Intraindividual Comparison of Image Artifacts of Two Generations of Rotatable Cochlear Implant Magnets.

Rayoung Kim,Simon Schleyer,Hans Bjoern Gehl,Holger Sudhoff,Peter Goon,Ingo Todt

Journal of International Advanced Otology（2024）SCI 4区

Department of Otolaryngology

Cited 0|Views3

Abstract

In cochlear implant recipients, the diagnostic value of magnetic resonance imaging (MRI) scans is reduced by image artifacts. The static magnetic field of a 3.0T scanner is associated with the risk of implant demagnetization. The development of rotatable implant magnets aimed to support the advancement of 3.0T MRI scanners and eliminate the risk of demagnetization of cochlear implant magnets. This study aimed to compare the image artifacts caused by first-t and second-generation rotatable cochlear implant magnets in 3.0T MRI. Three Tesla MRI T2W TSE sequences were performed on 3 subjects with first- and second-generation rotatable cochlear implant magnets. The cochlear implant was fixed to the head at the implantation position by a swim cap. The size of the image artifact was determined in the transverse plane. Intraindividual comparative analyses showed that within the margin of combined uncertainty of 5 mm at a resolution of 2 mm, the cochlear implant-induced image artifacts in all subjects showed for both (first- and second-generation rotatable cochlear implant magnets), the same maximum image artifact dimension of 125 mm. We could show that no difference in image artifact size was detected within the margin of error determined by resolution, localized induced shift of the scan, and reproducibility of the tilt angle of the head relative to the chest in a living subject. Assumed improved magnet attachment can be reached without compromising of the magnet artifact size.

Translated text

Key words

3T,cochlear implant,magnetic resonance imaging,image artifact,3T,cochlear implant,magnetic resonance imaging,image artifact

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper