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Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.
Zhang, Chi; Piccini, Davide; Demirel, Omer Burak; Bonanno, Gabriele; Roy, Christopher W; Yaman, Burhaneddin; Moeller, Steen; Shenoy, Chetan; Stuber, Matthias; Akçakaya, Mehmet.
Afiliación
  • Zhang C; Electrical and Computer Engineering, University of Minnesota, 200 Union Street S.E., Minneapolis, MN, 55455, USA.
  • Piccini D; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA.
  • Demirel OB; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
  • Bonanno G; Advanced Clinical Imaging Technology, Siemens Healthineers International, Lausanne, Switzerland.
  • Roy CW; Electrical and Computer Engineering, University of Minnesota, 200 Union Street S.E., Minneapolis, MN, 55455, USA.
  • Yaman B; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA.
  • Moeller S; Advanced Clinical Imaging Technology, Siemens Healthineers International, Lausanne, Switzerland.
  • Shenoy C; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
  • Stuber M; Electrical and Computer Engineering, University of Minnesota, 200 Union Street S.E., Minneapolis, MN, 55455, USA.
  • Akçakaya M; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA.
MAGMA ; 37(3): 429-438, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38743377
ABSTRACT
OBJECT To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability. MATERIALS AND

METHODS:

While PG-DL has emerged as a powerful image reconstruction method, its application to large-scale 3D non-Cartesian MRI is hindered by hardware limitations and limited availability of training data. We combine several recent advances in deep learning and MRI reconstruction to tackle the former challenge, and we further propose a 2.5D reconstruction using 2D convolutional neural networks, which treat 3D volumes as batches of 2D images to train the network with a limited amount of training data. Both 3D and 2.5D variants of the PG-DL networks were compared to conventional methods for high-resolution 3D kooshball coronary MRI.

RESULTS:

Proposed PG-DL reconstructions of 3D non-Cartesian coronary MRI with 3D and 2.5D processing outperformed all conventional methods both quantitatively and qualitatively in terms of image assessment by an experienced cardiologist. The 2.5D variant further improved vessel sharpness compared to 3D processing, and scored higher in terms of qualitative image quality.

DISCUSSION:

PG-DL reconstruction of large-scale 3D non-Cartesian MRI without compromising image size or network complexity is achieved, and the proposed 2.5D processing enables high-quality reconstruction with limited training data.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Imagen por Resonancia Magnética / Redes Neurales de la Computación / Vasos Coronarios / Imagenología Tridimensional / Aprendizaje Profundo Límite: Humans Idioma: En Revista: MAGMA Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Alemania
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Imagen por Resonancia Magnética / Redes Neurales de la Computación / Vasos Coronarios / Imagenología Tridimensional / Aprendizaje Profundo Límite: Humans Idioma: En Revista: MAGMA Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Alemania