Deep learning (DL) has driven innovation in the field of computational imaging. One of its bottlenecks is unavailable or insufficient training data. This article reviews an emerging paradigm, imaging physics-based data synthesis (IPADS), that can provide huge training data in biomedical magnetic resonance (MR) without or with few real data. Following the physical law of MR, IPADS generates signals from differential equations or analytical solution models, making learning more scalable and explainable and better protecting privacy. Key components of IPADS learning, including signal generation models, basic DL network structures, enhanced data generation, and learning methods, are discussed. Great IPADS potential has been demonstrated by representative applications in fast imaging, ultrafast signal reconstruction, and accurate parameter quantification. Finally, open questions and future work are discussed.

Ferroptosis has been realized in anticancer drug–induced acute cardiac/kidney injuries (ACI/AKI); however, molecular imaging approach to detect ferroptosis in ACI/AKI is a challenge. We report an artemisinin-based probe (Art-Gd) for contrast-enhanced magnetic resonance imaging of ferroptosis (feMRI) by exploiting the redox-active Fe(II) as a vivid chemical target. In vivo, the Art-Gd probe showed great feasibility in early diagnosis of anticancer drug–induced ACI/AKI, which was at least 24 and 48 hours earlier than the standard clinical assays for assessing ACI and AKI, respectively. Furthermore, the feMRI was able to provide imaging evidence for the different mechanisms of action of ferroptosis-targeted agents, either by blocking lipid peroxidation or depleting iron ions. This study presents a feMRI strategy with simple chemistry and robust efficacy for early evaluation of anticancer drug–induced ACI/AKI, which may shed light on the theranostics of a variety of ferroptosis-related diseases.

Quantitative magnetic resonance imaging (MRI) is of great value to both clinical diagnosis and scientific research. However, most MRI experiments remain qualitative, especially dynamic MRI, because repeated sampling with variable weighting parameter makes quantitative imaging time consuming and sensitive to motion artifacts. A single-shot quantitative T2 mapping method based on multiple overlapping-echo acquisition (dubbed MOLED-4) was proposed to obtain reliable T2 mapping in milliseconds. Different from traditional MRI acceleration methods, such as compressed sensing and parallel imaging, MOLED-4 accelerates quantitative T2 mapping via synchronized multisampling and then deep learning to map the complex nonlinear relationship that is difficult to solve by traditional optimization-based method. The results of simulation, phantom and in vivo human brain experiments show the great performance of the proposed method. The principle of MOLED-4 may be extended to other ultrafast quantitative parameter mappings, and potentially lead to new dynamic MRI with high efficiency to catch quantitative variation of tissue properties.

Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI). However, the challenge brought by domain gap between synthetic and real data is usually encountered, especially under complex experimental conditions. In this study, by combining Bloch simulation and general MRI models, we propose a framework for addressing the lack of training data in supervised learning scenarios, termed MOST-DL. A challenging application is demonstrated to verify the proposed framework and achieve motion-robust T2 mapping using single-shot overlapping-echo acquisition. We decompose the process into two main steps: (1) calibrationless parallel reconstruction for ultra-fast pulse sequence and (2) intra-shot motion correction for T2 mapping. To bridge the domain gap, realistic textures from a public database and various imperfection simulations were explored. The neural network was first trained with pure synthetic data and then evaluated with in vivo human brain. Both simulation and in vivo experiments show that the MOST-DL method significantly reduces ghosting and motion artifacts in T2 maps in the presence of unpredictable subject movement and has the potential to be applied to motion-prone patients in the clinic. Our code is available at https://github.com/qinqinyang/MOST-DL..

A novel image encoding approach based on linear frequency‐swept excitation has been recently proposed to overcome artifacts induced by various field perturbations in single‐shot echo planar imaging. In this article, we develop a new super‐resolved reconstruction method for it using the concepts of local k‐space and partial Fourier transform. This method is superior to the originally developed conjugate gradient algorithm in convenience, image quality, and stability of solution. Reduced field‐of‐view is applied to the phase encoding direction to further enhance the spatial resolution and field perturbation immunity of the image obtained. Effectiveness of this new combined reconstruction method is demonstrated with a series of experiments on biological samples. Two single‐shot sequences with different encoding features are tested. The results show that this reconstruction method maintains excellent field perturbation immunity and improves fidelity of the images. In vivo experiments on rat indicate that this solution is favorable for ultrafast imaging applications in which severe susceptibility heterogeneities around the tissue–air or tissue–bone interfaces, motion and oblique plane effects usually compromise the echo planar imaging image quality. Magn Reson Med, 2013.

A software package has been designed to simulate nuclear magnetic resonance spectra and images. Combining the product operator matrix with the non-linear Bloch equations, the software can efficiently simulate classical and quantum effects including scalar coupling, dipolar coupling, translational diffusion, chemical shift, radiation damping, transverse relaxation, and longitudinal relaxation. One of the most unique features of the software is its ability to incorporate effects of inter- and intra-molecular multiple quantum coherences in complex multiple-spin coupled systems, which are difficult with other existing software packages. The software, written in Visual C++, has a friendly graphical user interface and is easy to use.