Spin saturation artifact correction using slice-to-volume registration motion estimates for fMRI time series

Evaluation of functional magnetic resonance imaging (fMRI) as a reliable clinical imaging tool requires accurate assessment and correction of head motion artifacts. As the correction of bulk head motion is vital, the loss of signal strength from the confounding effect of head motion on spin magnetization may be an additional factor in activation analysis error. This study focuses on the evaluation and correction of the spin saturation artifact that occurs when parts of adjacent slices are selected due to changing head positions in single-shot multislice acquisitions. As a consequence of head movement, the acquired slices constituting a fMRI volume are no longer parallel to each other and the spin magnetization in fMRI voxels becomes dependent on head motion history. Motion corrections applying the same rigid motion estimates to all the slices in a volume may not be a reasonable approximation in cases where the magnitude of head motion exceeds a subvoxel range. For realistic ranges of motion in fMRI, an accurate estimate of rigid motion parameters for each echo planar imaging (EPI) slice is essential to correctly register voxel intensities. Previously we have implemented the map-slice-to-volume (MSV) motion correction method that maps each slice in a time series onto a reference anatomical volume, which proved to be effective in improving activation detection. To correctly evaluate the motion dependence of spin magnetization, each voxel is tracked with movement history that is available from MSV motion estimates. Relatively low in resolution, EPI voxels are composed of varying mixtures of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) and variations in the tissue composition give rise to voxel intensities that are functions of tissue T1 properties. We have developed a weighted-average spin saturation (WASS) correction method that can handle full rigid motion and account for the melange of different brain tissue isochromats at each EPI voxel location. We evaluated the effect of spin saturation artifacts and the performance of the WASS correction using simulated fMRI time series synthesized with known true activation, motion, and the associated spin saturation artifact. Two different ranges of head rotations, [-5,5] and [-2,2] deg, were introduced and the effect of the spin saturation artifact was quantified to show 18% and 13% reduction in activation detection rate, respectively. Following the MSV motion and WASS correction, results indicate that WASS correction can improve activation detection by 17% relative to MSV only correction.     

Correcting frequency and phase offsets in MRS data using robust spectral registration

An algorithm for retrospective correction of frequency and phase offsets in MRS data is presented. The algorithm, termed robust spectral registration (rSR), contains a set of subroutines designed to robustly align individual transients in a given dataset even in cases of significant frequency and phase offsets or unstable lipid contamination and residual water signals. Data acquired by complex multiplexed editing approaches with distinct subspectral profiles are also accurately aligned. Automated removal of unstable lipid contamination and residual water signals is applied first, when needed. Frequency and phase offsets are corrected in the time domain by aligning each transient to a weighted average reference in a statistically optimal order using nonlinear least‐squares optimization. The alignment of subspectra in edited datasets is performed using an approach that specifically targets subtraction artifacts in the frequency domain. Weighted averaging is then used for signal averaging to down‐weight poorer‐quality transients. Algorithm performance was assessed on one simulated and 67 in vivo pediatric GABA‐/GSH‐edited HERMES datasets and compared with the performance of a multistep correction method previously developed for aligning HERMES data. The performance of the novel approach was quantitatively assessed by comparing the estimated frequency/phase offsets against the known values for the simulated dataset or by examining the presence of subtraction artifacts in the in vivo data. Spectral quality was improved following robust alignment, especially in cases of significant spectral distortion. rSR reduced more subtraction artifacts than the multistep method in 64% of the GABA difference spectra and 75% of the GSH difference spectra. rSR overcomes the major challenges of frequency and phase correction.     

基于圆形网格化的磁共振PROPELLER旋转校正算法

PROPELLER数据采集成像技术利用K空间中心重叠采样区域的数据来估计受检查者的运动并加以校正,能较好地消除运动伪影。本文提出了一种基于圆形网格化的PROPELLER旋转校正算法,将中心重叠区域数据网格化到圆形的网格点上,避免了旋转估计时需多次网格化的缺点;并提出有效的相似性测度公式,通过计算测度值估计相应的旋转运动参数,据此对各数据带进行旋转校正。实验表明,与传统旋转校正算法相比,该算法运行速度快,成像质量好。     

CT image registration in sinogram space

Object displacement in a CT scan is generally reflected in CT projection data or sinogram. In this work, the direct relationship between object motion and the change of CT projection data (sinogram) is investigated and this knowledge is applied to create a novel algorithm for sinogram registration. Calculated and experimental results demonstrate that the registration technique works well for registering rigid 2D or 3D motion in parallel and fan beam samplings. Problem and solution for 3D sinogram-based registration of metallic fiducials are also addressed. Since the motion is registered before image reconstruction, the presented algorithm is particularly useful when registering images with metal or truncation artifacts. In addition, this algorithm is valuable for dealing with situations where only limited projection data are available, making it appealing for various applications in image guided radiation therapy.     

Correction of megavoltage cone-beam CT images for dose calculation in the head and neck region

Megavoltage cone-beam computed tomography (MVCBCT) imaging systems are now available for image-guided radiation therapy delivery and verification. In order to use the three-dimensional anatomical information for dose calculation, the MVCBCT image must provide accurate electron density. This work proposes a new method that has been developed to correct for the cupping and missing data artifacts seen on MVCBCT images of the head and neck region. It uses a conventional kilovoltage CT (kVCT) image as a reference for electron density and rigid registration with a MVCBCT image to obtain correction factors. Dose calculations performed on MVCBCT images corrected with the proposed method agree with calculations done on kVCT images within +/- 1% on phantoms. With patients images the agreement is within +/- 13% above the shoulders and +/- 5% below the shoulder line. This level of dose calculation accuracy allows the use of MVCBCT images for dose verification purposes.     

磁共振成像PROPELLER采样数据重建中的运动估计新算法

PROPELLER（推进器）采样技术能够利用K空间中心重叠采样区域的数据来估计采集过程中受检查者的运动进而加以补偿,对运动伪影的消除效果非常显著。然而,由于其重建时的运动估计是基于最大化频域空间上相关系数的配准算法,该算法为了实现旋转估计与平移估计的分离,在进行旋转估计时,仅仅采用K空间数据的模,在数据量有限的情况下造成估计精度较低,在重建图像上表现为模糊及星条状伪影。本研究基于最大化图像空间上的互信息提出一种PROPELLER采样数据的运动估计新算法,首先由每个K空间带进行傅立叶逆变换后取模重建出系列临时图像,对这些图像进行模糊增强后以互信息作为相似性测度迭代搜索最优的运动参数。实验证明,该方法能显著提高PROPELLER采样数据重建中运动估计与补偿的精度,从而更好地消除伪影,特别是用于有运动时T1加权头部成像时。     

MR图像刚性平移运动伪影的自动逆向迭代修正

在MR扫描过程中患者的自主或非自主运动使获得的采集数据的相位发生偏移,因而重建图像含有运动伪影,严重地影响临床诊断。由于平移和旋转是最基本的刚性运动,故几乎所有刚性运动都可由他们复合而成。本研究针对MR图像刚性平移运动伪影提出了一个新的后处理方法:逆向迭代修正法(IIC)。通过逆向模拟患者的平移运动,逐次修正信号在K空间中已偏移的相位,然后根据最小熵约束准则,判断所模拟的逆向运动平移量是否等于患者的实际运动平移量,即修正到原来的位置。实验表明,应用本研究提出的逆向迭代修正算法,运动伪影能被修正到一个较为理想的状态。在修正后图像质量及计算耗时方面,优于其他算法。     

Robust image registration for functional magnetic resonance imaging of the brain

Motion-related artifacts are still a major problem in data analysis of functional magnetic resonance imaging (FMRI) studies of brain activation. However, the traditional image registration algorithm is prone to inaccuracy when there are residual variations owing to counting statistics, partial volume effects or biological variation. In particular, susceptibility artifacts usually result in remarkable signal intensity variance, and they can mislead the estimation of motion parameters. In this study, Two robust estimation algorithms for the registration of FMRI images are described. The first estimation algorithm was based on the Newton method and used Tukey's biweight objective function. The second estimation algorithm was based on the Levenberg-Marquardt technique and used a skipped mean objective function. The robust M-estimators can suppress the effects of the outliers by scaling down their error magnitudes or completely rejecting outliers using a weighting function. The proposed registration methods consisted of the following steps: fast segmentation of the brain region from noisy background as a preprocessing step; pre-registration of the volume centroids to provide a good initial estimation; and two robust estimation algorithms and a voxel sampling technique to find the affine transformation parameters. The accuracy of the algorithms was within 0.5 mm in translation and within 0.5° in rotation. For the FMRI data sets, the performance of the algorithms was visually compared with the AIR 2.0 software, which is a software for image registration, using colour-coded statistical mapping by the Kolmogorov-Smirov method. Experimental results showed, that the algorithms provided significant improvement in correcting motion-related artifacts and can enhance the detection of real brain activation.     

Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation

A successful surface-based image-to-physical space registration in image-guided liver surgery (IGLS) is critical to provide reliable guidance information to surgeons and pertinent surface displacement data for use in deformation correction algorithms. The current protocol used to perform the image-to-physical space registration involves an initial pose estimation provided by a point based registration of anatomical landmarks identifiable in both the preoperative tomograms and the intraoperative presentation. The surface based registration is then performed via a traditional iterative closest point (ICP) algorithm between the preoperative liver surface, segmented from the tomographic image set, and an intraoperatively acquired point cloud of the liver surface provided by a laser range scanner. Using this more conventional method, the registration accuracy can be compromised by poor initial pose estimation as well as tissue deformation due to the laparotomy and liver mobilization performed prior to tumor resection. In order to increase the robustness of the current surface-based registration method used in IGLS, we propose the incorporation of salient anatomical features, identifiable in both the preoperative image sets and intraoperative liver surface data, to aid in the initial pose estimation and play a more significant role in the surface-based registration via a novel weighting scheme. Examples of such salient anatomical features are the falciform groove region as well as the inferior ridge of the liver surface. In order to validate the proposed weighted patch registration method, the alignment results provided by the proposed algorithm using both single and multiple patch regions were compared with the traditional ICP method using six clinical datasets. Robustness studies were also performed using both phantom and clinical data to compare the resulting registrations provided by the proposed algorithm and the traditional method under conditions of varying initial pose. The results provided by the robustness trials and clinical registration comparisons suggest that the proposed weighted patch registration algorithm provides a more robust method with which to perform the image-to-physical space registration in IGLS. Furthermore, the implementation of the proposed algorithm during surgical procedures does not impose significant increases in computation or data acquisition times.     

Simultaneous measurement of saturation and relaxation in human brain by repetitive magnetization transfer pulses

Magnetization transfer (MT) by equidistant pulse trains can be described as being analogous to progressive partial saturation, where 'direct' saturation of water is amplified by MT contributions that are dependent on macromolecular content and differential saturation. This concept was applied to study the transition to steady state in the human brain using similar MT-pulses as in imaging. Up to 41 bell-shaped MT-pulses of 12 ms duration were applied at frequency offsets between 0.5 and 15 kHz with flip angles between 1080 and 1440 degrees . Central white and parietal gray matter was studied in human subjects using STEAM for localized read-out (TE = 30 ms, TM = 13.7 ms). The apparent degree of saturation, delta(app), and the longitudinal relaxation of the water pool during the pulse repetition period (PR) were fitted to the transient behavior after signal correction for cerebro-spinal fluid. PR was varied between 15 and 100 ms to assess the PR-dependence of the fitted parameters. The MT-term in delta(app) exceeded the direct saturation and attained its maximum at PR > or = 100 ms. The macromolecular pool was only partially saturated by a single MT-pulse. The offset may be increased to 2.5 kHz to reduce direct saturation without sacrificing MT in white matter. The estimated relaxation rates (1.04 +/- 0.11 s(-1) in WM; 0.76 +/- 0.13 s(-1) in GM) were faster than are commonly observed at 1.5 T. The apparent saturation is a measure for MT that is not confounded by relaxation. To maximize MT in brain tissue, MT-pulses should be applied at PR = 100 ms or longer. At shorter PR, a larger steady state saturation is obtained at the cost of increased contributions from direct saturation. Since this accelerates the convergence, PR should be decreased to reach the steady state within a specified time. A faster transition can always be achieved at a reduced frequency offset via increased direct saturation.     

Digital subtraction angiogram registration method with local distortion vectors to decrease motion artifact.

