Abstract:To introduce a dual-domain reconstruction network with V-Net and K-Net for accurate MR image reconstruction from undersampled k-space data.Methods: Most state-of-the-art reconstruction methods apply U-Net or cascaded U-Nets in the image domain and/or k-space domain. Nevertheless, these methods have the following problems: (1) directly applying U-Net in the k-space domain is not optimal for extracting features; (2) classical image-domain-oriented U-Net is heavyweighted and hence inefficient when cascaded many t… Show more
“…Different from natural image processing that DL mainly operates in the spatial domain, DL for fast MR imaging can either directly learn the nonlinear mapping between the aliased MR images and the reference MR images, or between the undersampled k-space data and fully sampled k-space data, or conduct cross-domain dual-space learning. [21][22][23][24][25][26][27][28][29] The dual-space property of MRI provides opportunities for DL reconstruction methods operating flexibly in image space, k-space, or dual space. There are several publicly available MR image databases for tasks related to DL-based MR image processing or…”
Section: Fourier Imaging-dual Space Propertymentioning
Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MRI involves physics‐based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data‐driven approaches. Our review will introduce the significant challenges faced by such knowledge‐driven DL approaches in the context of fast MRI along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi‐supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.
“…Different from natural image processing that DL mainly operates in the spatial domain, DL for fast MR imaging can either directly learn the nonlinear mapping between the aliased MR images and the reference MR images, or between the undersampled k-space data and fully sampled k-space data, or conduct cross-domain dual-space learning. [21][22][23][24][25][26][27][28][29] The dual-space property of MRI provides opportunities for DL reconstruction methods operating flexibly in image space, k-space, or dual space. There are several publicly available MR image databases for tasks related to DL-based MR image processing or…”
Section: Fourier Imaging-dual Space Propertymentioning
Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MRI involves physics‐based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data‐driven approaches. Our review will introduce the significant challenges faced by such knowledge‐driven DL approaches in the context of fast MRI along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi‐supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.
“…A U-Net in its basic form is limited to image-to-image reconstruction without any domain-specific knowledge, such as k-space, included. However, dual-domain networks allow both image and k-space to be utilized, and scan-specific methods can restore missing k-space [ 9 , 16 , 17 ]. Fig.…”
Artificial intelligence has opened a new path of innovation in magnetic resonance (MR) image reconstruction of undersampled k-space acquisitions. This review offers readers an analysis of the current deep learning-based MR image reconstruction methods. The literature in this field shows exponential growth, both in volume and complexity, as the capabilities of machine learning in solving inverse problems such as image reconstruction are explored. We review the latest developments, aiming to assist researchers and radiologists who are developing new methods or seeking to provide valuable feedback. We shed light on key concepts by exploring the technical intricacies of MR image reconstruction, highlighting the importance of raw datasets and the difficulty of evaluating diagnostic value using standard metrics.Relevance statement Increasingly complex algorithms output reconstructed images that are difficult to assess for robustness and diagnostic quality, necessitating high-quality datasets and collaboration with radiologists.Key points• Deep learning-based image reconstruction algorithms are increasing both in complexity and performance.• The evaluation of reconstructed images may mistake perceived image quality for diagnostic value.• Collaboration with radiologists is crucial for advancing deep learning technology.
Graphical Abstract
Purpose
To develop a truly calibrationless reconstruction method that derives An Eigenvalue Approach to Autocalibrating Parallel MRI (ESPIRiT) maps from uniformly‐undersampled multi‐channel MR data by deep learning.
Methods
ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k‐space data using ESPIRiT maps that effectively represents coil sensitivity information. Accurate ESPIRiT map estimation requires quality coil sensitivity calibration or autocalibration data. We present a U‐Net based deep learning model to estimate the multi‐channel ESPIRiT maps directly from uniformly‐undersampled multi‐channel multi‐slice MR data. The model is trained using fully‐sampled multi‐slice axial brain datasets from the same MR receiving coil system. To utilize subject‐coil geometric parameters available for each dataset, the training imposes a hybrid loss on ESPIRiT maps at the original locations as well as their corresponding locations within the standard reference multi‐slice axial stack. The performance of the approach was evaluated using publicly available T1‐weighed brain and cardiac data.
Results
The proposed model robustly predicted multi‐channel ESPIRiT maps from uniformly‐undersampled k‐space data. They were highly comparable to the reference ESPIRiT maps directly computed from 24 consecutive central k‐space lines. Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps.
Conclusion
A new deep learning approach is developed to estimate ESPIRiT maps directly from uniformly‐undersampled MR data. It presents a general strategy for calibrationless parallel imaging reconstruction through learning from the coil and protocol‐specific data.
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