Template Registration Research Articles

The potential of preregistration in psychology: Assessing preregistration producibility and preregistration-study consistency.

Study preregistration has become increasingly popular in psychology, but its potential to restrict researcher degrees of freedom has not yet been empirically verified. We used an extensive protocol to assess the producibility (i.e., the degree to which a study can be properly conducted based on the available information) of preregistrations and the consistency between preregistrations and their corresponding papers for 300 psychology studies. We found that preregistrations often lack methodological details and that undisclosed deviations from preregistered plans are frequent. These results highlight that biases due to researcher degrees of freedom remain possible in many preregistered studies. More comprehensive registration templates typically yielded more producible preregistrations. We did not find that the producibility and consistency of preregistrations differed over time or between original and replication studies. Furthermore, we found that operationalizations of variables were generally preregistered more producible and consistently than other study parts. Inconsistencies between preregistrations and published studies were mainly encountered for data collection procedures, statistical models, and exclusion criteria. Our results indicate that, to unlock the full potential of preregistration, researchers in psychology should aim to write more producible preregistrations, adhere to these preregistrations more faithfully, and more transparently report any deviations from their preregistrations. This could be facilitated by training and education to improve preregistration skills, as well as the development of more comprehensive templates. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Quantitative evaluation of striatal uptake ratios using an adaptive template registration method for 123I-ioflupane dopamine transporter SPECT.

123I-FP-CIT (123I-Ioflupane) SPECT shows strong accumulation in the striatum, but morphological standardization is challenging due to low accumulation outside the striatum, particularly in subjects with marked striatal decline. In this study, morphological standardization without MRI was achieved using the adaptive template registration (ATR) method to create a subject-specific optimized template with weighted images of normal-type and egg-shape-type templates. The accuracy of a quantitative method for calculating the ratio with nonspecific accumulation in the occipital lobe was evaluated by placing voxels-of-interest (VOI) on standardized images, particularly targeting the striatum. The average images of eight subjects, demonstrating normal-type and egg-shape-type tracer accumulation in 123I-Ioflupane SPECT, were utilized as normal and disease templates, respectively. The study included 300 subjects that underwent both 123I-Ioflupane SPECT and MRI for the diagnosis of suspected Parkinson's disease or for exclusion diagnosis. Morphological standardization of SPECT images using structural MRI (MRI-based method) was considered the standard of truth (SOT). Three morphological standardizations without MRI were conducted. The first involved conventional morphological standardization using a normal template (fixed template method), the second employed the ATR method, with a weighted template, and the third used the split-ATR method, processing the left and right striatum separately to address asymmetrical accumulation. VOIs were set on the striatum, caudate, putamen as regions of specific accumulation, and on the occipital lobe as a reference region for nonspecific accumulation. Results showed significant and robust linearity in the striatal accumulation ratios for all templates when compared with the occipital lobe accumulation ratio when using the MRI-based method. Comparing intra-class correlations for different linearities, the ATR method and split-ATR method demonstrated higher linearity in the striatum, caudate, and putamen. The split-ATR method showed similar improvements, although more linearity than some of the ATR methods; the effectiveness of the Split-ATR method may vary by image quality, and further validation of its effectiveness in diverse asymmetric accumulation cases seemed warranted. The use of optimized templates, such as the ATR and split-ATR methods, improved reproducibility in fully automated processing and demonstrated superior linearity compared to that of MRI-based method, in the ratio to the occipital lobe. The ATR method, which enables morphological standardization when using SPECT images only, proved highly reproducible for clinical quantitative analysis of striatal accumulation, facilitating its clinical use.

Automated Craniofacial Biometry with 3D T2w Fetal MRI.

Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated landmark propagation pipeline using 3D motion-corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements. A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI. An MRI atlas with defined anatomical landmarks served as a template for subject registration, auto-labelling, and biometric calculation. We assessed 108 healthy controls and 24 fetuses with Down syndrome (T21) in the third trimester (29-36 weeks gestational age, GA) to identify meaningful biometrics in T21. Reliability and reproducibility were evaluated in 10 random datasets by four observers. Automated labels were produced for all 132 subjects with a 0.03% placement error rate. Seven measurements, including anterior base of skull length and maxillary length, showed significant differences with large effect sizes between T21 and control groups (ANOVA, p<0.001). Manual measurements took 25-35 minutes per case, while automated extraction took approximately 5 minutes. Bland-Altman plots showed agreement within manual observer ranges except for mandibular width, which had higher variability. Extended GA growth charts (19-39 weeks), based on 280 control fetuses, were produced for future research. This is the first automated atlas-based protocol using 3D SVR MRI for fetal craniofacial biometrics, accurately revealing morphological craniofacial differences in a T21 cohort. Future work should focus on improving measurement reliability, larger clinical cohorts, and technical advancements, to enhance prenatal care and phenotypic characterisation.

Template-Guided Hierarchical Multi-View Registration Framework of Unordered Bridge Terrestrial Laser Scanning Data.

The registration of bridge point cloud data (PCD) is an important preprocessing step for tasks such as bridge modeling, deformation detection, and bridge health monitoring. However, most existing research on bridge PCD registration only focused on pairwise registration, and payed insufficient attention to multi-view registration. In addition, to recover the overlaps of unordered multiple scans and obtain the merging order, extensive pairwise matching and the creation of a fully connected graph of all scans are often required, resulting in low efficiency. To address these issues, this paper proposes a marker-free template-guided method to align multiple unordered bridge PCD to a global coordinate system. Firstly, by aligning each scan to a given registration template, the overlaps between all the scans are recovered. Secondly, a fully connected graph is created based on the overlaps and scanning locations, and then a graph-partition algorithm is utilized to construct the scan-blocks. Then, the coarse-to-fine registration is performed within each scan-block, and the transformation matrix of coarse registration is obtained using an intelligent optimization algorithm. Finally, global block-to-block registration is performed to align all scans to a unified coordinate reference system. We tested our framework on different bridge point cloud datasets, including a suspension bridge and a continuous rigid frame bridge, to evaluate its accuracy. Experimental results demonstrate that our method has high accuracy.

A deep learning and radiomics based Alberta stroke program early CT score method on CTA to evaluate acute ischemic stroke.

Alberta stroke program early CT score (ASPECTS) is a semi-quantitative evaluation method used to evaluate early ischemic changes in patients with acute ischemic stroke, which can guide physicians in treatment decisions and prognostic judgments. We propose a method combining deep learning and radiomics to alleviate the problem of large inter-observer variance in ASPECTS faced by physicians and assist them to improve the accuracy and comprehensiveness of the ASPECTS. Our study used a brain region segmentation method based on an improved encoding-decoding network. Through the deep convolutional neural network, 10 regions defined for ASPECTS will be obtained. Then, we used Pyradiomics to extract features associated with cerebral infarction and select those significantly associated with stroke to train machine learning classifiers to determine the presence of cerebral infarction in each scored brain region. The experimental results show that the Dice coefficient for brain region segmentation reaches 0.79. Three radioactive features are selected to identify cerebral infarction in brain regions, and the 5-fold cross-validation experiment proves that these 3 features are reliable. The classifier trained based on 3 features reaches prediction performance of AUC = 0.95. Moreover, the intraclass correlation coefficient of ASPECTS between those obtained by the automated ASPECTS method and physicians is 0.86 (95% confidence interval, 0.56-0.96). This study demonstrates advantages of using a deep learning network to replace the traditional template registration for brain region segmentation, which can determine the shape and location of each brain region more precisely. In addition, a new brain region classifier based on radiomics features has potential to assist physicians in clinical stroke detection and improve the consistency of ASPECTS.

