Average Target Registration Error Research Articles

A Novel Ultrasound-Based Registration for Image-Guided Laparoscopic Liver Ablation.

Background Patient-to-image registration is a core process of image-guided surgery (IGS) systems. We present a novel registration approach for application in laparoscopic liver surgery, which reconstructs in real time an intraoperative volume of the underlying intrahepatic vessels through an ultrasound (US) sweep process. Methods An existing IGS system for an open liver procedure was adapted, with suitable instrument tracking for laparoscopic equipment. Registration accuracy was evaluated on a realistic phantom by computing the target registration error (TRE) for 5 intrahepatic tumors. The registration work flow was evaluated by computing the time required for performing the registration. Additionally, a scheme for intraoperative accuracy assessment by visual overlay of the US image with preoperative image data was evaluated. Results The proposed registration method achieved an average TRE of 7.2 mm in the left lobe and 9.7 mm in the right lobe. The average time required for performing the registration was 12 minutes. A positive correlation was found between the intraoperative accuracy assessment and the obtained TREs. Conclusions The registration accuracy of the proposed method is adequate for laparoscopic intrahepatic tumor targeting. The presented approach is feasible and fast and may, therefore, not be disruptive to the current surgical work flow.

Software-based PET-MR image coregistration: combined PET-MRI for the rest of us!

With the introduction of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI), a new imaging option to acquire multimodality images with complementary anatomical and functional information has become available. Compared with hybrid PET/computed tomography (CT), hybrid PET/MRI is capable of providing superior anatomical detail while removing the radiation exposure associated with CT. The early adoption of hybrid PET/MRI, however, has been limited. To provide a viable alternative to the hybrid PET/MRI hardware by validating a software-based solution for PET-MR image coregistration. A fully automated, graphics processing unit-accelerated 3-D deformable image registration technique was used to align PET (acquired as PET/CT) and MR image pairs of 17 patients (age range: 10months-21years, mean: 10years) who underwent PET/CT and body MRI (chest, abdomen or pelvis), which were performed within a 28-day (mean: 10.5days) interval. MRI data for most of these cases included single-station post-contrast axial T1-weighted images. Following registration, maximum standardized uptake value (SUVmax) values observed in coregistered PET (cPET) and the original PET were compared for 82 volumes of interest. In addition, we calculated the target registration error as a measure of the quality of image coregistration, and evaluated the algorithm's performance in the context of interexpert variability. The coregistration execution time averaged 97±45s. The overall relative SUVmax difference was 7% between cPET-MRI and PET/CT. The average target registration error was 10.7±6.6mm, which compared favorably with the typical voxel size (diagonal distance) of 8.0mm (typical resolution: 0.66mm × 0.66mm × 8mm) for MRI and 6.1mm (typical resolution: 3.65mm × 3.65mm × 3.27mm) for PET. The variability in landmark identification did not show statistically significant differences between the algorithm and a typical expert. We have presented a software-based solution that achieves the many benefits of hybrid PET/MRI scanners without actually needing one. The method proved to be accurate and potentially clinically useful.

TU‐AB‐202‐06: Quantitative Evaluation of Deformable Image Registration in MRI‐Guided Adaptive Radiation Therapy

