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Gómez PA, Cencini M, Golbabaee M, Schulte RF, Pirkl C, Horvath I, Fallo G, Peretti L, Tosetti M, Menze BH, Buonincontri G. Rapid three-dimensional multiparametric MRI with quantitative transient-state imaging. Sci Rep 2020; 10:13769. [PMID: 32792618 PMCID: PMC7427097 DOI: 10.1038/s41598-020-70789-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 06/22/2020] [Indexed: 11/30/2022] Open
Abstract
Novel methods for quantitative, transient-state multiparametric imaging are increasingly being demonstrated for assessment of disease and treatment efficacy. Here, we build on these by assessing the most common Non-Cartesian readout trajectories (2D/3D radials and spirals), demonstrating efficient anti-aliasing with a k-space view-sharing technique, and proposing novel methods for parameter inference with neural networks that incorporate the estimation of proton density. Our results show good agreement with gold standard and phantom references for all readout trajectories at 1.5 T and 3 T. Parameters inferred with the neural network were within 6.58% difference from the parameters inferred with a high-resolution dictionary. Concordance correlation coefficients were above 0.92 and the normalized root mean squared error ranged between 4.2 and 12.7% with respect to gold-standard phantom references for T1 and T2. In vivo acquisitions demonstrate sub-millimetric isotropic resolution in under five minutes with reconstruction and inference times < 7 min. Our 3D quantitative transient-state imaging approach could enable high-resolution multiparametric tissue quantification within clinically acceptable acquisition and reconstruction times.
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Affiliation(s)
- Pedro A Gómez
- Computer Science, Munich School of Bioengineering, Technical University of Munich, Munich, Germany.
| | - Matteo Cencini
- Imago7 Foundation, Pisa, Italy
- IRCCS Stella Maris, Pisa, Italy
| | | | | | - Carolin Pirkl
- Computer Science, Munich School of Bioengineering, Technical University of Munich, Munich, Germany
- GE Healthcare, Munich, Germany
| | - Izabela Horvath
- Computer Science, Munich School of Bioengineering, Technical University of Munich, Munich, Germany
- GE Healthcare, Munich, Germany
| | - Giada Fallo
- University of Pisa, Pisa, Italy
- Imago7 Foundation, Pisa, Italy
| | - Luca Peretti
- University of Pisa, Pisa, Italy
- Imago7 Foundation, Pisa, Italy
| | - Michela Tosetti
- Imago7 Foundation, Pisa, Italy
- IRCCS Stella Maris, Pisa, Italy
| | - Bjoern H Menze
- Computer Science, Munich School of Bioengineering, Technical University of Munich, Munich, Germany
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