Abstract #1711

Quantitative measurements of masseter fat infiltration in head and neck cancer using Dixon conjugated with machine learning auto-segmentation

Yu-Cheng Chang¹, Kai-Lun Cheng², Hsueh-Ju Lu³, Hui-Yu Wang², Ying-Hsiang Chou⁴, Yeu-Sheng Tyan⁵, and Ping-Huei Tsai⁶

¹Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan, Taichung, Taiwan, ²Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan, Taichung, Taiwan, ³Division of Medical Oncology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan, Taichung, Taiwan, ⁴Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan, Department of Radiation Oncology, Chung Shan Medical University Hospital, Taichung, Taiwan, Taichung, Taiwan, ⁵Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan, Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan, Taichung, Taiwan, ⁶Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan, Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan, Taichung, Taiwan

Synopsis

Keywords: Muscle, Aging, Dixon, fat fraction, texture analysis

Motivation: Pathological changes in the masseter muscle have been associated with head and neck cancer (HNC). Nevertheless, investigations on the quantification of fatty infiltration in the masseter muscle and its correlation with HNC is limited.

Goal(s): We aim to assess fatty infiltration, morphological characteristics, and texture features of the masseter muscle in HNC.

Approach: This study sought to employ the Dixon method for fat fraction estimation conjugated with a machine learning-based auto-segmentation of the masseter muscle.

Results: Our analysis revealed an elevated level of fatty infiltration in the masseter muscle among patients with head and neck cancer.

Impact: Dixon method conjugated with machine learning-based auto-segmentation should facilitate in reliably assessing masseter fat alteration in head and neck cancer (HNC), this may be beneficial in response prediction in HNC treatment.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.