Investigation of filter sets for supervised pixel classification of cephalometric landmarks by spatial spectroscopy

The diagnostic process of orthodontics requires the analysis of a cephalometric radiograph. Image landmarks on this two-dimensional lateral projection image of the patient's head are manually identified and spatial relationships are evaluated. This method is very time consuming. A reliable method for automatic computer landmark identification does not exist. Spatial Spectroscopy is a proposed method of automatic landmark identification on cephalometric radiographs, that decomposes an image by convolving it with a set of filters followed by a statistical decision process. The purpose of this paper is to discuss and test appropriate filter sets for the application of Spatial Spectroscopy for automatic identification of cephalometric radiographic landmarks. This study evaluated two different filter sets with 15 landmarks on fourteen images. Spatial Spectroscopy was able to consistently locate landmarks on all 14 cephalometric radiographs tested. The mean landmark identification error of 0.841±1.253 pixels for a Multiscale Derivative filter set and 0.912±1.364 pixels for an Offset Gaussian filter set was not significantly different. Furthermore, there were no significant differences between identification of individual landmarks for the Multiscale Derivative and the Offset Gaussian filter set (P>0.05). These results suggest that Spatial Spectroscopy may be useful in landmark identification tasks.     

Automatic Cobb Measurement of Scoliosis Based on Fuzzy Hough Transform with Vertebral Shape Prior

To reduce variability of Cobb angle measurement for scoliosis assessment, a computerized method was developed. This method automatically measured the Cobb angle on spinal posteroanterior radiographs after the brightness and the contrast of the image were adjusted, and the top and bottom of the vertebrae were selected. The automated process started with the edge detection of the vertebra by Canny edge detector. After that, the fuzzy Hough transform was used to find line structures in the vertebral edge images. The lines that fitted to the endplates of vertebrae were identified by selecting peaks in Hough space under the vertebral shape constraints. The Cobb angle was then calculated according to the directions of these lines. A total of 76 radiographs were respectively analyzed by an experienced surgeon using the manual measurement method and by two examiners using the proposed method twice. Intraclass correlation coefficients (ICC) showed high agreement between automatic and manual measurements (ICCs > 0.95). The mean absolute differences between automatic and manual measurements were less than 5°. In the interobserver analyses, ICCs were higher than 0.95, and mean absolute differences were less than 5°. In the intraobserver analyses, ICCs were 0.985 and 0.978, respectively, for each examiner, and mean absolute differences were less than 3°. These results demonstrated the validity and reliability of the proposed method.Key words: Cobb angle, scoliosis, fuzzy Hough transform (FHT), shape prior, radiograph     

The effect of automated landmark identification on morphometric analyses

Morphometric analysis of anatomical landmarks allows researchers to identify specific morphological differences between natural populations or experimental groups, but manually identifying landmarks is time‐consuming. We compare manually and automatically generated adult mouse skull landmarks and subsequent morphometric analyses to elucidate how switching from manual to automated landmarking will impact morphometric analysis results for large mouse (Mus musculus) samples (n = 1205) that represent a wide range of ‘normal’ phenotypic variation (62 genotypes). Other studies have suggested that the use of automated landmarking methods is feasible, but this study is the first to compare the utility of current automated approaches to manual landmarking for a large dataset that allows the quantification of intra‐ and inter‐strain variation. With this unique sample, we investigated how switching to a non‐linear image registration‐based automated landmarking method impacts estimated differences in genotype mean shape and shape variance‐covariance structure. In addition, we tested whether an initial registration of specimen images to genotype‐specific averages improves automatic landmark identification accuracy. Our results indicated that automated landmark placement was significantly different than manual landmark placement but that estimated skull shape covariation was correlated across methods. The addition of a preliminary genotype‐specific registration step as part of a two‐level procedure did not substantially improve on the accuracy of one‐level automatic landmark placement. The landmarks with the lowest automatic landmark accuracy are found in locations with poor image registration alignment. The most serious outliers within morphometric analysis of automated landmarks displayed instances of stochastic image registration error that are likely representative of errors common when applying image registration methods to micro‐computed tomography datasets that were initially collected with manual landmarking in mind. Additional efforts during specimen preparation and image acquisition can help reduce the number of registration errors and improve registration results. A reduction in skull shape variance estimates were noted for automated landmarking methods compared with manual landmarking. This partially reflects an underestimation of more extreme genotype shapes and loss of biological signal, but largely represents the fact that automated methods do not suffer from intra‐observer landmarking error. For appropriate samples and research questions, our image registration‐based automated landmarking method can eliminate the time required for manual landmarking and have a similar power to identify shape differences between inbred mouse genotypes.     

