An improved algorithm for femoropopliteal artery centerline restoration using prior knowledge of shapes and image space data

Accurate arterial centerline extraction is essential for comprehensive visualization in CT Angiography. Time consuming manual tracking is needed when automated methods fail to track centerlines through severely diseased and occluded vessels. A previously described algorithm, Partial Vector Space Projection (PVSP), which uses vessel shape information from a database to bridge occlusions of the femoropopliteal artery, has a limited accuracy in long (>100 mm) occlusions. In this article we introduce a new algorithm, Intermediate Point Detection (IPD), which uses calcifications in the occluded artery to provide additional information about the location of the centerline to facilitate improvement in PVSP performance. It identifies calcified plaque in image space to find the most useful point within the occlusion to improve the estimate from PVSP. In this algorithm candidates for calcified plaque are automatically identified on axial CT slices in a restricted region around the estimate obtained from PVSP. A modified Canny edge detector identifies the edge of the calcified plaque and a convex polygon fit is used to find the edge of the calcification bordering the wall of the vessel. The Hough transform for circles estimates the center of the vessel on the slice, which serves as a candidate intermediate point. Each candidate is characterized by two scores based on radius and relative position within the occluded segment, and a polynomial function is constructed to define a net score representing the potential benefit of using this candidate for improving the centerline. We tested our approach in 44 femoropopliteal artery occlusions of lengths up to 398 mm in 30 patients with peripheral arterial occlusive disease. Centerlines were tracked manually by four-experts, twice each, with their mean serving as the reference standard. All occlusions were first interpolated with PVSP using a database of femoropopliteal arterial shapes obtained from a total of 60 subjects. Occlusions longer than 80 mm (N = 20) were then processed with the IPD algorithm, provided calcifications were found (N = 14). We used the maximum point-wise distance of an interpolated curve from the reference standard as our error metric. The IPD algorithm significantly reduced the average error of the initial PVSP from 2.76 to 1.86 mm (p < 0.01). The error was less than the clinically desirable 3 mm (smallest radius of the femoropopliteal artery) in 13 of 14 occlusions. The IPD algorithm achieved results within the range of the human readers in 11 of 14 cases. We conclude that the additional use of sparse but specific image space information, such as calcified atherosclerotic plaque, can be used to substantially improve the performance of a previously described knowledge-based method to restore the centerlines of femoropopliteal arterial occlusions.     

Fast and robust computation of colon centerline in CT colonography

Although several methods for generating the centerline of a colon from CT colonographic scans have been proposed, in general they are time-consuming and do not take into account that the images of the colon may be of nonoptimal quality, with collapsed regions, and stool within the colon. Furthermore, the colonic lumen or wall, which is often used as a basis for computation of a centerline, is not always precisely segmented. In this study, we have developed an algorithm for computation of a colon centerline that is fast compared to the centerline algorithms presented in the reviewed literature, and that relies little on a complete colon segments identification. The proposed algorithm first extracts local maxima in a distance map of a segmented colonic lumen. The maxima are considered to be nodes in a set of graphs, and are iteratively linked together, based on a set of connection criteria, giving a minimum distance spanning tree. The connection criteria are computed from the distance from object boundary, the Euclidean distance between nodes and the voxel values on the pathway between pairs of nodes. After the last iteration, redundant branches are removed and end segments are recovered for each remaining graph. A subset of the initial maxima is used for distinguishing between the colon and noncolonic centerline segments among the set of graphs, giving the final centerline representation. A phantom study showed that, with respect to phantom variations, the algorithm achieved nearly constant computation time (2.3-2.9 s) except for the most extreme setting (20.2 s). The algorithm successfully found all, or most of, the centerline (93% - 100%). Displacement from optimum varied with colon diameter (1.2-6.6 mm). By use of 40 CT colonographic scans, the computer-generated centerlines were compared with the centerlines generated by three radiologists. The similarity was measured based on percent coverage and average displacement. The computer-generated centerlines, when compared with human-generated centerlines, had approximately the same displacement as when the human-generated centerlines were compared among each other (3.8 mm versus 4.0 mm). The coverage of the computer-generated centerlines was slightly less than that of the human-generated centerlines (92% versus 94%). The 40 centerlines were, on average, computed in 10.5 seconds, including computation time for the distance transform, with an Intel Pentium-based 800 MHz computer, as compared with 12-17 seconds or more (excluding computation time for the distance transform needed) per centerline as reported in other studies.     

