Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data

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 met...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Magma (New York, N.Y.) Ročník 37; číslo 3; s. 429 - 438
Hlavní autoři: Zhang, Chi, Piccini, Davide, Demirel, Omer Burak, Bonanno, Gabriele, Roy, Christopher W., Yaman, Burhaneddin, Moeller, Steen, Shenoy, Chetan, Stuber, Matthias, Akçakaya, Mehmet
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.07.2024
Témata:
Algorithms
Basic Science - Reconstruction algorithms and artificial intelligence
Biomedical Engineering and Bioengineering
Computer Appl. in Life Sciences
Coronary Vessels - diagnostic imaging
Deep Learning
Health Informatics
Humans
Image Processing, Computer-Assisted - methods
Imaging
Imaging, Three-Dimensional - methods
Magnetic Resonance Imaging - methods
Medicine
Medicine & Public Health
Neural Networks, Computer
Physics
Radiology
Research Article
Solid State Physics
Coronary MRI
Deep learning
Non-Cartesian
Cardiac MRI
Image reconstruction
ISSN:1352-8661, 0968-5243, 1352-8661
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Authors’ Contribution
Mehmet Akçakaya: Study conception and design, drafting of manuscript, critical revision
Chetan Shenoy: Analysis and interpretation of data, critical revision
Gabriele Bonanno: Acquisition of data, critical revision
Chi Zhang: Study conception and design, drafting of manuscript, critical revision
Omer Burak Demirel: Analysis and interpretation of data, critical revision
Davide Piccini: Acquisition of data, critical revision
Matthias Stuber: Acquisition of data, critical revision
Steen Moeller: Analysis and interpretation of data, critical revision
Burhaneddin Yaman: Analysis and interpretation of data, critical revision
ISSN:1352-8661
0968-5243
1352-8661
DOI:10.1007/s10334-024-01157-8