Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative MR imaging approach. Deep learning methods have been proposed for MRF and demonstrated improved performance over classical compressed sensing algorithms. However many of these end-to-end models are physics-free, while consistency of the predictions with respect to the physical forward model is crucial for reliably solving inverse problems. To address this, recently [1] proposed a proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within an unrolled learning mechanism. However, [1] only evaluated the unrolled model on synthetic data using Cartesian sampling trajectories. In this paper, as a complementary to [1], we investigate other choices of encoders to build the proximal neural network, and evaluate the deep unrolling algorithm on real accelerated MRF scans with non-Cartesian k-space sampling trajectories.
In quantitative Magnetic Resonance Imaging, traditional methods suffer from the so-called Partial Volume Effect (PVE) due to spatial resolution limitations. As a consequence of PVE, the parameters of the voxels containing more than one tissue are not correctly estimated. Magnetic Resonance Fingerprinting (MRF) is not an exception. The existing methods addressing PVE are neither scalable nor accurate. We propose to formulate the recovery of multiple tissues per voxel as a nonconvex constrained least-squares minimisation problem. To solve this problem, we develop a memory efficient, greedy appro…
Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring unique time signals for different tissues. Although the acquisition is highly accelerated, the reconstruction time remains a problem, as the state-of-the-art template matching compares every signal with a set of possible signals. To overcome this limitation, deep learning based approaches, e.g. Convolutional Neural Networks (CNNs) have been proposed. In this work, we investigate the applicability of Recurrent Neural Networks (RNNs) for this reconstruction problem, as the signals are correlated in time. Compared to previo…
Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times $T_1$ and $T_2$. Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Template matching methods are used to find the most similar signal to the measured one by comparing it to pre-simulated signals of possible parameter combinations in a discretized dictionary. Deep learning approaches can overcome this limitation, by providing the direct mapping from …
Deep unrolling is an emerging deep learning-based image reconstruction methodology that bridges the gap between model-based and purely deep learning-based image reconstruction methods. Although deep unrolling methods achieve state-of-the-art performance for imaging problems and allow the incorporation of the observation model into the reconstruction process, they do not provide any uncertainty information about the reconstructed image, which severely limits their use in practice, especially for safety-critical imaging applications. In this paper, we propose a learning-based image reconstructio…
Leiden University Medical Center（莱顿大学医学中心）；Philips Research Hamburg（飞利浦汉堡研究院）；Delft University of Technology（代尔夫特理工大学）
In Magnetic Resonance Fingerprinting (MRF) the quality of the estimated parameter maps depends on the encoding capability of the variable flip angle train. In this work we show how the dimensionality reduction technique Hierarchical Stochastic Neighbor Embedding (HSNE) can be used to obtain insight into the encoding capability of different MRF sequences. Embedding high-dimensional MRF dictionaries into a lower-dimensional space and visualizing them with colors, being a surrogate for location in low-dimensional space, provides a comprehensive overview of particular dictionaries and, in addition…
Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders
University of Bristol（布里斯托大学）；INFN Pisa division（意大利国家核物理研究所比萨分部）；University of Bath（巴斯大学）；GE HealthCare（GE医疗）；University of Zurich（苏黎世大学）；IRCCS Stella Maris（斯特拉·马里斯住院与治疗科学研究所）
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Case Western Reserve University（凯斯西储大学）
Magnetic resonance Fingerprinting (MRF) is a relatively new multi-parametric quantitative imaging method that involves a two-step process: (i) reconstructing a series of time frames from highly-undersampled non-Cartesian spiral k-space data and (ii) pattern matching using the time frames to infer tissue properties (e.g., T1 and T2 relaxation times). In this paper, we introduce a novel end-to-end deep learning framework to seamlessly map the tissue properties directly from spiral k-space MRF data, thereby avoiding time-consuming processing such as the nonuniform fast Fourier transform (NUFFT) a…
Magnetic Resonance Fingerprinting (MRF) is a novel technique that simultaneously estimates multiple tissue-related parameters, such as the longitudinal relaxation time T1, the transverse relaxation time T2, off resonance frequency B0 and proton density, from a scanned object in just tens of seconds. However, the MRF method suffers from aliasing artifacts because it significantly undersamples the k-space data. In this work, we propose a compressed sensing (CS) framework for simultaneously estimating multiple tissue-related parameters based on the MRF method. It is more robust to low sampling ra…
Imperial College London（帝国理工学院）；Weizmann Institute of Science（魏茨曼科学研究所）；Technion – Israel Institute of Technology（以色列理工学院）；University College London（伦敦大学学院）
Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on dictio-nary matching to map the temporal MRF signals to quantitative tissue parameters. Such approaches suffer from inherent discretization errors, as well as high computational complexity as the dictionary size grows. To alleviate these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting approach, referred to as HYDRA. Methods: HYDRA involves two stages: a model-based signature restoration phase and a learning-based parameter restoration phase. Signal restoration is implemented using low-rank based de-al…
Mathematical models are becoming increasingly important in magnetic resonance imaging (MRI), as they provide a mechanistic approach for making a link between tissue microstructure and signals acquired using the medical imaging instrument. The Bloch equations, which describes spin and relaxation in a magnetic field, is a set of integer order differential equations with a solution exhibiting mono-exponential behaviour in time. Parameters of the model may be estimated using a non-linear solver, or by creating a dictionary of model parameters from which MRI signals are simulated and then matched w…
Magnetic Resonance Fingerprinting (MRF) methods typically rely on dictionary matching to map the temporal MRF signals to quantitative tissue parameters. These methods suffer from heavy storage and computation requirements as the dictionary size grows. To address these issues, we proposed an end to end fully convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly employ linear dimensionality reduction and then use neural network to project the data into the tissue parameters manifold space. Experiments on the MAGIC data demonstrate the effectiveness of the method.
Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting
School of Electrical Engineering and Computer Science, University of Queensland（电气工程与计算机科学学院，昆士兰大学）；School of Computer Science and Engineering, Central South University（计算机科学与工程学院，中南大学）；Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney（Image X研究所，悉尼健康科学学院，医学与健康学院，悉尼大学）；Donders Institute for Brain Cognition and Behaviour, Radboud University（脑认知与行为研究所，拉德堡德大学）；Australian Institute for Bioengineering and Nanotechnology, University of Queensland（生物工程与纳米技术研究所，昆士兰大学）；School of Engineering, University of Newcastle（工程学院，新castle大学）
University of California, Berkeley（加州大学伯克利分校）；Philips Research Europe（飞利浦欧洲研究院）；UC Berkeley-UCSF（加州大学伯克利分校-加州大学旧金山分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；International Computer Science Institute（国际计算机科学研究所）；University of Michigan（密歇根大学）
Magnetic Resonance Fingerprinting (MRF) is an efficient quantitative MRI technique that can extract important tissue and system parameters such as T1, T2, B0, and B1 from a single scan. This property also makes it attractive for retrospectively synthesizing contrast-weighted images. In general, contrast-weighted images like T1-weighted, T2-weighted, etc., can be synthesized directly from parameter maps through spin-dynamics simulation (i.e., Bloch or Extended Phase Graph models). However, these approaches often exhibit artifacts due to imperfections in the mapping, the sequence modeling, and t…
University of Bern（伯尔尼大学）；Bern University Hospital（伯尔尼大学医院）；Institute of Myology（肌学研究所）；CEA（法国原子能与替代能源委员会）
Magnetic resonance fingerprinting (MRF) provides a unique concept for simultaneous and fast acquisition of multiple quantitative MR parameters. Despite acquisition efficiency, adoption of MRF into the clinics is hindered by its dictionary matching-based reconstruction, which is computationally demanding and lacks scalability. Here, we propose a convolutional neural network-based reconstruction, which enables both accurate and fast reconstruction of parametric maps, and is adaptable based on the needs of spatial regularization and the capacity for the reconstruction. We evaluated the method usi…
Purpose: To demonstrate an ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) method that can simultaneously quantify tissue relaxometries for muscle and bone in musculoskeletal systems and tissue components in brain and therefore can synthesize pseudo-CT images. Methods: A FISP-MRF sequence with half pulse excitation and half spoke radial acquisition was designed to sample fast T2 decay signals. Sinusoidal echo time (TE) pattern was applied to enhance MRF sensitivity for tissues with short and ultrashort T2 values. The performance of UTE-MRF was evaluated via simulations, phanto…
Robust MRI Reconstruction by Smoothed Unrolling (SMUG)
Biomedical Engineering (BME) Department at Michigan State University (MSU)（密歇根州立大学生物医学工程系）；Mathematics Department at MSU（密歇根州立大学数学系）；Computer Science and Engineering (CSE) Department at MSU（密歇根州立大学计算机科学与工程系）；Computational Mathematics, Science & Engineering (CMSE) Department at MSU and the Electrical Engineering & Computer Science Department at the University of Michigan（密歇根州立大学计算数学、科学与工程系及密歇根大学电气工程与计算机科学系）；CSE Department at MSU（密歇根州立大学计算机科学与工程系）；CMSE & BME Departments at MSU（密歇根州立大学计算数学、科学与工程系及生物医学工程系）
Magnetization transfer contrast magnetic resonance fingerprinting (MTC-MRF) is a novel quantitative imaging technique that simultaneously measures several tissue parameters of semisolid macromolecule and free bulk water. In this study, we propose an Only-Train-Once MR fingerprinting (OTOM) framework that estimates the free bulk water and MTC tissue parameters from MR fingerprints regardless of MRF schedule, thereby avoiding time-consuming process such as generation of training dataset and network training according to each MRF schedule. A recurrent neural network is designed to cope with two t…
Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equations) and might also use some additional low-rank or spatial regularization. However to our knowledge these three regularizations are not applied together in a joint reconstruction. The reason is that it is indeed challenging to incorporate effectively multiple regularizations in a single MRF optimization algorithm. As a result most of these metho…
University of Edinburgh（爱丁堡大学）；University of Bath（巴斯大学）
Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems. This consistency is mostly un-controlled in the current end-to-end deep learning methodologies proposed for the Magnetic Resonance Fingerprinting (MRF) problem. To address this, we propose ProxNet, a learned proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within a recurrent learning mechanism. The ProxNet adopts a compact neural proximal model for de-aliasing and quantitative inference, that can be flexibly t…
DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction
University of Nottingham Ningbo China（诺丁汉大学宁波校区）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）
ARTORG Center for Biomedical Research, University of Bern（伯尔尼大学ARTORG生物医学研究中心）；Insel Data Science Center, Inselspital, Bern University Hospital（伯尔尼大学医院因塞尔spital因塞尔数据科学中心）；Institute of Myology, Neuromuscular Investigation Center, Paris（巴黎肌病研究所神经肌肉研究中心）；CEA, DRF, IBFJ, MIRCen, Paris（法国原子能和替代能源委员会DRF部门IBFJ分部MIRCen中心）
Magnetic resonance fingerprinting (MRF) enables fast and multiparametric MR imaging. Despite fast acquisition, the state-of-the-art reconstruction of MRF based on dictionary matching is slow and lacks scalability. To overcome these limitations, neural network (NN) approaches estimating MR parameters from fingerprints have been proposed recently. Here, we revisit NN-based MRF reconstruction to jointly learn the forward process from MR parameters to fingerprints and the backward process from fingerprints to MR parameters by leveraging invertible neural networks (INNs). As a proof-of-concept, we …
Izmir University of Economics（伊兹密尔经济大学）；Izmir Democracy University（伊兹密尔民主大学）；King’s College London（伦敦国王学院）；Istanbul Technical University（伊斯坦布尔理工大学）
Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of MRF relies on varying acquisition parameters pseudo-randomly, so that each tissue generates its unique signal evolution during scanning. Even though MRF provides faster scanning, it has disadvantages such as erroneous and slow generation of the corresponding parametric maps, which needs to be improved. Moreover, there is a need for explainable architectures for understanding the guiding signals to generate accurate parametric maps. In t…
Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mapping of only a small number of parameters (proton density, T1 and T2 in general). Inspired by diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF sequence with embedded diffusion-encoding gradients along all three axes to effi…
In inverse problems we aim to reconstruct some underlying signal of interest from potentially corrupted and often ill-posed measurements. Classical optimization-based techniques proceed by optimizing a data consistency metric together with a regularizer. Current state-of-the-art machine learning approaches draw inspiration from such techniques by unrolling the iterative updates for an optimization-based solver and then learning a regularizer from data. This loop unrolling (LU) method has shown tremendous success, but often requires a deep model for the best performance leading to high memory c…
Purpose: To improve image quality and accelerate the acquisition of 3D MRF. Methods: Building on the multi-axis spiral-projection MRF technique, a subspace reconstruction with locally low rank (LLR) constraint and a modified spiral-projection spatiotemporal encoding scheme termed tiny-golden-angle-shuffling (TGAS) were implemented for rapid whole-brain high-resolution quantitative mapping. The LLR regularization parameter and the number of subspace bases were tuned using retrospective in-vivo data and simulated examinations, respectively. B0 inhomogeneity correction using multi-frequency inter…
University of Bath（巴斯大学）；Technical University of Munich（慕尼黑工业大学）；GE Healthcare（通用电气医疗集团）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）
Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern which is used for fast (compressed) acquisition. This may not be useful when the acquisition process is unknown during training of the deep learning model and/or changes during testing time. This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process. Spatiotemporal image priors are learned by an image denoiser i.e. a Convolutional Neura…
StoDIP: Efficient 3D MRF image reconstruction with deep image priors and stochastic iterations
University of Bristol（布里斯托尔大学）；INFN Pisa division（意大利国家核物理研究院比萨分部）；GE HealthCare（GE医疗）；Technische Hochschule Ingolstadt（英戈尔施塔特应用科学大学）；IRCCS Stella Maris（斯特拉·马里斯国立卫生研究所）；University of Zurich（苏黎世大学）