Pulmonary nodule registration in serial CT scans based on rib anatomy and nodule template matching

An automated method is being developed in order to identify corresponding nodules in serial thoracic CT scans for interval change analysis. The method uses the rib centerlines as the reference for initial nodule registration. A spatially adaptive rib segmentation method first locates the regions where the ribs join the spine, which define the starting locations for rib tracking. Each rib is tracked and locally segmented by expectation-maximization. The ribs are automatically labeled, and the centerlines are estimated using skeletonization. For a given nodule in the source scan, the closest three ribs are identified. A three-dimensional (3D) rigid affine transformation guided by simplex optimization aligns the centerlines of each of the three rib pairs in the source and target CT volumes. Automatically defined control points along the centerlines of the three ribs in the source scan and the registered ribs in the target scan are used to guide an initial registration using a second 3D rigid affine transformation. A search volume of interest (VOI) is then located in the target scan. Nodule candidate locations within the search VOI are identified as regions with high Hessian responses. The initial registration is refined by searching for the maximum cross-correlation between the nodule template from the source scan and the candidate locations. The method was evaluated on 48 CT scans from 20 patients. Experienced radiologists identified 101 pairs of corresponding nodules. Three metrics were used for performance evaluation. The first metric was the Euclidean distance between the nodule centers identified by the radiologist and the computer registration, the second metric was a volume overlap measure between the nodule VOIs identified by the radiologist and the computer registration, and the third metric was the hit rate, which measures the fraction of nodules whose centroid computed by the computer registration in the target scan falls within the VOI identified by the radiologist. The average Euclidean distance error was 2.7 +/- 3.3 mm. Only two pairs had an error larger than 10 mm. The average volume overlap measure was 0.71 +/- 0.24. Eighty-three of the 101 pairs had ratios larger than 0.5, and only two pairs had no overlap. The final hit rate was 93/101.     

基于互信息医学刚性图像配准方法的研究

目的:基于互信息的配准方法是医学图像配准领域的重要方法,具有鲁棒性,精度高等优点。本文探究医学刚性图像配准的有效算法和关键技术。方法:基于互信息配准方法,利用Powell多参数算法和改进的PV插值算法,得到两幅图像之间的最大互信息和最佳配准参数。结果:二维磨牙CT图像配准实验表明,配准速度快,精度提高,验证了插值方法的有效性。结论:方法和算法可提高配准速度,能有效抑制互信息目标函数的局部极值。     

Validation of Image-Based Method for Extraction of Coronary Morphometry

An accurate analysis of the spatial distribution of blood flow in any organ must be based on detailed morphometry (diameters, lengths, vessel numbers, and branching pattern) of the organ vasculature. Despite the significance of detailed morphometric data, there is relative scarcity of data on 3D vascular anatomy. One of the major reasons is that the process of morphometric data collection is labor intensive. The objective of this study is to validate a novel segmentation algorithm for semi-automation of morphometric data extraction. The utility of the method is demonstrated in porcine coronary arteries imaged by computerized tomography (CT). The coronary arteries of five porcine hearts were injected with a contrast-enhancing polymer. The coronary arterial tree proximal to 1 mm was extracted from the 3D CT images. By determining the centerlines of the extracted vessels, the vessel radii and lengths were identified for various vessel segments. The extraction algorithm described in this paper is based on a topological analysis of a vector field generated by normal vectors of the extracted vessel wall. With this approach, special focus is placed on achieving the highest accuracy of the measured values. To validate the algorithm, the results were compared to optical measurements of the main trunk of the coronary arteries with microscopy. The agreement was found to be excellent with a root mean square deviation between computed vessel diameters and optical measurements of 0.16 mm (<10% of the mean value) and an average deviation of 0.08 mm. The utility and future applications of the proposed method to speed up morphometric measurements of vascular trees are discussed.     

