Phantom validation of coregistration of PET and CT for image-guided radiotherapy

Radiotherapy treatment planning integrating positron emission tomography (PET) and computerized tomography (CT) is rapidly gaining acceptance in the clinical setting. Although hybrid systems are available, often the planning CT is acquired on a dedicated system separate from the PET scanner. A limiting factor to using PET data becomes the accuracy of the CT/PET registration. In this work, we use phantom and patient validation to demonstrate a general method for assessing the accuracy of CT/PET image registration and apply it to two multi-modality image registration programs. An IAEA (International Atomic Energy Association) brain phantom and an anthropomorphic head phantom were used. Internal volumes and externally mounted fiducial markers were filled with CT contrast and 18F-fluorodeoxyglucose (FDG). CT, PET emission, and PET transmission images were acquired and registered using two different image registration algorithms. CT/PET Fusion (GE Medical Systems, Milwaukee, WI) is commercially available and uses a semi-automated initial step followed by manual adjustment. Automatic Mutual Information-based Registration (AMIR), developed at our institution, is fully automated and exhibits no variation between repeated registrations. Registration was performed using distinct phantom structures; assessment of accuracy was determined from registration of the calculated centroids of a set of fiducial markers. By comparing structure-based registration with fiducial-based registration, target registration error (TRE) was computed at each point in a three-dimensional (3D) grid that spans the image volume. Identical methods were also applied to patient data to assess CT/PET registration accuracy. Accuracy was calculated as the mean with standard deviation of the TRE for every point in the 3D grid. Overall TRE values for the IAEA brain phantom are: CT/PET Fusion = 1.71 +/- 0.62 mm, AMIR = 1.13 +/- 0.53 mm; overall TRE values for the anthropomorphic head phantom are: CT/PET Fusion = 1.66 +/- 0.53 mm, AMIR = 1.15 +/- 0.48 mm. Precision (repeatability by a single user) measured for CT/PET Fusion: IAEA phantom = 1.59 +/- 0.67 mm and anthropomorphic head phantom = 1.63 +/- 0.52 mm. (AMIR has exact precision and so no measurements are necessary.) One sample patient demonstrated the following accuracy results: CT/PET Fusion = 3.89 +/- 1.61 mm, AMIR = 2.86 +/- 0.60 mm. Semi-automatic and automatic image registration methods may be used to facilitate incorporation of PET data into radiotherapy treatment planning in relatively rigid anatomic sites, such as head and neck. The overall accuracies in phantom and patient images are < 2 mm and < 4 mm, respectively, using either registration algorithm. Registration accuracy may decrease, however, as distance from the initial registration points (CT/PET fusion) or center of the image (AMIR) increases. Additional information provided by PET may improve dose coverage to active tumor subregions and hence tumor control. This study shows that the accuracy obtained by image registration with these two methods is well suited for image-guided radiotherapy.     

Matching PET and CT scans of the head and neck area: development of method and validation

Positron emission tomography (PET) provides important information on tumor biology, but lacks detailed anatomical information. Our aim in the present study was to develop and validate an automatic registration method for matching PET and CT scans of the head and neck. Three difficulties in achieving this goal are (1) nonrigid motions of the neck can hamper the use of automatic ridged body transformations; (2) emission scans contain too little anatomical information to apply standard image fusion methods; and (3) no objective way exists to quantify the quality of the match results. These problems are solved as follows: accurate and reproducible positioning of the patient was achieved by using a radiotherapy treatment mask. The proposed method makes use of the transmission rather than the emission scan. To obtain sufficient (anatomical) information for matching, two bed positions for the transmission scan were included in the protocol. A mutual information-based algorithm was used as a registration technique. PET and CT data were obtained in seven patients. Each patient had two CT scans and one PET scan. The datasets were used to estimate the consistency by matching PET to CT1, CT1 to CT2, and CT2 to PET using the full circle consistency test. It was found that using our method, consistency could be obtained of 4 mm and 1.3 degrees on average. The PET voxels used for registration were 5.15 mm, so the errors compared quite favorably with the voxel size. Cropping the images (removing the scanner bed from images) did not improve the consistency of the algorithm. The transmission scan, however, could potentially be reduced to a single position using this approach. In conclusion, the represented algorithm and validation technique has several features that are attractive from both theoretical and practical point of view, it is a user-independent, automatic validation technique for matching CT and PET scans of the head and neck, which gives the opportunity to compare different image enhancements.     

