AutoCorNN: An Unsupervised Physics-Aware Deep Learning Model for Geometric Distortion Correction of Brain MRI Images Towards MR-Only Stereotactic Radiosurgery

Geometric distortions in brain MRI images arising from susceptibility artifacts at air-tissue interfaces pose a significant challenge for high-precision radiation therapy modalities like stereotactic radiosurgery, necessitating sub-millimeter accuracy. To achieve this goal, we developed AutoCorNN, a...

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Veröffentlicht in:	Journal of imaging informatics in medicine Jg. 38; H. 1; S. 587 - 601
Hauptverfasser:	Hosseini, Mahboube Sadat, Aghamiri, Seyed Mahmoud Reza, Fatemi Ardekani, Ali, BagheriMofidi, Seyed Mehdi, Safari, Mojtaba
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.02.2025
Schlagworte:	Brain - diagnostic imaging Deep Learning Humans Image Processing, Computer-Assisted - methods Imaging Magnetic Resonance Imaging - methods Medicine Medicine & Public Health Neuroma, Acoustic - diagnostic imaging Neuroma, Acoustic - radiotherapy Radiology Radiosurgery - methods Signal-To-Noise Ratio Unsupervised Machine Learning Deep learning Stereotactic radiosurgery Geometric distortion correction Dosimetric evaluation Magnetic Resonance Imaging (MRI) Unsupervised learning
ISSN:	2948-2933, 2948-2925, 2948-2933
Online-Zugang:	Volltext
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