Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

PMID: 39929288

Abstract

PURPOSE: This study investigated the effect of multiple magnetic resonance (MR) sequences on the quality of deep-learning-based synthetic computed tomography (sCT) generation in the head and neck region.

Authors

Jacob Antunes

MIM Software Inc, Cleveland, OH, United States. Electronic address: jantunes@mimsoftware.com.
Tony Young

Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia; Ingham Institute, Sydney, Australia.
Dane Pittock

MIM Software Inc, Cleveland, OH, United States.
Paul Jacobs

MIM Software Inc., Cleveland, Ohio.
Aaron Nelson

Department of Neurology, New York University Langone Medical Center, New York, New York.
Jon Piper

MIM Software Inc, Cleveland, OH, United States.
Shrikant Deshpande

Ingham Institute, Sydney, Australia; South Western Sydney Clinical School, University of New South Wales, Sydney, Australia.

Keywords

Aged Deep Learning Female Head and Neck Neoplasms Humans Magnetic Resonance Imaging Male Middle Aged Radiotherapy Planning, Computer-Assisted Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (39929288)

Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers.

Abstract

Authors

Keywords

External Resources

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