基于CTVision分析不同配准方式对鼻咽癌摆位误差的影响

目的分析CTVision进行鼻咽癌图像引导时采用不同的配准方式对摆位误差的影响。方法采用两种不同的图像配准方式,对同一个进行调强放射治疗的鼻咽癌患者采集得到的治疗CT图像与计划CT图像进行配准分析。自动配准：系统自动调节窗宽、窗位,进行图像灰度配准。骨性配准：手动调节基于第一颈椎体为骨性标志进行配准。配准得到的摆位误差大小以均数和标准差的形式表示。结果分别计算得到两种方法在X、Y、Z三个方向上的摆位误差数据．自动配准和骨性配准结果分别是X轴为（0．2286±0．1496）cm和（0．0571±0．0976）cm,Y轴为（-0．1000±0．1000）cm和（-0．0714±0．1254）cm．Z轴为（0．1000±0．0816）cm和（0．1000±0．0577）cm。两种配准方法比较,靶区在X轴上的摆位误差差异有统计学意义（P〈0．05）,而在Y轴和Z轴上差异均无统计学意义（P〉0．05）。结论鼻咽癌进行图像引导时．基于骨性标志的手动配准和基于灰度的自动配准．在X轴方向的摆位误差校正差异明显．在Y轴和Z轴方向的无明显差异。采用骨性标志的配准是一种更为准确和有效的配准方式。     

用无约束优化薄板样条实现平滑的医学图像弹性配准

医学图像弹性配准是医学图像处理的一个重要研究方向。目前采用的方法多是手动选择对应标记点，然后用薄板样条插值方法计算配准变换。由于对应点的选取总是存在误差，所以配准的准确性受到影响，而且手动选点操作繁杂、耗时大。为此，我们根据最优化理论，改进了薄板样条插值方法，并在此基础上采用了一种自动标记点选择方法。将这两者结合，我们得到了一种自动、准确、鲁棒性好的配准方法，运用此方法进行医学图像的弹性配准，得到了更好的结果。     

Stereo image matching for facial feature measurement to aid in fetal alcohol syndrome screening

The facial phenotype associated with fetal alcohol syndrome is identified through the measurement of facial distances and their comparison to population norms. As an alternative to time-consuming direct manual measurements, stereo photogrammetry has been used to obtain the required facial measurements from stereo digital photographs. While stereo photogrammetric measurement is faster, it requires manual marking of relevant facial landmarks on digital images. We present an algorithm that automatically finds matched feature points on the second of a pair of stereo images, after manual marking of the first. Standard image processing tools are used for preprocessing. Matching is based on a simple exhaustive search in an image window, with the sum of squared differences of the pixel intensities in the two images as the objective function. Eye measurements, namely palpebral fissure length, interpupillary distance, inner canthal distance and outer canthal distance, as well as distances that can be used to approximate the circularity of the upper lip, were obtained using the manual method of marking both images, and the method of automatic marking of the second image. Comparison revealed mean differences less than 1mm.     

基于随机森林算法的小鼠micro-CT影像中骨骼关节特征点定位

随着小动物成像技术的发展,技术人员每天需要处理的小动物影像数量急剧增长,这使得自动化的小动物图像分析方法成为迫切的需求。在小鼠图像分析方面,小鼠灵活多变的身体姿态给自动化的图像分析带来困难。基于随机森林算法实现小鼠micro-CT图像中骨骼关节点的自动定位,为解决小鼠影像中身体姿态的自动识别打下基础。该算法主要分3步:先通过分类随机森林算法得到小鼠骨骼关节点的粗定位,再通过回归随机森林算法进一步减小定位误差,最后通过图匹配的方法在备选点中挑选正确位置上的关节点。对49例不同身体姿态的小鼠全身三维micro-CT图像进行测试,全身关节点定位的成功率为98.27%,定位误差的中值为0.68 mm。同时验证联合使用分类与回归随机森林的必要性,并探究训练数据的数量对不同骨关节的识别效果的影响。研究为小鼠micro-CT影像中身体姿态的识别提供一种新方法,为后续的自动化图像配准、图像分割以及自动化图像测量提供重要的定位信息。     

