Deep learning MRI-only synthetic-CT generation for pelvis, brain and head and neck cancers.
Radiother Oncol
; 191: 110052, 2024 02.
Article
in En
| MEDLINE
| ID: mdl-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.METHODS:
sCT generation models were trained using a cycle-GAN algorithm, using paired MRI-CT patient data. Input MRI sequences were T2 for pelvis, T1 with gadolinium (T1Gd) and T2 FLAIR for brain and T1 for H&N. Patient validation sCTs were generated for each site (49 - pelvis, 25 - brain and 30 - H&N). VMAT plans, following local clinical protocols, were calculated on planning CTs and recalculated on sCTs. HU and dosimetric differences were assessed, including DVH differences and gamma index (2 %/2mm).RESULTS:
Mean absolute error (MAE) HU differences were; 48.8 HU (pelvis), 118 HU (T2 FLAIR brain), 126 HU (T1Gd brain) and 124 HU (H&N). Mean primary PTV D95% dose differences for all sites were < 0.2 % (range -0.9 to 1.0 %). Mean 2 %/2mm and 1 %/1mm gamma pass rates for all sites were > 99.6 % (min 95.3 %) and > 97.3 % (min 80.1 %) respectively. For all OARs for all sites, mean dose differences were < 0.4 %.CONCLUSION:
Generated sCTs had excellent dosimetric accuracy for all sites and sequences. The cycle-GAN model, available on the research version of a commercial treatment planning system, is a feasible method for sCT generation with high clinical utility due to its ability to use variable input data from multiple scanners and sequences.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Blood Coagulation Disorders
/
Deep Learning
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Head and Neck Neoplasms
Limits:
Humans
Language:
En
Journal:
Radiother Oncol
Year:
2024
Document type:
Article
Country of publication:
Ireland