Mean Registration Research Articles

Automated analysis of small animal PET studies through deformable registration to an atlas

PurposeThis work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model.MethodsA non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed.ResultsThe Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6 mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10 % in most of the organs considered.ConclusionThe proposed automated quantification technique is reliable, robust and suitable for fast quantification of preclinical PET data in large serial studies.

Automated algorithm for reconstruction of the complete spine from multistation 7T MR data

Recent technical developments in high-field MRI have enabled high-resolution imaging of the whole spine within clinically acceptable times. However, analysis of such data requires intensity inhomogeneity correction and volume stitching, both of which are typically performed manually. In this work, an automated method for reconstruction of the complete spine from multistation 7T MR data is presented. The method consists of a number of image processing steps, in particular intensity inhomogeneity correction and image registration for recovery of unknown interscan bed translations, which result in high-quality spine volume reconstructions. The registration performance of the developed algorithm was validated on 18 datasets acquired in two or three stations. In all the test cases, our algorithm was able to produce correct reconstruction of the spine volume. The resulting mean registration error (0.53 mm) is found to be lower than the pixel size, demonstrating robustness and accuracy of the proposed method.

WE-E-213CD-03: Inverse-Consistent Symmetric Registration of Inner Colon Surfaces Derived from Prone and Supine CT Colonography.

Robust registration of prone and supine colonie surfaces acquired during CT colonography may lead to faster and more accurate detection of colorectal cancer and polyps. Any directional bias when registering one surface to the other could precipitate incorrect anatomical correspondence and engender reader error. Despite this, non-rigid registration methods are often implemented asymmetrically, which could negatively influence the registration. We aimed to reduce directional bias and so increase robustness by adapting a cylindrical registration algorithm to be both symmetric and inverse-consistent. The registration task can be simplified by mapping both prone and supine colonie surfaces onto regular cylinders. Spatial correspondence can then be established in cylindrical space using the original surfaces' local shape indices. We implemented a symmetric formulation of the popular non-rigid B-spline image registration method in cylindrical space. A symmetric similarity measure computes the sum of squared differences between both cylindrical representations of prone-to-supine and supine-to-prone directions simultaneously. Inverse consistency of the transformation is enforced by adding an appropriately weighted penalty term to the optimisation function. We selected 8 CT colonography patient cases with marked variation in luminal distension and surface morphology. We randomly allocated 4 of these for tuning an optimal set of registration parameters and 4 for validation. The mean inverse-consistency error was reduced by 32% from 4.8mm to 3.2mm by the new symmetric formulation. The mean registration error improved from 8.2mm to 7.3mm for 330 manually chosen reference points on the 4 validation sets. A symmetric formulation of prone and supine surface registration improves the quality of registration. Information from both prone-to-supine and supine-to-prone directions helps enforce convergence towards a more accurate solution due to reduced directional bias. A more robust and accurate registration will facilitate interpretation of CT colonography and has the potential to improve existing computer-aided detection methods. The authors gratefully acknowledge financial support for this work from the NIHR program: “Imaging diagnosis of colorectal cancer: Interventions for efficient and acceptable diagnosis in symptomatic and screening populationsâ€.

MO-F-BRA-04: Voxel-Based Statistical Analysis of Deformable Image Registration Error via a Finite Element Method.

Purpose Clinical implementation of adaptive treatment planning is limited by the lack of quantitative tools to assess deformable image registration errors (R-ERR). The purpose of this study was to develop a method, using finite element modeling (FEM), to estimate registration errors based on mechanical changes resulting from them. Methods An experimental platform to quantify the correlation between registration errors and their mechanical consequences was developed as follows: diaphragm deformation was simulated on the CT images in patients with lung cancer using a finite element method (FEM). The simulated displacement vector fields (F-DVF) were used to warp each CT image to generate a FEM image. B-Spline based (Elastix) registrations were performed from reference to FEM images to generate a registration DVF (R-DVF). The F- DVF was subtracted from R-DVF. The magnitude of the difference vector was defined as the registration error, which is a consequence of mechanically unbalanced energy (UE), computed using 'in-house-developed' FEM software. A nonlinear regression model was used based on imaging voxel data and the analysis considered clustered voxel data within images. Results A regression model analysis showed that UE was significantly correlated with registration error, DVF and the product of registration error and DVF respectively with R̂2=0.73 (R=0.854). The association was verified independently using 40 tracked landmarks. A linear function between the means of UE values and R- DVF*R-ERR has been established. The mean registration error (N=8) was 0.9 mm. 85.4% of voxels fit this model within one standard deviation. Conclusions An encouraging relationship between UE and registration error has been found. These experimental results suggest the feasibility of UE as a valuable tool for evaluating registration errors, thus supporting 4D and adaptive radiotherapy. The research was supported by NIH/NCI R01CA140341.

Feature‐based multibody rigid registration of CT and ultrasound images of lumbar spine

Fusion of intraprocedure ultrasound and preprocedure CT data is proposed for guidance in percutaneous spinal injections, a common procedure for pain management. CT scan of the lumbar spine is usually collected in a supine position, whereas spinal injections are performed in prone or sitting positions. This leads to a difference in the spine curvature between CT and ultrasound images; as such, a single-body rigid registration approach cannot be used for the whole lumbar vertebrae. To compensate for the difference in the spinal curvature between the two imaging modalities, a multibody rigid registration algorithm is presented. The approach utilizes a point-based registration technique based on the unscented Kalman filter (UKF), taking as input segmented vertebrae surfaces in both CT and ultrasound data. Ultrasound images are automatically segmented using a dynamic programming method, while the CT images are semiautomatically segmented using thresholding. The registration approach is designed to simultaneously align individual groups of points segmented from each vertebra in the two imaging modalities. A biomechanical model is developed to constrain the vertebrae transformation parameters during the registration and to ensure convergence. The proposed methodology is evaluated on human spine phantoms and a sheep cadaver. Registrations on phantom data have a mean target registration error (TRE) of 1.99 mm in the region of interest and converged in 87% of cases. Registrations on sheep cadaver have a mean target registration error of 2.2 mm and converged in 82% of cases. The proposed technique can robustly and simultaneously register several vertebrae extracted from CT images to the ultrasound volumes. The registration error below 2.2 mm is sufficient for most spinal injections.

Elastic image registration for guiding focal laser ablation of prostate cancer: Preliminary results

PurposeTo guide ultrasound-driven prostate photodynamic therapy using information from MRI-based treatment planning. MethodsRobust points matching (RPM) and thin plate splines (TPS) are used to solve correspondences and to map optimally positioned landmarks from MR images to transrectal ultrasound (TRUS) images. The algorithm uses a reduced number of anatomical markers that are initialized on the images. ResultsBoth phantom and patient data were used to evaluate precision and robustness of the method. Mean registration error (±standard deviation) was of 2.18±0.25mm and 1.55±0.31mm for patient prostate and urethra, respectively. Repeated tests with different markers initialization conditions showed that the quality of registration was neither influenced by the number of markers nor to the human observer. ConclusionThis method allows for satisfyingly accurate and robust non rigid registration of MRI and TRUS and provides practitioners with substantial help in mapping treatment planning from pre-operative MRI to interventional TRUS.

Electromagnetic Tracking for Registration and Navigation in Endovascular Aneurysm Repair: A Phantom Study

ObjectiveTo assess the feasibility of using an electromagnetic tracking for both registration and navigation in endovascular aneurysm repair. Materials and methodsA registration process was implemented to align computed tomography (CT) data and electromagnetic tracking data. Two abdominal aortic aneurysm (AAA) phantoms were used, a rigid plastic AAA model (phantom A) and a soft silicon AAA model (phantom B). A pre-procedural CT volume was acquired for each phantom. Intra-operative simulation was performed by placing each phantom in the magnetic field of the tracking device. Using a modified electromagnetic catheter, a set of three-dimensional positions was acquired in the phantom's aortic lumen. Pre-procedural CT images and intra-procedural tracked positions were registered. Four reference points were used to calculate the registration accuracy of phantom A. Three surgeons simulated catheterisation of the left renal artery with phantom B using only image-guided procedure software. ResultsThe mean registration error was 1.3 mm (range 0.88–1.89). The median time for left renal catheterisation was 22 s (range 15–59). ConclusionRegistration of CT data and electromagnetic tracking data is feasible using catheter positions in the aorto-iliac structure as landmark. This navigation system could reduce X-ray exposure time and the use of contrast medium injections.

Prostate: Registration of Digital Histopathologic Images to in Vivo MR Images Acquired by Using Endorectal Receive Coil

To develop and evaluate a technique for the registration of in vivo prostate magnetic resonance (MR) images to digital histopathologic images by using image-guided specimen slicing based on strand-shaped fiducial markers relating specimen imaging to histopathologic examination. The study was approved by the institutional review board (the University of Western Ontario Health Sciences Research Ethics Board, London, Ontario, Canada), and written informed consent was obtained from all patients. This work proposed and evaluated a technique utilizing developed fiducial markers and real-time three-dimensional visualization in support of image guidance for ex vivo prostate specimen slicing parallel to the MR imaging planes prior to digitization, simplifying the registration process. Means, standard deviations, root-mean-square errors, and 95% confidence intervals are reported for all evaluated measurements. The slicing error was within the 2.2 mm thickness of the diagnostic-quality MR imaging sections, with a tissue block thickness standard deviation of 0.2 mm. Rigid registration provided negligible postregistration overlap of the smallest clinically important tumors (0.2 cm(3)) at histologic examination and MR imaging, whereas the tested nonrigid registration method yielded a mean target registration error of 1.1 mm and provided useful coregistration of such tumors. This method for the registration of prostate digital histopathologic images to in vivo MR images acquired by using an endorectal receive coil was sufficiently accurate for coregistering the smallest clinically important lesions with 95% confidence.

MRI to X-ray mammography registration using a volume-preserving affine transformation

X-ray mammography is routinely used in national screening programmes and as a clinical diagnostic tool. Magnetic Resonance Imaging (MRI) is commonly used as a complementary modality, providing functional information about the breast and a 3D image that can overcome ambiguities caused by the superimposition of fibro-glandular structures associated with X-ray imaging. Relating findings between these modalities is a challenging task however, due to the different imaging processes involved and the large deformation that the breast undergoes. In this work we present a registration method to determine spatial correspondence between pairs of MR and X-ray images of the breast, that is targeted for clinical use. We propose a generic registration framework which incorporates a volume-preserving affine transformation model and validate its performance using routinely acquired clinical data. Experiments on simulated mammograms from 8 volunteers produced a mean registration error of 3.8±1.6mm for a mean of 12 manually identified landmarks per volume. When validated using 57 lesions identified on routine clinical CC and MLO mammograms (n=113 registration tasks) from 49 subjects the median registration error was 13.1mm. When applied to the registration of an MR image to CC and MLO mammograms of a patient with a localisation clip, the mean error was 8.9mm. The results indicate that an intensity based registration algorithm, using a relatively simple transformation model, can provide radiologists with a clinically useful tool for breast cancer diagnosis.

Automatic three-dimensional registration of intravascular optical coherence tomography images

Intravascular optical coherence tomography (IV-OCT) is a catheter-based high-resolution imaging technique able to visualize the inner wall of the coronary arteries and implanted devices in vivo with an axial resolution below 20 μm. IV-OCT is being used in several clinical trials aiming to quantify the vessel response to stent implantation over time. However, stent analysis is currently performed manually and corresponding images taken at different time points are matched through a very labor-intensive and subjective procedure. We present an automated method for the spatial registration of IV-OCT datasets. Stent struts are segmented through consecutive images and three-dimensional models of the stents are created for both datasets to be registered. The two models are initially roughly registered through an automatic initialization procedure and an iterative closest point algorithm is subsequently applied for a more precise registration. To correct for nonuniform rotational distortions (NURDs) and other potential acquisition artifacts, the registration is consecutively refined on a local level. The algorithm was first validated by using an in vitro experimental setup based on a polyvinyl-alcohol gel tubular phantom. Subsequently, an in vivo validation was obtained by exploiting stable vessel landmarks. The mean registration error in vitro was quantified to be 0.14 mm in the longitudinal axis and 7.3-deg mean rotation error. In vivo validation resulted in 0.23 mm in the longitudinal axis and 10.1-deg rotation error. These results indicate that the proposed methodology can be used for automatic registration of in vivo IV-OCT datasets. Such a tool will be indispensable for larger studies on vessel healing pathophysiology and reaction to stent implantation. As such, it will be valuable in testing the performance of new generations of intracoronary devices and new therapeutic drugs.

Augmenting intraoperative ultrasound with preoperative magnetic resonance planning models for percutaneous renal access

BackgroundUltrasound (US) is a commonly-used intraoperative imaging modality for guiding percutaneous renal access (PRA). However, the anatomy identification and target localization abilities of the US imaging are limited. This paper evaluates the feasibility and efficiency of a proposed image-guided PRA by augmenting the intraoperative US with preoperative magnetic resonance (MR) planning models.MethodsFirst, a preoperative surgical planning approach is presented to define an optimal needle trajectory using MR volume data. Then, a MR to US registration is proposed to transfer the preoperative planning into the intraoperative context. The proposed registration makes use of orthogonal US slices to avoid local minima while reduce processing time. During the registration, a respiratory gating method is used to minimize the impact of kidney deformation. By augmenting the intraoperative US with preoperative MR models and a virtual needle, a visual guidance is provided to guarantee the correct execution of the surgical planning. The accuracy, robustness and processing time of the proposed registration were evaluated by four urologists on human data from four volunteers. Furthermore, the PRA experiments were performed by the same four urologists on a kidney phantom. The puncture accuracy in terms of the needle-target distance was measured, while the perceptual quality in using the proposed image guidance was evaluated according to custom scoring method.ResultsThe mean registration accuracy in terms of the root mean square (RMS) target registration error (TRE) is 3.53 mm. The RMS distance from the registered feature points to their average is 0.81 mm. The mean operating time of the registration is 6'4". In the phantom evaluation, the mean needle-target distance is 2.08 mm for the left lesion and 1.85 mm for the right one. The mean duration for all phantom PRA tests was 4'26". According to the custom scoring method, the mean scores of the Intervention Improvement, Workflow Impact, and Clinical Relevance were 4.0, 3.3 and 3.9 respectively.ConclusionsThe presented image guidance is feasible and promising for PRA procedure. With careful setup it can be efficient for overcoming the limitation of current US-guided PRA.

Monitoring tumor motion by real time 2D/3D registration during radiotherapy

Background and purposeIn this paper, we investigate the possibility to use X-ray based real time 2D/3D registration for non-invasive tumor motion monitoring during radiotherapy. Materials and methodsThe 2D/3D registration scheme is implemented using general purpose computation on graphics hardware (GPGPU) programming techniques and several algorithmic refinements in the registration process. Validation is conducted off-line using a phantom and five clinical patient data sets. The registration is performed on a region of interest (ROI) centered around the planned target volume (PTV). ResultsThe phantom motion is measured with an rms error of 2.56mm. For the patient data sets, a sinusoidal movement that clearly correlates to the breathing cycle is shown. Videos show a good match between X-ray and digitally reconstructed radiographs (DRR) displacement. Mean registration time is 0.5s. ConclusionsWe have demonstrated that real-time organ motion monitoring using image based markerless registration is feasible.

Multi-modal registration of speckle-tracked freehand 3D ultrasound to CT in the lumbar spine

A method for registration of speckle-tracked freehand 3D ultrasound (US) to preoperative CT volumes of the spine is proposed. We register the US volume to the CT volume by creating individual US "sub-volumes", each consisting of a small section of the entire US volume. The registration proceeds incrementally from the beginning of the US volume to the end, registering every sub-volume, where each sub-volume contains overlapping images with the previous sub-volume. Each registration is performed by generating simulated US images from the CT volume. As a by-product of our registration, the significant drift error common in speckle-tracked US volumes is corrected for. Results are validated through a phantom study of plastic spine phantoms created from clinical patient CT data as well as an animal study using a lamb cadaver. Results demonstrate that we were able to successfully register a speckle-tracked US volume to CT volume in four out of five phantoms with a success rate of greater than 98%. The final error of the registered US volumes decreases by over 50 percent from the speckle tracking error to consistently below 3 mm. Studies on the lamb cadaver showed a mean registration error consistently below 2 mm.

Fusion and visualization of intraoperative cortical images with preoperative models for epilepsy surgical planning and guidance

Objective: During epilepsy surgery it is important for the surgeon to correlate the preoperative cortical morphology (from preoperative images) with the intraoperative environment. Augmented Reality (AR) provides a solution for combining the real environment with virtual models. However, AR usually requires the use of specialized displays, and its effectiveness in the surgery still needs to be evaluated. The objective of this research was to develop an alternative approach to provide enhanced visualization by fusing a direct (photographic) view of the surgical field with the 3D patient model during image guided epilepsy surgery.Materials and Methods: We correlated the preoperative plan with the intraoperative surgical scene, first by a manual landmark-based registration and then by an intensity-based perspective 3D-2D registration for camera pose estimation. The 2D photographic image was then texture-mapped onto the 3D preoperative model using the solved camera pose. In the proposed method, we employ direct volume rendering to obtain a perspective view of the brain image using GPU-accelerated ray-casting. The algorithm was validated by a phantom study and also in the clinical environment with a neuronavigation system.Results: In the phantom experiment, the 3D Mean Registration Error (MRE) was 2.43 ± 0.32 mm with a success rate of 100%. In the clinical experiment, the 3D MRE was 5.15 ± 0.49 mm with 2D in-plane error of 3.30 ± 1.41 mm. A clinical application of our fusion method for enhanced and augmented visualization for integrated image and functional guidance during neurosurgery is also presented.Conclusions: This paper presents an alternative approach to a sophisticated AR environment for assisting in epilepsy surgery, whereby a real intraoperative scene is mapped onto the surface model of the brain. In contrast to the AR approach, this method needs no specialized display equipment. Moreover, it requires minimal changes to existing systems and workflow, and is therefore well suited to the OR environment. In the phantom and in vivo clinical experiments, we demonstrate that the fusion method can achieve a level of accuracy sufficient for the requirements of epilepsy surgery.

Registration of 3D trans-esophageal echocardiography to X-ray fluoroscopy using image-based probe tracking

Two-dimensional (2D) X-ray imaging is the dominant imaging modality for cardiac interventions. However, the use of X-ray fluoroscopy alone is inadequate for the guidance of procedures that require soft-tissue information, for example, the treatment of structural heart disease. The recent availability of three-dimensional (3D) trans-esophageal echocardiography (TEE) provides cardiologists with real-time 3D imaging of cardiac anatomy. Increasingly X-ray imaging is now supported by using intra-procedure 3D TEE imaging. We hypothesize that the real-time co-registration and visualization of 3D TEE and X-ray fluoroscopy data will provide a powerful guidance tool for cardiologists. In this paper, we propose a novel, robust and efficient method for performing this registration. The major advantage of our method is that it does not rely on any additional tracking hardware and therefore can be deployed straightforwardly into any interventional laboratory. Our method consists of an image-based TEE probe localization algorithm and a calibration procedure. While the calibration needs to be done only once, the GPU-accelerated registration takes approximately from 2 to 15s to complete depending on the number of X-ray images used in the registration and the image resolution. The accuracy of our method was assessed using a realistic heart phantom. The target registration error (TRE) for the heart phantom was less than 2mm. In addition, we assess the accuracy and the clinical feasibility of our method using five patient datasets, two of which were acquired from cardiac electrophysiology procedures and three from trans-catheter aortic valve implantation procedures. The registration results showed our technique had mean registration errors of 1.5-4.2mm and 95% capture range of 8.7-11.4mm in terms of TRE.

Direct application of MR images to computer-assisted bone tumor surgery

BackgroundWe describe a method for the direct application of MR images to navigation-assisted bone tumor surgery as an alternative to CT-MRI fusion. MethodSix patients with an orthopedic malignancy were employed for this method during navigation-assisted tumor resection. Tumor types included osteosarcoma (4), high-grade chondrosarcoma (1), and adamantinoma (1). Mean patient age was 25.3 years (range 18–52 years). Mean duration of follow-up was 25.8 months (range 18–32 months). Resorbable pin placement and rapid 3-dimensional spoiled gradient echo sequences made the direct application of MR images to computer-assisted bone tumor surgery without CT-MR image fusion possible. A paired-point registration technique was employed for patient-image registration in all patients. ResultsIt took 20 min on average to set up the navigation (range 15–25 min). The mean registration error was 0.98 mm (range 0.4–1.7 mm). On histologic examination, distances from tumors to resection margins were in accord with preoperative plans. No patient had a local recurrence or distant metastasis at the last follow-up. ConclusionDirect patient-to-MRI registration is a very useful method for bone tumor surgery, permitting the application of MR images to intraoperative visualization without any additional costs or exposure of the patient to radiation from the preoperative CT scan.

Microwave ablation assisted by a computerised tomography–ultrasonography fusion imaging system for liver lesions: An ex vivo experimental study

Purpose: To investigate the feasibility and validity of real-time guidance using a fusion imaging system that combines ultrasonography (US) and computerised tomography (CT) information in the targeting and subsequent microwave ablation of a liver target inconspicuous on US.Materials and methods: The study was an experimental ex vivo study in calf livers with internal targets, simulating a focal liver lesion, focused on the accuracy of real-time US using a multimodality fusion imaging system in combination with 15 gauge (G) microwave antennae. US image and pre-procedural CT image were fused by the external markers registration procedure. Microwave antennae were inserted into the liver to ablate the target assisted by the CT-US fusion imaging system. Finally, a post-procedural CT with needles in situ and multiplanar reconstructions were performed to compare with pre-procedural CT information in order to calculate the accuracy of positioning (distance between the needle tip and the target).Results: Eight insertions were planned and eight ablations were performed in four calf livers. The calf livers were undertaken successfully on external markers registration procedure. The mean registration error in the four livers was 2.1 ± 0.1 mm, 2.8 ± 0.1 mm, 3.4 ± 0.1 mm and 2.3 ± 0.1 mm, respectively. The accuracy of the matched US-CT images was very satisfactory in the fact that it was found there was a mean discrepancy of 1.63 ± 1.06 mm.Conclusion: Real-time registration and fusion of pre-procedural CT volume images with intraprocedural US is feasible and accurate for microwave (MW) ablation in experimental setting. Further studies are warranted to validate the system under clinical conditions.

Spatio-temporal (2D+T) non-rigid registration of real-time 3D echocardiography and cardiovascular MR image sequences

In this paper we describe a method to non-rigidly co-register a 2D slice sequence from real-time 3D echocardiography with a 2D cardiovascular MR image sequence. This is challenging because the imaging modalities have different spatial and temporal resolution. Non-rigid registration is required for accurate alignment due to imprecision of cardiac gating and natural motion variations between cardiac cycles. In our approach the deformation field between the imaging modalities is decoupled into temporal and spatial components. First, temporal alignment is performed to establish temporal correspondence between a real-time 3D echocardiography frame and a cardiovascular MR frame. Spatial alignment is then performed using an adaptive non-rigid registration algorithm based on local phase mutual information on each temporally aligned image pair. Experiments on seven volunteer datasets are reported. Evaluation of registration errors based on expert-identified landmarks shows that the spatio-temporal registration algorithm gives a mean registration error of 3.56 ± 0.49 and 3.54 ± 0.27 mm for the short and long axis sequences, respectively.

A feasibility study of a molecular-based patient setup verification method using a parallel-plane PET system

A feasibility study of a novel PET-based molecular image guided radiation therapy (m-IGRT) system was conducted by comparing PET-based digitally reconstructed planar image (PDRI) registration with radiographic registration. We selected a pair of opposing parallel-plane PET systems for the practical implementation of this system. Planar images along the in-plane and cross-plane directions were reconstructed from the parallel-plane PET data. The in-plane and cross-plane FWHM of the profile of 2 mm diameter sources was approximately 1.8 and 8.1 mm, respectively. Therefore, only the reconstructed in-plane image from the parallel-plane PET data was used in the PDRI registration. In the image registration, five different sizes of 18F cylindrical sources (diameter: 8, 12, 16, 24, 32 mm) were used to determine setup errors. The data acquisition times were 1, 3 and 5 min. Image registration was performed by five observers to determine the setup errors from PDRI registration and radiographic registration. The majority of the mean registration errors obtained from the PDRI registration were not significantly different from those obtained from the radiographic registration. Acquisition time did not appear to result in significant differences in the mean registration error. The mean registration error for the PDRI registration was found to be 0.93 ± 0.33 mm. This is not statistically different from the radiographic registration which had a mean registration error of 0.92 ± 0.27 mm. Our results suggest that m-IGRT image registration using PET-based reconstructed planar images along the in-plane direction is feasible for clinical use if PDRI registration is performed at two orthogonal gantry angles.

Skull-fixated fiducial markers improve accuracy in staged frameless stereotactic epilepsy surgery in children

Surgery to monitor and resect epileptogenic foci may be undertaken in 2 stages, providing an opportunity to use skull-fixated fiducials implanted during the first stage to improve the accuracy of cortical resection during the second stage. This study compared the intrinsic accuracy of skin-based and skull-fixated fiducial markers in registering frameless stereotaxy during pediatric epilepsy surgery. To the authors' knowledge, these modalities of registration have not previously been directly compared in this population. The authors undertook a retrospective review of pediatric patients who underwent resection of epileptogenic foci in 2 stages with frameless stereotactic assistance, performed by a single surgeon at Oregon Health & Science University. For the first stage (subdural grid implantation), 9 skin fiducial markers were used to register anatomical data in a frameless stereotactic station. Intraoperatively, four 3-mm screws were placed circumferentially around the craniotomy. Postoperatively, thin-slice brain MR and CT images were obtained and fused. For the second stage, the 4 screws were used as fiducial markers to register the stereotactic anatomical data. For both stages, accuracy (difference in millimeters from zero of the manual fiducial registration compared with the computer model) was determined using navigation software. The intrinsic accuracy of these 2 methods of fiducial registration was compared using a paired Student t-test. Between 2004 and 2009, 40 pediatric patients with epilepsy underwent frameless stereotactic surgical procedures. Fourteen patients who had 2-stage procedures using skin-based and skull-fixated registration with complete accuracy data were included in this retrospective review. Mean registration error was significantly lower using skull-fixated fiducials (1.35 mm, 95% CI 1.09-1.60 mm) than using skin-based fiducials (1.85 mm, 95% CI 1.56-2.13 mm; p = 0.0016). A significantly higher degree of accuracy was achieved using 4 skull-fixated fiducials compared with using 9 skin-based fiducials. This simple and accurate method for registering frameless stereotactic anatomical data does not involve the potential time, expense, discomfort, and morbidity of extraoperative skull-fixated fiducial placement. The method described in this paper could also be extrapolated to other planned 2-stage cranial surgical procedures such as combined skull base approaches.