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Muscles of the Anterior Neck

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The anterior neck muscles are the group of muscles covering the front part of the neck. These muscles are classified into three subgroups. The first one is the superficial muscles, the most visible muscles in the front of the neck. It includes the platysma and sternocleidomastoid. The second group is the suprahyoid muscles, located above the hyoid bone. This group comprises the digastric, mylohyoid, geniohyoid, and stylohyoid. Lastly, the infrahyoid muscles are found below the hyoid bone and...

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Related Experiment Video

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Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

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An improved articulated registration method for neck images.

A du Bois d'Aische¹, M De Craene, B Macq

¹Communications and Remote Sensing Laboratory, Université catholique de Louvain, Louvain-la-Neuve, Belgium.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 7, 2007

Summary

This study introduces a novel method for estimating tissue deformation in articulated body parts like the neck. The technique accurately tracks rigid bone movement and soft tissue changes across medical images.

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Area of Science:

Medical imaging analysis
Computational anatomy
Biomechanical modeling

Background:

Accurate medical image analysis requires distinguishing rigid motion of bony structures from soft tissue deformation.
Existing methods may struggle to precisely quantify displacement fields in articulated regions.

Purpose of the Study:

To develop and validate a comprehensive method for estimating displacement fields in articulated anatomical structures.
To accurately model both rigid and deformable components within medical images.

Main Methods:

A three-step registration process: initial rigid body alignment, deformation propagation via tetrahedral mesh, and mutual information-based optical flow refinement.
Utilizes the Insight Segmentation and Registration Toolkit (ITK) framework with stochastic gradient descent optimization.
Employs mutual information as the metric to maximize during registration.

Main Results:

The method successfully estimates displacement fields in complex articulated regions.
Demonstrated accuracy across various 3D imaging modalities including CT, MR, and PET scans.
The approach effectively handles the distinct motion patterns of bony and soft tissues.

Conclusions:

This articulated registration method provides accurate displacement field estimation for medical imaging.
The technique is robust and applicable to multi-modal 3D datasets.
Offers a valuable tool for analyzing anatomical changes in areas like the cervical spine.