Tomographic motion detection and correction directly in sinogram space

Patient motion, especially respiratory motion, results in various artefacts such as blurring and streaks in tomographic images. The interplay of the movement of the beam aperture and variations of organ anatomy during delivery can create 'hot' and 'cold' spots throughout the field in intensity-modulated radiation therapy (IMRT). Detection and correction of patient motion is extremely important in tomographic imaging and IMRT. Tomographic projection data (sinogram) encode not only the patient anatomy information, but also the intra-scanning motion information. In this paper, we developed an algorithm to detect and correct the in-plane respiratory motion directly in sinogram space. The respiratory motion is modelled as time-varying scaling along the x and y directions. Its effects on the sinogram are discussed. Based on the traces of some nodal points in the sinogram, the intra-scanning motion is determined. The motion correction is also implemented in sinogram space. The motion-corrected sinogram is used for reconstruction by the filtered back-projection (FBP) method. Computer simulations validate the motion detection and correction algorithm. The reconstructed images from the motion-corrected sinogram eliminate the majority of the artefacts. The method could be applied to projection data used in CT and ECT, as well as in tomotherapy delivery modification and dose reconstruction.     

基于投影图像和CT容积图像的三维冠状动脉运动跟踪新方法

目的利用双平面X射线投影图像序列和参考CT容积图像进行冠状动脉的三维运动跟踪建模。方法①提取投影图像序列中的冠状动脉树;②利用多尺度滤波和血管函数提取CT容积图像中的动脉血管;③采用基于B样条配准的方法进行三维运动建模。结果将双投影图像中血管的运动估计结果的三维重建形态与三维运动模型预测出的结果进行了量化比较,调整配准的B样条参数后,两者误差处于有效范围之内。结论由此表明采用投影图像和对应容积图像的联合先验知识进行冠状动脉的三维运动建模是有效的。     

Motion correction for improved target localization with on-board cone-beam computed tomography

On-board imager (OBI) based cone-beam computed tomography (CBCT) has become available in radiotherapy clinics to accurately identify the target in the treatment position. However, due to the relatively slow gantry rotation (typically about 60 s for a full 360 degrees scan) in acquiring the CBCT projection data, the patient's respiratory motion causes serious problems such as blurring, doubling, streaking and distortion in the reconstructed images, which heavily degrade the image quality and the target localization. In this work, we present a motion compensation method for slow-rotating CBCT scans by incorporating into image reconstruction a patient-specific motion model, which is derived from previously obtained four-dimensional (4D) treatment planning CT images of the same patient via deformable registration. The registration of the 4D CT phases results in transformations representing a temporal sequence of three-dimensional (3D) deformation fields, or in other words, a 4D model of organ motion. The algorithm was developed heuristically in two-dimensional (2D) parallel-beam geometry and extended to 3D cone-beam geometry. By simulations with digital phantoms capable of translational motion and other complex motion, we demonstrated that the algorithm can reduce the motion artefacts locally, and restore the tumour size and shape, which may thereby improve the accuracy of target localization and patient positioning when CBCT is used as the treatment guidance.     

Registration of 2D x-ray images to 3D MRI by generating pseudo-CT data

Spatial and soft tissue information provided by magnetic resonance imaging can be very valuable during image-guided procedures, where usually only real-time two-dimensional (2D) x-ray images are available. Registration of 2D x-ray images to three-dimensional (3D) magnetic resonance imaging (MRI) data, acquired prior to the procedure, can provide optimal information to guide the procedure. However, registering x-ray images to MRI data is not a trivial task because of their fundamental difference in tissue contrast. This paper presents a technique that generates pseudo-computed tomography (CT) data from multi-spectral MRI acquisitions which is sufficiently similar to real CT data to enable registration of x-ray to MRI with comparable accuracy as registration of x-ray to CT. The method is based on a k-nearest-neighbors (kNN)-regression strategy which labels voxels of MRI data with CT Hounsfield Units. The regression method uses multi-spectral MRI intensities and intensity gradients as features to discriminate between various tissue types. The efficacy of using pseudo-CT data for registration of x-ray to MRI was tested on ex vivo animal data. 2D-3D registration experiments using CT and pseudo-CT data of multiple subjects were performed with a commonly used 2D-3D registration algorithm. On average, the median target registration error for registration of two x-ray images to MRI data was approximately 1 mm larger than for x-ray to CT registration. The authors have shown that pseudo-CT data generated from multi-spectral MRI facilitate registration of MRI to x-ray images. From the experiments it could be concluded that the accuracy achieved was comparable to that of registering x-ray images to CT data.     