Coronary x-ray angiographic reconstruction and image orientation

We have developed an interactive geometric method for 3D reconstruction of the coronary arteries using multiple single-plane angiographic views with arbitrary orientations. Epipolar planes and epipolar lines are employed to trace corresponding vessel segments on these views. These points are utilized to reconstruct 3D vessel centerlines. The accuracy of the reconstruction is assessed using: (1) near-intersection distances of the rays that connect x-ray sources with projected points, (2) distances between traced and projected centerlines. These same two measures enter into a fitness function for a genetic search algorithm (GA) employed to orient the angiographic image planes automatically in 3D avoiding local minima in the search for optimized parameters. Furthermore, the GA utilizes traced vessel shapes (as opposed to isolated anchor points) to assist the optimization process. Differences between two-view and multiview reconstructions are evaluated. Vessel radii are measured and used to render the coronary tree in 3D as a surface. Reconstruction fidelity is demonstrated via (1) virtual phantom, (2) real phantom, and (3) patient data sets, the latter two of which utilize the GA. These simulated and measured angiograms illustrate that the vessel center-lines are reconstructed in 3D with accuracy below 1 mm. The reconstruction method is thus accurate compared to typical vessel dimensions of 1-3 mm. The methods presented should enable a combined interpretation of the severity of coronary artery stenoses and the hemodynamic impact on myocardial perfusion in patients with coronary artery disease.     

基于多对比度磁共振颈动脉图像的配准算法研究

基于在血管壁增强显示方面的强大潜力,近年来多对比度核磁共振成像已成为斑块分析研究的有力工具,但其效能受到多序列图像血管不匹配的影响。为实现多序列图像的准确血管配准,在管腔分割的基础上提出一种由粗到精的两步配准策略:先采用迭代最近点实现多对比度图像中心线的刚性配准,再采用薄板样条实现基于血管边界点的非刚性配准。在第二步配准中,为准确寻找不同序列血管边界的匹配点,创新使用形状上下文描述子对边界点进行筛选,并应用确定性退火技术进行全局优化。采用新型三维多对比度磁共振血管成像序列,对提出算法的有效性进行定量评价。结果表明,配准后不同序列的边界重合度均达到95%以上,平均表面距离0.12 mm,可有效提高配准精度,为后续斑块成分的分析奠定基础。     

An enhanced block matching algorithm for fast elastic registration in adaptive radiotherapy

Image registration has many medical applications in diagnosis, therapy planning and therapy. Especially for time-adaptive radiotherapy, an efficient and accurate elastic registration of images acquired for treatment planning, and at the time of the actual treatment, is highly desirable. Therefore, we developed a fully automatic and fast block matching algorithm which identifies a set of anatomical landmarks in a 3D CT dataset and relocates them in another CT dataset by maximization of local correlation coefficients in the frequency domain. To transform the complete dataset, a smooth interpolation between the landmarks is calculated by modified thin-plate splines with local impact. The concept of the algorithm allows separate processing of image discontinuities like temporally changing air cavities in the intestinal track or rectum. The result is a fully transformed 3D planning dataset (planning CT as well as delineations of tumour and organs at risk) to a verification CT, allowing evaluation and, if necessary, changes of the treatment plan based on the current patient anatomy without time-consuming manual re-contouring. Typically the total calculation time is less than 5 min, which allows the use of the registration tool between acquiring the verification images and delivering the dose fraction for online corrections. We present verifications of the algorithm for five different patient datasets with different tumour locations (prostate, paraspinal and head-and-neck) by comparing the results with manually selected landmarks, visual assessment and consistency testing. It turns out that the mean error of the registration is better than the voxel resolution (2 x 2 x 3 mm(3)). In conclusion, we present an algorithm for fully automatic elastic image registration that is precise and fast enough for online corrections in an adaptive fractionated radiation treatment course.     

Correlation of projection radiographs in radiation therapy using open curve segments and points.

