PRIM: An Efficient Preconditioning Iterative Reweighted Least Squares Method for Parallel Brain MRI Reconstruction

Xu, Zheng; Wang, Sheng; Li, Yeqing; Zhu, Fayan; Huang, Junzhou

doi:10.1007/s12021-017-9354-9

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Also, a highly appropriate SIMD algorithm that operates on thousands of threads concurrently is executed by today's GPUs [9]. On the other hand, the most frequently used medical imaging modality for brain imaging is magnetic resonance imaging (MRI), followed by computed tomography (CT), positron emission tomography (PET), and ultrasound [10][11][12][13]. In basic terms, MRI has been widely utilized to analyze the anatomy of the entire brain [14].…”

Section: Introductionmentioning

confidence: 99%

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

Kirimtat,

Krejcar

2024

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Kirimtat,

Krejcar

2024

Sensors

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“…Examples of such sparsifying transformations are the total variation operator, wavelet transformations or a combination of these 1–5 . Preconditioning has been proposed in the past to accelerate CS reconstructions 6–9 . This technique can reduce the number of iterations that are needed to converge to the optimal solution 10 .…”

Section: Introductionmentioning

confidence: 99%

“… 1 , 2 , 3 , 4 , 5 Preconditioning has been proposed in the past to accelerate CS reconstructions. 6 , 7 , 8 , 9 This technique can reduce the number of iterations that are needed to converge to the optimal solution. 10 Designing an efficient preconditioner, however, is not straightforward.…”

Section: Introductionmentioning

confidence: 99%

Learning a preconditioner to accelerate compressed sensing reconstructions in MRI

Koolstra

Remis

2021

Magnetic Resonance in Med

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Purpose To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions. Methods A convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain images and 50% white Gaussian noise images. Each multichannel training example contains a simulated sampling mask, complex coil sensitivity maps, and two regularization parameter maps. The trained model was integrated in the preconditioned conjugate gradient (PCG) method as part of the split Bregman CS method. The acceleration performance was compared with that of a circulant PI‐CS preconditioner for varying undersampling factors, number of coil elements and anatomies. Results The learned preconditioner reduces the number of PCG iterations by a factor of 4, yielding a similar acceleration as an efficient circulant preconditioner. The method generalizes well to different sampling schemes, coil configurations and anatomies. Conclusion It is possible to learn adaptable preconditioners for PI and CS reconstructions that meet the performance of state‐of‐the‐art preconditioners. Further acceleration could be achieved by optimizing the network architecture and the training set. Such a preconditioner could also be integrated in fully learned reconstruction methods to accelerate the training process of unrolled networks.

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“…To extend the TV reconstruction problem, consider the special structure of undersampling matrix A in reconstruction, Chen et al [25] proposed fast iterative reweighted least squares (FIRLS) method, could be easily applied to the TV and JTV sparsity patterns, outperformed in accuracy and computer speed. Xu et al [26] exploit the JTV regularization to increase sampling speed for pMRI, JTV model demonstrate also superior performance in both accuracy and efficiency. Since practical MR data are complex-valued, some of these methods solely work for real-valued images.…”

Section: Introductionmentioning

confidence: 99%

Rosette Trajectories for Fast MRI Based on an Adaptive Reconstruction Method

Yang

et al. 2021

IEEE Access

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Non-Cartesian MRI k-space trajectories provide faster and more motion-robust data acquisitions than those of Cartesian trajectories. In this paper, we focus on the rosette trajectory and generalize the weighted rosette trajectories for fast undersampled k-space data acquisition. However, single-slice imaging using the rosette trajectory will be affected by the off-resonance effect. To reduce the artifacts from offresonance slices, this paper introduced an adaptive iterative reconstruction method derived from the TV and nuclear norm regularization terms for raw MRI data reconstruction. This reconstruction problem was separated into two subproblems and solved with an adaptive regularization parameter. The results show that the weighted rosette trajectory that offers short acquisition times and good off-resonance behaviors with little blurring can be used for reconstruction with adaptive regularization parameters and achieve a superior performance.

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PRIM: An Efficient Preconditioning Iterative Reweighted Least Squares Method for Parallel Brain MRI Reconstruction

Cited by 7 publications

References 20 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

Learning a preconditioner to accelerate compressed sensing reconstructions in MRI

Rosette Trajectories for Fast MRI Based on an Adaptive Reconstruction Method

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