PET/CT image registration: preliminary tests for its application to clinical dosimetry in radiotherapy

The quality of dosimetry in radiotherapy treatment requires the accurate delimitation of the gross tumor volume. This can be achieved by complementing the anatomical detail provided by CT images through fusion with other imaging modalities that provide additional metabolic and physiological information. Therefore, use of multiple imaging modalities for radiotherapy treatment planning requires an accurate image registration method. This work describes tests carried out on a Discovery LS positron emission/computed tomography (PET/CT) system by General Electric Medical Systems (GEMS), for its later use to obtain images to delimit the target in radiotherapy treatment. Several phantoms have been used to verify image correlation, in combination with fiducial markers, which were used as a system of external landmarks. We analyzed the geometrical accuracy of two different fusion methods with the images obtained with these phantoms. We first studied the fusion method used by the PET/CT system by GEMS (hardware fusion) on the basis that there is satisfactory coincidence between the reconstruction centers in CT and PET systems; and secondly the fiducial fusion, a registration method, by means of least-squares fitting algorithm of a landmark points system. The study concluded with the verification of the centroid position of some phantom components in both imaging modalities. Centroids were estimated through a calculation similar to center-of-mass, weighted by the value of the CT number and the uptake intensity in PET. The mean deviations found for the hardware fusion method were: deltax/ +/-sigma = 3.3 mm +/- 1.0 mm and /deltax/ +/-sigma = 3.6 mm +/- 1.0 mm. These values were substantially improved upon applying fiducial fusion based on external landmark points: /deltax/ +/-sigma = 0.7 mm +/- 0.8 mm and /deltax/ +/-sigma = 0.3 mm 1.7 mm. We also noted that differences found for each of the fusion methods were similar for both the axial and helical CT image acquisition protocols.     

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.     

Incorporation of Preprocedural PET into CT-Guided Radiofrequency Ablation of Hepatic Metastases: a Nonrigid Image Registration Validation Study

This study evaluates the accuracy of augmenting initial intraprocedural computed tomography (CT) during radiofrequency ablation (RFA) of hepatic metastases with preprocedural positron emission tomography (PET) through a hardware-accelerated implementation of an automatic nonrigid PET–CT registration algorithm. The feasibility of augmenting intraprocedural CT with preprocedural PET to improve localization of CT-invisible but PET-positive tumors with images from actual RFA was explored. Preprocedural PET and intraprocedural CT images from 18 cases of hepatic RFA were included. All PET images in the study originated from a hybrid PET/CT scanner, and PET–CT registration was performed in two ways: (1) direct registration of preprocedural PET with intraprocedural CT and (2) indirect registration of preprocedural CT (i.e., the CT of hybrid PET/CT scan) with intraprocedural CT. A hardware-accelerated registration took approximately 2 min. Calculated registration errors were 7.0 and 8.4 mm for the direct and indirect methods, respectively. Overall, the direct registration was found to be statistically not distinct from that performed by a group of clinical experts. The accuracy, execution speed, and compactness of our implementation of nonrigid image registration suggest that existing PET can be overlaid on intraprocedural CT, promising a novel, technically feasible, and clinically viable approach for PET augmentation of CT guidance of RFA.     

Fast Mutual Information Registration Method of 3-D Medical Image

INTRODUCTION　　Multimodal registration,which brings images from different modality into spa-tial correspondence,is of importance in many clinical applications.Over the years,research of multimodal registration has produced a lotof differentmethods.Surveysof medical image registration with a classification have been made〔1,2〕.The registra-tion methods can be classified as frame- based,point- landmark- based,surface- basedand voxel- based.Voxel- based methods achieve registration by iden…     