Purpose:To assess the performance of the deformable image registration algorithm used for MRI‐guided adaptive radiation therapy using image feature analysis.Methods:MR images were collected from five patients treated on the MRIdian (ViewRay, Inc., Oakwood Village, OH), a three head Cobalt‐60 therapy machine with an 0.35 T MR system. The images were acquired immediately prior to treatment with a uniform 1.5 mm resolution. Treatment sites were as follows: head/neck, lung, breast, stomach, and bladder. Deformable image registration was performed using the ViewRay software between the first fraction MRI and the final fraction MRI, and the DICE similarity coefficient (DSC) for the skin contours was reported. The SIFT and Harris feature detection and matching algorithms identified point features in each image separately, then found matching features in the other image. The target registration error (TRE) was defined as the vector distance between matched features on the two image sets. Each deformation was evaluated based on comparison of average TRE and DSC.Results:Image feature analysis produced between 2000–9500 points for evaluation on the patient images. The average (± standard deviation) TRE for all patients was 3.3 mm (±3.1 mm), and the passing rate of TRE<3 mm was 60% on the images. The head/neck patient had the best average TRE (1.9 mm±2.3 mm) and the best passing rate (80%). The lung patient had the worst average TRE (4.8 mm±3.3 mm) and the worst passing rate (37.2%). DSC was not significantly correlated with either TRE (p=0.63) or passing rate (p=0.55).Conclusions:Feature matching provides a quantitative assessment of deformable image registration, with a large number of data points for analysis. The TRE of matched features can be used to evaluate the registration of many objects throughout the volume, whereas DSC mainly provides a measure of gross overlap.We have a research agreement with ViewRay Inc.

SU‐F‐J‐85: Evaluation of the Velocity Deformable Image Registration Algorithm

Purpose:To perform validation and commissioning of a commercial deformable image registration (DIR) algorithm (Velocity, Varian Medical Systems) for numerous clinical sites using single and multi‐modality images.Methods:In this retrospective study, the DIR algorithm was evaluated for 10 patients in each of the following body sites: head and neck (HN), prostate, liver, and gynecological (GYN). HN DIRs were evaluated from planning (p)CT to re‐pCT and pCTs to daily CBCTs using dice similarity coefficients (DSC) of corresponding anatomical structures. Prostate DIRs were evaluated from pCT to CBCTs using DSC and target registration error (TRE) of implanted RF beacons within the prostate. Liver DIRs were evaluated from pMR to pCT using DSC and TRE of vessel bifurcations. GYN DIRs were evaluated between fractionated brachytherapy MRIs using DSC of corresponding anatomical structures.Results:Analysis to date has given average DSCs for HN pCT‐to‐(re)pCT DIR for the brainstem, cochleas, constrictors, spinal canal, cord, esophagus, larynx, parotids, and submandibular glands as 0.88, 0.65, 0.67, 0.91, 0.77, 0.69, 0.77, 0.87, and 0.71, respectively. Average DSCs for HN pCT‐to‐CBCT DIR for the constrictors, spinal canal, esophagus, larynx, parotids, and submandibular glands were 0.64, 0.90, 0.62, 0.82, 0.75, and 0.69, respectively. For prostate pCT‐to‐CBCT DIR the DSC for the bladder, femoral heads, prostate, and rectum were 0.71, 0.82, 0.69, and 0.61, respectively. Average TRE using implanted beacons was 3.35 mm. For liver pCT‐to‐pMR, the average liver DSC was 0.94 and TRE was 5.26 mm. For GYN MR‐to‐MR DIR the DSC for the bladder, sigmoid colon, GTV, and rectum were 0.79, 0.58, 0.67, and 0.76, respectively.Conclusion:The Velocity DIR algorithm has been evaluated over a number of anatomical sites. This work functions to document the uncertainties in the DIR in the commissioning process so that these can be accounted for in the development of downstream clinical processes.This work was supported in part by a co‐development agreement with Varian Medical Systems

Marker-less tracking of brain surface deformations by non-rigid registration integrating surface and vessel/sulci features