Unsupervised Anthropometric Measurements from 3D Scans Using Automated Template Registration

Three dimensional digital avatars can be valuable assets in various industrial fields: from telepresence, entertainment, fashion, to wellness and medicine. Accurate geometric representations of a person’s body shape are crucial for extracting reliable anthropometric information applicable in contexts ranging from virtual try-ons to medical-grade applications, where precision can significantly impact outcomes. This paper presents two different automated procedures to perform anthropometrics measurements, both based on an non-rigid, human template mesh registration method that requires no human supervision, centered around a custom human body model. Both procedures were evaluated using datasets from six participants, comparing a fully automated registration with a manual registration performed by a trained operator. The results, analyzed and benchmarked against manual digital tape measurements, confirm that the accuracy of the automated methods aligns closely with ground truth, affirming the reliability of these measurements across diverse body shapes. This highlights the potential of fully automated methods in applications that require precise body measurements.

Florida 4-H Food Challenge: Event Hosting Guide

This guide will assist county Extension agents who conduct a Florida 4-H Food Challenge event. It contains contest guidelines, contests responsibilities, sample schedule, orientation talking points, registration template, score tally sheet, Judging questions, contest resources, scoresheets, and worksheets.

Unrolled and rapid motion-compensated reconstruction for cardiac CINE MRI

In recent years Motion-Compensated MR reconstruction (MCMR) has emerged as a promising approach for cardiac MR (CMR) imaging reconstruction. MCMR estimates cardiac motion and incorporates this information in the reconstruction. However, two obstacles prevent the practical use of MCMR in clinical situations: First, inaccurate motion estimation often leads to inferior CMR reconstruction results. Second, the motion estimation frequently leads to a long processing time for the reconstruction. In this work, we propose a learning-based and unrolled MCMR framework that can perform precise and rapid CMR reconstruction. We achieve accurate reconstruction by developing a joint optimization between the motion estimation and reconstruction, in which a deep learning-based motion estimation framework is unrolled within an iterative optimization procedure. With progressive iterations, a mutually beneficial interaction can be established in which the reconstruction quality is improved with more accurate motion estimation. Further, we propose a groupwise motion estimation framework to speed up the MCMR process. A registration template based on the cardiac sequence average is introduced, while the motion estimation is conducted between the cardiac frames and the template. By applying this framework, cardiac sequence registration can be accomplished with linear time complexity. Experiments on 43 in-house acquired 2D CINE datasets indicate that the proposed unrolled MCMR framework can deliver artifacts-free motion estimation and high-quality CMR reconstruction even for imaging acceleration rates up to 20x. We compare our approach with state-of-the-art reconstruction methods and it outperforms them quantitatively and qualitatively in all adapted metrics across all acceleration rates.

Template-based CT reconstruction with optimal transport and total generalized variation

x-ray computed tomography (CT) has been widely used in clinical diagnosis as a modality of medical imaging. To decrease the radiation dose patients suffering from, sparse-view CT has gained much attention in medical imaging field. In this paper, we propose to design a variational model based on dynamic optimal transportation and total generalized variation for CT reconstruction. This is a joint task involving inverse problem and template registration. The final state image of the optimal transport problem is unknown and needs to be reconstructed through CT inversion, while the given initial state can be regarded as a template to provide some structural information for the final one. Moreover, the existence and stability of minimizers to our proposed model are shown in continuous space. In discretization with the continuity equation, we utilize the well-known staggered grid in fluid mechanics and develop a first-order algorithm based on primal-dual method for numerically solving the proposed model. Finally, numerical experiments for sparse-view CT reconstruction are exhibited to show the performance of our proposed model in recovering images with high quality and structure preservation.