Validation of 3D multimodality roadmapping in interventional neuroradiology

Three-dimensional multimodality roadmapping is entering clinical routine utilization for neuro-vascular treatment. Its purpose is to navigate intra-arterial and intra-venous endovascular devices through complex vascular anatomy by fusing pre-operative computed tomography (CT) or magnetic resonance (MR) with the live fluoroscopy image. The fused image presents the real-time position of the intra-vascular devices together with the patient's 3D vascular morphology and its soft-tissue context. This paper investigates the effectiveness, accuracy, robustness and computation times of the described methods in order to assess their suitability for the intended clinical purpose: accurate interventional navigation. The mutual information-based 3D-3D registration proved to be of sub-voxel accuracy and yielded an average registration error of 0.515 mm and the live machine-based 2D-3D registration delivered an average error of less than 0.2 mm. The capture range of the image-based 3D-3D registration was investigated to characterize its robustness, and yielded an extent of 35 mm and 25° for >80% of the datasets for registration of 3D rotational angiography (3DRA) with CT, and 15 mm and 20° for >80% of the datasets for registration of 3DRA with MR data. The image-based 3D-3D registration could be computed within 8 s, while applying the machine-based 2D-3D registration only took 1.5 μs, which makes them very suitable for interventional use.     

Effects of x-ray and CT image enhancements on the robustness and accuracy of a rigid 3D/2D image registration

A rigid body three-dimensional/two-dimensional (3D/2D) registration method has been implemented using mutual information, gradient ascent, and 3D texturemap-based digitally reconstructed radiographs. Nine combinations of commonly used x-ray and computed tomography (CT) image enhancement methods, including window leveling, histogram equalization, and adaptive histogram equalization, were examined to assess their effects on accuracy and robustness of the registration method. From a set of experiments using an anthropomorphic chest phantom, we were able to draw several conclusions. First, the CT and x-ray preprocessing combination with the widest attraction range was the one that linearly stretched the histograms onto the entire display range on both CT and x-ray images. The average attraction ranges of this combination were 71.3 mm and 61.3 deg in the translation and rotation dimensions, respectively, and the average errors were 0.12 deg and 0.47 mm. Second, the combination of the CT image with tissue and bone information and the x-ray images with adaptive histogram equalization also showed subvoxel accuracy, especially the best in the translation dimensions. However, its attraction ranges were the smallest among the examined combinations (on average 36 mm and 19 deg). Last the bone-only information on the CT image did not show convergency property to the correct registration.     

Assessment of rigid multi-modality image registration consistency using the multiple sub-volume registration (MSR) method

Registration of different imaging modalities such as CT, MRI, functional MRI (fMRI), positron (PET) and single photon (SPECT) emission tomography is used in many clinical applications. Determining the quality of any automatic registration procedure has been a challenging part because no gold standard is available to evaluate the registration. In this note we present a method, called the 'multiple sub-volume registration' (MSR) method, for assessing the consistency of a rigid registration. This is done by registering sub-images of one data set on the other data set, performing a crude non-rigid registration. By analysing the deviations (local deformations) of the sub-volume registrations from the full registration we get a measure of the consistency of the rigid registration. Registration of 15 data sets which include CT, MR and PET images for brain, head and neck, cervix, prostate and lung was performed utilizing a rigid body registration with normalized mutual information as the similarity measure. The resulting registrations were classified as good or bad by visual inspection. The resulting registrations were also classified using our MSR method. The results of our MSR method agree with the classification obtained from visual inspection for all cases (p < 0.02 based on ANOVA of the good and bad groups). The proposed method is independent of the registration algorithm and similarity measure. It can be used for multi-modality image data sets and different anatomic sites of the patient.     

A method of image registration for small animal, multi-modality imaging

Many research institutions have a full suite of preclinical tomographic scanners to answer biomedical questions in vivo. Routine multi-modality imaging requires robust registration of images generated by various tomographs. We have implemented a hardware registration method for preclinical imaging that is similar to that used in the combined positron emission tomography (PET)/computed tomography (CT) scanners in the clinic. We designed an imaging chamber which can be rigidly and reproducibly mounted on separate microPET and microCT scanners. We have also designed a three-dimensional grid phantom with 1288 lines that is used to generate the spatial transformation matrix from software registration using a 15-parameter perspective model. The imaging chamber works in combination with the registration phantom synergistically to achieve the image registration goal. We verified that the average registration error between two imaging modalities is 0.335 mm using an in vivo mouse bone scan. This paper also estimates the impact of image misalignment on PET quantitation using attenuation corrections generated from misregistered images. Our technique is expected to produce PET quantitation errors of less than 5%. The methods presented are robust and appropriate for routine use in high throughput animal imaging facilities.     