Automatic segmentation of carotid B-mode images using fuzzy classification

This paper presents a new method for the automatic segmentation of the common carotid artery in B-mode images. This method uses the instantaneous coefficient of variation edge detector, fuzzy classification of edges and dynamic programming. Several discriminating features of the intima and adventitia boundaries are considered, like the edge strength, the intensity gradient orientation, the valley shaped intensity profile and contextual information of the region delimited by those boundaries. The adopted fuzzy classification of edges helps avoiding low-pass filtering. The method is suited to real-time processing and user interaction is not required. Both the near and far wall boundaries can be detected in arteries with plaques of different types and sizes. Both expert manual and automatic tracings are significantly better for the far wall, due to the better visibility of the intima and adventitia boundaries. The automatic detection of the far wall shows an accuracy similar to the manual detections. For this wall, the error coefficient of variation for the mean intima-media thickness is in the range [5.6, 6.6?%] for automatic detections and in [6.7, 7.1?%] for manual ones. In the case of the near wall, the same coefficient of variation is in [11.2, 13.0?%] for automatic detections and in [5.9, 9.0?%] for manual detections. The mean intima-media thickness measurement errors observed for the far wall [Formula: see text] are among the best values reported for other fully automatic approaches. The application of this approach in clinical practice is encouraged by the results for the far wall and the short processing time (mean of 2.1?s per image).     

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.     

Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patient-specific bladder model

In multiple plan adaptive radiotherapy (ART) strategies of bladder cancer, a library of plans corresponding to different bladder volumes is created based on images acquired in early treatment sessions. Subsequently, the plan for the smallest PTV safely covering the bladder on cone-beam CT (CBCT) is selected as the plan of the day. The aim of this study is to develop an automatic bladder segmentation approach suitable for CBCT scans and test its ability to select the appropriate plan from the library of plans for such an ART procedure. Twenty-three bladder cancer patients with a planning CT and on average 11.6 CBCT scans were included in our study. For each patient, all CBCT scans were matched to the planning CT on bony anatomy. Bladder contours were manually delineated for each planning CT (for model building) and CBCT (for model building and validation). The automatic segmentation method consisted of two steps. A patient-specific bladder deformation model was built from the training data set of each patient (the planning CT and the first five CBCT scans). Then, the model was applied to automatically segment bladders in the validation data of the same patient (the remaining CBCT scans). Principal component analysis (PCA) was applied to the training data to model patient-specific bladder deformation patterns. The number of PCA modes for each patient was chosen such that the bladder shapes in the training set could be represented by such number of PCA modes with less than 0.1?cm mean residual error. The automatic segmentation started from the bladder shape of a reference CBCT, which was adjusted by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to fit the bladder in the validation image. A cost function was defined by the absolute difference between the directional gradient field of reference CBCT sampled on the corresponding bladder contour and the directional gradient field of validation CBCT sampled on the segmentation contour candidate. The cost function measured the goodness of fit of the segmentation on the validation image and was minimized using a simplex optimizer. For each validation CBCT image, the segmentations were done five times using a different reference CBCT. The one with the lowest cost function was selected as the final bladder segmentation. Volume- and distance-based metrics and the accuracy of plan selection were evaluated to quantify the performance. Two to four PCA modes were needed to represent the bladder shape variation with less than 0.1?cm average residual error for the training data of each patient. The automatically segmented bladders had a 78.5% mean conformity index with the manual delineations. The mean SD of the local residual error over all patients was 0.24?cm. The agreement of plan selection between automatic and manual bladder segmentations was 77.5%. PCA is an efficient method to describe patient-specific bladder deformation. The statistical-shape-based segmentation approach is robust to handle the relatively poor CBCT image quality and allows for fast and reliable automatic segmentation of the bladder on CBCT for selecting the appropriate plan from a library of plans.     