A Contrastive-Learning-Based Method for the Few-Shot Identification of Ship-Radiated Noises

For identifying each vessel from ship-radiated noises with only a very limited number of data samples available, an approach based on the contrastive learning was proposed. The input was sample pairs in the training, and the parameters of the models were optimized by maximizing the similarity of sample pairs from the same vessel and minimizing that from different vessels. In practical inference, the method calculated the distance between the features of testing samples and those of registration templates and assigned the testing sample into the closest templates for it to achieve the parameter-free classification. Experimental results on different sea-trial data demonstrated the advantages of the proposed method. On the five-ship identification task based on the open-source data, the proposed method achieved an accuracy of 0.68 when only five samples per vessel were available, that was significantly higher than conventional solutions with accuracies of 0.26 and 0.48. Furthermore, the convergence of the method and the behavior of its performance with increasing data samples available for the training were discussed empirically.

Automatic Segmentation of Parkinson Disease Therapeutic Targets Using Nonlinear Registration and Clinical MR Imaging: Comparison of Methodology, Presence of Disease, and Quality Control

Introduction: Accurate and precise delineation of the globus pallidus pars interna (GPi) and subthalamic nucleus (STN) is critical for the clinical treatment and research of Parkinson’s disease (PD). Automated segmentation is a developing technology which addresses limitations of visualizing deep nuclei on MR imaging and standardizing their definition in research applications. We sought to compare manual segmentation with three workflows for template-to-patient nonlinear registration providing atlas-based automatic segmentation of deep nuclei. Methods: Bilateral GPi, STN, and red nucleus (RN) were segmented for 20 PD and 20 healthy control (HC) subjects using 3T MRIs acquired for clinical purposes. The automated workflows used were an option available in clinical practice and two common research protocols. Quality control (QC) was performed on registered templates via visual inspection of readily discernible brain structures. Manual segmentation using T1, proton density, and T2 sequences was used as “ground truth” data for comparison. Dice similarity coefficient (DSC) was used to assess agreement between segmented nuclei. Further analysis was done to compare the influences of disease state and QC classifications on DSC. Results: Automated segmentation workflows (CIT-S, CRV-AB, and DIST-S) had the highest DSC for the RN and lowest for the STN. Manual segmentations outperformed automated segmentation for all workflows and nuclei; however, for 3/9 workflows (CIT-S STN, CRV-AB STN, and CRV-AB GPi) the differences were not statically significant. HC and PD only showed significant differences in 1/9 comparisons (DIST-S GPi). QC classification only demonstrated significantly higher DSC in 2/9 comparisons (CRV-AB RN and GPi). Conclusion: Manual segmentations generally performed better than automated segmentations. Disease state does not appear to have a significant effect on the quality of automated segmentations via nonlinear template-to-patient registration. Notably, visual inspection of template registration is a poor indicator of the accuracy of deep nuclei segmentation. As automatic segmentation methods continue to evolve, efficient and reliable QC methods will be necessary to support safe and effective integration into clinical workflows.

Attenuation Correction Using Template PET Registration for Brain PET: A Proof-of-Concept Study.

NeuroLF is a dedicated brain PET system with an octagonal prism shape housed in a scanner head that can be positioned around a patient's head. Because it does not have MR or CT capabilities, attenuation correction based on an estimation of the attenuation map is a crucial feature. In this article, we demonstrate this method on [18F]FDG PET brain scans performed with a low-resolution proof of concept prototype of NeuroLF called BPET. We perform an affine registration of a template PET scan to the uncorrected emission image, and then apply the resulting transform to the corresponding template attenuation map. Using a whole-body PET/CT system as reference, we quantitively show that this method yields comparable image quality (0.893 average correlation to reference scan) to using the reference µ-map as obtained from the CT scan of the imaged patient (0.908 average correlation). We conclude from this initial study that attenuation correction using template registration instead of a patient CT delivers similar results and is an option for patients undergoing brain PET.

