Magnetic resonance imaging basedcomputer-guideddental implant surgery-A clinical pilot study

Background: Computer-guided implant surgery is currently based on radiographic techniques exposing patients to ionizing radiation. Purpose: To assess, whether computer-assisted 3D implant planning with template-guided placement of dental implants based on magnetic resonance imaging (MRI) is feasible. Materials and methods: 3-Tesla MRI was performed in 12 subjects as a basis for prosthetically driven virtual planning and subsequent guided implant surgery. To evaluate the transferability of the virtually planned implant position, deviations between virtually planned and resulting implant position were studied. Matching of occlusal surfaces was assessed by comparing surface scans with MRI-derived images. In addition, the overall image quality and the ability of depicting anatomically important structures were rated. Results: MRI-based guided implant surgery with subsequent prosthetic treatment was successfully performed in nine patients. Mean deviations between virtually planned and resulting implant position (error at entry point 0.8 +/- 0.3 mm, error at apex 1.2 +/- 0.6 mm, angular deviation 4.9 +/- 3.6 degrees), mean deviation of occlusal surfaces between surface scans and MRI-based tooth reconstructions (mean 0.254 +/- 0.026 mm) as well as visualization of important anatomical structures were acceptable for clinical application. Conclusion Magnetic resonance imaging (MRI) based computer-assisted implant surgery is a feasible and accurate procedure that avoids exposure to ionizing radiation

Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs

Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation. Image segmentation methods have proven to help quantify the disease burden and even help predict the outcome. The availability of longitudinal CT series may also result in an efficient and effective method to reliably assess the progression of COVID-19, monitor the healing process and the response to different therapeutic strategies. In this paper, we propose a new framework to identify infection at a voxel level (identification of healthy lung, consolidation, and ground-glass opacity) and visualize the progression of COVID-19 using sequential low-dose non-contrast CT scans. In particular, we devise a longitudinal segmentation network that utilizes the reference scan information to improve the performance of disease identification. Experimental results on a clinical longitudinal dataset collected in our institution show the effectiveness of the proposed method compared to the static deep neural networks for disease quantification.Comment: MICCAI 202

High resolution MRI for quantitative assessment of inferior alveolar nerve impairment in course of mandible fractures: an imaging feasibility study

The purpose of this study was to evaluate a magnetic resonance imaging (MRI) protocol for direct visualization of the inferior alveolar nerve in the setting of mandibular fractures. Fifteen patients suffering from unilateral mandible fractures involving the inferior alveolar nerve (15 affected IAN and 15 unaffected IAN from contralateral side) were examined on a 3 T scanner (Elition, Philips Healthcare, Best, the Netherlands) and compared with 15 healthy volunteers (30 IAN in total). The sequence protocol consisted of a 3D STIR, 3D DESS and 3D T1 FFE sequence. Apparent nerve-muscle contrast-to-noise ratio (aNMCNR), apparent signal-to-noise ratio (aSNR), nerve diameter and fracture dislocation were evaluated by two radiologists and correlated with nerve impairment. Furthermore, dislocation as depicted by MRI was compared to computed tomography (CT) images. Patients with clinically evident nerve impairment showed a significant increase of aNMCNR, aSNR and nerve diameter compared to healthy controls and to the contralateral side (p < 0.05). Furthermore, the T1 FFE sequence allowed dislocation depiction comparable to CT. This prospective study provides a rapid imaging protocol using the 3D STIR and 3D T1 FFE sequence that can directly assess both mandible fractures and IAN damage. In patients with hypoesthesia following mandibular fractures, increased aNMCN R, aSNR and nerve diameter on MRI imaging may help identify patients with a risk of prolonged or permanent hypoesthesia at an early time

Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans

Consistent segmentation of COVID-19 patient's CT scans across multiple time points is essential to assess disease progression and response to therapy accurately. Existing automatic and interactive segmentation models for medical images only use data from a single time point (static). However, valuable segmentation information from previous time points is often not used to aid the segmentation of a patient's follow-up scans. Also, fully automatic segmentation techniques frequently produce results that would need further editing for clinical use. In this work, we propose a new single network model for interactive segmentation that fully utilizes all available past information to refine the segmentation of follow-up scans. In the first segmentation round, our model takes 3D volumes of medical images from two-time points (target and reference) as concatenated slices with the additional reference time point segmentation as a guide to segment the target scan. In subsequent segmentation refinement rounds, user feedback in the form of scribbles that correct the segmentation and the target's previous segmentation results are additionally fed into the model. This ensures that the segmentation information from previous refinement rounds is retained. Experimental results on our in-house multiclass longitudinal COVID-19 dataset show that the proposed model outperforms its static version and can assist in localizing COVID-19 infections in patient's follow-up scans.Comment: 10 pages, 11 figures, 4 table

Bone regeneration of minipig mandibular defect by adipose derived mesenchymal stem cells seeded tri-calcium phosphate- poly(D,L-lactide-co-glycolide) scaffolds

Reconstruction of bone defects represents a serious issue for orthopaedic and maxillofacial surgeons, especially in extensive bone loss. Adipose-derived mesenchymal stem cells (ADSCs) with tri-calcium phosphates (TCP) are widely used for bone regeneration facilitating the formation of bone extracellular matrix to promote reparative osteogenesis. The present study assessed the potential of cell-scaffold constructs for the regeneration of extensive mandibular bone defects in a minipig model. Sixteen skeletally mature miniature pigs were divided into two groups: Control group and scaffolds seeded with osteogenic differentiated pADSCs (n=8/group). TCP-PLGA scaffolds with or without cells were integrated in the mandibular critical size defects and fixed by titanium osteosynthesis plates. After 12 weeks, ADSCs seeded scaffolds (n=7) demonstrated significantly higher bone volume (34.8%+/- 4.80%) than scaffolds implanted without cells (n=6, 22.4%+/- 9.85%) in the micro-CT (p < 0.05). Moreover, an increased amount of osteocalcin deposition was found in the test group in comparison to the control group (27.98 +/- 2.81% vs 17.10 +/- 3.57%, p < 0.001). In conclusion, ADSCs seeding on ceramic/polymer scaffolds improves bone regeneration in large mandibular defects. However, further improvement with regard to the osteogenic capacity is necessary to transfer this concept into clinical use