Registration Of Scans Research Articles

Real-Time Registration of 3D Underwater Sonar Scans

Due to space and energy restrictions, lightweight autonomous underwater vehicles (AUVs) are usually fitted with low-power processing units, which limits the ability to run demanding applications in real time during the mission. However, several robotic perception tasks reveal a parallel nature, where the same processing routine is applied for multiple independent inputs. In such cases, leveraging parallel execution by offloading tasks to a GPU can greatly enhance processing speed. This article presents a collection of generic matrix manipulation kernels, which can be combined to develop parallelized perception applications. Taking advantage of those building blocks, we report a parallel implementation for the 3DupIC algorithm—a probabilistic scan matching method for sonar scan registration. Tests demonstrate the algorithm’s real-time performance, enabling 3D sonar scan matching to be executed in real time onboard the EVA AUV.

Accuracy of digital jaw relation determination in different occlusal conditions - an in vitro study.

In orthodontics, accurate registration of jaw relationships is essential for correct diagnosis and treatment planning. Therefore, accuracy of the digital spatial registration of maxillary and mandibular models and - for the first time-the influence of dentition stage and malocclusion type on this procedure were investigated under controlled conditions. Eight pairs of jaw models, representing different occlusal and developmental statuses (m1-m8), were scanned using two IOS types (PS: Primescan; TR: Trios4). Buccal scans for registering maxillary and mandibular models were repeated (n = 3). Reference scans were obtained using a desktop scanner (RDS; Ceramill Map 600). Arch-specific 3D coordinate systems were used to calculate the linear and angular deviations among different registrations. Trueness of registration by PS and TR was calculated using a statistical mixed-effect model (random-effect: model-type). Precision values of IOS registrations across m1-m8 were characterized as standard deviations (SDs). As maximum deviations compared to RDS, PS showed caudal translation (0.11 ± 0.02mm), while TR showed ventral translation (0.08 ± 0.06mm), of the maxillary relative to the mandibular model. Maximum rotational values were calculated for tilting around the transverse axis (PS: anteinclination (0.25 ± 0.16°), TR: retroinclination (0.27 ± 0.16°)). These deviations varied with the malocclusion type. The lowest IOS precision was recorded for sagittal translation (PS: 0.013 ± 0.005mm, TR: 0.021 ± 0.010mm) and rotation around the transverse axis (PS: 0.051 ± 0.013°, TR: 0.076 ± 0.031°). Registrations using buccal IOS scans showed quantifiable but clinically negligible 3D deviations from reference scan registrations, whereby the type of tooth and jaw misalignment did not appear to have a clinically relevant influence. Therefore, the examined IOSs appear to be suitable for digital jaw relation determination in everyday clinical orthodontic practice.

Application of intraoral scanning registration implant robot in dental implant surgery.

This paper aims to investigate the application of intraoral scanning and cone beam computed tomography (CBCT) registration implant robot in dental implant surgery. The data of 40 cases with dental defect of robot-assisted implantation from November 2023 to May 2024 were retrospectively analyzed. Before the operation, the intraoral scan data and CBCT data of the positioning markers were automatically fused with the initial CBCT images, and the registration error was calculated. The average registration error of positioning markers was determined during the operation, and the implantation accuracy was analyzed after the operation. The intraoral scan data and CBCT data of 40 patients with dental defect wearing positioning markers were successfully registered with the initial CBCT image, and the registration errors were (0.157±0.026) mm and (0.154±0.033) mm, respectively. Statistical analysis showed no statistical significance between them. The registration errors of the marker was (0.037 3±0.003 6) mm. A total of 55 implants were performed, and the total deviations of the implant point and the apical point were (0.78±0.41) and (0.89±0.28) mm, respectively. The transverse deviations of the implant point and the apical point were (0.44±0.36) and (0.58±0.25) mm, respectively. The depth deviations of the implant point and the apical point were (0.51±0.32) and (0.54±0.36) mm, respectively. The deviation of the implant angle was 1.24°±0.67°. The fusion technology based on intraoral scanning and CBCT registration can meet the accuracy requirements of preoperative registration of oral implant robots. The technology increases the choice of registration methods before robot-assisted dental implant surgery and reduces the multiple radiation exposuresof the patient.

A digital workflow for integrating diagnostic dentures into dynamic navigation-guided implant surgery.

The dual cone beam computed tomography (CBCT) method is the established way of planning prosthetically driven implant treatment for edentulous jaws. Although effective, it is time-consuming and complex for dynamic real-time navigation surgery procedures. This article presents a novel technique that describes a single CBCT scan registration method integrating diagnostic denture information into dynamic real-time navigation to streamline the implant planning process effectively.

Culture system for longitudinal monitoring of bone dynamics ex vivo.

To quantify and visualize both bone formation and resorption within osteochondral explants cultured ex vivo is challenging with the current analysis techniques. An approach that enables monitoring of bone remodeling dynamics is longitudinal microcomputed tomography (µCT), a non-destructive technique that relies on repeated µCT scanning and subsequent registration of consecutive scans. In this study, a two-compartment culture system suitable for osteochondral explants that allowed for µCT scanning during ex vivo culture was established. Explants were scanned repeatedly in a fixed orientation, which allowed assessment of bone remodeling due to adequate image registration. Using this method, bone formation was found to be restricted to the outer surfaces when cultured statically. To demonstrate that the culture system could capture differences in bone remodeling, explants were cultured statically and under dynamic compression as loading promotes osteogenesis. No quantitative differences between static and dynamic culture were revealed. Still, only in dynamic conditions, bone formation was visualized on trabecular surfaces located within the inner cores, suggesting enhanced bone formation towards the center of the explants upon mechanical loading. Taken together, the ex vivo culture system in combination with longitudinal µCT scanning and subsequent registration of images demonstrated potential for evaluating bone remodeling within explants.

Rest Tremor in Parkinson's Disease Is Associated with Ipsilateral Striatal Dopamine Transporter Binding.

The cardinal motor symptoms of Parkinson's disease (PD) include rigidity, bradykinesia, and rest tremor. Rigidity and bradykinesia correlate with contralateral nigrostriatal degeneration and striatal dopamine deficit, but association between striatal dopamine function and rest tremor has remained unclear. The aim of this study was to investigate the possible link between dopamine function and rest tremor using Parkinson's Progression Markers Initiative dataset, the largest prospective neuroimaging cohort of patients with PD. Clinical, [123I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane ([123I]FP-CIT) single photon emission computed tomography (SPECT), and structural magnetic resonance imaging data from 354 early PD patients and 166 healthy controls were included in this study. We employed a novel approach allowing nonlinear registration of individual scans accurately to a standard space and voxelwise analyses of the association between motor symptoms and striatal dopamine transporter (DAT) binding. Severity of both rigidity and bradykinesia was negatively associated with contralateral striatal DAT binding (PFWE < 0.05 [FWE, family-wise error corrected]). However, rest tremor amplitude was positively associated with increased ipsilateral DAT binding (PFWE < 0.05). The association between rest tremor and binding remained the same controlling for Hoehn & Yahr stage, Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III score, bradykinesia-rigidity score, or motor phenotype. The association between rest tremor and binding was independent of bradykinesia-rigidity and replicated using 2-year follow-up data (PFWE < 0.05). In agreement with the existing literature, we did not find a consistent association between rest tremor and contralateral dopamine defect. However, our results demonstrate a link between rest tremor and increased or less decreased ipsilateral DAT binding. Our findings provide novel information about the association between dopaminergic function and parkinsonian rest tremor. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Evaluation of gingival thickness obtained from intraoral scanning registration with cone beam computed tomography at different acquisition modes

To investigate the reliability of gingival thickness measurements obtained from the registration of intraoral scans with cone beam computed tomography (CBCT) examinations at different acquisition modes. CBCT examinations of 9 porcine hemimandibles were acquired using the OP300 Maxio unit operating at the highest-dose protocol with the smallest voxel size as the reference standard for visualizing the gingival surface. Subsequently, the hemimandibles were surrounded by water to simulate soft tissue attenuation of radiation, and additional CBCT examinations were acquired in 4 modes: Endo, High-resolution, Standard-resolution, and Low-dose. These CBCT datasets were registered with corresponding intraoral scans obtained with the Carestream Dental 3600 intraoral scanning system using the Blue Sky Plan 4 software. Four oral radiologists measured the buccal gingival thickness on cross-sectional reconstructions at 4 measurement sites and 2 distances from the gingival margin in the CBCT reference standard examinations and examinations obtained with water and intraoral scan registration. Multifactorial analysis of variance was used to assess the influence of acquisition mode, measurement site, and distance from the gingival margin on measurements (α = 5%; statistical power = 90%). Buccal gingival thickness measurements obtained via CBCT after registration with intraoral scanning were not significantly influenced by acquisition mode (P = .153) or measurement site (P = .089). Gingival thickness measurements derived from the registration of intraoral scans with CBCT examinations at different acquisition modes appear to be reliable.

Validation of a novel AI-based automated multimodal image registration of CBCT and intraoral scan aiding presurgical implant planning.

The objective of this study is to assess accuracy, time-efficiency and consistency of a novel artificial intelligence (AI)-driven automated tool for cone-beam computed tomography (CBCT) and intraoral scan (IOS) registration compared with manual and semi-automated approaches. A dataset of 31 intraoral scans (IOSs) and CBCT scans was used to validate automated IOS-CBCT registration (AR) when compared with manual (MR) and semi-automated registration (SR). CBCT scans were conducted by placing cotton rolls between the cheeks and teeth to facilitate gingival delineation. The time taken to perform multimodal registration was recorded in seconds. A qualitative analysis was carried out to assess the correspondence between hard and soft tissue anatomy on IOS and CBCT. In addition, a quantitative analysis was conducted by measuring median surface deviation (MSD) and root mean square (RMS) differences between registered IOSs. AR was the most time-efficient, taking 51.4 ± 17.2 s, compared with MR (840 ± 168.9 s) and SR approaches (274.7 ± 100.7 s). Both AR and SR resulted in significantly higher qualitative scores, favoring perfect IOS-CBCT registration, compared with MR (p = .001). Additionally, AR demonstrated significantly superior quantitative performance compared with SR, as indicated by low MSD (0.04 ± 0.07 mm) and RMS (0.19 ± 0.31 mm). In contrast, MR exhibited a significantly higher discrepancy compared with both AR (MSD = 0.13 ± 0.20 mm; RMS = 0.32 ± 0.14 mm) and SR (MSD = 0.11 ± 0.15 mm; RMS = 0.40 ± 0.30 mm). The novel AI-driven method provided an accurate, time-efficient, and consistent multimodal IOS-CBCT registration, encompassing both soft and hard tissues. This approach stands as a valuable alternative to manual and semi-automated registration approaches in the presurgical implant planning workflow.

Computer-Assisted Implant Planning: A Review of Data Registration Techniques.

Computer-assisted implant planning allows for a comprehensive treatment plan by combining radiographic data provided by a Cone Beam Computed Tomography (CBCT) with surface optical scan (IOs) data that includes patient intraoral situation and the intended restorative planning. Integrating a tailored restorative design with the patient's anatomical conditions through virtual implant planning allows for an ideal bio-restorative treatment planning to maximize biological, functional, and esthetic outcomes. This article discusses dataset registration techniques that combine radiographic CBCT data with restorative information as the main path to create a virtual patient. The described techniques include the use of removable radiographic templates with radiopaque markers, dual scan technique, and direct digital file registration of intra-oral scans using anatomical references. Depending on the individual clinical situation, different factors must be considered to appropriately select methods that achieve an optimal registration of diverse datasets. An inherent challenge lies in the presence of scattering artifacts in CBCT scans. Two approaches are proposed for these situations - the use of chairside-fabricated composite resin markers or adhesive spot-markers fabricated for the use with CBCT scans. Both techniques exhibit limitations that need to be taken into consideration. Further approaches should be developed for situations involving scattering in CBCT.