Custom 3D fMRI Registration Template Construction Method Based on Time-Series Fusion

As the brain standard template for medical image registration has only been constructed with an MRI template, there is no three-dimensional fMRI standard template for use, and when the subject’s brain structure is quite different from the standard brain structure, the registration to the standard space will lead to large errors. Registration to an individual space can avoid this problem. However, in the current fMRI registration algorithm based on individual space, the reference image is often selected by researchers or randomly selected fMRI images at a certain time point. This makes the quality of the reference image very dependent on the experience and ability of the researchers and has great contingency. Whether the reference image is appropriate and reasonable affects the rationality and accuracy of the registration results to a great extent. Therefore, a method for constructing a 3D custom fMRI template is proposed. First, the data are preprocessed; second, by taking a group of two-dimensional slices corresponding to the same layer of the brain in three-dimensional fMRI images at multiple time points as image sequences, each group of slice sequences are registered and fused; and finally, a group of fused slices corresponding to different layers of the brain are obtained. In the process of registration, in order to make full use of the correlation information between the sequence data, the feature points of each two slices of adjacent time points in the sequence are matched, and then according to the transformation relationship between the adjacent images, they are recursively forwarded and mapped to the same space. Then, the fused slices are stacked in order to form a three-dimensional customized fMRI template with individual pertinence. Finally, in the classic registration algorithm, the difference in the registration accuracy between using a custom fMRI template and different standard spaces is compared, which proves that using a custom template can improve the registration effect to a certain extent.

An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery.

Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional features is an important step towards morphogenetic brain structure characterization of murine models in neurobiological studies. State-of-the-art image segmentation methods register image volumes to standard presegmented templates or well-characterized highly detailed image atlases. Performance of these methods depends critically on the quality of skull-stripping, which is the digital removal of tissue signal exterior to the brain. This is, however, tedious to do manually and challenging to automate. Registration-based segmentation, in addition, performs poorly on small structures, low resolution images, weak signals, or faint boundaries, intrinsic to in vivo MRI scans. To address these issues, we developed an automated end-to-end pipeline called DeepBrainIPP (deep learning-based brain image processing pipeline) for 1) isolating brain volumes by stripping skull and tissue from T2w MRI images using an improved deep learning-based skull-stripping and data augmentation strategy, which enables segmentation of large brain regions by atlas or template registration, and 2) address segmentation of small brain structures, such as the paraflocculus, a small lobule of the cerebellum, for which DeepBrainIPP performs direct segmentation with a dedicated model, producing results superior to the skull-stripping/atlas-registration paradigm. We demonstrate our approach on data from both in vivo and ex vivo samples, using an in-house dataset of 172 images, expanded to 4,040 samples through data augmentation. Our skull stripping model produced an average Dice score of 0.96 and residual volume of 2.18%. This facilitated automatic registration of the skull-stripped brain to an atlas yielding an average cross-correlation of 0.98. For small brain structures, direct segmentation yielded an average Dice score of 0.89 and 5.32% residual volume error, well below the tolerance threshold for phenotype detection. Full pipeline execution is provided to non-expert users via a Web-based interface, which exposes analysis parameters, and is powered by a service that manages job submission, monitors job status and provides job history. Usability, reliability, and user experience of DeepBrainIPP was measured using the Customer Satisfaction Score (CSAT) and a modified PYTHEIA Scale, with a rating of excellent. DeepBrainIPP code, documentation and network weights are freely available to the research community.

Driver's Head Pose and Gaze Zone Estimation Based on Multi-Zone Templates Registration and Multi-Frame Point Cloud Fusion.

Head pose and eye gaze are vital clues for analysing a driver’s visual attention. Previous approaches achieve promising results from point clouds in constrained conditions. However, these approaches face challenges in the complex naturalistic driving scene. One of the challenges is that the collected point cloud data under non-uniform illumination and large head rotation is prone to partial facial occlusion. It causes bad transformation during failed template matching or incorrect feature extraction. In this paper, a novel estimation method is proposed for predicting accurate driver head pose and gaze zone using an RGB-D camera, with an effective point cloud fusion and registration strategy. In the fusion step, to reduce bad transformation, continuous multi-frame point clouds are registered and fused to generate a stable point cloud. In the registration step, to reduce reliance on template registration, multiple point clouds in the nearest neighbor gaze zone are utilized as a template point cloud. A coarse transformation computed by the normal distributions transform is used as the initial transformation, and updated with particle filter. A gaze zone estimator is trained by combining the head pose and eye image features, in which the head pose is predicted by point cloud registration, and the eye image features are extracted via multi-scale spare coding. Extensive experiments demonstrate that the proposed strategy achieves better results on head pose tracking, and also has a low error on gaze zone classification.

Declaration of common standards for the preregistration of animal research-speeding up the scientific progress.

Preregistration of studies is a recognized tool in clinical research to improve the quality and reporting of all gained results. In preclinical research, preregistration could boost the translation of published results into clinical breakthroughs. When studies rely on animal testing or form the basis of clinical trials, maximizing the validity and reliability of research outcomes becomes in addition an ethical obligation. Nevertheless, the implementation of preregistration in animal research is still slow. However, research institutions, funders, and publishers start valuing preregistration, and thereby level the way for its broader acceptance in the future. A total of 3 public registries, the OSF registry, preclinicaltrials.eu, and animalstudyregistry.org already encourage the preregistration of research involving animals. Here, they jointly declare common standards to make preregistration a valuable tool for better science. Registries should meet the following criteria: public accessibility, transparency in their financial sources, tracking of changes, and warranty and sustainability of data. Furthermore, registration templates should cover a minimum set of mandatory information and studies have to be uniquely identifiable. Finally, preregistered studies should be linked to any published outcome. To ensure that preregistration becomes a powerful instrument, publishers, funders, and institutions should refer to registries that fulfill these minimum standards.

Pancreas MRI Segmentation Into Head, Body, and Tail Enables Regional Quantitative Analysis of Heterogeneous Disease.

Quantitative imaging studies of the pancreas have often targeted the three main anatomical segments, head, body, and tail, using manual region of interest strategies to assess geographic heterogeneity. Existing automated analyses have implemented whole-organ segmentation, providing overall quantification but failing to address spatial heterogeneity. To develop and validate an automated method for pancreas segmentation into head, body, and tail subregions in abdominal MRI. Retrospective. One hundred and fifty nominally healthy subjects from UK Biobank (100 subjects for method development and 50 subjects for validation). A separate 390 UK Biobank triples of subjects including type 2 diabetes mellitus (T2DM) subjects and matched nondiabetics. A 1.5 T, three-dimensional two-point Dixon sequence (for segmentation and volume assessment) and a two-dimensional axial multiecho gradient-recalled echo sequence. Pancreas segments were annotated by four raters on the validation cohort. Intrarater agreement and interrater agreement were reported using Dice overlap (Dice similarity coefficient [DSC]). A segmentation method based on template registration was developed and evaluated against annotations. Results on regional pancreatic fat assessment are also presented, by intersecting the three-dimensional parts segmentation with one available proton density fat fraction (PDFF) image. Wilcoxon signed rank test and Mann-Whitney U-test for comparisons. DSC and volume differences for evaluation. A P value &lt; 0.05 was considered statistically significant. Good intrarater (DSC mean, head: 0.982, body: 0.940, tail: 0.961) agreement and interrater (DSC mean, head: 0.968, body: 0.905, tail: 0.943) agreement were observed. No differences (DSC, head: P=0.4358, body: P=0.0992, tail: P=0.1080) were observed between the manual annotations and our method's segmentations (DSC mean, head: 0.965, body: 0.893, tail: 0.934). Pancreatic body PDFF was different between T2DM and nondiabetics matched by body mass index. The developed segmentation's performance was no different from manual annotations. Application on type 2 diabetes subjects showed potential for assessing pancreatic disease heterogeneity. 4 TECHNICAL EFFICACY STAGE: 3.

OpenEHR modelling applied to Complementary Diagnostics Requests

Complementary Diagnostic Requests (CDRs) are required for disease identification, monitoring, and prognosis. Diagnostic tests misuse, on the other hand, can lead to negative health outcomes as well as additional costs. Inappropriate diagnostic test requests are primarily the result of a lack of interoperability between Healthcare Information Systems (HIS). On one hand, clinicians can be mislead into which test is the best option for each clinical case, on the other hand missing previous results, leads to duplication or unnecessary tests. HIS is increasingly relying on standards based on dual architecture to promote interoperability as well as the structuring and consistency of clinical and demographic data. The OpenEHR standard's duo-based architecture allows for concise modelling of archetypes and templates for a given clinical case, which was used in this study. As a result, the purpose of this research was to build an openEHR template for the CDR registration as well as the architecture of a Data Warehouse (DW) system capable of storing all of the information needed for the diagnostic test request process. Afterwards, Business Intelligence (BI) indicators was developed in order to answers the needs for test registration and execution.

Measurement of Lead Localization Accuracy Based on Magnetic Resonance Imaging.

Post-implantation localization of deep brain stimulation (DBS) lead based on a magnetic resonance (MR) image is widely used. Existing localization methods use artifact center method or template registration method, which may lead to a considerable deviation of > 2 mm, and result in severe side effects or even surgical failure. Accurate measurement of lead position can instantly inform surgeons of the imprecise implantation. This study aimed to identify the influencing factors in DBS lead post-implantation localization approach, analyze their influence, and describe a localization approach that uses the individual template method to reduce the deviation. We verified that reconstructing direction should be parallel or perpendicular to lead direction, instead of the magnetic field. Besides, we used simplified relationship between magnetic field angle and deviation error to correct the localization results. The mean localization error can be reduced after correction and favors the feasibility of direct localization of DBS lead using MR images. We also discussed influence of in vivo noise on localization frequency and the possibility of using only MR images to localize the contacts.

ЭКСПЕРИМЕНТАЛЬНО-МАТЕМАТИЧЕСКОЕ ОБОСНОВАНИЕ ПРИМЕНЕНИЯ ЦИФРОВЫХ ТЕХНОЛОГИЙ ДЛЯ РЕГИСТРАЦИИ ПОЛОЖЕНИЯ НИЖНЕЙ ЧЕЛЮСТИ ПРИ ТОТАЛЬНОМ ПРОТЕЗИРОВАНИИ С ИСПОЛЬЗОВАНИЕМ ДЕНТАЛЬНЫХ ИМПЛАНТАТОВ

Subject. When prosthetics of toothless jaws are performed, the physiological position of the lower jaw must be determined before the final structures are made. In some cases, the use of temporary prostheses for several weeks may be useful to check for a new occlusion in a centric relationship. When the correct ratio is achieved, it is difficult to transfer them from the preliminary prostheses to the final ones. This article presents a case and a technique used to communicate information about the position of the lower jaw using a digital workflow.&#x0D; To determine the optimal position of the lower jaw, a computer tomogram of the temporomandibular joint was used to mathematically measure the correct position of the head of the lower jaw.&#x0D; Purpose. To describe a technique designed to transfer the inter-occlusal relations of the upper and lower jaws, using digital technologies in the prosthetics of full adentia using dental implants.&#x0D; Methodology. On the example of a clinical case of rehabilitation of a patient, total removal of failed teeth was performed, followed by prosthetics of complete adentia with the installation of six implants on the upper and lower jaws. After the completion of the integration period, a cone-beam computed tomography was performed with the capture of the temporomandibular joint, and a scan of plaster models with installed gum shapers was performed. The obtained data were compared in a specialized program for mathematical calculation of the optimal position of the lower jaw. This information was used to produce registration templates for the manufacture of fixed structures supported by implants in the resulting central jaw ratio.&#x0D; Results. A digital protocol for determining the central ratio of the jaws allowed us to transfer the information obtained in a virtual model of the temporomandibular joint to a permanent restoration based on implants.&#x0D; Conclusion. The approach described in this article predictably conveys information about the optimal position of the lower jaw during prosthetics and allows you to perform the final restoration on implants with optimal occlusal ratios, based on a mathematical calculation performed in specialized programs.