Mean Registration Error Research Articles

Validation and Safety Profile of a Novel, Noninvasive Fiducial Attachment for Stereotactic Robotic-Guided Stereoelectroencephalography: A Case Series.

We developed, tested, and validated a novel, noninvasive, Leksell G frame-based fiducial attachment, for use in stereotactic registration for stereoelectroencephalography (sEEG). Use of the device increased the number of fixed reference points available for registration, while obviating the need for additional scalp incisions. We report here on our experience and safety profile of using the device. We collected registration data using the fiducial device across 25 adult and pediatric patients with epilepsy consecutively undergoing robotic-guided sEEG for invasive epilepsy monitoring, treated between May 2022 and July 2023. ROSA One Brain was used for trajectory planning and electrode implantation. Postoperative clinical and radiographic data were computed and quantified, including mean registration error for all patients. Entry point, target point (TP), and angular errors were measured. Descriptive statistics and correlation coefficients for error were calculated. Twenty-five patients underwent robotic-guided sEEG implantation (11 patients, bilateral; 10 patients, left unilateral; 4 patients, right). The mean number of electrodes per patient was 18 ± 3. The average mean registration error was 0.77 ± 0.11 mm. All patients were implanted with Ad-Tech depth electrodes. No clinically relevant complications were reported. Analysis of trajectory error was performed on 446 electrodes. The median entry point error was 1.03 mm (IQR 0.69-1.54). The median TP error was 2.26 mm (IQR 1.63-2.93). The mean angular error was 0.03 radians (IQR 0.02-0.05). There was no significant correlation between root mean square error and lead error. Root mean square error did not appreciably change over time, nor were there any significant changes in average angular, entry point, or TP error metrics. A novel, noninvasive, Leksell G frame-based fiducial attachment was developed, tested, and validated, facilitating O-arm-based stereotactic registration for sEEG. This simple innovation maintained an excellent accuracy and safety profile for sEEG procedures in epilepsy patients, with the added advantages of providing additional reference points for stereotactic registration, without requiring additional scalp incisions.

Surgical Navigation in the Anterior Skull Base Using 3-Dimensional Endoscopy and Surface Reconstruction

Image guidance is an important adjunct for endoscopic sinus and skull base surgery. However, current systems require bulky external tracking equipment, and their use can interrupt efficient surgical workflow. To evaluate a trackerless surgical navigation system using 3-dimensional (3D) endoscopy and simultaneous localization and mapping (SLAM) algorithms in the anterior skull base. This interventional deceased donor cohort study and retrospective clinical case study was conducted at a tertiary academic medical center with human deceased donor specimens and a patient with anterior skull base pathology. Participants underwent endoscopic endonasal transsphenoidal dissection and surface model reconstruction from stereoscopic video with registration to volumetric models segmented from computed tomography (CT) and magnetic resonance imaging. To assess the fidelity of surface model reconstruction and accuracy of surgical navigation and surface-CT model coregistration, 3 metrics were calculated: reconstruction error, registration error, and localization error. In deceased donor models (n = 9), high-fidelity surface models of the posterior wall of the sphenoid sinus were reconstructed from stereoscopic video and coregistered to corresponding volumetric CT models. The mean (SD; range) reconstruction, registration, and localization errors were 0.60 (0.24; 0.36-0.93), 1.11 (0.49; 0.71-1.56) and 1.01 (0.17; 0.78-1.25) mm, respectively. In a clinical case study of a patient who underwent a 3D endoscopic endonasal transsphenoidal resection of a tubercular meningioma, a high-fidelity surface model of the posterior wall of the sphenoid was reconstructed from intraoperative stereoscopic video and coregistered to a volumetric preoperative fused CT magnetic resonance imaging model with a root-mean-square error of 1.38 mm. The results of this study suggest that SLAM algorithm-based endoscopic endonasal surgery navigation is a novel, accurate, and trackerless approach to surgical navigation that uses 3D endoscopy and SLAM-based algorithms in lieu of conventional optical or electromagnetic tracking. While multiple challenges remain before clinical readiness, a SLAM algorithm-based endoscopic endonasal surgery navigation system has the potential to improve surgical efficiency, economy of motion, and safety.

Virtual clinical trial-based study for clinical evaluation of projection-reduced low-dose cone-beam CT for image guided radiotherapy.

Repeated cone-beam CT (CBCT) scans for image-guided radiotherapy (IGRT) increase the health risk of radiation-induced malignancies. Patient-enrolled studies to optimize scan protocols are inadequate. We proposed a virtual clinical trial-based approach to evaluate projection-reduced low-dose CBCT for IGRT. A total of 71 patients were virtually enrolled with 26 head, 23 thorax and 22 pelvis scans. Projection numbers of full-dose CBCT scans were reduced to 1/2, 1/4, and 1/8 of the original to simulate low-dose scans. Contrast-to-noise ratio (CNR) values in fat and muscle were measured in the full-dose and low-dose images. CBCT images were registered to planning CT to derive 6-degree-of-freedom couch shifts. Registration errors were statistically analyzed with the Wilcoxon paired signed-rank test. As projection numbers were reduced, CNR values descended and the magnitude of registration errors increased. The mean CNR values of full-dose and half-dose CBCT were >3.0. For full-dose and low-dose CBCT (i.e. 1/2, 1/4 and 1/8 full-dose), the mean registration errors were< ± 0.4 mm in translational directions (LAT, LNG, VRT) and ±0.2 degree in rotational directions (Pitch, Roll, Yaw); the mean magnitude of registration errors were< 1 mm in translation and< 0.5 degree in rotation. The couch shift differences between full-dose and low-dose CBCT were not statistically significant (p>0.05) in all the directions. The results indicate that while the impact of dose-reduction on CBCT couch shifts is not significant, the impact on CNR values is significant. Further validation on optimizing CBCT imaging dose is required.

Application of augmented reality using automatic markerless registration for facial plastic and reconstructive surgery

This study aimed to present a novel markerless augmented reality (AR) system using automatic registration based on machine-learning algorithms that visualize the facial region and provide an intraoperative guide for facial plastic and reconstructive surgeries.This study prospectively enrolled 20 patients scheduled for facial plastic and reconstructive surgeries. The AR system visualizes computed tomographic data in three-dimensional (3D) space by aligning with the point clouds captured by a 3D camera. Point cloud registration consists of two stages: the preliminary registration gives an initial estimate of the transformation using landmark detection, followed by the precise registration using Iterative Closest Point algorithms. Computed Tomography (CT) data can be visualized as two-dimensional slice images or 3D images by the AR system. The AR registration error was defined as the cloud-to-cloud distance between the surface data obtained from the CT and 3D camera. The error was calculated in each facial territory, including the upper, middle, and lower face, while patients were awake and orally intubated, respectively.The mean registration errors were 1.490 ± 0.384 mm and 1.948 ± 0.638 mm while patients were awake and orally intubated, respectively. There was a significant difference in the errors in the lower face between patients while they were awake (1.502 ± 0.480 mm) and orally intubated (2.325 ± 0.971 mm) when stratified by facial territories (p = 0.006).The markerless AR can accurately visualize the facial region with a mean overall registration error of 1–2 mm, with a slight increase in the lower face due to errors arising from tube intubation.

Accuracy of an electromagnetic tracking enabled afterloader based on the automated registration with CT phantom images.