To compensate for brain shift in image-guided neurosurgery, we propose a new non-rigid registration method that integrates surface and vessel/sulci feature to noninvasively track the brain surface. Textured brain surfaces were acquired using phase-shift three-dimensional (3D) shape measurement, which offers 2D image pixels and their corresponding 3D points directly. Measured brain surfaces were noninvasively tracked using the proposed method by minimizing a new energy function, which is a weighted combination of 3D point corresponding estimation and surface deformation constraints. Initially, the measured surfaces were divided into featured and non-featured parts using a Frangi filter. The corresponding feature/non-feature points between intraoperative brain surfaces were estimated using the closest point algorithm. Subsequently, smoothness and rigidity constraints were introduced in the energy function for a smooth surface deformation and local surface detail conservation, respectively. Our 3D shape measurement accuracy was evaluated using 20 spheres for bias and precision errors. In addition, the proposed method was evaluated based on root mean square error (RMSE) and target registration error (TRE) with five porcine brains for which deformations were produced by gravity and pushing with different displacements in both the vertical and horizontal directions. The minimum and maximum bias errors were 0.32 and 0.61mm, respectively. The minimum and maximum precision errors were 0.025 and 0.30mm, respectively. Quantitative validation with porcine brains showed that the average RMSE and TRE were 0.1 and 0.9mm, respectively. The proposed method appeared to be advantageous in integrating vessels/sulci feature, robust to changes in deformation magnitude and integrated feature numbers, and feasible in compensating for brain shift deformation in surgeries.

Progressive internal landmark registration for surgical navigation in laparoscopic gastrectomy for gastric cancer.

A surgical navigation system supports the comprehension of anatomical information during surgery. Patient-to-image registration is the alignment process between CT volume and patient coordinate systems. Achieving accurate registration in the surgical navigation of laparoscopic surgery is very challenging due to soft tissue deformation. This paper presents a new patient-to-image registration method based on internal anatomical landmarks for improving registration accuracy in the surgical navigation of laparoscopic gastrectomy for gastric cancer. Our proposed registration method progressively utilizes internal anatomical landmarks. In laparoscopic gastrectomy for gastric cancer, the surgeon cuts the blood vessels around the stomach. The positions of the cut vessels are sequentially used as fiducials for registration during surgery. The proposed method uses a weighted point-based registration method for computing the transformation matrix using the fiducials both on the body surface and on the blood vessels. When a blood vessel is cut during surgery, the proposed progressive registration method measures the cut vessel's position and computes a transformation matrix by adding the cut vessel as a fiducial. We applied our proposed progressive registration method using the positional information of the blood vessels acquired during laparoscopic gastrectomy in 20 cases. We evaluated it using target registration error in four blood vessels. The average target registration error in the four blood vessels was 12.6mm and ranged from 2.1 to 32.9mm. Since the proposed progressive registration can reduce registration error, our proposed method is very useful for the surgical navigation of laparoscopic gastrectomy. Our proposed progressive registration method might increase the accuracy of surgical navigation in laparoscopic gastrectomy.

The Utility of Cloud Computing in Analyzing GPU-Accelerated Deformable Image Registration of CT and CBCT Images in Head and Neck Cancer Radiation Therapy.

The images generated during radiation oncology treatments provide a valuable resource to conduct analysis for personalized therapy, outcomes prediction, and treatment margin optimization. Deformable image registration (DIR) is an essential tool in analyzing these images. We are enhancing and examining DIR with the contributions of this paper: 1) implementing and investigating a cloud and graphic processing unit (GPU) accelerated DIR solution and 2) assessing the accuracy and flexibility of that solution on planning computed tomography (CT) with cone-beam CT (CBCT). Registering planning CTs and CBCTs aids in monitoring tumors, tracking body changes, and assuring that the treatment is executed as planned. This provides significant information not only on the level of a single patient, but also for an oncology department. However, traditional methods for DIR are usually time-consuming, and manual intervention is sometimes required even for a single registration. In this paper, we present a cloud-based solution in order to increase the data analysis throughput, so that treatment tracking results may be delivered at the time of care. We assess our solution in terms of accuracy and flexibility compared with a commercial tool registering CT with CBCT. The latency of a previously reported mutual information-based DIR algorithm was improved with GPUs for a single registration. This registration consists of rigid registration followed by volume subdivision-based nonrigid registration. In this paper, the throughput of the system was accelerated on the cloud for hundreds of data analysis pairs. Nine clinical cases of head and neck cancer patients were utilized to quantitatively evaluate the accuracy and throughput. Target registration error (TRE) and structural similarity index were utilized as evaluation metrics for registration accuracy. The total computation time consisting of preprocessing the data, running the registration, and analyzing the results was used to evaluate the system throughput. Evaluation showed that the average TRE for GPU-accelerated DIR for each of the nine patients was from 1.99 to 3.39 mm, which is lower than the voxel dimension. The total processing time for 282 pairs on an Amazon Web Services cloud consisting of 20 GPU enabled nodes took less than an hour. Beyond the original registration, the cloud resources also included automatic registration quality checks with minimal impact to timing. Clinical data were utilized in quantitative evaluations, and the results showed that the presented method holds great potential for many high-impact clinical applications in radiation oncology, including adaptive radio therapy, patient outcomes prediction, and treatment margin optimization.

Vision-based markerless registration using stereo vision and an augmented reality surgical navigation system: a pilot study.

BackgroundThis study evaluated the use of an augmented reality navigation system that provides a markerless registration system using stereo vision in oral and maxillofacial surgery.MethodA feasibility study was performed on a subject, wherein a stereo camera was used for tracking and markerless registration. The computed tomography data obtained from the volunteer was used to create an integral videography image and a 3-dimensional rapid prototype model of the jaw. The overlay of the subject’s anatomic site and its 3D-IV image were displayed in real space using a 3D-AR display. Extraction of characteristic points and teeth matching were done using parallax images from two stereo cameras for patient-image registration.ResultsAccurate registration of the volunteer’s anatomy with IV stereoscopic images via image matching was done using the fully automated markerless system, which recognized the incisal edges of the teeth and captured information pertaining to their position with an average target registration error of < 1 mm. These 3D-CT images were then displayed in real space with high accuracy using AR. Even when the viewing position was changed, the 3D images could be observed as if they were floating in real space without using special glasses.ConclusionTeeth were successfully used for registration via 3D image (contour) matching. This system, without using references or fiducial markers, displayed 3D-CT images in real space with high accuracy. The system provided real-time markerless registration and 3D image matching via stereo vision, which, combined with AR, could have significant clinical applications.Electronic supplementary materialThe online version of this article (doi:10.1186/s12880-015-0089-5) contains supplementary material, which is available to authorized users.

TU‐CD‐BRA‐08: Single‐Energy Computed Tomography‐Based Pulmonary Perfusion Imaging: Proof‐Of‐Principle in a Canine Model

Purpose:Pulmonary perfusion imaging has provided significant insights into pulmonary diseases, and can be useful in radiotherapy. The purpose of this study was to prospectively establish proof‐of‐principle in a canine model for single‐energy CT‐based perfusion imaging, which has the potential for widespread clinical implementation.Methods:Single‐energy CT perfusion imaging is based on: (1) acquisition of inspiratory breath‐hold CT scans before and after intravenous injection of iodinated contrast medium, (2) deformable image registration (DIR) of the two CT image data sets, and (3) subtraction of the pre‐contrast image from post‐contrast image, yielding a map of Hounsfield unit (HU) enhancement. These subtraction image data sets hypothetically represent perfused blood volume, a surrogate for perfusion. In an IACUC‐approved clinical trial, we acquired pre‐ and post‐contrast CT scans in the prone posture for six anesthetized, mechanically‐ventilated dogs. The elastix algorithm was used for DIR. The registration accuracy was quantified using the target registration errors (TREs) for 50 pulmonary landmarks in each dog. The gradient of HU enhancement between gravity‐dependent (ventral) and non‐dependent (dorsal) regions was evaluated to quantify the known effect of gravity, i.e., greater perfusion in ventral regions.Results:The lung volume difference between the two scans was 4.3±3.5% on average (range 0.3%–10.1%). DIR demonstrated an average TRE of 0.7±1.0 mm. HU enhancement in lung parenchyma was 34±10 HU on average and varied considerably between individual dogs, indicating the need for improvement of the contrast injection protocol. HU enhancement in ventral (gravity‐dependent) regions was found to be greater than in dorsal regions. A population average ventral‐to‐dorsal gradient of HU enhancement was strong (R2=0.94) and statistically significant (p&lt;0.01).Conclusion:This canine study demonstrated relatively accurate DIR and a strong ventral‐to‐dorsal gradient of HU enhancement, providing proof‐of‐principle for single‐energy CT pulmonary perfusion imaging. This ongoing study will enroll more dogs and investigate the physiological significance.This study was supported by a Philips Healthcare/Radiological Society of North America (RSNA) Research Seed Grant (RSD1458).