Diffeomorphic transformer-based abdomen MRI-CT deformable image registration.

Stereotactic body radiotherapy (SBRT) is a well-established treatment modality for liver metastases in patients unsuitable for surgery. Both CT and MRI are useful during treatment planning for accurate target delineation and to reduce potential organs-at-risk (OAR) toxicity from radiation. MRI-CT deformable image registration (DIR) is required to propagate the contours defined on high-contrast MRI to CT images. An accurate DIR method could lead to more precisely defined treatment volumes and superior OAR sparing on the treatment plan. Therefore, it is beneficial to develop an accurate MRI-CT DIR for liver SBRT. To create a new deep learning model that can estimate the deformation vector field (DVF) for directly registering abdominal MRI-CT images. The proposed method assumed a diffeomorphic deformation. By using topology-preserved deformation features extracted from the probabilistic diffeomorphic registration model, abdominal motion can be accurately obtained and utilized for DVF estimation. The model integrated Swin transformers, which have demonstrated superior performance in motion tracking, into the convolutional neural network (CNN) for deformation feature extraction. The model was optimized using a cross-modality image similarity loss and a surface matching loss. To compute the image loss, a modality-independent neighborhood descriptor (MIND) was used between the deformed MRI and CT images. The surface matching loss was determined by measuring the distance between the warped coordinates of the surfaces of contoured structures on the MRI and CT images. To evaluate the performance of the model, a retrospective study was carried out on a group of 50 liver cases that underwent rigid registration of MRI and CT scans. The deformed MRI image was assessed against the CT image using the target registration error (TRE), Dice similarity coefficient (DSC), and mean surface distance (MSD) between the deformed contours of the MRI image and manual contours of the CT image. When compared to only rigid registration, DIR with the proposed method resulted in an increase of the mean DSC values of the liver and portal vein from 0.850±0.102 and 0.628±0.129 to 0.903±0.044 and 0.763±0.073, a decrease of the mean MSD of the liver from 7.216±4.513mm to 3.232±1.483mm, and a decrease of the TRE from 26.238±2.769mm to 8.492±1.058mm. The proposed DIR method based on a diffeomorphic transformer provides an effective and efficient way to generate an accurate DVF from an MRI-CT image pair of the abdomen. It could be utilized in the current treatment planning workflow for liver SBRT.

The 3-Dimensional Intelligent Structured Light Technique: A New Registration Method in Stereotactic Neurosurgery.

Surface-based facial scanning registration emerged as an essential registration method in the robot-assisted neuronavigation surgery, providing a marker-free way to align a patient's facial surface with the imaging data. The 3-dimensional (3D) structured light was developed as an advanced registration method based on surface-based facial scanning registration. We aspire to introduce the 3D structured light as a new registration method in the procedure of the robot-assisted neurosurgery and assess the accuracy, efficiency, and safety of this method by analyzing the relative operative results. We analyzed the results of 47 patients who underwent Ommaya reservoir implantation (n = 17) and stereotactic biopsy (n = 30) assisted by 3D structured light at our hospital from January 2022 to May 2023. The accuracy and additional operative results were analyzed. For the Ommaya reservoir implantation, the target point error was 3.2 ± 2.2 mm and the entry point error was 3.3 ± 2.4 mm, while the operation duration was 35.8 ± 8.3 minutes. For the stereotactic biopsy, the target point error was 2.3 ± 1.3 mm and the entry point error was 2.7 ± 1.2 mm, while the operation duration was 24.5 ± 6.3 minutes. The 3D structured light technique reduces the patients' discomfort and offers the advantage of a simpler procedure, which can improve the clinical efficiency with the sufficient accuracy and safety to meet the clinical requirements of the puncture and navigation.

Evaluation of joint kinematics in patients with condylar reconstruction using mandibular motion simulation based on intraoral scanning registration.

To summarize the features of condylar kinematics in patients with condylar reconstruction using a mandibular motion simulation method based on intraoral scanning registration. Patients undergoing unilateral segmental mandibulectomy and autogenous bone reconstruction as well as healthy volunteers were enrolled in the study. Patients were grouped based on whether the condyles were reconstructed. Mandibular movements were recorded using a jaw tracking system, and kinematic models were simulated after registration. The path inclination of the condyle point, margin of border movement, deviation, and chewing cycle were analyzed. A t test and one-way analysis of variance (ANOVA) were carried out. A total of 20 patients, including 6 with condylar reconstruction and 14 with condylar preservation as well as 10 healthy volunteers were included. The patients with condylar reconstruction showed flatter movement paths of the condyle points. The mean inclination angle of the condylar movement paths of the patients with condylar reconstruction (0.57 ± 12.54 degrees) was significantly smaller than that of those with condylar preservation (24.70 ± 3.90 degrees, P = 0.014) during both maximum opening and protrusion (7.04 ± 12.21 degrees and 31.12 ± 6.79 degrees, respectively, P = 0.022). The inclination angle of the condylar movement paths of the healthy volunteers was 16.81 ± 3.97 degrees during maximum opening and 21.54 ± 2.80 degrees during protrusion; no significant difference compared with the patients. The condyles of the affected side tended to deviate laterally in all patients during mouth opening and protrusion. Patients with condylar reconstruction showed more severe symptoms of mouth opening limitation and mandibular movement deviation as well as shorter chewing cycles than patients with condylar preservation. Patients with condylar reconstruction showed flatter movement paths of the condyle points, greater lateral motion range, and shorter chewing cycles than those with condylar preservation. The method of mandibular motion simulation based on intraoral scanning registration was feasible to simulate condylar movement.

Deep-learning-based deformable image registration of head CT and MRI scans

This work is motivated by the lack of publications on the direct application of multimodal image registration with deep-learning techniques for the enhancement of treatment planning in particle therapy. An unsupervised workflow, which seeks to improve image alignment, was developed and evaluated for computed tomography and magnetic resonance imaging scans of the head. The scans of 39 paediatric patients with brain tumours were available. The focus of the two-step workflow, including preprocessing of the scans for normalisation, is deformable image registration (DIR) with a deep neural network, which generates deformation vector fields (DVFs). To obtain a suitable configuration of the network, parameter tuning is performed by varying its parameters, e.g., layer size, regularisation (λ) of the DVF and learning rate (α). Image similarity was determined with the Dice similarity coefficient, mDSC, using segmented images and the mutual-information metric, mMI. The performance of the deep-learning models was assessed with the inverse consistency, mIC, and the Jacobian determinant, mJD. Inverse consistency is obtained for mIC = 0 mm, while the determinant of a deformed image is expected to be unity. The deep-learning models passed both performance checks, indicated by the mean values m̄IC=(0.57±1.00)mm and m̄JD=(1.00±0.07). Models with λ ≥ 1 yielded higher mDSC values than models with lower λ values. A small-architecture model with α = 10–4 was found to be most suitable for DIR, as improvement in image similarity of up to 12% was obtained in terms of mMI. The direct application of deep-learning models produced registered images improving image alignment between scans of different modalities.

A robust and interpretable deep learning framework for multi-modal registration via keypoints.

We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for registration often are not robust to large misalignments, are not interpretable, and do not incorporate the symmetries of the problem. In addition, most models produce only a single prediction at test-time. Our core insight which addresses these shortcomings is that corresponding keypoints between images can be used to obtain the optimal transformation via a differentiable closed-form expression. We use this observation to drive the end-to-end learning of keypoints tailored for the registration task, and without knowledge of ground-truth keypoints. This framework not only leads to substantially more robust registration but also yields better interpretability, since the keypoints reveal which parts of the image are driving the final alignment. Moreover, KeyMorph can be designed to be equivariant under image translations and/or symmetric with respect to the input image ordering. Finally, we show how multiple deformation fields can be computed efficiently and in closed-form at test time corresponding to different transformation variants. We demonstrate the proposed framework in solving 3D affine and spline-based registration of multi-modal brain MRI scans. In particular, we show registration accuracy that surpasses current state-of-the-art methods, especially in the context of large displacements. Our code is available at https://github.com/alanqrwang/keymorph.

CT-Based Micromotion Analysis After Locking Plate Fixation of AO Type C Distal Radius Fractures

BackgroundVolar locking plate fixation (VLPF) is the most common method for operative fixation of distal radius fractures (DRF). The dorsal ulnar corner (DUC) can be difficult to stabilize as the fragment is small and not exposed when using the volar approach. The purpose of this study was to study fracture fragment migration after VLPF of AO type C DRF, using a volume registration technique of paired CT scans with special focus on the DUC fragment.Materials and MethodsThis pilot study included ten patients with AO type C DRF, all operated with VLPF. The primary outcome was radiographic outcome. Postoperative and 1-year scans were compared and analyzed. Fragment migration was assessed with CT-based micromotion analysis (CTMA), a software technique used for volume registration of paired CT scans.ResultsAll plates were stable over time. Two patients showed signs of screw movement (0.2–0.35 mm and 0.35– > 1 mm respectively). Postoperative reduction was maintained, and there was no fragment migration at the 1-year follow-up except for one case with increased dorsal tilt. The DUC fragment was found in 8/10 cases, fixated in 7/8 cases, and not dislocated in any case at the 1-year follow-up.ConclusionThe CTMA results indicate that variable-angle VLPF after AO type C DRF can yield and maintain a highly stable reduction of the fracture fragments. The DUC fragment remained stable regardless of the number of screws through the fragment. CT volume registration can be a valuable tool in the detailed assessment of fracture fragment migration following volar plate fixation of DRFs.

Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT.

Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.

Keypoint-based registration of TLS point clouds using a statistical matching approach

Abstract Laser scanning is a wide-spread practice to capture the environment. Besides the fields of robotics and self-driving cars, it has been applied in the field of engineering geodesy for documentation and monitoring purposes for many years. The registration of scans is still one of the main sources of uncertainty in the final point cloud. This paper presents a new keypoint-based method for terrestrial laser scan (TLS) registration for high-accuracy applications. Based on detected 2D-keypoints, we introduce a new statistical matching approach that tests wheter keypoints, scanned from two scan stations, can be assumed to be identical. This approach avoids the use of keypoint descriptors for matching and also handles wide distances between different scanner stations. The presented approach requires a good coarse registration as initial input, which can be achieved for example by artificial laser scanning targets. By means of two evaluation data sets, we show that our keypoint-based registration leads to the smallest loop closure error when traversing several stations compared to target-based and ICP registrations. Due to the high number of observations compared to the target-based registration, the reliability of the our keypoint-based registration can be increased significantly and the precision of the registration can be increased by about 25 % on average.

Synthetic MRI Generation from CT Scans for Stroke Patients

CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning models are developed, trained, and tested using a dataset of 181 CT/MR pairs from stroke patients. The resultant synthetic MRIs generated by these models are compared through a variety of qualitative and quantitative methods. The synthetic MRIs generated by a 3D UNet model consistently demonstrated superior performance across all methods of evaluation. Overall, the generation of synthetic MRIs from CT scans using the methods described in this paper produces realistic MRIs that can guide the registration of CT scans to MRI atlases. The synthetic MRIs enable the segmentation of white matter, grey matter, and cerebrospinal fluid by using algorithms designed for MRIs, exhibiting a high degree of similarity to true MRIs.

P07.04.A ELASTIC IMAGE FUSION OF INTRAOPERATIVE ULTRASOUND AND PREOPERATIVE MRI FOR IMPROVING NAVIGATION ACCURACY IN BRAIN TUMOR SURGERY

Abstract BACKGROUND In brain surgery, neuronavigation based on preoperative MRI is limited by various physical and operational factors, such as surgery-induced brain shift. Using navigated intraoperative ultrasound (iUS) imaging and 3D iUS image reconstruction may help improve neuronavigation. However, the registration of MRI and US scans can lead to spatial inconsistencies between the two modalities. This study aims to evaluate the clinical integration of navigated iUS imagining and its potential to improve the accuracy of resection during brain tumor surgery by comparing the performance of rigid and elastic MRI-iUS image fusion algorithms during retrospective evaluation. MATERIAL AND METHODS N = 50 patients with brain tumors were prospectively enrolled in this trial. The neuronavigation system (BrainLab AG) used in the study allowed for the reconstruction and integration of 3D iUS imaging, which was acquired multiple times throughout the surgery using a tracked ultrasound probe (BK Medical). Prototypes of automatic rigid and elastic MRI-iUS image fusion algorithms (Brainlab Elements Image Fusion) were retrospectively applied to improve co-registration accuracy between the MRI and iUS data. To evaluate the image co-registration accuracy, anatomical landmarks were defined in the MRI and iUS scans and used to measure Euclidean distances between each landmark pair. RESULTS Improvements in the image quality over the course of the study indicated that skills in US handling are essential. Contrast behavior and tumor structure influence the visibility of the tumor in the US. Artifacts caused by hemostatic agents challenge the US image quality and the fluid-filled resection cavity requires experience in probe handling. In terms of image fusion, significant mismatches of the iUS and MRI data were reported. For scans acquired before resection, rigid fusion using a novel co-registration algorithm may enhance the registration accuracy with mean target registration errors for two representative patients of 6.5/8.9 mm and 3.7/3.4 mm before and after registration update, respectively. For iUS scans acquired after tumor resection, rigid fusion is inherently limited due to non-linear brain shifts. Therefore, elastic image fusion has been applied to provide deformable MR-iUS image co-registration. CONCLUSION Navigated iUS imaging can be integrated into clinical routine and the use of MR-iUS image co-registration for updating navigation is a promising tool to compensate brainshift during surgical resection. However, further studies are needed to quantify the accuracy and assess whether iUS could potentially replace the use of iMRI in resection control of selected cases.

Fast adaptive multimodal feature registration (FAMFR): an effective high-resolution point clouds registration workflow for cultural heritage interiors

Accurate registration of 3D scans is crucial in creating precise and detailed 3D models for various applications in cultural heritage. The dataset used in this study comprised numerous point clouds collected from different rooms in the Museum of King Jan III’s Palace in Warsaw using a structured light scanner. Point clouds from three relatively small rooms at Wilanow Palace: The King’s Chinese Cabinet, The King’s Wardrobe, and The Queen’s Antecabinet exhibit intricate geometric and decorative surfaces with diverse colour and reflective properties. As a result, creating a high-resolution full 3D model require a complex and time-consuming registration process. This process often consists of several steps: data preparation, registering point clouds, final relaxation, and evaluation of the resulting model. Registering two-point clouds is the most fundamental part of this process; therefore, an effective registration workflow capable of precisely registering two-point clouds representing various cultural heritage interiors is proposed in this paper. Fast Adaptive Multimodal Feature Registration (FAMFR) workflow is based on two different handcrafted features, utilising the colour and shape of the object to accurately register point clouds with extensive surface geometry details or geometrically deficient but with rich colour decorations. Furthermore, this work emphasises the challenges associated with high-resolution point clouds registration, providing an overview of various registration techniques ranging from feature-based classic approaches to new ones based on deep learning. A comparison shows that the algorithm explicitly created for this data achieved much better results than traditional feature-based or deep learning methods by at least 35%.