Automatic recognition of vertebral landmarks in fluoroscopic sequences for analysis of intervertebral kinematics

Intervertebral kinematics closely relates to the functionality of the spinal segments. Direct measurement of the intervertebral kinematics in vivo is very problematic. The use of a fluoroscopic device can provide continuous screening of the lumbar tract during patient spontaneous motion, with an acceptable, low X-ray dose. The kinematic analysis is intended to be limited to planar motion. Kinematic parameters are computed from vertebral landmarks on each frame of the image sequence. Landmarks are normally selected manually in spite of the fact that this is subjective, tedious to perform and regarded as one of the major contributors to errors in the computed kinematic parameters. The aim of this work is to present an innovative method for the automatic recognition of vertebral landmarks throughout a fluoroscopic image sequence to provide an objective and more precise quantification of intervertebral kinematics. The recognition procedure is based upon comparing vertebral features in two adjacent frames by means of a cross-correlation index, which is also robust despite the low signal-to-noise ratio of the lumbar fluoroscopic images. To provide a quantitative assessment of this method a calibration model was used which consisted of two lumbar vertebrae linked by a universal joint. The reliability and accuracy of the kinematic measurements have been investigated. The errors are of the order of a millimetre for the localisation of the intervertebral centre of rotation and tenths of a degree for the intervertebral angle. Error analysis suggests that this method improves the accuracy of the intervertebral kinematic calculations and has the potential to automate the selection of anatomical landmarks.     

A non-invasive technique for the accurate measurement of leg length in animals.

A novel, non-invasive technique for accurate measurements in animals (Kyniklometry) is presented. Kyniklometry (derived from greek ? kúvikloz the rabbit) determines the distance between soft tissue landmarks in conscious rabbits, in particular the rear lower leg. Each measurement consists of six subsequent and independent estimations of this distance, with a technical error of 79 microns (study I), respectively 83 microns (study II). The angle of the relaxed sitting animal's knee is approximately 45 infinity, and remains individually almost constant during subsequent measurements. The precision of the device was compared with X-ray stereophotogrammetry (technical error 30 microns). Five female New Zealand White rabbits were measured for 13 consecutive days at 24-hour-intervals both by kyniklometry and X-ray stereophotogrammetry (study I). The mean increment of 5 kyniklometric series of ten (3rd to 13th day) 24-hour-increments was 0.988 mm, the mean 24-hour-variance was 0.244 mm2. Sixty point five percent of this variance could be explained by parallel right/left leg soft tissue variation. Only 5% of the variance was explicable by the technical error. The 24-hour-correlation between kyniklometry and X-ray stereophotogrammetry was significant with r = 0.889 and p less than 0.001. Kyniklometric measurements were also performed in 5 female rabbits for 56 days at 24-hour-intervals (study II). We found spontaneous periodicity once every 8 to 14 days. There was a diurnal variation of rear lower leg increment with maxima in the early morning hours.     

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.     

Accuracy of an Automatic Patient-Positioning System Based on the Correlation of Two Edge Images in Radiotherapy

We have clinically evaluated the accuracy of an automatic patient-positioning system based on the image correlation of two edge images in radiotherapy. Ninety-six head & neck images from eight patients undergoing proton therapy were compared with a digitally reconstructed radiograph (DRR) of planning CT. Two edge images, a reference image and a test image, were extracted by applying a Canny edge detector algorithm to a DRR and a 2D X-ray image, respectively, of each patient before positioning. In a simulation using a humanoid phantom, performed to verify the effectiveness of the proposed method, no registration errors were observed for given ranges of rotation, pitch, and translation in the x, y, and z directions. For real patients, however, there were discrepancies between the automatic positioning method and manual positioning by physicians or technicians. Using edged head coronal- and sagittal-view images, the average differences in registration between these two methods for the x, y, and z directions were 0.11 cm, 0.09 cm and 0.11 cm, respectively, whereas the maximum discrepancies were 0.34 cm, 0.38 cm, and 0.50 cm, respectively. For rotation and pitch, the average registration errors were 0.95° and 1.00°, respectively, and the maximum errors were 3.6° and 2.3°, respectively. The proposed automatic patient-positioning system based on edge image comparison was relatively accurate for head and neck patients. However, image deformation during treatment may render the automatic method less accurate, since the test image many differ significantly from the reference image.     

Registration of electronic portal images for patient set-up verification

Images acquired from an electronic portal imaging device are aligned with digitally reconstructed radiographs (DRRs) or other portal images to verify patient positioning during radiation therapy. Most of the currently available computer aided registration methods are based on the manual placement of corresponding landmarks. The purpose of the paper is twofold: (a) the establishment of a methodology for patient set-up verification during radiotherapy based on the registration of electronic portal images, and (b) the evaluation of the proposed methodology in a clinical environment. The estimation of set-up errors, using the proposed methodology, can be accomplished by matching the portal image of the current fraction of the treatment with the portal image of the baseline treatment (reference portal image) using a nearly automated technique. The proposed registration method is tested on a number of phantom data as well as on data from four patients. The phantom data included portal images that corresponded to various positions of the phantom on the treatment couch. For each patient, a set of 30 portal images was used. For the phantom data (for both transverse and lateral portal images), the maximum absolute deviations of the translational shifts were within 1.5 mm, whereas the in-plane rotation angle error was less than 0.5 degrees. The two-way Anova revealed no statistical significant variability both within observer and between-observer measurements (P > 0.05). For the patient data, the mean values obtained with manual and the proposed registration methods were within 0.5 mm. In conclusion, the proposed registration method has been incorporated within a system, called ESTERR-PRO. Its image registration capability achieves high accuracy and both intra- and inter-user reproducibility. The system is fully operational within the Radiotherapy Department of 'HYGEIA' Hospital in Athens and it could be easily installed in any other clinical environment since it requires standardized hardware specifications and minimal human intervention.     

Automatic noise robust registration of radiographs for subtraction using strategic local correlation: an application to radiographs of dental implants

Most of digital subtraction methods in dental radiography are based on registration using manual landmarks. We have developed an automatic registration method without using the manual selection of landmarks. By restricting a geometrical matching of images to a region of interest (ROI), we compare the cross-correlation coefficient only between the ROIs. The affine or perspective transform parameters satisfying maximum of cross-correlation between the local regions are searched iteratively by a fast searching strategy. The parameters are searched on the 14 scale image coarsely and then, the fine registration is performed on the original scale image. The developed method can match the images corrupted by Gaussian noise with the same accuracy for the images without any transform simulation. The registration accuracy of the perspective method shows a 17% improvement over the manual method. The application of the developed method to radiographs of dental implants provides an automatic noise robust registration with high accuracy in almost real time.     

Automated analysis of phantom images for the evaluation of long-term reproducibility in digital mammography

The performance of an automatic software package was evaluated with phantom images acquired by a full-field digital mammography unit. After the validation, the software was used, together with a Leeds TORMAS test object, to model the image acquisition process. Process modelling results were used to evaluate the sensitivity of the method in detecting changes of exposure parameters from routine image quality measurements in digital mammography, which is the ultimate purpose of long-term reproducibility tests. Image quality indices measured by the software included the mean pixel value and standard deviation of circular details and surrounding background, contrast-to-noise ratio and relative contrast; detail counts were also collected. The validation procedure demonstrated that the software localizes the phantom details correctly and the difference between automatic and manual measurements was within few grey levels. Quantitative analysis showed sufficient sensitivity to relate fluctuations in exposure parameters (kV(p) or mAs) to variations in image quality indices. In comparison, detail counts were found less sensitive in detecting image quality changes, even when limitations due to observer subjectivity were overcome by automatic analysis. In conclusion, long-term reproducibility tests provided by the Leeds TORMAS phantom with quantitative analysis of multiple IQ indices have been demonstrated to be effective in predicting causes of deviation from standard operating conditions and can be used to monitor stability in full-field digital mammography.     

Blind deblurring of spiral CT images-comparative studies on edge-to-noise ratios

A recently developed blind deblurring algorithm based on the edge-to-noise ratio has been applied to improve the quality of spiral CT images. Since the discrepancy measure used to quantify the edge and noise effects is not symmetric, there are several ways to formulate the edge-to-noise ratio. This article is to investigate the performance of those ratios with phantom and patient data. In the phantom study, it is shown that all the ratios share similar properties, validating the blind deblurring algorithm. The image fidelity improvement varies from 29% to 33% for different ratios, according to the root mean square error (RMSE) criterion; the optimal iteration number determined for each ratio varies from 25 to 35. Those ratios that are associated with most satisfactory performance are singled out for the image fidelity improvement of about 33% in the numerical simulation. After automatic blind deblurring with the selected ratios, the spatial resolution of CT is substantially refined in all the cases tested.     

Characteristics of the human isoplanatic patch and implications for adaptive optics retinal imaging

Conventional adaptive optics enables correction of high-order aberrations of the eye, but only for a single retinal point. When imaging extended regions of the retina, aberrations increase away from this point and degrade image quality. The zone over which aberrations do not change significantly is called the "isoplanatic patch." Literature concerning the human isoplanatic patch is incomplete. We determine foveal isoplanatic patch characteristics by performing Hartmann-Shack aberrometry in 1 deg increments in 8 directions on 7 human eyes. Using these measurements, we establish the correction quality required to yield at least 80% of the potential patch size for a given eye. Single-point correction systems (conventional adaptive optics) and multiple-point correction systems (multiconjugate adaptive optics) are simulated. Results are compared to a model eye. Using the Marechal criterion for 555-nm light, average isoplanatic patch diameter for our subjects is 0.80+/-0.10 deg. The required order of aberration correction depends on desired image quality over the patch. For the more realistically achievable criterion of 0.1 mum root mean square (rms) wavefront error over a 6.0-mm pupil, correction to at least sixth order is recommended for all adaptive optics systems. The most important aberrations to target for a multiconjugate correction are defocus, astigmatism, and coma.     

Validity of the line-pair bar-pattern method in the measurement of the modulation transfer function (MTF) in megavoltage imaging

The measurement of the modulation transfer function (MTF) of an imaging device is a common requirement in evaluating radiographic detector performance. Such measurements are considered mandatory in detector development research, and may also be carried out as part of routine quality assurance (QA) checks of image quality. Traditionally, MTF measurement has been performed by imaging either a narrow slit or a sharp edge in order to generate a line spread function, whose Fourier transform provides the MTF on a near-continuous frequency domain. Much less commonly employed is the method of square-wave line-pair modulations, in which the modulation response to bar resolution targets contained in a bar pattern is used to estimate the MTF at discrete spatial frequencies. While the slit and edge methods offer advantages of accuracy and a well-know standardized protocol for measurement based on several decades of development, their major limitation is the difficult and time-consuming experimental setup that is necessary to ensure accurate measurements. On the other hand, the bar pattern offers the advantage of a quick, simple, and easy measurement without the need for a complex experimental setup, with the main disadvantages of the technique being a pseudo-normalization that may lead to an overestimated MTF, and corrections for removing higher-order frequency harmonics that require interpolating between discrete spatial frequencies. Therefore, bar patterns are traditionally used for qualitative imaging applications like detector QA in terms of relative and arbitrarily defined spatial resolution metrics, while slit and edge methods are preferred for quantitative MTF measurements. Compared to diagnostic x rays, MTF measurements using megavoltage x rays are further complicated by low x-ray attenuation and excessive Compton scattering. In this work, a method to measure the MTF of megavoltage x-ray detectors based on imaging square-wave line pairs with improved near-zero-frequency normalization was developed as an adaptation to previously reported methods. Monte Carlo simulations were used to identify an improved normalization condition with which the accuracy of the MTF determined from line-pair modulations could be enhanced considerably compared to previously used techniques. Slit, edge, and bar-pattern measurements were performed to obtain the MTF of commercial megavoltage imaging devices including portal film and electronic portal imaging devices. A comparison of the MTF measurements from the three techniques was used to ascertain the validity of the proposed bar-pattern method for accurate and reliable measurement of MTF for megavoltage imagers. Statistical analyses revealed no significant differences between the bar-pattern method and the standard slit and edge techniques, indicating very good agreement (mean difference within +/- 3%). These results indicated the potential for line-pair bar patterns to be used more effectively than in the past for traditional QA imaging as well as for quantitative MTF measurement in detector development research.