Electromagnetic tracking (EMT) has been researched for brachytherapy applications, showing a great potential for automating implant reconstruction, and overcoming image-based limitations such as contrast and spatial resolution. One of the challenges of this technology is that it does not intrinsically share the same reference frame as the patient's medical imaging. To present a novel phantom that can be used for a comprehensive quality assurance (QA) program of brachytherapy EMT systems and use this phantom to validate a novel applicator-based registration method of EMT and image reference frames for gynecological (GYN) interstitial brachytherapy. Eleven 6F-catheters (20cm long), one 6F round tip catheter (29.4cm long) and a tandem and ring gynecological applicator (Elekta, CT/MR 60°, 40mm long tandem, 30mm diameter ring) were placed in a rigid custom-made phantom (Elekta Brachytherapy, Veenendaal, The Netherlands) to reconstruct their geometry using a five-degree of freedom EMT sensor attached to an afterloader's check cable. All EMT reconstructions were done in three different environments: disturbance free (no metal nearby), computed tomography (CT)-on-rails brachytherapy suite and magnetic resonance imaging (MRI) brachytherapy suite. Implants were placed parallel to a magnetic field generatorand were reconstructed using two different acquisition methods: step-and-record and continuous motion. In all cases, the acquisition is performed at a rate of approximately 40Hz. A CT scan of the phantom inside a water cube was obtained. In the treatment planning system (TPS), all catheters in the CT images were manually reconstructed and the applicator reconstruction was achieved by manually placing its solid 3D model, found in the applicator library of the TPS. The Iterative Closest Point and the Coherent Point Drift algorithms were used, initialized with four known points, to register both EMT and CT scan reference frames using corresponding points from the EMT and CT based reconstructions of the phantom, following three approaches: one gynecological applicator, four interstitial catheters inside four calibration plates having an S-shaped path, and four 5mm diameter ceramic marbles found in each of the four calibration plates. Once registered, the registration error (perpendicular distance) was computed. The absolute median deviation from the expected value for EMT measurements in the disturbance free environment, CT-on-rails brachytherapy suite, and MRI-brachytherapy suite are 0.41, 0.23, and 0.31mm, respectively, while for the CT scan it is 0.18mm. These values significantly lie below the sensor's expected accuracy of 0.70mm (p<0.001), suggesting that the environment did not have a significant impact on the measurements, given that care is taken in the immediate surroundings. In all three environments, the two acquisitions and three registration approaches have mean and median registration errors that lie at or below 1mm, which is lower than the clinical acceptable threshold of 2mm. The novel phantom allowed to successfully evaluate the accuracy of EMT-based reconstructions of catheters and a GYN tandem and ring applicator in different clinical environments. A registration method based only on the applicator geometry, reconstructed withan EMT sensor and the TPS solid applicator library, was validated and shows clinically acceptable accuracy, comparable to CT-based reconstruction but within a few minutes. Since the applicator is also visible in MRI, this method could potentially be used in clinics in an EMT-MR interstitial GYN brachytherapy workflow.

Automatically tracking brain metastases after stereotactic radiosurgery.

Patients with brain metastases (BMs) are surviving longer and returning for multiple courses of stereotactic radiosurgery. BMs are monitored after radiation with follow-up magnetic resonance (MR) imaging every 2-3months. This study investigated whether it is possible to automatically track BMs on longitudinal imaging and quantify the tumor response after radiotherapy. The METRO process (MEtastasis Tracking with Repeated Observations was developed to automatically process patient data and track BMs. A longitudinal intrapatient registration method for T1 MR post-Gd was conceived and validated on 20 patients. Detections and volumetric measurements of BMs were obtained from a deep learning model. BM tracking was validated on 32 separate patients by comparing results with manual measurements of BM response and radiologists' assessments of new BMs. Linear regression and residual analysis were used to assess accuracy in determining tumor response and size change. A total of 123 irradiated BMs and 38 new BMs were successfully tracked. 66 irradiated BMs were visible on follow-up imaging 3-9months after radiotherapy. Comparing their longest diameter changes measured manually vs. METRO, the Pearson correlation coefficient was 0.88 (p<0.001); the mean residual error was -8±17%. The mean registration error was 1.5±0.2mm. Automatic, longitudinal tracking of BMs using deep learning methods is feasible. In particular, the software system METRO fulfills a need to automatically track and quantify volumetric changes of BMs prior to, and in response to, radiation therapy.

Robot-assisted percutaneous balloon compression for trigeminal neuralgia- preliminary experiences

ObjectivesThis study aims to discuss the availability of robot-assisted percutaneous balloon compression (PBC) for trigeminal neuralgia (TN) and share our preliminary experiences.MethodsPatients with TN who underwent robot-assisted PBC from June to September 2022 were enrolled. We designed a fixing plug for robot-assisted PBC, three-dimensional structured light registration was used, puncture trajectory was the line connects the medial third of inner and outer aperture of foramen ovale. Numerical Rating Scale (NRS), Barrow Neurological Institute (BNI) pain and numbness intensity score were used to evaluate the facial pain and numbness.ResultsEventually, nine patients were enrolled, the structured light registrations were successfully finished in all patients with a mean registration error of 0.68 mm. All the punctures of foramen ovales were successfully done one-time. Of note, the balloons were all got pear-shaped followed by 150 to 180 s compression. Though, postoperatively, all the patients complained of facial numbness and four patients suffered from transient masseter weakness, all patients got fully or mostly pain relief. It should be noted that is the numbness and weakness gradually relieved during follow-up.ConclusionThree-dimensional structured light registration and robot assisted PBC is an effective choice for patients with TN. Extension line between the medial third of the inner and outer aperture of foramen ovale might be a safe and effective puncture trajectory to this procedure.

Color fundus photograph registration based on feature and intensity for longitudinal evaluation of diabetic retinopathy progression

Longitudinal disease progression evaluation between follow-up examinations relies on precise registration of medical images. Compared to other medical imaging methods, color fundus photograph, a common retinal examination, is easily affected by eye movements while shooting, for which we think it is necessary to develop a reliable longitudinal registration method for this modality. Thus, the purpose of this study was to propose a robust registration method for longitudinal color fundus photographs and establish a longitudinal retinal registration dataset. In the proposed algorithm, radiation-variation insensitive feature transform (RIFT) feature points were calculated and aligned, followed by further refinement using a normalized total gradient (NTG). Experiments and ablation analyses were conducted on both public and private datasets, using the mean registration error and registration success plot as the main evaluation metrics. The results showed that our proposed method was comparable to other state-of-the-art registration algorithms and was particularly accurate for longitudinal images with disease progression. We believe the proposed method will be beneficial for the longitudinal evaluation of fundus images.

A Neuronavigation System Using a Mobile Augmented Reality Solution

Image-guided surgery has shown great utility in neurosurgery, especially in allowing for more accurate surgical planning and navigation. The current gold standard for image-guided neurosurgery is neuronavigation, which provides millimetric accuracy on such tasks. However, these approaches often require a complicated setup and have high cost, hindering their potential in low- and middle-income countries. The aim of this study was to develop and evaluate the performance of a mobile-based augmented reality neuronavigation solution under different conditions in a preclinical environment. The application was developed using the Swift programming language and was tested on a replica of a human scalp under variable lighting, with different numbers of registration points and target point position conditions. For each condition, reference points were input into the application, and the target points were computed for 10 iterations. The mean registration error and target error were used to assess the performance of the application. In the best-case scenario, the proposed solution had a mean target error of 2.6 ± 1.6 mm. Our approach provides a viable, low-cost, easy-to-use, portable method for locating points on the scalp surface with an accuracy of 2.6 ± 1.6 mm in the best-case scenario.

Surgical Navigation System for Hypertensive Intracerebral Hemorrhage Based on Mixed Reality.

Hypertensive intracerebral hemorrhage (HICH) is an intracerebral bleeding disease that affects 2.5 per 10,000 people worldwide each year. An effective way to cure this disease is puncture through the dura with a brain puncture drill and tube; the accuracy of the insertion determines the quality of the surgery. In recent decades, surgical navigation systems have been widely used to improve the accuracy of surgery and minimize risks. Augmented reality- and mixed reality-based surgical navigation is a promising new technology for surgical navigation in the clinic, aiming to improve the safety and accuracy of the operation. In this study, we present a novel multimodel mixed reality navigation system for HICH surgery in which medical images and virtual anatomical structures can be aligned intraoperatively with the actual structures of the patient in a head-mounted device and adjusted when the patient moves in real time while under local anesthesia; this approach can help the surgeon intuitively perform intraoperative navigation. A novel registration method is used to register the holographic space and serves as an intraoperative optical tracker, and a method for calibrating the HICH surgical tools is used to track the tools in real time. The results of phantom experiments revealed a mean registration error of 1.03mm and an average time consumption of 12.9min. In clinical usage, the registration error was 1.94mm, and the time consumption was 14.2min, showing that this system is sufficiently accurate and effective for clinical application.

3D Registration Accuracy of a Novel Marker-Free Technique for Double-Scan Protocol.

The aim of this retrospective study was to introduce a novel technique of a double-scan protocol with markerfree registration and compare it to the already-used techniques regarding the accuracy of registration. Fifty-nine fully edentulous patients underwent double-scan procedures by three different methods: the barium sulfate method for 11 patients; the gutta-percha method for 26 patients; and the marker-free method for 22 patients. Point-to-point registration of the two scans was followed by a voxel-based surface "best fit" registration. The mean registration error of each case was digitally recorded. Differences in registration error between groups were evaluated using one-way analysis of variance (ANOVA). The accuracy of the registration showed no significant differences according to the method (P = .719). The marker-free procedure was presented as a novel technique for registration of the scans in the double-scan protocol. There was no significant difference in the accuracy of the registration between the three techniques: marker-free, gutta-percha markers, and fully radiopaque barium sulfate scan appliance. Therefore, the marker-free technique might be a viable option for the double-scan protocol.

Design and evaluation of an intelligent reduction robot system for the minimally invasive reduction in pelvic fractures

IntroductionPelvic fracture is a severe high-energy injury with the highest disability and mortality of all fractures. Traditional open surgery is associated with extensive soft tissue damages and many complications. Minimally invasive surgery potentially mitigates the risks of open surgical procedures and is becoming a new standard for pelvic fracture treatment. The accurate reduction has been recognized as the cornerstone of minimally invasive surgery for pelvic fracture. At present, the closed reduction in pelvic fractures is limited by the current sub-optimal 2D intra-operative imaging (fluoroscopy) and by the high forces of soft tissue involved in the fragment manipulation, which might result in fracture malreduction. To overcome these shortcomings and facilitate pelvic fracture reduction, we developed an intelligent robot-assisted fracture reduction (RAFR) system for pelvic fracture.MethodsThe presented method is divided into three parts. The first part is the preparation of 20 pelvic fracture models. In the second part, we offer an automatic reduction algorithm of our robotic reduction system, including Intraoperative real-time 3D navigation, reduction path planning, control and fixation, and robotic-assisted fracture reduction. In the third part, image registration accuracy and fracture reduction accuracy were calculated and analyzed.ResultsAll 20 pelvic fracture bone models were reduced by the RAFR system; the mean registration error E1 of the 20 models was 1.29 ± 0.57 mm. The mean reduction error E2 of the 20 models was 2.72 ± 0.82 mm. The global error analysis of registration and reduction results showed that higher errors are mainly located at the edge of the pelvis, such as the iliac wing.ConclusionThe accuracy of image registration error and fracture reduction error in our study was excellent, which could reach the requirements of the clinical environment. Our study demonstrated the precision and effectiveness of our RAFR system and its applicability and usability in clinical practice, thus paving the way toward robot minimally invasive pelvic fracture surgeries.

Fiducial marker recovery and detection from severely truncated data in navigation-assisted spine surgery.

Fiducial markers are commonly used in navigation-assisted minimally invasive spine surgery and they help transfer image coordinates into real-world coordinates. In practice, these markers might be located outside the field-of-view (FOV) of C-arm cone-beam computed tomography (CBCT) systems used in intraoperative surgeries, due to the limited detector sizes. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle fornavigation. In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method). For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task-specific data preparation strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. The networks in both methods are trained based on simulated data. For the direct method, 6800 images and 10000 images are generated, respectively, to train the U-Net and ResNet50. For the recovery method, the training set includes 1360 images for FBPConvNet and Pix2pixGAN. The simulated data set with 166 markers and four cadaver cases with real fiducials are used forevaluation. The two methods are evaluated on simulated data and real cadaver data. The direct method achieves 100% detection rates within 1mm detection error on simulated data with normal truncation and simulated data with heavier noise, but only detect 94.6% markers in extremely severe truncation case. The recovery method detects all the markers successfully in three test data sets and around 95% markers are detected within 0.5mm error. For real cadaver data, both methods achieve 100% marker detection rates with mean registration error below 0.2mm. Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with the task-specific data preparation strategy has high robustness and generalizability on various data sets. The task-specific data preparation is able to reconstruct structures of interest outside the FOV from severely truncated data better than conventional datapreparation.

Registration Accuracy Using the Main Portal-vein Branch as Vascular Landmark Acquired by Intraoperative Ultrasound for 3D-Navigated Liver Resection

Introduction: The accuracy of image registration for 3D-navigated liver resection is crucial. Registration with intraoperative ultrasound (IOUS) using the right or left main portal-vein branch (PV) as landmark has the potential to optimize the accuracy and reduce errors compared to traditional liver surface landmark acquisition. Aim of this study was to analyze the IOUS registration accuracy regarding the distance to the portal vein branches and compare the use of a commercial vs. an semi-automatic in-house 3d-liver model.MethodsImage analysis of the intraoperative use of the IOUS registration process in patients who underwent 3D-navigated combined liver resection/thermal ablation. Results: 108 measurements were performed. Accuracy defined by the mean registration error was 0.75cm±0.48cm, using the left portal vein branch it was 0.65cm± 0.40cm, the right branch 0.95cm±0.54cm(p=0.001). Correlation between distance to the acquired portal vein branch landmarks and the resulting registration error was low (r2=0.36) showing a high accuracy in a distance up to 6cm to the PV. Registration error comparing the commercial 3D-liver model vs. the semi-automatic in-house 3D-liver model was not different (0.91±0.51 vs 0.99±0.58,p=0.64) Conclusion: IOUS registration algorithm shows a high accuracy in a distance up to 6cm to the region of interest in the left liver lobe using in-house 3D-image reconstruction. Accuracy in the right liver lobe was lower.

Validation and application of a novel in vivo cervical spine kinematics analysis technique

To validate the accuracy of Cone beam computed tomography (CBCT) cervical spine modeling with three dimensional (3D)-3D registration for in vivo measurements of cervical spine kinematics. CBCT model accuracy was validated by superimposition with computed tomography (CT) models in 10 healthy young adults, and then cervical vertebrae were registered in six end positions of functional movements, versus a neutral position, in 5 healthy young adults. Registration errors and six degrees of freedom (6-DOF) kinematics were calculated and reported. Relative to CT models, mean deviations of the CBCT models were < 0.6 mm. Mean registration errors between end positions and the reference neutral position were < 0.7 mm. During flexion–extension (F–E), the translation in the three directions was small, mostly < 1 mm, with coupled LB and AR both < 1°. During lateral bending (LB), the bending was distributed roughly evenly, with coupled axial rotation (AR) opposite to the LB at C1–C2, and minimal coupled F–E. During AR, most of the rotation occurred in the C1–C2 segment (29.93 ± 7.19° in left twist and 31.38 ± 8.49° in right twist) and coupled LB was observed in the direction opposite to that of the AR. Model matching demonstrated submillimeter accuracy in cervical spine kinematics data. The presently evaluated low-radiation-dose CBCT technique can be used to measure 3D spine kinematics in vivo across functional F–E, AR, and LB positions, which has been especially challenging for the upper cervical spine.

Fully Automatic Registration Methods for Chest X-Ray Images

PurposeImage registration is important in medical applications accomplished by improving healthcare technology in recent years. Various studies have been proposed in medical applications, including clinical track of events and updating the treatment plan for radiotherapy and surgery. This study presents a fully automatic registration system for chest X-ray images to generate fusion results for difference analysis. Using the accurate alignment of the proposed system, the fusion result indicates the differences in the thoracic area during the treatment process.MethodsThe proposed method consists of a data normalization method, a hybrid L-SVM model to detect lungs, ribs and clavicles for object recognition, a landmark matching algorithm, two-stage transformation approaches and a fusion method for difference analysis to highlight the differences in the thoracic area. In evaluation, a preliminary test was performed to compare three transformation models, with a full evaluation process to compare the proposed method with two existing elastic registration methods.ResultsThe results show that the proposed method produces significantly better results than two benchmark methods (P-value le 0.001). The proposed system achieves the lowest mean registration error distance (MRED) (8.99 mm, 23.55 pixel) and the lowest mean registration error ratio (MRER) w.r.t. the length of image diagonal (1.61%) compared to the two benchmark approaches with MRED (15.64 mm, 40.97 pixel) and (180.5 mm, 472.69 pixel) and MRER (2.81%) and (32.51%), respectively.ConclusionsThe experimental results show that the proposed method is capable of accurately aligning the chest X-ray images acquired at different times, assisting doctors to trace individual health status, evaluate treatment effectiveness and monitor patient recovery progress for thoracic diseases.

P.167 Application of the Anatomical Fiducials Framework to a Clinical Dataset of Patients with Parkinson’s Disease

Background: Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications. A point-based set of anatomical fiducials (AFIDs) was recently developed and validated to provide quantitative measures of image registration. We applied the AFIDs protocol to magnetic resonance images (MRIs) obtained from patients with Parkinson’s Disease (PD). Methods: Two expert and three novice raters placed AFIDs on MRIs of 39 PD patients. Localization and registration errors were calculated. To investigate for unique morphometric features, pairwise distances between AFIDs were calculated and compared to 30 controls who previously had AFIDs placed. Wilcoxon rank-sum tests with Bonferroni corrections were used. Results: 6240 AFIDs were placed with a mean localization error (±SD) of 1.57mm±1.16mm and mean registration error of 3.34mm±1.94mm. Out of the 496 pairwise distances, 40 were statistically significant (p&lt;0.05/496). PD patients had a decreased pairwise distance between the left temporal horn, brainstem and pineal gland. Conclusions: AFIDs can be successfully applied with millimetric accuracy in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence, facilitating accurate aggregation of imaging datasets and comparisons between various neurological conditions.

Soft tissue-based registration of intraoral scan with cone beam computed tomography scan

The aim of this study was to evaluate a novel soft tissue-based method to register an intraoral scan (IOS) with a cone beam computed tomography (CBCT) scan. IOS and CBCT data were obtained from eight dentate patients (mean age 21±2 years; three male, five female) and 14 fully edentulous patients (mean age 56±9 years; eight male, six female). An algorithm was developed to create a soft tissue model of the CBCT scan, which allowed a soft tissue-based registration to be performed with the IOS. First, validation was performed on dentate jaws with registration of the palatal mucosal surface and accuracy evaluation at the level of the teeth. Second, fully edentulous jaws were registered using both the palatal and alveolar crest mucosal surfaces. Distance maps were created to measure the method accuracy. The mean registration error was 0.49±0.26mm for the dentate jaws. Registration of the fully edentulous jaws had a mean error of 0.16±0.08mm at the palate and 0.16±0.05mm at the alveolar crest. In conclusion, the high accuracy of this registration method may allow the digital workflow to be optimized when no teeth are available to perform a regular registration procedure.

Hierarchical registration of laser point clouds between airborne and vehicle-borne data considering building eave attributes.

Laser point cloud registration is a key step in multisource laser scanning data fusion and application. Aimed at the problems of fewer overlapping regional features and the influence of building eaves on registration accuracy, a hierarchical registration algorithm of laser point clouds that considers building eave attributes is proposed in this paper. After extracting the building feature points of airborne and vehicle-borne light detection and ranging data, the similarity measurement model is constructed to carry out coarse registration based on pseudo-conjugate points. To obtain the feature points of the potential eaves (FPPE), the building contour lines of the vehicle-borne data are extended using the direction prediction algorithm. The FPPE data are regarded as the search set, in which the iterative closest point (ICP) algorithm is employed to match the true conjugate points between the airborne laser scanning data and vehicle-borne laser scanning data. The ICP algorithm is used again to complete the fine registration. To evaluate the registration performance, the developed method was applied to the data processing near Shandong University of Science and Technology, Qingdao, China. The experimental results showed that the FPPE dataset can effectively address the coarse registration accuracy effects on the convergence of the iterative ICP. Before considering eave attributes, the mean registration errors (MREs) of the proposed method in the xoz plane, yoz plane, and xoy plane are 0.318, 0.96, and 0.786 m, respectively. After considering eave attributes, the MREs decrease to 0.129, 0.187, and 0.169 m, respectively. The developed method can effectively improve the registration accuracy of the laser point clouds, which not only solves the problem of matching true conjugate points under the effects of the eaves but also avoids converging to a local minimum due to ICP's poor coarse registration.

Development of a mechatronic guidance system for targeted ultrasound-guided biopsy under high-resolution positron emission mammography localization.

Image-guided needle biopsy of small, detectable lesions is crucial for early-stage diagnosis, treatment planning, and management of breast cancer. High-resolution positron emission mammography (PEM) is a dedicated functional imaging modality that can detect breast cancer independent of breast tissue density, but anatomical context and real-time needle visualization are not yet available to guide biopsy. We propose a mechatronic guidance system integrating an ultrasound (US)-guided core-needle biopsy (CNB) with high-resolution PEM localization to improve the spatial sampling of breast lesions. This paper presents the benchtop testing and phantom studies to evaluate the accuracy of the system and its constituent components for targeted PEM-US-guided biopsy under simulated high-resolution PEM localization. A mechatronic guidance system was developed to operate with the Radialis PEM system and a conventional US system. The system includes a user-operated guidance arm and end-effector biopsy device, integrating a US transducer and CNB gun, with its needle focused on a remote center of motion (RCM). Custom software modules were developed to track, display, and guide the end-effector biopsy device. Registration of the mechatronic guidance system to a simulated PEM detector plate was performed using a landmark-based method. Testing was performed with fiducials positioned in the peripheral and central regions of the simulated detector plate and registration error was quantified. Breast phantom experiments were performed under ideal detection and localization to evaluate for bias in the end-effector biopsy device. The accuracy of the complete mechatronic guidance system to perform targeted breast biopsy was assessed using breast phantoms with simulated lesions. Three-dimensional positioning error was quantified, and principal component analysis assessed for directional trends in 3D space within 95% prediction intervals. Targeted breast biopsies with test phantoms were performed and an overall in-plane needle targeting error was quantified. The mean registration errors were 0.63mm (N=44) and 0.73mm (N=72) in the peripheral and central regions of the simulated PEM detector plate, respectively. A 3D 95% prediction ellipsoid shows an error volume <2.0mm in diameter, centered on the mean registration error. Under ideal detection and localization, targets <1.0mm in diameter can be sampled with 95% confidence. The complete mechatronic guidance system was able to successfully spatially sample simulated breast lesions, 4mm and 6mm in diameter and height (N=20) in known 3D positions in the PEM image coordinate space. The 3D positioning error was 0.85mm (N=20) with 0.64mm in-plane and 0.44mm cross-plane component errors. Targeted breast biopsies resulted in a mean in-plane needle targeting error of 1.08mm (N=15) allowing for targets 1.32mm in radius to be sampled with 95% confidence. We demonstrated the utility of our mechatronic guidance system for targeted breast biopsy under high-resolution PEM localization. Breast phantom studies showed the ability to accurately guide, position, and target breast lesions with the accuracy to spatially sample targets <3.0mm in diameter with 95% confidence. Future work will integrate the developed system with the Radialis PEM system toward combined PEM-US-guided breast biopsy.