Robust and fast shell registration in PET and MR/CT brain images

A robust and fast hybrid method using a shell volume that consists of high contrast voxels with their neighbors is proposed for registering PET and MR/CT brain images. Whereas conventional hybrid methods find the best matched pairs from several manually selected or automatically extracted local regions, our method automatically selects a shell volume in the PET image, and finds the best matched corresponding volume using normalized mutual information (NMI) in overlapping volumes while transforming the shell volume into an MR or CT image. A shell volume not only can reduce irrelevant corresponding voxels between two images during optimization of transformation parameters, but also brings a more robust registration with less computational cost. Experimental results on clinical data sets showed that our method successfully aligned all PET and MR/CT image pairs without losing any diagnostic information, while the conventional registration methods failed in some cases.     

Deformable and rigid registration of MRI and microPET images for photodynamic therapy of cancer in mice

We are investigating imaging techniques to study the tumor response to photodynamic therapy (PDT). Positron emission tomography (PET) can provide physiological and functional information. High-resolution magnetic resonance imaging (MRI) can provide anatomical and morphological changes. Image registration can combine MRI and PET images for improved tumor monitoring. In this study, we acquired high-resolution MRI and microPET 18F-fluorodeoxyglucose (FDG) images from C3H mice with RIF-1 tumors that were treated with Pc 4-based PDT. We developed two registration methods for this application. For registration of the whole mouse body, we used an automatic three-dimensional, normalized mutual information algorithm. For tumor registration, we developed a finite element model (FEM)-based deformable registration scheme. To assess the quality of whole body registration, we performed slice-by-slice review of both image volumes; manually segmented feature organs, such as the left and right kidneys and the bladder, in each slice; and computed the distance between corresponding centroids. Over 40 volume registration experiments were performed with MRI and microPET images. The distance between corresponding centroids of organs was 1.5 +/- 0.4 mm which is about 2 pixels of microPET images. The mean volume overlap ratios for tumors were 94.7% and 86.3% for the deformable and rigid registration methods, respectively. Registration of high-resolution MRI and microPET images combines anatomical and functional information of the tumors and provides a useful tool for evaluating photodynamic therapy.     

基于自由变形法的多模态医学图像的配准与融合

本研究提出了一种自动识别颈部PET-CT图像特征点的算法，它应用自由变形（FFD）方法以CT图像的特征点为参考使PET图像产生变形，再结合最大互信息法对颈部PET与CT图像进行非刚体配准，最后用改进的小波图像融合法把两者进行融合得出视觉效果比较理想的融合图像。经实际计算得出的变形PET图像与对应CT图像的互信息量大于原始PET图像，并且最后用改进的小波图像融合法得出的融合图像的信息量比一般小波融合大，由此证明本研究所用方法是有效的。     

基于血管匹配的三维超声与CT图像的配准方法

提出一种基于血管匹配的三维超声与CT图像配准的新方法.首先,基于水平集方法自动分割出CT图像中的血管；其次,由于超声图像中的声影与血管均属于低回声区域,我们结合声影形成的物理原理及图像纹理特性,自动检测出声影区域,以提高配准的鲁棒性；最后,采用进化算法,将CT图像中分割出的血管与超声图像中低回声区域进行匹配.在肝脏体模和临床脾脏数据上进行了实验验证,自动配准的成功率在95％以上,平均目标配准误差在2 mm以内,实验结果验证了本方法的可行性.     

Multi-Modal Medical Image Integration to Optimize Radiotherapy Planning in Lung Cancer Treatment

This work presents a method for CT and PET image registration, and multi-modal analysis, to optimize radiotherapy planning in lung cancer treatment. The method relies on an image registration technique based on fiducial external markers to realign, spatially, PET images with the CT spatial reference system. The method was set up for clinical use in radiotherapy, allowing minimal modifications to be introduced in the management of patients undergoing radiation treatment. The accuracy of the registration technique was evaluated on patient studies in terms of Target Registration Error and was found to be less than 6.40 mm. The method was applied in the treatment planning of five patients affected by non-small-cell lung cancer, revealing the usefulness of PET/CT integration in delineating the extension of both the tumor mass and the tissues involved in the neoplastic process. Moreover, the functional information provided by PET often led to alterations in the treatment planning, changing the size and/or direction of radiation portals. The proposed method for PET/CT integration has been confirmed as being useful for optimizing radiotherapy planning in lung cancer treatment.     