A method for determining differences in patient position between projection radiographs such as those routinely used in radiation therapy has been developed. Determination of a transformation relating two radiographs permits registration of simulation and portal images and the transfer of information between them. The algorithm is based on spatially registering segments of open curves or points seen on both images, and does not require identification of corresponding curve endpoints. The method as implemented is both fast and accurate. After user definition of the curves or points to be registered, the optimal transformation is calculated in approximately 1 s. Calculational experiments indicate that corresponding points on open curves are registered to better than 2 mm, even when random errors (FWHM 1 mm) in digitization are included. Experiments on the registration of clinical portal and simulation images (pixel size = 0.5 by 0.5 mm) indicate an accuracy on the order of 2 mm or less in translation and 2 deg or less in rotation. Analysis of portal and simulation radiographs of the brain, thorax, and pelvis indicates this algorithm to be robust and clinically applicable. The rapid and accurate registration of portal and simulation images is potentially important in the application of real time portal imaging devices in radiation therapy.     

Clonal diversity and stability of subgingival Eikenella corrodens

Eikenella corrodens is a commensal subgingival bacterium commonly found in both periodontally nondiseased and diseased subjects. The present study examined the clonal diversity and stability of subgingival E. corrodens over time. Ninety-five subjects were enrolled at the baseline examination, including 44 periodontally nondiseased subjects and 51 subjects with aggressive periodontitis. Twenty-two nondiseased subjects and 11 subjects with aggressive periodontitis were subsequently reexamined after an average interval of 14 months. Two subgingival plaque samples were obtained from each subject to determine the total cultivable bacteria. In addition, multiple E. corrodens isolates from each sample were recovered for clonal analysis by arbitrarily primed PCR. The mean numbers of distinct E. corrodens clones harbored by nondiseased subjects and subjects with aggressive periodontitis were 1.3 and 3.0, respectively. Thirty-nine percent of the nondiseased subjects and 63% of the subjects with aggressive periodontitis harbored multiple clones of E. corrodens. The numbers of distinct E. corrodens clones increased significantly (Mann-Whitney ranking test, P < 0.05) in sites from patients with aggressive periodontitis, in sites with pocket depths of 4 mm or greater, in sites with a clinical attachment loss of 2 mm or greater, and in sites coinfected with Porphyromonas gingivalis. Comparison of E. corrodens clones recovered at the baseline and those recovered at the follow-up examination showed that E. corrodens colonization was not stable. Thirty-eight of the 66 follow-up samples (58%) showed a complete change (including de novo colonization of the sites or complete elimination of the organism from the sites) of the subgingival E. corrodens clonal types between the baseline and the follow-up examinations. Our results suggest a complexity of subgingival microbiota not seen previously.     

Optimization of three-dimensional angiographic data obtained by self-calibration of multiview imaging

Stroke is one of the leading causes of death in the U.S. The treatment of stroke often involves vascular interventions in which devices are guided to the intervention site often through tortuous vessels based on two-dimensional (2-D) angiographic images. Three dimensional (3-D) vascular information may facilitate these procedures. Methods have been proposed for the self-calibrating determination of 3-D vessel trees from biplane and multiple plane images and the geometric relationships between the views (imaging geometries). For the biplane analysis, four or more corresponding points must be identified in the biplane images. For the multiple view technique, multiple vessels must be indicated and only the translation vectors relating the geometries are calculated. We have developed methods for the calculation of the 3-D vessel data and the full transformations relating the multiple views (rotations and translations) obtained during interventional procedures, and the technique does not require indication of corresponding points, but only the indication of a single vessel, e.g., the vessel of interest. Multiple projection views of vessel trees are obtained and transferred to the analysis computer. The vessel or vessels of interest are indicated by the user. Using the initial imaging geometry determined from the gantry information, 3-D vessel centerlines are calculated using the indicated centerlines in pairs of images. The imaging geometries are then iteratively adjusted and 3-D centerlines recalculated until the root-mean-square (rms) difference between the calculated 3-D centerlines is minimized. Simulations indicate that the 3-D centerlines can be accurately determined (to within 1 mm) even for errors in indication of the vessel endpoints as large as 5 mm. In phantom studies, the average rms difference between the pairwise calculated 3-D centerlines is approximately 7.5 mm prior to refinement (i.e., using the gantry information alone), whereas the average rms difference is usually below 1 mm after refinement. Accuracies and reliabilities of better than 1 mm were also determined by comparing centerlines determined using multiview and rotational angiography reconstruction and clinical data sets. These results indicate that the multiview approach will provide accurate and reliable 3-D centerlines for indicated vessel(s) without increasing the dose to the patient.     

Algorithms for automated oximetry along the retinal vascular tree from dual-wavelength fundus images

We present an automated method to perform accurate, rapid, and objective measurement of the blood oxygen saturation over each segment of the retinal vascular hierarchy from dual-wavelength fundus images. Its speed and automation (2 s per entire image versus 20 s per segment for manual methods) enables detailed level-by-level measurements over wider areas. An automated tracing algorithm is used to estimate vessel centerlines, thickness, directions, and locations of landmarks such as bifurcations and crossover points. The hierarchical structure of the vascular network is recovered from the trace fragments and landmarks by a novel algorithm. Optical densities (OD) are measured from vascular segments using the minimum reflected intensities inside and outside the vessel. The OD ratio (ODR=OD600/OD570) bears an inverse relationship to systemic HbO2 saturation (SO2). The sensitivity for detecting saturation change when breathing air versus pure oxygen was calculated from the measurements made on six subjects and was found to be 0.0226 ODR units, which is in good agreement with previous manual measurements by the dual-wavelength technique, indicating the validity of the automation. A fully automated system for retinal vessel oximetry would prove useful to achieve early assessments of risk for progression of disease conditions associated with oxygen utilization.     

Patient position verification using CT images.

The use of ions in the radiotherapy of cancer patients requires an accurate patient positioning in order to exploit its potential benefits. Using CT images as the basis for the setup verification offers the advantage of a high in-plane resolution in combination with a geometrically accurate, volumetric information. Before each fraction a single CT slice is acquired at the isocenter level after the positioning procedure. This single slice is registered to the planning CT cube using automated image registration algorithms. Thus any erreonous translation or rotation can be detected and quantified. The registration process involves the interpolation of the volumetric data, the calculation of an energy function, and the minimization of this energy function. Several data interpolation functions as well as minimization algorithms were compared. CT studies with a head phantom were performed in which defined translations and rotations were simulated by moving a motor-driven treatment chair. Different slice thicknesses and anatomical sites were studied to investigate their potential influence on the registration accuracy. The accuracy of the registration was found to be a fraction of a voxel size for suitable combinations of algorithms (typically better than 0.16 mm/deg). A significant dependancy of the registration accuracy on the CT slice thickness and the anatomical site was found (the accuracy ranges from 0.05 mm/deg to 0.16 mm/deg depending on the site). The calculation time is dependant on the used algorithms and the magnitude of the setup error. For the standard combination of algorithms as proposed by the authors (Downhill Simplex minimization with Trilinear interpolation) the typical calculation time is about 20 s for a Sun UltraSPARC processor. Taking into account the mechanical accuracy of the setup device (motor-driven chair) the registration of CT images is thus a useful tool for detecting and quantifying any significant error in the patient position.     

具紧支撑性质的薄板样条插值函数及其应用

目的：改进薄板样条插值函数，提高薄板样条插值方法的效率；方法：针对薄板样条插值方法的不足。提出了新的处理方法，使薄板样条插值具有紧支撑性，并应用于图象配准过程中；结果：实现了图象快速、准确配准。简化了配准过程；结论：新的插值函数可进一步提高配准的鲁棒性和适用性。     

Scalp-Recorded EEG Localization in MRI Volume Data

Scalp-recorded EEG is a noninvasive and widely available tool for studying normal and dysfunctional human neurophysiology with unsurpassed temporal resolution. However, scalp-recorded EEG data is difficult to correlate with anatomy, and most current display and neural source estimation algorithms are based on unrealistic spherical or elliptical models of the head. It is possible to measure the positions of electrodes on the patient's scalp, and to register those electrode positions into the space of a high-resolution MRI volume, and to then use the patient-specific anatomy as the basis for display and estimation of neural sources. We use a surface matching algorithm to register digitized electrode and scalp surface coordinates to a three-dimensional MRI volume. This study uses fiducial markers in phantom and volunteer studies to quantitatively estimate the accuracy of the electrode registration method. Our electrode registration procedure is accurate to 2.21 mm for a realistic head phantom and accurate to 4.16 mm on average for five volunteers. This level of accuracy is considered within acceptable limits for clinical applications.     

Model-independent, multimodality deformable image registration by local matching of anatomical features and minimization of elastic energy

With respect to the demands of adaptive and 4D-radiotherapy applications, an algorithm is proposed for a fully automatic, multimodality deformable registration that follows the concept of translational relocation of regularly distributed image subvolumes governed by local anatomical features. Thereby, the problem of global deformable registration is broken down to multiple independent local registration steps which allows for straightforward parallelization of the algorithm. In a subsequent step, possible local misregistrations are corrected for by minimization of the elastic energy of the displacement field under consideration of image information. The final displacement field results from interpolation of the subvolume shift vectors. The algorithm can employ as a similarity measure both the correlation coefficient and mutual information. The latter allows the application to intermodality deformable registration problems. The typical calculation time on a modern multiprocessor PC is well below 1 min, which facilitates almost-interactive, "online" usage. CT-to-MRI and CT-to-cone-beam-CT registrations of head-and-neck data sets are presented, as well as inhale-to-exhale registrations of lung CT data sets. For quantitative evaluation of registration accuracy, a virtual thorax phantom was developed; additionally, a landmark-based evaluation on four lung respiratory-correlated CT data sets was performed. This consistently resulted in average registration residuals on the order of the voxel size or less (3D-residuals approximately 1-2 mm). Summarizing, the presented algorithm allows an accurate multimodality deformable registration with calculation times well below 1 min, and thus bears promise as a versatile basic tool in adaptive and 4D-radiotherapy applications.     

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.     

Rigid Registration of Medical Images Using 1D and 2D Binary Projections

Image registration is a necessary procedure in everyday clinical practice. Several techniques for rigid and non-rigid registration have been developed and tested and the state-of-the-art is evolving from the research setting to incorporate image registration techniques into clinically useful tools. In this paper, we develop a novel rigid medical image registration technique which incorporates binary projections. This technique is tested and compared to the standard mutual information (MI) methods. Results show that the method is significantly more accurate and robust compared to MI methods. The accuracy is well below 0.5° and 0.5 mm. This method introduces two more improvements over MI methods: (1)for 2D registration with the use of 1D binary projections, we use minimal interpolation; and (2) for 3D registration with the use of 2D binary projections the method converges to stable final positions, independent of the initial misregistration.     

Deformable structure registration of bladder through surface mapping

Cumulative dose distributions in fractionated radiation therapy depict the dose to normal tissues and therefore may permit an estimation of the risk of normal tissue complications. However, calculation of these distributions is highly challenging because of interfractional changes in the geometry of patient anatomy. This work presents an algorithm for deformable structure registration of the bladder and the verification of the accuracy of the algorithm using phantom and patient data. In this algorithm, the registration process involves conformal mapping of genus zero surfaces using finite element analysis, and guided by three control landmarks. The registration produces a correspondence between fractions of the triangular meshes used to describe the bladder surface. For validation of the algorithm, two types of balloons were inflated gradually to three times their original size, and several computerized tomography (CT) scans were taken during the process. The registration algorithm yielded a local accuracy of 4 mm along the balloon surface. The algorithm was then applied to CT data of patients receiving fractionated high-dose-rate brachytherapy to the vaginal cuff, with the vaginal cylinder in situ. The patients' bladder filling status was intentionally different for each fraction. The three required control landmark points were identified for the bladder based on anatomy. Out of an Institutional Review Board (IRB) approved study of 20 patients, 3 had radiographically identifiable points near the bladder surface that were used for verification of the accuracy of the registration. The verification point as seen in each fraction was compared with its predicted location based on affine as well as deformable registration. Despite the variation in bladder shape and volume, the deformable registration was accurate to 5 mm, consistently outperforming the affine registration. We conclude that the structure registration algorithm presented works with reasonable accuracy and provides a means of calculating cumulative dose distributions.     

Registration of FA and T1-Weighted MRI Data of Healthy Human Brain Based on Template Matching and Normalized Cross-Correlation

In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual information-based parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure.