to MRI for seizure focus localization in partial epilepsy

Peri-ictal single-photon emission computed tomography (SPECT) of the brain is increasingly used in localizing the seizure focus in presurgical evaluation of patients with partial epilepsy. However, traditional side-by-side visual interpretation of ictal and interictal SPECT films is hampered by differences in slice location and tracer activity. Precise correlation of the seizure focus with a high-quality image of the underlying brain anatomy can improve the physician's understanding of seizure neurophysiology and assist in surgical planning. Computer-based methods have been developed for aligning, normalizing, and subtracting digital ictal and interictal SPECT images of the patient's brain to produce a map of the blood flow changes occurring between the seizure and resting states. These maps are then aligned with a high-resolution magnetic resonance image (MRI) of the patient's brain anatomy and fused to identify anatomical regions involved in the seizure. The purpose of this article is to review the technical components and clinical implementation of subtraction ictal SPECT, as well as to discuss recent technological advances that could extend and improve the diagnostic and localizing capacity of this method.     

Magnetic resonance imaging appropriate for construction of subject-specific head models for diffuse optical tomography

Subject-specific head models of which their geometry is based on structural magnetic resonance images are essential to accurately estimate the spatial sensitivity profiles for image reconstruction in diffuse optical tomography. T1-weighted magnetic resonance images, which are commonly used for structural imaging, are not sufficient for the threshold-based segmentation of the superficial tissues. Two types of pulse sequences, which provide a high contrast among the superficial tissues, are introduced to complement the segmentation to construct the subject-specific head models. The magnetic resonance images acquired by the proposed pulse sequences are robust to the threshold level and adequate for the threshold-based segmentation of the superficial tissues compared to the T1- and T2-weighted images. The total scan time of the proposed pulse sequences is less than one-fourth of that for the T2-weighted pulse sequence.OCIS codes: (170.2655) Functional monitoring and imaging, (170.3660) Light propagation in tissues