Open-source image registration for MRI-TRUS fusion-guided prostate interventions.

We propose two software tools for non-rigid registration of MRI and transrectal ultrasound (TRUS) images of the prostate. Our ultimate goal is to develop an open-source solution to support MRI-TRUS fusion image guidance of prostate interventions, such as targeted biopsy for prostate cancer detection and focal therapy. It is widely hypothesized that image registration is an essential component in such systems. The two non-rigid registration methods are: (1) a deformable registration of the prostate segmentation distance maps with B-spline regularization and (2) a finite element-based deformable registration of the segmentation surfaces in the presence of partial data. We evaluate the methods retrospectively using clinical patient image data collected during standard clinical procedures. Computation time and Target Registration Error (TRE) calculated at the expert-identified anatomical landmarks were used as quantitative measures for the evaluation. The presented image registration tools were capable of completing deformable registration computation within 5 min. Average TRE was approximately 3 mm for both methods, which is comparable with the slice thickness in our MRI data. Both tools are available under nonrestrictive open-source license. We release open-source tools that may be used for registration during MRI-TRUS-guided prostate interventions. Our tools implement novel registration approaches and produce acceptable registration results. We believe these tools will lower the barriers in development and deployment of interventional research solutions and facilitate comparison with similar tools.

TH-C-18A-03: Novel Technique for Dynamic Lung Ventilation Imaging Based On Wide Coverage 4D CT

Purpose: An emerging lung ventilation imaging method based on conventional 4D CT has a high translational potential and advantages over other competing modalities, however suffers from binning artifacts and irregular breathing during a scan. We propose a novel technique to compute dynamic ventilation based upon wide coverage 320-slice 4D CT, which provides high spatial and temporal resolution and binning artifact-free images. Methods: Wide coverage 4D CT images were acquired using a commercial 320-slice CT scanner, with 160 mm craniocaudal coverage and 0.35 s rotation time, during tidal breathing for five patients with thoracic cancer. The image at each of 51 time points was deformably registered to a peak-exhalation phase image using B-spline deformable image registration (DIR). For each time-point, the Jacobian determinant of deformation acted as a surrogate for regional ventilation. Dynamic ventilation was then computed by comparing time-resolved ventilation images. The accuracy of DIR was quantified by calculating target registration errors (TREs) of 100 anatomic landmarks per patient. Results: The proposed technique provided regional ventilation information of high spatial and temporal resolution. The average TRE of five patients was 1.4±1.3 mm. Dynamic properties of regional ventilation were found to be spatially heterogeneous. For example, ventilation increased during inhalation and decreased during exhalation in well-ventilated normal lung regions. However, in poorly-ventilated emphysematous regions, ventilation showed relatively minor changes with time around zero. Furthermore, ventilation resolved at this timescale demonstrated a signal that appeared to come from the heartbeat, especially in proximity to the heart. Conclusion: 320-slice 4D CT-based ventilation imaging provides regional ventilation information of high spatial and temporal resolution. Dynamic ventilation information obtained by this technique may be useful in creating a model of dynamic ventilation that could be used to compensate for errors in conventional 4D CT-based ventilation imaging, and may also provide new insights into respiratory physiology. Supported in part by Free to Breathe Young Investigator Research Grant.

