3D interfractional patient position verification using 2D-3D registration of orthogonal images

Reproducible positioning of the patient during fractionated external beam radiation therapy is imperative to ensure that the delivered dose distribution matches the planned one. In this paper, we expand on a 2D-3D image registration method to verify a patient's setup in three dimensions (rotations and translations) using orthogonal portal images and megavoltage digitally reconstructed radiographs (MDRRs) derived from CT data. The accuracy of 2D-3D registration was improved by employing additional image preprocessing steps and a parabolic fit to interpolate the parameter space of the cost function utilized for registration. Using a humanoid phantom, precision for registration of three-dimensional translations was found to be better than 0.5 mm (1 s.d.) for any axis when no rotations were present. Three-dimensional rotations about any axis were registered with a precision of better than 0.2 degrees (1 s.d.) when no translations were present. Combined rotations and translations of up to 4 degrees and 15 mm were registered with 0.4 degrees and 0.7 mm accuracy for each axis. The influence of setup translations on registration of rotations and vice versa was also investigated and mostly agrees with a simple geometric model. Additionally, the dependence of registration accuracy on three cost functions, angular spacing between MDRRs, pixel size, and field-of-view, was examined. Best results were achieved by mutual information using 0.5 degrees angular spacing and a 10 x 10 cm2 field-of-view with 140 x 140 pixels. Approximating patient motion as rigid transformation, the registration method is applied to two treatment plans and the patients' setup errors are determined. Their magnitude was found to be < or = 6.1 mm and < or = 2.7 degrees for any axis in all of the six fractions measured for each treatment plan.     

Automated 2D-3D registration of portal images and CT data using line-segment enhancement

In prostate radiotherapy, setup errors with respect to the patient's bony anatomy can be reduced by aligning 2D megavoltage (MV) portal images acquired during treatment to a reference 3D kilovoltage (kV) CT acquired for treatment planning purposes. The purpose of this study was to evaluate a fully automated 2D-3D registration algorithm to quantify setup errors in 3D through the alignment of line-enhanced portal images and digitally reconstructed radiographs computed from the CT. The line-enhanced images were obtained by correlating the images with a filter bank of short line segments, or "sticks" at different orientations. The proposed methods were validated on (1) accurately collected gold-standard data consisting of a 3D kV cone-beam CT scan of an anthropomorphic phantom of the pelvis and 2D MV portal images in the anterior-posterior (AP) view acquired at 15 different poses and (2) a conventional 3D kV CT scan and weekly 2D MV AP portal images of a patient over 8 weeks. The mean (and standard deviation) of the absolute registration error for rotations around the right-lateral (RL), inferior-superior (IS), and posterior-anterior (PA) axes were 0.212 degree (0.214 degree), 0.055 degree (0.033 degree) and 0.041 degree (0.039 degree), respectively. The corresponding registration errors for translations along the RL, IS, and PA axes were 0.161 (0.131) mm, 0.096 (0.033) mm, and 0.612 (0.485) mm. The mean (and standard deviation) of the total registration error was 0.778 (0.543) mm. Registration on the patient images was successful in all eight cases as determined visually. The results indicate that it is feasible to automatically enhance features in MV portal images of the pelvis for use within a completely automated 2D-3D registration framework for the accurate determination of patient setup errors. They also indicate that it is feasible to estimate all six transformation parameters from a 3D CT of the pelvis and a single portal image in the AP view.     

Combination of intensity-based image registration with 3D simulation in radiation therapy

Modern techniques of radiotherapy like intensity modulated radiation therapy (IMRT) make it possible to deliver high dose to tumors of different irregular shapes at the same time sparing surrounding healthy tissue. However, internal tumor motion makes precise calculation of the delivered dose distribution challenging. This makes analysis of tumor motion necessary. One way to describe target motion is using image registration. Many registration methods have already been developed previously. However, most of them belong either to geometric approaches or to intensity approaches. Methods which take account of anatomical information and results of intensity matching can greatly improve the results of image registration. Based on this idea, a combined method of image registration followed by 3D modeling and simulation was introduced in this project. Experiments were carried out for five patients 4DCT lung datasets. In the 3D simulation, models obtained from images of end-exhalation were deformed to the state of end-inhalation. Diaphragm motions were around -25 mm in the cranial-caudal (CC) direction. To verify the quality of our new method, displacements of landmarks were calculated and compared with measurements in the CT images. Improvement of accuracy after simulations has been shown compared to the results obtained only by intensity-based image registration. The average improvement was 0.97 mm. The average Euclidean error of the combined method was around 3.77 mm. Unrealistic motions such as curl-shaped deformations in the results of image registration were corrected. The combined method required less than 30 min. Our method provides information about the deformation of the target volume, which we need for dose optimization and target definition in our planning system.     

A new algorithm for the registration of portal images to planning images in the verification of radiotherapy, as validated in prostate treatments

The manual verification of a radiotherapy treatment, where a portal image is matched onto a planning image, is very time consuming and subject to inter- and intraobserver variability. Therefore, a fully automatic matching procedure (image registration) is required. Existing automatic matching algorithms are confounded, however, by irrelevant information in the portal images (i.e., air in the intestines). Therefore, we have developed a new method, which is an extension of chamfer matching and uses, apart from the distance to the nearest edge, additional information on the correspondence of the gradient angle and magnitude of the edges, making the method less sensitive to confounding information in the images. To validate the automatic matching procedure in clinical practice, we applied the new method on 157 images of 29 randomly selected patients treated for carcinoma of the prostate. Three experts manually matched these images in consensus. Subsequently, the same observers assessed the results of the automatic registration. When regular chamfer matching is used for the fully automatic matching procedure, only 5% of the image pairs could be matched correctly, whereas the new method successfully registered 80% by using additional information on the angle of the edges. From the results of the validation study it can be concluded that a significant reduction in workload for the physicians and technicians can be achieved with this method.     

2D-3D registration of coronary angiograms for cardiac procedure planning and guidance

We present a completely automated 2D-3D registration technique that accurately maps a patient-specific heart model, created from preoperative images, to the patient's orientation in the operating room. This mapping is based on the registration of preoperatively acquired 3D vascular data with intraoperatively acquired angiograms. Registration using both single and dual-plane angiograms is explored using simulated but realistic datasets that were created from clinical images. Heart deformations and cardiac phase mismatches are taken into account in our validation using a digital 4D human heart model. In an ideal situation where the pre- and intraoperative images were acquired at identical time points within the cardiac cycle, the single-plane and the dual-plane registrations resulted in 3D root-mean-square (rms) errors of 1.60 +/- 0.21 and 0.53 +/- 0.08 mm, respectively. When a 10% timing offset was added between the pre- and the intraoperative acquisitions, the single-plane registration approach resulted in inaccurate registrations in the out-of-plane axis, whereas the dual-plane registration exhibited a 98% success rate with a 3D rms error of 1.33 +/- 0.28 mm. When all potential sources of error were included, namely, the anatomical background, timing offset, and typical errors in the vascular tree reconstruction, the dual-plane registration performed at 94% with an accuracy of 2.19 +/- 0.77 mm.     

A fast, accurate, and automatic 2D-3D image registration for image-guided cranial radiosurgery

The authors developed a fast and accurate two-dimensional (2D)-three-dimensional (3D) image registration method to perform precise initial patient setup and frequent detection and correction for patient movement during image-guided cranial radiosurgery treatment. In this method, an approximate geometric relationship is first established to decompose a 3D rigid transformation in the 3D patient coordinate into in-plane transformations and out-of-plane rotations in two orthogonal 2D projections. Digitally reconstructed radiographs are generated offline from a preoperative computed tomography volume prior to treatment and used as the reference for patient position. A multiphase framework is designed to register the digitally reconstructed radiographs with the x-ray images periodically acquired during patient setup and treatment. The registration in each projection is performed independently; the results in the two projections are then combined and converted to a 3D rigid transformation by 2D-3D geometric backprojection. The in-plane transformation and the out-of-plane rotation are estimated using different search methods, including multiresolution matching, steepest descent minimization, and one-dimensional search. Two similarity measures, optimized pattern intensity and sum of squared difference, are applied at different registration phases to optimize accuracy and computation speed. Various experiments on an anthropomorphic head-and-neck phantom showed that, using fiducial registration as a gold standard, the registration errors were 0.33 +/- 0.16 mm (s.d.) in overall translation and 0.29 degrees +/- 0.11 degrees (s.d.) in overall rotation. The total targeting errors were 0.34 +/- 0.16 mm (s.d.), 0.40 +/- 0.2 mm (s.d.), and 0.51 +/- 0.26 mm (s.d.) for the targets at the distances of 2, 6, and 10 cm from the rotation center, respectively. The computation time was less than 3 s on a computer with an Intel Pentium 3.0 GHz dual processor.     

基于2D-3D配准的术中腓骨旋转不良检测方法

目的通常检测腓骨旋转不良的方法是对术中踝关节2D X射线影像做目视评估,但主观判断往往导致结果误差较大;目前公认的准确检测腓骨旋转不良的方法需借助术后3D CT数据,但会为患者引入较大的辐射量。为此,本文提出了一种基于2D-3D配准技术准确识别术中腓骨旋转不良的低剂量、低成本的精准复位腓骨的可行方法,以此验证所提方法的可行性。方法研究对象为单一腓骨CT数据,初始位置定义为参考位,然后借助Mimics软件对其做不同程度的旋转变换,生成12组腓骨旋转畸形测试位的CT数据,模拟术中腓骨的不同姿态,包括6组腓骨内旋和6组腓骨外旋。测试位腓骨术中C臂图像由投影仿真程序生成。通过将术中C臂图像和参考位CT数据做2D-3D配准来识别这12组测试位相对于参考位的旋转不良程度。得到的结果和金标准比对,从而评估2D-3D配准的准确性;其中,金标准为参考位和12组测试位的3D-3D配准结果。另外,因为与投影轴平行方向检测位移不敏感,故本文用两幅正交位投影的配准结果做补偿。结果 10次测试12组数据配准结果在绕x轴、y轴和z轴旋转的平均角度(及沿3个方向的平均位移)误差分别为1. 19°(0. 56 mm)、0. 72°(0. 84 mm)和0. 81°(0. 65 mm);标准差依次为0. 43°(0. 38 mm)、0. 51°(0. 47 mm)和0. 58°(0. 5 mm);最大误差分别是2. 13°(1. 76 mm)、2. 74°(1. 90 mm)和2. 10°(2. 16 mm)。结论 2D-3D配准方法可为临床腓骨复位提供精度更高的监控工具,其误差远小于目前10°旋转的目测误差。相比于术前CT做手术规划、术后CT做手术评估,本文方法借助术前CT和术中C臂图像不仅可达到准确评估的目的,而且可实现术中动态评估,故其辐射剂量更低,患者医疗成本更低,治疗更及时、更有效。     

A stochastic approach for automatic registration and fusion of left atrial electroanatomic maps with 3D CT anatomical images

The integration of electroanatomic maps with highly resolved computed tomography cardiac images plays an important role in the successful planning of the ablation procedure of arrhythmias. In this paper, we present and validate a fully-automated strategy for the registration and fusion of sparse, atrial endocardial electroanatomic maps (CARTO maps) with detailed left atrial (LA) anatomical reconstructions segmented from a pre-procedural MDCT scan. Registration is accomplished by a parameterized geometric transformation of the CARTO points and by a stochastic search of the best parameter set which minimizes the misalignment between transformed CARTO points and the LA surface. The subsequent fusion of electrophysiological information on the registered CT atrium is obtained through radial basis function interpolation. The algorithm is validated by simulation and by real data from 14 patients referred to CT imaging prior to the ablation procedure. Results are presented, which show the validity of the algorithmic scheme as well as the accuracy and reproducibility of the integration process. The obtained results encourage the application of the integration method in post-intervention ablation assessment and basic AF research and suggest the development for real-time applications in catheter guiding during ablation intervention.     

Real-time 3D surface-image-guided beam setup in radiotherapy of breast cancer

We describe an approach for external beam radiotherapy of breast cancer that utilizes the three-dimensional (3D) surface information of the breast. The surface data of the breast are obtained from a 3D optical camera that is rigidly mounted on the ceiling of the treatment vault. This 3D camera utilizes light in the visible range therefore it introduces no ionization radiation to the patient. In addition to the surface topographical information of the treated area, the camera also captures gray-scale information that is overlaid on the 3D surface image. This allows us to visualize the skin markers and automatically determine the isocenter position and the beam angles in the breast tangential fields. The field sizes and shapes of the tangential, supraclavicular, and internal mammary gland fields can all be determined according to the 3D surface image of the target. A least-squares method is first introduced for the tangential-field setup that is useful for compensation of the target shape changes. The entire process of capturing the 3D surface data and subsequent calculation of beam parameters typically requires less than 1 min. Our tests on phantom experiments and patient images have achieved the accuracy of 1 mm in shift and 0.5 degrees in rotation. Importantly, the target shape and position changes in each treatment session can both be corrected through this real-time image-guided system.     