Metallic copper as a fiducial marker for both CT and PET

There is great interest in augmenting computed tomography (CT) with information gained from other imaging modalities. Positron emission tomography (PET) provides valuable data related to patient physiology to aid in the delineation of tumor volumes. Combining the information provided by these imaging modalities requires accurate spatial registration of the two data sets. Fiducial based mapping provides straightforward registration based on corresponding landmark points or fiducials in the two image sets. When external fiducials are employed, consistent intermodality marker placement and centroid identification are essential to achieving an accurate and reliable registration. Similarity of marker design between modalities greatly aides in achieving this goal. Solid copper may serve as a fiducial marker in both CT and PET. Small spheres or wires of copper are readily visible in CT while neutron activation of these same markers produces positron emitting Copper-64 for detection by PET. The use of identical shaped markers in both imaging modalities greatly simplifies the task of intermodality centroid matching. Copper has excellent machining properties and, prior to activation, is easy and safe to handle. The feasibility of Cu as a marker for both CT and PET is demonstrated using imaging phantoms.     

Evaluation of linear registration algorithms for brain SPECT and the errors due to hypoperfusion lesions

The semiquantitative analysis of perfusion single-photon emission computed tomography (SPECT) images requires a reproducible, objective method. Automated spatial standardization (registration) of images is a prerequisite to this goal. A source of registration error is the presence of hypoperfusion defects, which was evaluated in this study with simulated lesions. The brain perfusion images measured by 99mTc-HMPAO SPECT from 21 patients with probable Alzheimer's disease and 35 control subjects were retrospectively analyzed. An automatic segmentation method was developed to remove external activity. Three registration methods, robust least squares, normalized mutual information (NMI), and count difference were implemented and the effects of simulated defects were compared. The tested registration methods required segmentation of the cerebrum from external activity, and the automatic and manual methods differed by a three-dimensional displacement of 1.4+/-1.1 mm. NMI registration proved to be least adversely effected by simulated defects with 3 mm average displacement caused by severe defects. The error in quantifying the patient-template parietal ratio due to misregistration was 2.0% for large defects (70% hypoperfusion) and 0.5% for smaller defects (85% hypoperfusion).     

Robust feature-based registration using a Gaussian-weighted distance map and brain feature points for brain PET/CT images

Feature-based registration is an effective technique for clinical use, because it can greatly reduce computational costs. However, this technique, which estimates the transformation by using feature points extracted from two images may cause misalignments, particularly in brain PET and CT images that have low correspondence rates between features due to differences in image characteristics. To cope with this limitation, we propose a robust feature-based registration technique using a Gaussian-weighted distance map (GWDM) that finds the best alignment of feature points even when features of two images are mismatched. A GWDM is generated by propagating the value of the Gaussian-weighted mask from feature points of CT images and leads the feature points of PET images to be aligned on an optimal location even though there is a localization error between feature points extracted from PET and CT images. Feature points are extracted from two images by our automatic brain segmentation method. In our experiments, simulated and clinical data sets were used to compare our method with conventional methods such as normalized mutual information (NMI)-based registration and chamfer matching in accuracy, robustness, and computational time. Experimental results showed that our method aligned the images robustly even in cases where conventional methods failed to find optimal locations. In addition, the accuracy of our method was comparable to that of the NMI-based registration method.     

Improved quantitation for PET/CT image reconstruction with system modeling and anatomical priors

Accurate quantitation of positron emission tomography (PET) tracer uptake levels in tumors is important for staging and monitoring response to treatment. Quantitative accuracy in PET is particularly poor for small tumors because of system partial volume errors and smoothing operations. This work proposes a reconstruction algorithm to reduce the quantitative errors due to limited system resolution and due to necessary image noise reduction. We propose a method for finding and using the detection system response in the projection matrix of a statistical reconstruction algorithm. In addition, we use aligned anatomical information, available in PET/CT scanners, to govern the penalty term applied during each image update. These improvements are combined with Fourier rebinning in a clinically feasible algorithm for reconstructing fully three-dimensional PET data. Results from simulation and measured studies show improved quantitation of tumor values in terms of bias and variance across multiple tumor sizes and activity levels with the proposed method. At common clinical image noise levels for the detection task, the proposed method reduces the error in maximum tumor values by 11% compared to filtered back-projection and 5% compared to conventional iterative methods.