MO‐G‐18C‐03: Evaluation of Deformable Image Registration for Lung Motion Estimation Using Hyperpolarized Gas Tagging MRI

Purpose:Hyperpolarized gas (HP) tagging MRI is a novel imaging technique for direct measurement of lung motion during breathing. This study aims to quantitatively evaluate the accuracy of deformable image registration (DIR) in lung motion estimation using HP tagging MRI as references.Methods:Three healthy subjects were imaged using the HP MR tagging, as well as a high‐resolution 3D proton MR sequence (TrueFISP) at the end‐of‐inhalation (EOI) and the end‐of‐exhalation (EOE). Ground truth of lung motion and corresponding displacement vector field (tDVF) was derived from HP tagging MRI by manually tracking the displacement of tagging grids between EOI and EOE. Seven different DIR methods were applied to the high‐resolution TrueFISP MR images (EOI and EOE) to generate the DIR‐based DVFs (dDVF). The DIR methods include Velocity (VEL), MIM, Mirada, multi‐grid B‐spline from Elastix (MGB) and 3 other algorithms from DIRART toolbox (Double Force Demons (DFD), Improved Lucas‐Kanade (ILK), and Iterative Optical Flow (IOF)). All registrations were performed by independent experts. Target registration error (TRE) was calculated as tDVF – dDVF. Analysis was performed for the entire lungs, and separately for the upper and lower lungs.Results:Significant differences between tDVF and dDVF were observed. Besides the DFD and IOF algorithms, all other dDVFs showed similarity in deformation magnitude distribution but away from the ground truth. The average TRE for entire lung ranged 2.5−23.7mm (mean=8.8mm), depending on the DIR method and subject's breathing amplitude. Larger TRE (13.3–23.7mm) was found in subject with larger breathing amplitude of 45.6mm. TRE was greater in lower lung (2.5−33.9 mm, mean=12.4mm) than that in upper lung (2.5−11.9 mm, mean=5.8mm).Conclusion:Significant differences were observed in lung motion estimation between the HP gas tagging MRI method and the DIR methods, especially when lung motion is large. Large variation among different DIR methods was also observed.

A bronchoscopic navigation system using bronchoscope center calibration for accurate registration of electromagnetic tracker and CT volume without markers.

Various bronchoscopic navigation systems are developed for diagnosis, staging, and treatment of lung and bronchus cancers. To construct electromagnetically navigated bronchoscopy systems, registration of preoperative images and an electromagnetic tracker must be performed. This paper proposes a new marker-free registration method, which uses the centerlines of the bronchial tree and the center of a bronchoscope tip where an electromagnetic sensor is attached, to align preoperative images and electromagnetic tracker systems. The chest computed tomography (CT) volume (preoperative images) was segmented to extract the bronchial centerlines. An electromagnetic sensor was fixed at the bronchoscope tip surface. A model was designed and printed using a 3D printer to calibrate the relationship between the fixed sensor and the bronchoscope tip center. For each sensor measurement that includes sensor position and orientation information, its corresponding bronchoscope tip center position was calculated. By minimizing the distance between each bronchoscope tip center position and the bronchial centerlines, the spatial alignment of the electromagnetic tracker system and the CT volume was determined. After obtaining the spatial alignment, an electromagnetic navigation bronchoscopy system was established to real-timely track or locate a bronchoscope inside the bronchial tree during bronchoscopic examinations. The electromagnetic navigation bronchoscopy system was validated on a dynamic bronchial phantom that can simulate respiratory motion with a breath rate range of 0-10 min(-1). The fiducial and target registration errors of this navigation system were evaluated. The average fiducial registration error was reduced from 8.7 to 6.6 mm. The average target registration error, which indicates all tracked or navigated bronchoscope position accuracy, was much reduced from 6.8 to 4.5 mm compared to previous registration methods. An electromagnetically navigated bronchoscopy system was constructed with accurate registration of an electromagnetic tracker and the CT volume on the basis of an improved marker-free registration approach that uses the bronchial centerlines and bronchoscope tip center information. The fiducial and target registration errors of our electromagnetic navigation system were about 6.6 and 4.5 mm in dynamic bronchial phantom validation.

Model-based registration for assessment of spinal deformities in idiopathic scoliosis

Detailed analysis of spinal deformity is important within orthopaedic healthcare, in particular for assessment of idiopathic scoliosis. This paper addresses this challenge by proposing an image analysis method, capable of providing a full three-dimensional spine characterization. The proposed method is based on the registration of a highly detailed spine model to image data from computed tomography. The registration process provides an accurate segmentation of each individual vertebra and the ability to derive various measures describing the spinal deformity. The derived measures are estimated from landmarks attached to the spine model and transferred to the patient data according to the registration result. Evaluation of the method provides an average point-to-surface error of 0.9 mm ± 0.9 (comparing segmentations), and an average target registration error of 2.3 mm ± 1.7 (comparing landmarks). Comparing automatic and manual measurements of axial vertebral rotation provides a mean absolute difference of 2.5° ± 1.8, which is on a par with other computerized methods for assessing axial vertebral rotation. A significant advantage of our method, compared to other computerized methods for rotational measurements, is that it does not rely on vertebral symmetry for computing the rotational measures. The proposed method is fully automatic and computationally efficient, only requiring three to four minutes to process an entire image volume covering vertebrae L5 to T1. Given the use of landmarks, the method can be readily adapted to estimate other measures describing a spinal deformity by changing the set of employed landmarks. In addition, the method has the potential to be utilized for accurate segmentations of the vertebrae in routine computed tomography examinations, given the relatively low point-to-surface error.

Automatic Generation of Statistical Pose and Shape Models for Articulated Joints

Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of 0.34 ±0.27 mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity.

Rapid on-site evaluation and low registration error enhance the success of electromagnetic navigation bronchoscopy

BACKGROUND:Electromagnetic navigation bronchoscopy (EMN) is a novel technology which allows localizing peripheral lung lesions and mediastinal lymph nodes for sampling and thus increasing diagnostic yield of Flexible Bronchoscopy.OBJECTIVES:A prospective study was conducted to investigate the diagnostic yield of EMN with lower average fiducial target registration error (AFTRE) and rapid on-site evaluation (ROSE).METHODS:Consecutive patients with peripheral lung lesion (PL) or enlarged mediastinal lymph node (MLN) which could not be diagnosed by conventional techniques and/or if the patients were not suitable for such interventions were included. The navigation procedure was continued once registration error was reached below/equal to the absolute value of 5 mm. ROSE was performed by an expert cytopathologist.RESULTS:A total of 76 patients; 22 having only PLs, 41 having only MLNs, and 13 having both PLs and MLNs together were enrolled. Thirty-two of 35 PLs (91.4%) and 85 of 102 MLNs (83.3%) were successfully sampled. Overall diagnostic yield was 89.5%. PLs and MLNs were further grouped according to their size (PLs: <20 mm vs ≥20 mm, MLNs: <15 mm vs ≥15 mm). The sampling yield was independent of size for both PL and MLN (P = 1.00, P = 0.38). In diagnostic EMN cases, mean AFTRE was 4.33 ± 0.71 mm, whereas it was 5.16 ± 0.05 mm (P = 0.008) in nondiagnostics. The total duration of procedure was 36.17 ± 9.13 min. Pneumothorax was observed in three patients (3.9%).CONCLUSION:EMN with low AFTRE in combination with ROSE is a reliable method with high sampling and/or diagnostic rate in PLs and MLNs.

Fiducial Optimization for Minimal Target Registration Error in Image-Guided Neurosurgery

This paper presents new methods for the optimal selection of anatomical landmarks and optimal placement of fiducial markers in image-guided neurosurgery. These methods allow the surgeon to optimally plan fiducial marker locations on routine diagnostic images before preoperative imaging and to intraoperatively select the set of fiducial markers and anatomical landmarks that minimize the expected target registration error (TRE). The optimization relies on a novel empirical simulation-based TRE estimation method built on actual fiducial localization error (FLE) data. Our methods take the guesswork out of the registration process and can reduce localization error without additional imaging and hardware. Our clinical experiments on five patients who underwent brain surgery with a navigation system show that optimizing one marker location and the anatomical landmarks configuration reduced the TRE. The average TRE values using the usual fiducials setup and using the suggested method were 4.7 mm and 3.2 mm, respectively. We observed a maximum improvement of 4 mm. Reducing the target registration error has the potential to support safer and more accurate minimally invasive neurosurgical procedures.

Design of the Image-Guided Biopsy Marking System for Gastroscopy

Endoscopists currently rely on an invasive biopsy tattooing method to identify previously biopsied sites. In order to better guide endoscopists to find the biopsy positions in follow-ups, we proposed a non-invasive image guided biopsy marking system for gastroscopy. Using an electromagnetic tracking device, the position of the gastroscope relative to the stomach was acquired and displayed in the guidance interface. The biopsy positions were recorded in computer for the use of guidance in follow-ups. The accuracy of the system was evaluated by both phantom experiments and in vivo experiments. The average target registration errors on the test animal and the volunteer are 13.4mm and 11.2mm respectively. Although the positioning error is slightly larger than current biopsy tattooing method, it satisfies the need for guidance. In the near future, we will validate the system by measuring how much it saves examination time.

Prostate multimodality image registration based on B-splines and quadrature local energy

Needle biopsy of the prostate is guided by Transrectal Ultrasound (TRUS) imaging. The TRUS images do not provide proper spatial localization of malignant tissues due to the poor sensitivity of TRUS to visualize early malignancy. Magnetic Resonance Imaging (MRI) has been shown to be sensitive for the detection of early stage malignancy, and therefore, a novel 2D deformable registration method that overlays pre-biopsy MRI onto TRUS images has been proposed. The registration method involves B-spline deformations with Normalized Mutual Information (NMI) as the similarity measure computed from the texture images obtained from the amplitude responses of the directional quadrature filter pairs. Registration accuracy of the proposed method is evaluated by computing the Dice Similarity coefficient (DSC) and 95% Hausdorff Distance (HD) values for 20 patients prostate mid-gland slices and Target Registration Error (TRE) for 18 patients only where homologous structures are visible in both the TRUS and transformed MR images. The proposed method and B-splines using NMI computed from intensities provide average TRE values of 2.64 ± 1.37 and 4.43 ± 2.77 mm respectively. Our method shows statistically significant improvement in TRE when compared with B-spline using NMI computed from intensities with Student's t test p = 0.02. The proposed method shows 1.18 times improvement over thin-plate splines registration with average TRE of 3.11 ± 2.18 mm. The mean DSC and the mean 95% HD values obtained with the proposed method of B-spline with NMI computed from texture are 0.943 ± 0.039 and 4.75 ± 2.40 mm respectively. The texture energy computed from the quadrature filter pairs provides better registration accuracy for multimodal images than raw intensities. Low TRE values of the proposed registration method add to the feasibility of it being used during TRUS-guided biopsy.

Accuracy assessment for navigated maxillo-facial surgery using an electromagnetic tracking device

Purpose To evaluate the accuracy and the usability of an electromagnetic tracking device in maxillo-facial surgery through testing on a phantom skull under operating room (OR) conditions. Material and methods A standard plastic skull phantom was equipped with a custom made model of the maxilla and with target markers and dental brackets. Imaging was performed with a computed tomography (CT) scanner. The extent and robustness of the electromagnetic tracking system’s target registration error (TRE) was evaluated under various conditions. Results For each measurement a total of 243 registrations were performed with 5 point registration and 4374 registrations with 6 point registration. The average target registration error for the 5 point registration under OR conditions was 2.1 mm (SD 0.86) and 1.03 (SD 0.53) for the 6 point registration. Metallic instruments applied to the skull increased the TRE significantly in both registration methods. Conclusion The electromagnetic tracking device showed a high accuracy and performed stable in both registration methods. Electromagnetic interference due to metallic instruments was significant but the extent of TRE was still acceptable in comparison to optical navigation devices. A benefit of EM tracking is the absence of line-of-sight hindrance. The test setting simulating OR conditions has proven suitable for further studies.