A deformable image registration method to handle distended rectums in prostate cancer radiotherapy

In image-guided adaptive radiotherapy, it is important to have the capability to automatically and accurately delineate the rectal wall, which is a major dose-limiting organ in prostate cancer radiotherapy. As image registration is a process to find the spatial correspondence between two images, a major challenge in intensity-based deformable image registration is to deal with the situation where no correspondence exists for some objects between the two images to be registered. One example is the variation of rectal contents due to the presence and absence of bowel gas. The intensity-based deformable image registration methods alone cannot create the correct spatial transformation if there is no correspondence between the source and target images. In this study we implemented an automatic image intensity modification procedure to create artificial gas pockets in the planning computed tomography (CT) images. A diffusion-based deformable image registration algorithm was developed to use an adaptive smoothing algorithm to better handle large organ deformations. The process was tested in 15 prostate cancer cases and 30 daily CT images containing the largest distended rectums. The manually delineated rectums agreed well with the autodelineated rectums when using the image-intensity modification procedure.     

Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery

Surgical targeting of the incorrect vertebral level (wrong-level surgery) is among the more common wrong-site surgical errors, attributed primarily to the lack of uniquely identifiable radiographic landmarks in the mid-thoracic spine. The conventional localization method involves manual counting of vertebral bodies under fluoroscopy, is prone to human error and carries additional time and dose. We propose an image registration and visualization system (referred to as LevelCheck), for decision support in spine surgery by automatically labeling vertebral levels in fluoroscopy using a GPU-accelerated, intensity-based 3D-2D (namely CT-to-fluoroscopy) registration. A gradient information (GI) similarity metric and a CMA-ES optimizer were chosen due to their robustness and inherent suitability for parallelization. Simulation studies involved ten patient CT datasets from which 50?000 simulated fluoroscopic images were generated from C-arm poses selected to approximate the C-arm operator and positioning variability. Physical experiments used an anthropomorphic chest phantom imaged under real fluoroscopy. The registration accuracy was evaluated as the mean projection distance (mPD) between the estimated and true center of vertebral levels. Trials were defined as successful if the estimated position was within the projection of the vertebral body (namely mPD <5?mm). Simulation studies showed a success rate of 99.998% (1 failure in 50?000 trials) and computation time of 4.7?s on a midrange GPU. Analysis of failure modes identified cases of false local optima in the search space arising from longitudinal periodicity in vertebral structures. Physical experiments demonstrated the robustness of the algorithm against quantum noise and x-ray scatter. The ability to automatically localize target anatomy in fluoroscopy in near-real-time could be valuable in reducing the occurrence of wrong-site surgery while helping to reduce radiation exposure. The method is applicable beyond the specific case of vertebral labeling, since any structure defined in pre-operative (or intra-operative) CT or cone-beam CT can be automatically registered to the fluoroscopic scene.     

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

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

The accuracy and repeatability of an automatic 2D-3D fluoroscopic image-model registration technique for determining shoulder joint kinematics

Fluoroscopic imaging, using single plane or dual plane images, has grown in popularity to measure dynamic in vivo human shoulder joint kinematics. However, no study has quantified the difference in spatial positional accuracy between single and dual plane image-model registration applied to the shoulder joint. In this paper, an automatic 2D-3D image-model registration technique was validated for accuracy and repeatability with single and dual plane fluoroscopic images. Accuracy was assessed in a cadaver model, kinematics found using the automatic registration technique were compared to those found using radiostereometric analysis. The in vivo repeatability of the automatic registration technique was assessed during the dynamic abduction motion of four human subjects. The in vitro data indicated that the error in spatial positional accuracy of the humerus and the scapula was less than 0.30mm in translation and less than 0.58° in rotation using dual plane images. Single plane accuracy was satisfactory for in-plane motion variables, but out-of-plane motion variables on average were approximately 8 times less accurate. The in vivo test indicated that the repeatability of the automatic 2D-3D image-model registration was 0.50mm in translation and 1.04° in rotation using dual images. For a single plane technique, the repeatability was 3.31mm in translation and 2.46° in rotation for measuring shoulder joint kinematics. The data demonstrate that accurate and repeatable shoulder joint kinematics can be obtained using dual plane fluoroscopic images with an automatic 2D-3D image-model registration technique; and that out-of-plane motion variables are less accurate than in-plane motion variables using a single plane technique.     

