Dataset Information

Three-Dimensional Assessment of Temporomandibular Joint Using MRI-CBCT Image Registration.

ABSTRACT: To introduce a new approach to reconstruct a 3D model of the TMJ using magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) registered images, and to evaluate the intra-examiner reproducibility values of reconstructing the 3D models of the TMJ.MRI and CBCT images of five patients (10 TMJs) were obtained. Multiple MRIs and CBCT images were registered using a mutual information based algorithm. The articular disc, condylar head and glenoid fossa were segmented at two different occasions, at least one-week apart, by one investigator, and 3D models were reconstructed. Differences between the segmentation at two occasions were automatically measured using the surface contours (Average Perpendicular Distance) and the volume overlap (Dice Similarity Index) of the 3D models. Descriptive analysis of the changes at 2 occasions, including means and standard deviation (SD) were reported to describe the intra-examiner reproducibility.The automatic segmentation of the condyle revealed maximum distance change of 1.9±0.93 mm, similarity index of 98% and root mean squared distance of 0.1±0.08 mm, and the glenoid fossa revealed maximum distance change of 2±0.52 mm, similarity index of 96% and root mean squared distance of 0.2±0.04 mm. The manual segmentation of the articular disc revealed maximum distance change of 3.6±0.32 mm, similarity index of 80% and root mean squared distance of 0.3±0.1 mm.The MRI-CBCT registration provides a reliable tool to reconstruct 3D models of the TMJ's soft and hard tissues, allows quantification of the articular disc morphology and position changes with associated differences of the condylar head and glenoid fossa, and facilitates measuring tissue changes over time.

SUBMITTER: Al-Saleh MA

PROVIDER: S-EPMC5241008 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Three-Dimensional Assessment of Temporomandibular Joint Using MRI-CBCT Image Registration.

Al-Saleh Mohammed A Q MA Punithakumar Kumaradevan K Lagravere Manuel M Boulanger Pierre P Jaremko Jacob L JL Major Paul W PW

PloS one 20170117 1

<h4>Purpose</h4>To introduce a new approach to reconstruct a 3D model of the TMJ using magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) registered images, and to evaluate the intra-examiner reproducibility values of reconstructing the 3D models of the TMJ.<h4>Methods</h4>MRI and CBCT images of five patients (10 TMJs) were obtained. Multiple MRIs and CBCT images were registered using a mutual information based algorithm. The articular disc, condylar head and glenoid fossa ...[more]

PMID: 28095486

Similar Datasets

Project description:Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2 ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.

Project description:SignificancePhotoacoustic tomography (PAT) has great potential in monitoring disease progression and treatment response in breast cancer. However, due to variations in breast repositioning, there is a chance of geometric misalignment between images. Further, poor repositioning can affect light fluence distribution and imaging field-of-view, making images different from one another. The net effect is that it becomes challenging to distinguish between image changes due to repositioning effects and those due to true biological variations.AimThe aim is to develop a three-dimensional image registration framework for geometrically aligning repeated PAT volumetric images, which are potentially affected by repositioning effects such as misalignment, changed radiant exposure conditions, and different fields-of-view.ApproachThe proposed framework involves the use of a coordinate-based neural network to represent the displacement field between pairs of PAT volumetric images. A loss function based on normalized cross correlation and Frangi vesselness feature extraction at multiple scales was implemented. We refer to our image registration framework as MUVINN-reg, which stands for multiscale vesselness-based image registration using neural networks. The approach was tested on a longitudinal dataset of healthy volunteer breast PAT images acquired with the hybrid photoacoustic-ultrasound Photoacoustic Mammoscope 3 imaging system. The registration performance was also tested under unfavorable repositioning conditions such as intentional mispositioning, and variation in breast-supporting cup size between measurements.ResultsA total of 13 pairs of repeated PAT scans were included in this study. MUVINN-reg showed excellent performance in co-registering each pair of images. The proposed framework was shown to be robust to image intensity shifts and field-of-view changes. Furthermore, MUVINN-reg could align vessels at imaging depths greater than 4 cm.ConclusionsThe proposed framework will enable the use of PAT for quantitative and reproducible monitoring of disease progression and treatment response.

Project description:BackgroundAvailable knowledge about disorders of temporomandibular joint structures and their association with orthodontic variables are still lacking.ObjectivesThis article is aimed at to identifying studies and presenting current information on the relationship between morphology diversity and the occurrence of degenerative changes in structures of the temporomandibular joint (TMJ) assessed by cone-beam computed tomography (CBCT) in the context of craniofacial morphology and malocclusion. Search Methods. The review was conducted by analyzing the PubMed (including Medline), Cochrane Library, Web of Science, and Scopus electronic databases up to November 2021 using two different comprehensive search strategies based on keywords as well as additional manual searches. Eligibility Criteria. Selection of the literature was carried out according to the PRISMA-ScR checklist. Methodological quality of the selected studies was evaluated using JBI Critical Appraisal Tool.ResultsThe electronic databases search revealed 3331 records. After applying the eligibility criteria and JBI assessment, a total of 33 studies were extracted and selected to the study. The review was divided into 4 parts, in which the following correlations were assessed in terms of orthodontic variables: TMJ degenerative changes, joint space and condylar position, condylar shape, TMJ articular eminence, and fossa.ConclusionsSkeletal and dental class II malocclusion with a retrognathic mandible, a hypodivergent skeletal pattern with a steep mandibular plane, and significant lateral mandibular displacement can be risk factors for developing radiographically detectable degenerative changes. Patients with skeletal and dental class III malocclusion as well as a hyperdivergent skeletal pattern may be at greater risk of TMD occurrence compared with other groups. Further studies are necessary to clarify the relationship between the position of the condylar processes and the presence of degenerative changes in the temporomandibular joints among orthodontic patients.

Dataset Information

Three-Dimensional Assessment of Temporomandibular Joint Using MRI-CBCT Image Registration.

Publications

Three-Dimensional Assessment of Temporomandibular Joint Using MRI-CBCT Image Registration.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets