Compressed sensing regularized calibrationless parallel magnetic resonance imaging via deep learning

Islam, Sheikh Rafiul; Maity, Santi P.; Ray, Ajoy Kumar

doi:10.1016/j.bspc.2020.102399

Cited by 6 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Figure 3 , we show the number of MRI sequences used in the previous studies from Table 2 . According to this figure, the most used MRI sequence type is T1-weighted, and the sequence was used for different purposes such as segmentation [ 43 ] in 2023, image restoration in 2019 [ 52 ], reconstruction both in 2021 and 2022 [ 45 , 46 ], surface mapping in 2022 [ 44 ], and feature extraction in 2019 [ 54 ]. The second most used MRI sequence type is T2-weighted, and it was used for various tasks such as pulse sequence simulation in 2023 [ 42 ], reconstruction in 2021 [ 47 ], and simulation of a human head in 2021 [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

“…A study by [ 46 ] proposed a calibrationless Compressed Sensing (CS) regularized by SENSE (SENSitivity Encoding) pMRI for providing MRI images with accurate clinical information. According to the simulation results, the proposed method enabled an important visual quality improvement on the MRIs in comparison with methods in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances

Kirimtat,

Krejcar

2024

Sensors

View full text Add to dashboard Cite

The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image classification, object detection, image segmentation, registration, and content-based image retrieval, since the GPU-based parallel processing approach allows for time-efficient computation by a software, allowing multiple computations to be completed at once. On the other hand, a non-invasive imaging technology that may depict the shape of an anatomy and the biological advancements of the human body is known as magnetic resonance imaging (MRI). Implementing GPU-based parallel processing approaches in brain MRI analysis with medical imaging techniques might be helpful in achieving immediate and timely image capture. Therefore, this extended review (the extension of the IWBBIO2023 conference paper) offers a thorough overview of the literature with an emphasis on the expanding use of GPU-based parallel processing methods for the medical analysis of brain MRIs with the imaging techniques mentioned above, given the need for quicker computation to acquire early and real-time feedback in medicine. Between 2019 and 2023, we examined the articles in the literature matrix that include the tasks, techniques, MRI sequences, and processing results. As a result, the methods discussed in this review demonstrate the advancements achieved until now in minimizing computing runtime as well as the obstacles and problems still to be solved in the future.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances

Kirimtat,

Krejcar

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…This work proposed to alternatively update sparse representation, sensitivity encoded images, and K-space data. SCDAE [27] was developed to estimate coil-wise sensitivity maps via convolutions and fully connected layers. The reconstruction employed a TV-based minimization algorithm that is solved by the Bregman iteration technique.…”

Section: Related Workmentioning

confidence: 99%

“…Quantitative evaluations of the reconstructions on the Coronal FSPD & PD data using different variations of the proposed methods (Labels of variations are explained in Tabel 5. The experiments without being labeled loss functions are trained with(27)). …”

mentioning

confidence: 99%

An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities

Bian¹,

Chen²,

Ye³

2021

Preprint

View full text Add to dashboard Cite

This work aims at developing a novel calibration-free fast parallel MRI (pMRI) reconstruction method incorporate with discrete-time optimal control framework. The reconstruction model is designed to learn a regularization that combines channels and extracts features by leveraging the information sharing among channels of multi-coil images. We propose to recover both magnitude and phase information by taking advantage of structured multiplayer convolutional networks in image and Fourier spaces. Methods: We develop a novel variational model with a learnable objective function that integrates an adaptive multi-coil image combination operator and effective image regularization in the image and Fourier spaces. We cast the reconstruction network as a structured discrete-time optimal control system, resulting in an optimal control formulation of parameter training where the parameters of the objective function play the role of control variables. We demonstrate that the Lagrangian method for solving the control problem is equivalent to back-propagation, ensuring the local convergence of the training algorithm. Results: We conduct a large number of numerical experiments of the proposed method with comparisons to several state-of-the-art pMRI reconstruction networks on real pMRI datasets. The numerical results demonstrate the promising performance of the proposed method evidently. Conclusion: The proposed method provides a general deep network design and training framework for efficient joint-channel pMRI reconstruction. Significance: By learning multi-coil image combination operator and performing regularizations in both image domain and k-space domain, the proposed method achieves a highly efficient image reconstruction network for pMRI.

show abstract

Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction

Tong

et al. 2021

Computers in Biology and Medicine

View full text Add to dashboard Cite

Compressed sensing regularized calibrationless parallel magnetic resonance imaging via deep learning

Cited by 6 publications

References 33 publications

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances

An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities

Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction

Contact Info

Product

Resources

About