A robotic approach to 4D real-time tumor tracking for radiotherapy

Respiratory and cardiac motions induce displacement and deformation of the tumor volumes in various internal organs. To accommodate this undesired movement and other errors, physicians incorporate a large margin around the tumor to delineate the planning target volume, so that the clinical target volume receives the prescribed radiation dose under any scenario. Consequently, a large volume of healthy tissue is irradiated and sometimes it is difficult to spare critical organs adjacent to the tumor. In this study we have proposed a novel approach to the 4D active tracking and dynamic delivery incorporating the tumor motion prediction technique. This method has been applied to the two commercially available robotic treatment couches. The proposed algorithm can predict the tumor position and the robotic systems are able to continuously track the tumor during radiation dose delivery. Therefore a precise dose is given to a moving target while the dose to the nearby critical organs is reduced to improve the patient treatment outcome. The efficacy of the proposed method has been investigated by extensive computer simulation. The tumor tracking method is simulated for two couches: HexaPOD robotic couch and ELEKTA Precise Table. The comparison results have been presented in this paper. In order to assess the clinical significance, dosimetric effects of the proposed method have been analyzed.     

A practical approach to inverse planning for high-precision dose escalated conformal prostate radiotherapy

The problem of choosing the best gantry angles and beam weights for dose-escalated conformal prostate treatment planning is formulated using a mixed-integer linear programming approach, to account for tumour dose homogeneity and dose-volume constraints. The formulation allows the number of beams to be restricted and for some of the beams to be compulsory. The present planning algorithm interfaces with and utilizes the three-dimensional planning capabilities of a commercial treatment planning system. A case study is illustrated, which represents a particularly challenging planning problem due to a large planning target volume and an unusually small bladder. Treatment plans with different numbers of beams are generated to compare with each other and with the standard six-field plan. Significant improvement is shown in the reduction of hot regions within the femoral heads and rectal wall, while not unduly compromising homogeneity constraints for the tumour.     

2D/3D registration of endoscopic ultrasound to CT volume data

This paper describes a computer-aided navigation system using image fusion to support endoscopic interventions such as the accurate collection of biopsy specimens. An endoscope provides the physician with real-time ultrasound (US) and a video image. An image slice that corresponds to the corresponding image from the US scan head is derived from a preoperative computed tomography (CT) or magnetic resonance image volume data set using oblique reformatting and displayed side by side with the US image. The position of the image acquired by the US scan head is determined by a miniaturized electromagnetic tracking system (EMTS) after calibrating the endoscope's scan head. The transformation between the patient coordinate system and the preoperative data set is calculated using a 2D/3D registration. This is achieved by calibrating an intraoperative interventional CT slice with an optical tracking system (OTS) using the same algorithm as for the US calibration. The slice is then used for 2D/3D registration with the coordinate system of the preoperative volume. The fiducial registration error (FRE) for the US calibration was 2.0 mm +/- 0.4 mm; the interventional CT FRE was 0.36 +/- 0.12 mm; and the 2D/3D registration target registration error (TRE) was 1.8 +/- 0.3 mm. The point-to-point registration between the OTS and the EMTS had an FRE of 0.9 +/- 0.4 mm. Finally, we found an overall TRE for the complete system to be 3.9 +/- 0.6 mm.     

Preprocessing of control portal images for patient setup verification during the treatments in external radiotherapy

The main problem in processing the control portal images is their poor quality. We have developed a way of improving the image quality to allow a segmentation stage. Three items were studied for this purpose the background gradient of intensity correction, the noise reduction, and the image restoration. The background was corrected by subtracting a smoothed version of the image from the original. We tested 15 noise reduction methods. The most appropriate for control portal images was found to be the truncated average. Finally, four restoration techniques were compared. The maximum a posteriori (MAP) algorithm was the most efficient. The algorithms were tested over a wide range of conditions (image quality). They produced a great improvement in anatomic detail for all the imaging systems, energies, and anatomical zones tested. For example, the signal-to-noise ratio of a SRI-100 pelvis image, acquired with 4 monitor units (MU) at 10 MV (very low quality image), increased from 0.97 to 42.84 after preprocessing. We found that the improvement in image quality facilitated or even enabled segmentation of the control portal images. The percentage of segments belonging to a structure increased from 30% to 65% in the example cited. The preprocessing of control portal images is the first step in checking the patient setup.