We have been investigating registration methods for improving digital subtraction angiography (DSA) images to extract blood vessels by reducing artifacts due to body motion, such as rotation, contraction, and dilation. In this paper, we propose a new and simple DSA registration algorithm with local distortion vectors to reduce artifacts. According to the results, the proposed method works well for vascular system around the nasal cavity and the orbit of the head and neck DSA images, which cannot be observed clearly by conventional methods. Additionally, we have applied the proposed method to abdominal and leg DSA images.     

Image registration with auto-mapped control volumes

Many image registration algorithms rely on the use of homologous control points on the two input image sets to be registered. In reality, the interactive identification of the control points on both images is tedious, difficult, and often a source of error. We propose a two-step algorithm to automatically identify homologous regions that are used as a priori information during the image registration procedure. First, a number of small control volumes having distinct anatomical features are identified on the model image in a somewhat arbitrary fashion. Instead of attempting to find their correspondences in the reference image through user interaction, in the proposed method, each of the control regions is mapped to the corresponding part of the reference image by using an automated image registration algorithm. A normalized cross-correlation (NCC) function or mutual information was used as the auto-mapping metric and a limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) was employed to optimize the function to find the optimal mapping. For rigid registration, the transformation parameters of the system are obtained by averaging that derived from the individual control volumes. In our deformable calculation, the mapped control volumes are treated as the nodes or control points with known positions on the two images. If the number of control volumes is not enough to cover the whole image to be registered, additional nodes are placed on the model image and then located on the reference image in a manner similar to the conventional BSpline deformable calculation. For deformable registration, the established correspondence by the auto-mapped control volumes provides valuable guidance for the registration calculation and greatly reduces the dimensionality of the problem. The performance of the two-step registrations was applied to three rigid registration cases (two PET-CT registrations and a brain MRI-CT registration) and one deformable registration of inhale and exhale phases of a lung 4D CT. Algorithm convergence was confirmed by starting the registration calculations from a large number of initial transformation parameters. An accuracy of approximately 2 mm was achieved for both deformable and rigid registration. The proposed image registration method greatly reduces the complexity involved in the determination of homologous control points and allows us to minimize the subjectivity and uncertainty associated with the current manual interactive approach. Patient studies have indicated that the two-step registration technique is fast, reliable, and provides a valuable tool to facilitate both rigid and nonrigid image registrations.     

A fast inverse consistent deformable image registration method based on symmetric optical flow computation

Deformable image registration is widely used in various radiation therapy applications including daily treatment planning adaptation to map planned tissue or dose to changing anatomy. In this work, a simple and efficient inverse consistency deformable registration method is proposed with aims of higher registration accuracy and faster convergence speed. Instead of registering image I to a second image J, the two images are symmetrically deformed toward one another in multiple passes, until both deformed images are matched and correct registration is therefore achieved. In each pass, a delta motion field is computed by minimizing a symmetric optical flow system cost function using modified optical flow algorithms. The images are then further deformed with the delta motion field in the positive and negative directions respectively, and then used for the next pass. The magnitude of the delta motion field is forced to be less than 0.4 voxel for every pass in order to guarantee smoothness and invertibility for the two overall motion fields that are accumulating the delta motion fields in both positive and negative directions, respectively. The final motion fields to register the original images I and J, in either direction, are calculated by inverting one overall motion field and combining the inversion result with the other overall motion field. The final motion fields are inversely consistent and this is ensured by the symmetric way that registration is carried out. The proposed method is demonstrated with phantom images, artificially deformed patient images and 4D-CT images. Our results suggest that the proposed method is able to improve the overall accuracy (reducing registration error by 30% or more, compared to the original and inversely inconsistent optical flow algorithms), reduce the inverse consistency error (by 95% or more) and increase the convergence rate (by 100% or more). The overall computation speed may slightly decrease, or increase in most cases because the new method converges faster. Compared to previously reported inverse consistency algorithms, the proposed method is simpler, easier to implement and more efficient.     

A rigidity penalty term for nonrigid registration

Medical images that are to be registered for clinical application often contain both structures that deform and ones that remain rigid. Nonrigid registration algorithms that do not model properties of different tissue types may result in deformations of rigid structures. In this article a local rigidity penalty term is proposed which is included in the registration function in order to penalize the deformation of rigid objects. This term can be used for any representation of the deformation field capable of modelling locally rigid transformations. By using a B-spline representation of the deformation field, a fast algorithm can be devised. The proposed method is compared with an unconstrained nonrigid registration algorithm. It is evaluated on clinical three-dimensional CT follow-up data of the thorax and on two-dimensional DSA image sequences. The results show that nonrigid registration using the proposed rigidity penalty term is capable of nonrigidly aligning images, while keeping user-defined structures locally rigid.     

Adaptive Voxel Matching for Temporal CT Subtraction

Temporal subtraction (TS) technique calculates a subtraction image between a pair of registered images acquired from the same patient at different times. Previous studies have shown that TS is effective for visualizing pathological changes over time; therefore, TS should be a useful tool for radiologists. However, artifacts caused by partial volume effects degrade the quality of thick-slice subtraction images, even with accurate image registration. Here, we propose a subtraction method for reducing artifacts in thick-slice images and discuss its implementation in high-speed processing. The proposed method is based on voxel matching, which reduces artifacts by considering gaps in discretized positions of two images in subtraction calculations. There are two different features between the proposed method and conventional voxel matching: (1) the size of a searching region to reduce artifacts is determined based on discretized position gaps between images and (2) the searching region is set on both images for symmetrical subtraction. The proposed method is implemented by adopting an accelerated subtraction calculation method that exploit the nature of liner interpolation for calculating the signal value at a point among discretized positions. We quantitatively evaluated the proposed method using synthetic data and qualitatively using clinical data interpreted by radiologists. The evaluation showed that the proposed method was superior to conventional methods. Moreover, the processing speed using the proposed method was almost unchanged from that of the conventional methods. The results indicate that the proposed method can improve the quality of subtraction images acquired from thick-slice images.     

MRI刚性平移运动模糊图像建模与恢复

磁共振成像过程中,患者的轻微运动可产生运动伪影。运动伪影会使MR图像模糊,从而影响医生对病灶区域的准确检测。传统抑制运动伪影的方法大多基于运动模型已知的情况下,忽略频率编码期间产生的运动,对K空间数据进行处理达到修正伪影的目的。本研究针对任意的刚性平移运动可由无数的匀速直线运动复合而成的特点,建立了匀速直线运动模糊图像的数学模型,并尝试在图像域内用状态空间的方法和Hough变换的理论进行处理,对小幅度水平匀速运动模糊图像的修正取得了满意的结果。在处理任意方向的匀速直线运动时,由于谱线在旋转时不可避免地进行近似插值处理,虽然校正后的图像也有较大的改善,但还有待进一步研究。另外,对较大幅度水平匀速运动模糊图像的修正,还需要寻求更好的方法来准确估计谱线中相邻暗线之间的距离。通过上述理论及实验的分析验证可以看出,本研究所提出的方法对处理MRI运动模糊图像的研究有一定的理论意义。     

Non-rigid image registration using a median-filtered coarse-to-fine displacement field and a symmetric correlation ratio

Conventional approaches to image registration are generally limited to image-wide rigid transformations. However, the body and its internal organs are non-rigid structures that change shape due to changes in the body's posture during image acquisition, and due to normal, pathological and treatment-related variations. Inter-subject matching also constitutes a non-rigid registration problem. In this paper, we present a fully automated non-rigid image registration method that maximizes a local voxel-based similarity metric. Overlapping image blocks are defined on a 3D grid. The transformation vector field representing image deformation is found by translating each block so as to maximize the local similarity measure. The resulting sparsely sampled vector field is median filtered and interpolated by a Gaussian function to ensure a locally smooth transformation. A hierarchical strategy is adopted to progressively establish local registration associated with image structures at diminishing scale. Simulation studies were carried out to evaluate the proposed algorithm and to determine the robustness of various voxel-based cost functions. Mutual information, normalized mutual information, correlation ratio (CR) and a new symmetric version of CR were evaluated and compared. A T1-weighted magnetic resonance (MR) image was used to test intra-modality registration. Proton density and T2-weighted MR images of the same subject were used to evaluate inter-modality registration. The proposed algorithm was tested on the 2D MR images distorted by known deformations and 3D images simulating inter-subject distortions. We studied the robustness of cost functions with respect to image sampling. Results indicate that the symmetric CR gives comparable registration to mutual information in intra- and inter-modality tasks at full sampling and is superior to mutual information in registering sparsely sampled images.     

