Simultaneous Parameter Mapping, Modality Synthesis, and Anatomical Labeling of the Brain with MR Fingerprinting

Gómez, Pedro A.; Molina-Romero, Miguel; Ulaş, Çağdaş; Bounincontri, Guido; Sperl, Jonathan I.; Jones, Derek K.; Menzel, Marion I.; Menze, Bjoern H.

doi:10.1007/978-3-319-46726-9_67

Cited by 8 publications

(14 citation statements)

References 6 publications

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“…It is noticeable that our spatiotemporal approach yields a less noisy reconstruction than the fingerprint-wise approach of Cohen et al [3] (similar noisy reconstructions were obtained for Hoppe et al [5] but not reported in Figure 3). The dictionarybased approach [4] yields a qualitatively coarser reconstruction than our method.…”

Section: Resultsmentioning

confidence: 79%

“…Baselines We compare our method to recent approaches for MRF reconstruction using the fully-connected neural network [3], the CNN [5], and the spatiotemporal dictionary matching [4]. For the deep learning-based methods, we performed the same leave-one-out cross-validation and the data underwent the same pre-processing as for our method.…”

Section: Discussionmentioning

confidence: 99%

“…The approaches were implemented as proposed in the papers. For [4], we also perform a leave-one-out cross-validation, i.e. construct a dictionary using five brain scans and reconstruct the left-out brain scan with following parameters: W n = 11 × 11 × 3, P = 3 × 3 × 3, C = 5, and α = 0.5 with two iterations.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, three approaches have been proposed aiming to overcome the issues associated with dictionary matching during the MRF reconstruction. Gómez et al [4] proposed a spatiotemporal dictionary matching that matches a spatial neighborhood of fingerprints instead of using a fingerprint-wise approach. They additionally improve the computational efficiency by limiting the matching to a local search window.…”

Section: Introductionmentioning

confidence: 99%

“…We propose a MRF reconstruction approach that exploits the spatiotemporal relationship between neighboring signal evolutions motivated by noisy reconstructions of fingerprint-wise approaches and the findings of [4]. Our approach bases on CNNs and yields fast and more accurate reconstructions than recently proposed methods on six healthy brain MRF images with three NMR maps: proton density (PD), T1 relaxation time (T1), and T2 relaxation time (T2).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks

Balsiger

Konar

Chikop

et al. 2018

Lecture Notes in Computer Science

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Magnetic resonance fingerprinting (MRF) quantifies multiple nuclear magnetic resonance parameters in a single and fast acquisition. Standard MRF reconstructs parametric maps using dictionary matching, which lacks scalability due to computational inefficiency. We propose to perform MRF map reconstruction using a spatiotemporal convolutional neural network, which exploits the relationship between neighboring MRF signal evolutions to replace the dictionary matching. We evaluate our method on multiparametric brain scans and compare it to three recent MRF reconstruction approaches. Our method achieves state-of-the-art reconstruction accuracy and yields qualitatively more appealing maps compared to other reconstruction methods. In addition, the reconstruction time is significantly reduced compared to a dictionarybased approach.

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Section: Resultsmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks

Balsiger

Konar

Chikop

et al. 2018

Lecture Notes in Computer Science

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Deep learning for accelerated and robust MRI reconstruction

Heckel,

Jacob,

Chaudhari

et al. 2024

Magn Reson Mater Phy

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Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.Affiliations [3 and 6] has been split into two different affiliations. Please check if action taken is appropriate and amend if necessary.looks good

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Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks

Golbabaee

Buonincontri²,

Pirkl

et al. 2021

Medical Image Analysis

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Simultaneous Parameter Mapping, Modality Synthesis, and Anatomical Labeling of the Brain with MR Fingerprinting

Cited by 8 publications

References 6 publications

Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks

Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks

Deep learning for accelerated and robust MRI reconstruction

Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks

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