Correcting organ motion artifacts in x-ray CT systems based on tracking of motion phase by the spatial overlap correlator. II. Experimental study

This paper presents the experimental part of an investigation on tracking and eliminating organ motion artifacts in x-ray CT cardiac applications with emphasis on imaging coronary calcification. The system methodology consists of a software implementation of the spatial overlap correlator (SSOC) concept in x-ray CT scanners to track the net amplitude and phase of organ motion during the CT data acquisition process. A coherent sinogram synthesis (CSS) method is then used to identify the repeated phases of a periodic organ motion from the information provided by the SSOC process and hence synthesize a new sinogram with no motion effects. Since the SSOC scheme is capable of tracking cardiac motion, it identifies also the projection points associated with minimum amplitude cardiac motion effects. These points are used to identify a 180 degrees plus the fan angle sinogram for image reconstruction. This leads to a retrospective gating (RG) scheme that is based on the output of the SSOC process. Performance comparison of the proposed methodology with the retrospective ECG gating using real data sets with phantoms and human patients provides a performance assessment of the merits of the proposed methods. Real results demonstrate that the new methodology eliminates the requirement for ECG gating. Moreover, the CSS and the new RG methods do not require breath holding and they can be implemented in x-ray CT scanners to image coronary calcification and the heart's ventricles.     

A faster method for 3D/2D medical image registration--a simulation study

3D/2D patient-to-computed-tomography (CT) registration is a method to determine a transformation that maps two coordinate systems by comparing a projection image rendered from CT to a real projection image. Iterative variation of the CT's position between rendering steps finally leads to exact registration. Applications include exact patient positioning in radiation therapy, calibration of surgical robots, and pose estimation in computer-aided surgery. One of the problems associated with 3D/2D registration is the fact that finding a registration includes solving a minimization problem in six degrees of freedom (dof) in motion. This results in considerable time requirements since for each iteration step at least one volume rendering has to be computed. We show that by choosing an appropriate world coordinate system and by applying a 2D/2D registration method in each iteration step, the number of iterations can be grossly reduced from n6 to n5. Here, n is the number of discrete variations around a given coordinate. Depending on the configuration of the optimization algorithm, this reduces the total number of iterations necessary to at least 1/3 of it's original value. The method was implemented and extensively tested on simulated x-ray images of a tibia, a pelvis and a skull base. When using one projective image and a discrete full parameter space search for solving the optimization problem, average accuracy was found to be 1.0 +/- 0.6(degrees) and 4.1 +/- 1.9 (mm) for a registration in six parameters, and 1.0 +/- 0.7(degrees) and 4.2 +/- 1.6 (mm) when using the 5 + 1 dof method described in this paper. Time requirements were reduced by a factor 3.1. We conclude that this hardware-independent optimization of 3D/2D registration is a step towards increasing the acceptance of this promising method for a wide number of clinical applications.     

4D-CT motion estimation using deformable image registration and 5D respiratory motion modeling

Four-dimensional computed tomography (4D-CT) imaging technology has been developed for radiation therapy to provide tumor and organ images at the different breathing phases. In this work, a procedure is proposed for estimating and modeling the respiratory motion field from acquired 4D-CT imaging data and predicting tissue motion at the different breathing phases. The 4D-CT image data consist of series of multislice CT volume segments acquired in ciné mode. A modified optical flow deformable image registration algorithm is used to compute the image motion from the CT segments to a common full volume 3D-CT reference. This reference volume is reconstructed using the acquired 4D-CT data at the end-of-exhalation phase. The segments are optimally aligned to the reference volume according to a proposed a priori alignment procedure. The registration is applied using a multigrid approach and a feature-preserving image downsampling maxfilter to achieve better computational speed and higher registration accuracy. The registration accuracy is about 1.1 +/- 0.8 mm for the lung region according to our verification using manually selected landmarks and artificially deformed CT volumes. The estimated motion fields are fitted to two 5D (spatial 3D+tidal volume+airflow rate) motion models: forward model and inverse model. The forward model predicts tissue movements and the inverse model predicts CT density changes as a function of tidal volume and airflow rate. A leave-one-out procedure is used to validate these motion models. The estimated modeling prediction errors are about 0.3 mm for the forward model and 0.4 mm for the inverse model.     

