Deep learning MRI-only synthetic-CT generation for pelvis, brain and head and neck cancers.

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

PMID: 38096921

Abstract

BACKGROUND AND PURPOSE: MRI-only planning relies on dosimetrically accurate synthetic-CT (sCT) generation to allow dose calculation. Here we validated the dosimetric accuracy of sCTs generated using a deep learning algorithm for pelvic, brain and head and neck (H&N) cancer sites using variable MRI data from multiple scanners.

Authors

David Bird

Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, United Kingdom; Radiotherapy Research Group, Leeds Institute of Medical Research, University of Leeds, United Kingdom. Electronic address: David.bird3@nhs.net.
Richard Speight

Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, United Kingdom.
Sebastian Andersson

RaySearch AB, Stockholm, Sweden.
Jenny Wingqvist

RaySearch AB, Stockholm, Sweden.
Bashar Al-Qaisieh

Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, United Kingdom.

Keywords

Blood Coagulation Disorders Brain Deep Learning Head and Neck Neoplasms Humans Magnetic Resonance Imaging Pelvis Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (38096921)

Deep learning MRI-only synthetic-CT generation for pelvis, brain and head and neck cancers.

Abstract

Authors

Keywords

External Resources

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