A Spatial Off-Resonance Correction in Spirals for Magnetic Resonance Fingerprinting

Coronado, Ronal; Cruz, Gastão; Castillo-Passi, Carlos; Tejos, Cristián; Uribe, Sergio; Prieto, Claudia; Irarrázaval, Pablo

doi:10.1109/tmi.2021.3100293

Cited by 4 publications

(5 citation statements)

References 48 publications

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“…As a result, the network may require convolution operations with larger receptive fields to capture more global and detailed information. Additionally, compared with the original DIFFnet paper, it is worth noting that there is no standard method for the calculation of the NRMSE (Zhou et al 2020, Coronado et al 2021. Different calculation methods yield different scaling error.…”

Section: Discussionmentioning

confidence: 99%

FlexDTI: flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning

Wu,

Wang,

Chen

et al. 2024

Phys. Med. Biol.

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Objective:Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in the training data. This work aims to develop and evaluate a novel dynamic-convolution-based method called FlexDTI for highly efficient diffusion tensor reconstruction with flexible diffusion encoding gradient scheme.Approach:FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The method used dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. Furthermore, it realized the generalization of a flexible number of diffusion gradient directions by setting the maximum number of input channels of the network. The network was trained and tested using datasets from the Human Connectome Project and local hospitals. Results from FlexDTI and other advanced tensor parameter estimation methods were compared.Main results:Compared to other methods, FlexDTI successfully achieves high-quality diffusion tensor-derived parameters even if the number and directions of diffusion encoding gradients change. It reduces normalized root mean squared error (NRMSE) by about 50% on fractional anisotropy (FA) and 15% on mean diffusivity (MD), compared with the state-of-the-art deep learning method with flexible diffusion encoding gradient scheme.Significance:FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient scheme. Both flexibility and reconstruction quality can be taken into account in this network.

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Section: Discussionmentioning

confidence: 99%

FlexDTI: flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning

Wu,

Wang,

Chen

et al. 2024

Phys. Med. Biol.

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“…The low‐rank image reconstruction (Equation 3 ) can be easily expanded to 3D‐acquisitions or to include other regularization terms 19 , 43 and spiral blurring correction methods. 44 , 45 , 46 …”

Section: Discussionmentioning

confidence: 99%

“…The low-rank image reconstruction (Equation 3) can be easily expanded to 3D-acquisitions or to include other regularization terms 19,43 and spiral blurring correction methods. [44][45][46] Some limitations of the performed work can be identified. A limitation of the SPIJN and k-SPIJN algorithms is that potentially important components could be discarded in early iterations.…”

Section: Discussionmentioning

confidence: 99%

“…A wavelet regularization term with a relatively small regularization value was used in all the reconstructions to suppress small spiral artifacts in the low‐rank images. The low‐rank image reconstruction (Equation ) can be easily expanded to 3D‐acquisitions or to include other regularization terms 19,43 and spiral blurring correction methods 44‐46 …”

Section: Discussionmentioning

confidence: 99%

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Multicomponent MR fingerprinting reconstruction using joint‐sparsity and low‐rank constraints

Nagtegaal

Hartsema

Koolstra

et al. 2022

Magnetic Resonance in Med

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To develop an efficient algorithm for multicomponent MR fingerprinting (MC-MRF) reconstructions directly from highly undersampled data without making prior assumptions about tissue relaxation times and expected number of tissues. Methods:The proposed method reconstructs MC-MRF maps from highly undersampled data by iteratively applying a joint-sparsity constraint to the estimated tissue components. Intermediate component maps are obtained by a low-rank multicomponent alternating direction method of multipliers (MC-ADMM) including the non-negativity of tissue weights as an extra regularization term. Over iterations, the used dictionary compression is adjusted.The proposed method (k-SPIJN) is compared with a two-step approach in which image reconstruction and multicomponent estimations are performed sequentially and tested in numerical simulations and in vivo by applying different undersampling factors in eight healthy volunteers. In the latter case, fully sampled data serves as the reference. Results: The proposed method shows improved precision and accuracy in simulations compared with a state-of-art sequential approach. Obtained in vivo magnetization fraction maps for different tissue types show reduced systematic errors and reduced noise-like effects. Root mean square errors in estimated magnetization fraction maps significantly reduce from 13.0%± 5.8% with the conventional, two-step approach to 9.6%± 3.9% and 9.6%± 3.2% with the proposed MC-ADMM and k-SPIJN methods, respectively. Mean standard deviation in homogeneous white matter regions reduced significantly from 8.6% to 2.9% (two step vs. k-SPIJN). Conclusion:The proposed MC-ADMM and k-SPIJN reconstruction methods estimate MC-MRF maps from highly undersampled data resulting in improved image quality compared with the existing method.

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“…It improves the estimation of

and

parametric maps in bSSFP-based MRF sequences. In [ 72 ], off-resonance artifacts are modeled as a point spread function over the true magnetization. The authors use conventional MRF acquisition to estimate

, and

.…”

Section: Emerging Trends In Mrf Reconstructionmentioning

confidence: 99%

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review

Monga,

Singh,

de Moura

et al. 2024

Bioengineering

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Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.

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A Spatial Off-Resonance Correction in Spirals for Magnetic Resonance Fingerprinting

Cited by 4 publications

References 48 publications

FlexDTI: flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning

FlexDTI: flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning

Multicomponent MR fingerprinting reconstruction using joint‐sparsity and low‐rank constraints

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review

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