Performance studies of four-dimensional cone beam computed tomography

Four-dimensional cone beam computed tomography (4DCBCT) has been proposed to characterize the breathing motion of tumors before radiotherapy treatment. However, when the acquired cone beam projection data are retrospectively gated into several respiratory phases, the available data to reconstruct each phase is under-sampled and thus causes streaking artifacts in the reconstructed images. To solve the under-sampling problem and improve image quality in 4DCBCT, various methods have been developed. This paper presents performance studies of three different 4DCBCT methods based on different reconstruction algorithms. The aims of this paper are to study (1) the relationship between the accuracy of the extracted motion trajectories and the data acquisition time of a 4DCBCT scan and (2) the relationship between the accuracy of the extracted motion trajectories and the number of phase bins used to sort projection data. These aims will be applied to three different 4DCBCT methods: conventional filtered backprojection reconstruction (FBP), FBP with McKinnon-Bates correction (MB) and prior image constrained compressed sensing (PICCS) reconstruction. A hybrid phantom consisting of realistic chest anatomy and a moving elliptical object with known 3D motion trajectories was constructed by superimposing the analytical projection data of the moving object to the simulated projection data from a chest CT volume dataset. CBCT scans with gantry rotation times from 1 to 4 min were simulated, and the generated projection data were sorted into 5, 10 and 20 phase bins before different methods were used to reconstruct 4D images. The motion trajectories of the moving object were extracted using a fast free-form deformable registration algorithm. The root mean square errors (RMSE) of the extracted motion trajectories were evaluated for all simulated cases to quantitatively study the performance. The results demonstrate (1) longer acquisition times result in more accurate motion delineation for each method; (2) ten or more phase bins are necessary in 4DCBCT to ensure sufficient temporal resolution in tumor motion and (3) to achieve the same performance as FBP-4DCBCT with a 4 min data acquisition time, MB-4DCBCT and PICCS-4DCBCT need about 2- and 1 min data acquisition times, respectively.     

Automated 2D-3D registration of a radiograph and a cone beam CT using line-segment enhancement

The objective of this study was to develop a fully automated two-dimensional (2D)-three-dimensional (3D) registration framework to quantify setup deviations in prostate radiation therapy from cone beam CT (CBCT) data and a single AP radiograph. A kilovoltage CBCT image and kilovoltage AP radiograph of an anthropomorphic phantom of the pelvis were acquired at 14 accurately known positions. The shifts in the phantom position were subsequently estimated by registering digitally reconstructed radiographs (DRRs) from the 3D CBCT scan to the AP radiographs through the correlation of enhanced linear image features mainly representing bony ridges. Linear features were enhanced by filtering the images with "sticks," short line segments which are varied in orientation to achieve the maximum projection value at every pixel in the image. The mean (and standard deviations) of the absolute errors in estimating translations along the three orthogonal axes in millimeters were 0.134 (0.096) AP(out-of-plane), 0.021 (0.023) ML and 0.020 (0.020) SI. The corresponding errors for rotations in degrees were 0.011 (0.009) AP, 0.029 (0.016) ML (out-of-plane), and 0.030 (0.028) SI (out-of-plane). Preliminary results with megavoltage patient data have also been reported. The results suggest that it may be possible to enhance anatomic features that are common to DRRs from a CBCT image and a single AP radiography of the pelvis for use in a completely automated and accurate 2D-3D registration framework for setup verification in prostate radiotherapy. This technique is theoretically applicable to other rigid bony structures such as the cranial vault or skull base and piecewise rigid structures such as the spine.     

A Projection-Based Image Registration Algorithm and Its Application

Summary: Proposed is a projection-based image registration technique where, by rearranging the projections of characteristic images, the image registration is implemented with two independent steps - rotation and translation, to perform the two-dimensional or three-dimensional rigid-body image registration addressing the head motion problem in functional magnetic resonance imaging (fMRI). For a 2D problem, the approach is based on a one-dimensional projection of a segmented two-dimensional characteristic image, in which the translation and rotation parameters are obtained with a one-dimensional cross-correlation-based estimator. This is then used to compute the cross-correlation between the projection of an image and a registration table that is created by rearranged projections of a selected two-dimensional image with various rotation angles. In this approach, the translation registration table may be created by rearranged projections of sub-voxel level two-dimensional images with various sub-voxel level parameters, and so it may be applied into a sub-voxel registration. Such an approach replaced the general multi-dimensional optimization procedure with a linear projection calculation and a finite cross-correlation with a registration table, thus the amount of computation is considerably reduced. The performance of this method was confirmed by simulation study different SNRs and applications to 2D and 3D actual functional MRI images. Supported by the 973 Project number 2003CB716106, NSFC 90208003, #30130180 and #30200059, TRAPOYT, Doctor training Fund of MOE, PRC, Fok Ying Tong Education Foundation (91041). The authors wish to thank the Wellcome Department of Imaging Neuroscience for the permission of using the fMRI experimental data.     

基于单幅X线图像和CT数据的2D/3D配准系统

目的建立基于统一计算架构(CUDA)下以单幅X线图像及CT扫描数据为数据源的2D/3D配准系统,并应用于膝关节在体运动及植入假体稳定性研究。方法首先应用张正友标定法对采集X线图像设备进行标定;其次基于CUDA构架利用光线跟踪算法生成数字影像重建图像,以相关性函数为相似性测度计算2D/3D配准参数;最后以三维激光扫描仪所获得的点云数据进行3D/3D配准,以验证2D/3D配准结果。结果以标本整体位置变换进行配准实验,6自由度平均误差中,位移小于1mm,旋转小于1°。结论此2D/3D配准系统达到了运动检测精度的要求,可以作为研究膝关节运动情况和假体在体稳定性研究的计算平台。     

Automatic registration between 3D intra-operative ultrasound and pre-operative CT images of the liver based on robust edge matching

The registration of a three-dimensional (3D) ultrasound (US) image with a computed tomography (CT) or magnetic resonance image is beneficial in various clinical applications such as diagnosis and image-guided intervention of the liver. However, conventional methods usually require a time-consuming and inconvenient manual process for pre-alignment, and the success of this process strongly depends on the proper selection of initial transformation parameters. In this paper, we present an automatic feature-based affine registration procedure of 3D intra-operative US and pre-operative CT images of the liver. In the registration procedure, we first segment vessel lumens and the liver surface from a 3D B-mode US image. We then automatically estimate an initial registration transformation by using the proposed edge matching algorithm. The algorithm finds the most likely correspondences between the vessel centerlines of both images in a non-iterative manner based on a modified Viterbi algorithm. Finally, the registration is iteratively refined on the basis of the global affine transformation by jointly using the vessel and liver surface information. The proposed registration algorithm is validated on synthesized datasets and 20 clinical datasets, through both qualitative and quantitative evaluations. Experimental results show that automatic registration can be successfully achieved between 3D B-mode US and CT images even with a large initial misalignment.     

Evaluation of deformable image registration and a motion model in CT images with limited features

Deformable image registration (DIR) is increasingly used in radiotherapy applications and provides the basis for a previously described model of patient-specific respiratory motion. We examine the accuracy of a DIR algorithm and a motion model with respiration-correlated CT (RCCT) images of software phantom with known displacement fields, physical deformable abdominal phantom with implanted fiducials in the liver and small liver structures in patient images. The motion model is derived from a principal component analysis that relates volumetric deformations with the motion of the diaphragm or fiducials in the RCCT. Patient data analysis compares DIR with rigid registration as ground truth: the mean ± standard deviation 3D discrepancy of liver structure centroid positions is 2.0 ± 2.2 mm. DIR discrepancy in the software phantom is 3.8 ± 2.0 mm in lung and 3.7 ± 1.8 mm in abdomen; discrepancies near the chest wall are larger than indicated by image feature matching. Marker's 3D discrepancy in the physical phantom is 3.6 ± 2.8 mm. The results indicate that visible features in the images are important for guiding the DIR algorithm. Motion model accuracy is comparable to DIR, indicating that two principal components are sufficient to describe DIR-derived deformation in these datasets.     

当前图像重建研究中的几个热点

在当前的图像处理的研究中,图像重建是一个热点,其中研究方向又可归结以几个方面（１）利用时频约束去除重建前和重建后的噪声。（２）运动器官的断层像重建研究。（３）不完全投影数据下的重建。（４）直接体积重建及三维显示（５）非Ｘ线ＣＴ的研究。（６）并行计算ＣＴ的研究。本文对这几方面作了简要的概述。     

Automatic 3D-to-2D registration for CT and dual-energy digital radiography for calcification detection

We are investigating three-dimensional (3D) to two-dimensional (2D) registration methods for computed tomography (CT) and dual-energy digital radiography (DEDR). CT is an established tool for the detection of cardiac calcification. DEDR could be a cost-effective alternative screening tool. In order to utilize CT as the "gold standard" to evaluate the capability of DEDR images for the detection and localization of calcium, we developed an automatic, intensity-based 3D-to-2D registration method for 3D CT volumes and 2D DEDR images. To generate digitally reconstructed radiography (DRR) from the CT volumes, we developed several projection algorithms using the fast shear-warp method. In particular, we created a Gaussian-weighted projection for this application. We used normalized mutual information (NMI) as the similarity measurement. Simulated projection images from CT values were fused with the corresponding DEDR images to evaluate the localization of cardiac calcification. The registration method was evaluated by digital phantoms, physical phantoms, and clinical data sets. The results from the digital phantoms show that the success rate is 100% with a translation difference of less than 0.8 mm and a rotation difference of less than 0.2 degrees. For physical phantom images, the registration accuracy is 0.43 +/- 0.24 mm. Color overlay and 3D visualization of clinical images show that the two images registered well. The NMI values between the DRR and DEDR images improved from 0.21 +/- 0.03 before registration to 0.25 +/- 0.03 after registration. Registration errors measured from anatomic markers decreased from 27.6 +/- 13.6 mm before registration to 2.5 +/- 0.5 mm after registration. Our results show that the automatic 3D-to-2D registration is accurate and robust. This technique can provide a useful tool for correlating DEDR with CT images for screening coronary artery calcification.     

Extension of the reconstruction field of view and truncation correction using sinogram decomposition

We propose a novel truncation correction algorithm that completes unmeasured data outside of the scan field of view, which allows extending the reconstruction field of view. When a patient extends outside the detector coverage the projection data are transversely truncated, which causes severe artifacts. The proposed method utilizes the idea of sinogram decomposition, where we consider sinogram curves corresponding to image points outside the field of view. We propose two ways to estimate the truncated data, one based on the minimum value along the sinogram curve, and the other based on the data values near the edge of truncation. Both estimation methods are combined to achieve uniform image quality improvement from the edge of truncation to the outer side of the extended region. In our evaluation with simulated and real projection data we compare the proposed method with existing methods and investigate the dependence on the amount of truncation. The evaluation shows that the proposed method handles cases when truncation is present on both sides of the detector, or when a high-contrast object is located outside the field of view.     

Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation

Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the accuracies reported elsewhere. Unlike most prior work, segmentation of the tumor is also presented. The clinical implementation of 4D treatment planning is critically dependent on automatic segmentation, for which is offered one of the most accurate algorithms yet presented.     

Automatic nonrigid registration of whole body CT mice images

Three-dimensional intra- and intersubject registration of image volumes is important for tasks that include quantification of temporal/longitudinal changes, atlas-based segmentation, computing population averages, or voxel and tensor-based morphometry. While a number of methods have been proposed to address this problem, few have focused on the problem of registering whole body image volumes acquired either from humans or small animals. These image volumes typically contain a large number of articulated structures, which makes registration more difficult than the registration of head images, to which the majority of registration algorithms have been applied. This article presents a new method for the automatic registration of whole body computed tomography (CT) volumes, which consists of two main steps. Skeletons are first brought into approximate correspondence with a robust point-based method. Transformations so obtained are refined with an intensity-based nonrigid registration algorithm that includes spatial adaptation of the transformation's stiffness. The approach has been applied to whole body CT images of mice, to CT images of the human upper torso, and to human head and neck CT images. To validate the authors method on soft tissue structures, which are difficult to see in CT images, the authors use coregistered magnetic resonance images. They demonstrate that the approach they propose can successfully register image volumes even when these volumes are very different in size and shape or if they have been acquired with the subjects in different positions.