Empirical cupping correction: a first-order raw data precorrection for cone-beam computed tomography

We propose an empirical cupping correction (ECC) algorithm to correct for CT cupping artifacts that are induced by nonlinearities in the projection data. The method is raw data based, empirical, and requires neither knowledge of the x-ray spectrum nor of the attenuation coefficients. It aims at linearizing the attenuation data using a precorrection function of polynomial form. The coefficients of the polynomial are determined once using a calibration scan of a homogeneous phantom. Computing the coefficients is done in image domain by fitting a series of basis images to a template image. The template image is obtained directly from the uncorrected phantom image and no assumptions on the phantom size or of its positioning are made. Raw data are precorrected by passing them through the once-determined polynomial. As an example we demonstrate how ECC can be used to perform water precorrection for an in vivo micro-CT scanner (TomoScope 30 s, VAMP GmbH, Erlangen, Germany). For this particular case, practical considerations regarding the definition of the template image are given. ECC strives to remove the cupping artifacts and to obtain well-calibrated CT values. Although ECC is a first-order correction and cannot compete with iterative higher-order beam hardening or scatter correction algorithms, our in vivo mouse images show a significant reduction of bone-induced artifacts as well. A combination of ECC with analytical techniques yielding a hybrid cupping correction method is possible and allows for channel-dependent correction functions.     

Use of the CT component of PET-CT to improve PET-MR registration: demonstration in soft-tissue sarcoma

We have investigated improvements to PET-MR image registration offered by PET-CT scanning. Ten subjects with suspected soft-tissue sarcomas were scanned with an in-line PET-CT and a clinical MR scanner. PET to CT, CT to MR and PET to MR image registrations were performed using a rigid-body external marker technique and rigid and non-rigid voxel-similarity algorithms. PET-MR registration was also performed using transformations derived from the registration of CT to MR. The external marker technique gave fiducial registration errors of 2.1 mm, 5.1 mm and 5.3 mm for PET-CT, PET-MR and CT-MR registration. Target registration errors were 3.9 mm, 9.0 mm and 9.3 mm, respectively. Voxel-based algorithms were evaluated by measuring the distance between corresponding fiducials after registration. Registration errors of 6.4 mm, 14.5 mm and 9.5 mm, respectively, for PET-CT, PET-MR and CT-MR were observed for rigid-body registration while non-rigid registration gave errors of 6.8 mm, 16.3 mm and 7.6 mm for the same modality combinations. The application of rigid and non-rigid CT to MR transformations to accompanying PET data gives significantly reduced PET-MR errors of 10.0 mm and 8.5 mm, respectively. Visual comparison by two independent observers confirmed the improvement over direct PET-MR registration. We conclude that PET-MR registration can be more accurately and reliably achieved using the hybrid technique described than through direct rigid-body registration of PET to MR.     

Motion clustering for deblurring multispectral optoacoustic tomography images of the mouse heart

Cardiac imaging in small animals is a valuable tool in basic biological research and drug discovery for cardiovascular disease. Multispectral optoacoustic tomography (MSOT) represents an emerging imaging modality capable of visualizing specific tissue chromophores at high resolution and deep in tissues in vivo by separating their spectral signatures. Whereas single-wavelength images can be acquired by multielement ultrasound detection in real-time imaging, using multiple wavelengths at separate times can lead to image blurring due to motion during acquisition. Therefore, MSOT imaging of the heart results in degraded resolution because of the heartbeat. In this work, we applied a clustering algorithm, k-means, to automatically separate a sequence of single-pulse images at multiple excitation wavelengths into clusters corresponding to different stages of the cardiac cycle. We then performed spectral unmixing on each cluster to obtain images of tissue intrinsic chromophores at different cardiac stages, showing reduced sensitivity to motion compared to signal averaging without clustering. We found that myocardium images of improved resolution and contrast can be achieved using MSOT motion clustering correction. The correction method presented could be generally applied to other MSOT imaging applications prone to motion artifacts, for example, by respiration and heartbeat.