With the development of deep-learning techniques, the application of deep learning in MR imaging processing seems to be growing.Accordingly, deep learning has also been introduced in motion correction and seemed to work as well as do conventional motion-compensation methods.In this article, we review the motion-correction methods based on deep learning, focusing especially on the motion-simulation methods adopted.We then propose a new motion-simulation tool, which we call view2Dmotion.
Magnetic resonance imaging (MRI) can extract the tissue conductivity values from in vivo data using the so-called phase-based magnetic resonance electrical properties tomography (MR-EPT). However, this procedure suffers from noise amplification caused by the use of the Laplacian operator. To counter this issue, we propose a novel preprocessing denoiser for magnetic resonance transceive phase images, operating in an unsupervised manner. Inspired by the deep image prior approach, we apply the random initialization of a convolutional neural network, which enforces an implicit regularization. Additionally, we introduce Stein's unbiased risk estimator, which is the unbiased estimator of the mean square error for optimizing the network without the need for label images. This modification not only tackles the overfitting problem inherent in the deep image prior approach but also operates within a purely unsupervised framework. In addition, instead of using phase images, we use real and imaginary images, which aligns with the theoretical model of the risk estimator. Our generative model needs neither the preparation of training datasets nor prior training procedure, and it maintains adaptability across various resolutions and signal-to-noise ratio levels. In testing. our method significantly diminished residual error remaining in phase maps, quantitatively as well as qualitatively, for both phantom and simulated brain data. Furthermore, it outperformed other denoising methods in reducing noise amplification and boundary error. When applied to healthy volunteer and patient data, the proposed method revealed reduced error in the reconstructed conductivity maps, with conductivity values aligning well with established literature values. To the best of our knowledge, this is the first blind approach using a purely unsupervised denoising framework that can implement a 2D phase-based MR-EPT reconstruction algorithm. The source code is available at https://github.com/Yonsei-MILab/Implicit-Regularization-forMREPT-with-SURE.
Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is widely used to visualize brain activation regions by detecting hemodynamic responses associated with increased metabolic demand. While alternative MRI methods have been employed to monitor functional activities, the investigation of in-vivo electrical property changes during brain function remains limited. In this study, we explored the relationship between fMRI signals and electrical conductivity (measured at the Larmor frequency) changes using phase-based electrical properties tomography (EPT). Our results revealed consistent patterns: conductivity changes showed negative correlations, with conductivity decreasing in the functionally active regions whereas B1 phase mapping exhibited positive correlations around activation regions. These observations were consistent across both motor and visual cortex activations. To further substantiate these findings, we conducted electromagnetic radio-frequency simulations that modeled activation states with varying conductivity, which demonstrated trends similar to our in-vivo results for both B1 phase and conductivity. These findings suggest that in-vivo electrical conductivity changes can indeed be measured during brain activity. However, further investigation is needed to fully understand the underlying mechanisms driving these measurements.
Magnetic resonance electrical properties tomography (MR-EPT) is a non-invasive measurement technique that derives the electrical properties (EPs, e.g., conductivity or permittivity) of tissues in the radiofrequency range (64 MHz for 1.5 T and 128 MHz for 3 T MR systems). Clinical studies have shown the potential of tissue conductivity as a biomarker. To date, model-based conductivity reconstructions rely on numerical assumptions and approximations, leading to inaccuracies in the reconstructed maps. To address such limitations, we propose an artificial neural network (ANN)-based non-linear conductivity estimator trained on simulated data for conductivity brain imaging. Network training was performed on 201 synthesized T2-weighted spin-echo (SE) data obtained from the finite-difference time-domain (FDTD) electromagnetic (EM) simulation. The dataset was composed of an approximated T2-w SE magnitude and transceive phase information. The proposed method was tested three in-silico and in-vivo on two volunteers and three patients' data. For comparison purposes, various conventional phase-based EPT reconstruction methods were used that ignore
Motivation: Conductivity reconstructions based on polynomial fitting methods are mostly 2D leading to inaccurate reconstructions as information arising from the through-plane dimension is missing. Goal(s): To include conductivity contributions from three-dimensions for deep-learning patch-based polynomial fitting reconstructions. Approach: A DL-informed polynomial fitting reconstruction method including $$$B_{1}^{+}$$$ magnitude information is presented. This method leverages neural networks to jointly predict optimal fitting coefficients enabling joint 2D-polynomial-fitting in three-orthogonal-planes, hence we call it 2.5D. Results: The proposed method demonstrates superior-performance compared to fitting-based 2D/3D fitting approaches and is computationally efficient for 3D-reconstructions. Impact: A 2.5-dimensional neural network informed fitting approach is used for MR-based conductivity reconstructions. Conductivity reconstruction accuracy as well as structural information are improved compared to physics-based and deep learning-based fitting methods.
Phase-based electrical property tomography (EPT) is a technique that allows conductivity reconstruction with only phase of the B1 field under the assumption that the magnitude of the B1 fields are homogeneous. The more this assumption is violated, the less accurate the reconstructed conductivity. Thus, a method that ensures homogeneity of |B1-|$| {{\rm{B}}_1^ - } |$ field is important for breast image using multi-receiver coil.To develop a method for multi-receiver combination for phase-based EPT usable for breast EPT imaging in the clinic.Theory of the proposed method is presented. To validate the proposed method, the phantom and in-vivo experiments were conducted. Conductivity images were reconstructed using the transceive phase of the combined image and results were compared with another combination method.The proposed method's conductivity results were more stable than those of the previous method when |B1+|$| {{\rm{B}}_1^ + } |$ was not homogeneous and when the homogeneous contrast region was small. The phantom and in-vivo results indicate that the proposed method produces improved conductivity images than the previous method. The proposed combination method also increased the conductivity contrast between benign and cancerous tissues.The proposed method produced more stable multi-receiver combination for phase-based EPT of the breast in a clinical environment.
Phase-based Electrical properties tomography is a non-invasive imaging technique that uses MRI systems to measure the tissue conductivity. However, the conductivity reconstruction process causes problems such as noise amplification and boundary artifact. To address such limitations, several DL-based reconstruction methods were proposed. Building upon these works, we propose an ANN-based conductivity reconstruction method trained only on simulation dataset. The proposed method was studied with the aim of: (a) approaching ground-truth conductivity values, (b) noise-robustness, (c) higher image resolution, (d) generalization to clinical data. The feasibility was investigated on simulations and TSE in-vivo data (one healthy volunteer, two meningioma cases).
Motivation: In Electrical Properties Tomography, often 2D reconstructions ignoring derivatives in the slice direction (often z) are performed instead of 3D reconstructions, without proper compensation. Goal(s): In this work, we investigate the quantitative influence on the reconstructed conductivity. Approach: This is done by experiments in a cylindrical phantom with homogeneous electrical properties in simulation and measurement. Furthermore, using simulations an indication is given of the importance of 3D reconstruction in several anatomical areas. Results: The contribution of the third dimension on the reconstructed conductivity is shown to be highly dependent on sample geometry. Therefore, disregarding this can only be done in specific cases. Impact: This work shows that the assumption of a negligible third dimension contribution as done in 2D EPT reconstruction is only accurate in specific cases. For most applications 3D reconstructions or proper compensation is needed.
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