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Automated inversion time selection for late gadolinium-enhanced cardiac magnetic resonance imaging.
Xie, Cheng; Zhang, Rory; Mensink, Sebastian; Gandharva, Rahul; Awni, Mustafa; Lim, Hester; Kachel, Stefan E; Cheung, Ernest; Crawley, Richard; Churilov, Leonid; Bettencourt, Nuno; Chiribiri, Amedeo; Scannell, Cian M; Lim, Ruth P.
Afiliación
  • Xie C; Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.
  • Zhang R; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia.
  • Mensink S; Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.
  • Gandharva R; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia.
  • Awni M; Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.
  • Lim H; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia.
  • Kachel SE; Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.
  • Cheung E; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia.
  • Crawley R; Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.
  • Churilov L; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia.
  • Bettencourt N; Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.
  • Chiribiri A; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia.
  • Scannell CM; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia.
  • Lim RP; Melbourne Medical School, The University of Melbourne, Parkville, VIC, Australia.
Eur Radiol ; 34(9): 5816-5828, 2024 Sep.
Article en En | MEDLINE | ID: mdl-38337070
ABSTRACT

OBJECTIVES:

To develop and share a deep learning method that can accurately identify optimal inversion time (TI) from multi-vendor, multi-institutional and multi-field strength inversion scout (TI scout) sequences for late gadolinium enhancement cardiac MRI. MATERIALS AND

METHODS:

Retrospective multicentre study conducted on 1136 1.5-T and 3-T cardiac MRI examinations from four centres and three scanner vendors. Deep learning models, comprising a convolutional neural network (CNN) that provides input to a long short-term memory (LSTM) network, were trained on TI scout pixel data from centres 1 to 3 to identify optimal TI, using ground truth annotations by two readers. Accuracy within 50 ms, mean absolute error (MAE), Lin's concordance coefficient (LCCC) and reduced major axis regression (RMAR) were used to select the best model from validation results, and applied to holdout test data. Robustness of the best-performing model was also tested on imaging data from centre 4.

RESULTS:

The best model (SE-ResNet18-LSTM) produced accuracy of 96.1%, MAE 22.9 ms and LCCC 0.47 compared to ground truth on the holdout test set and accuracy of 97.3%, MAE 15.2 ms and LCCC 0.64 when tested on unseen external (centre 4) data. Differences in vendor performance were observed, with greatest accuracy for the most commonly represented vendor in the training data.

CONCLUSION:

A deep learning model was developed that can identify optimal inversion time from TI scout images on multi-vendor data with high accuracy, including on previously unseen external data. We make this model available to the scientific community for further assessment or development. CLINICAL RELEVANCE STATEMENT A robust automated inversion time selection tool for late gadolinium-enhanced imaging allows for reproducible and efficient cross-vendor inversion time selection. KEY POINTS • A model comprising convolutional and recurrent neural networks was developed to extract optimal TI from TI scout images. • Model accuracy within 50 ms of ground truth on multi-vendor holdout and external data of 96.1% and 97.3% respectively was achieved. • This model could improve workflow efficiency and standardise optimal TI selection for consistent LGE imaging.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Medios de Contraste / Aprendizaje Profundo / Gadolinio Límite: Female / Humans / Male / Middle aged Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Alemania
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Medios de Contraste / Aprendizaje Profundo / Gadolinio Límite: Female / Humans / Male / Middle aged Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Alemania