1
|
Liao X, Huang B, Wang S, Liang D, Liu Q. Variable augmentation network for invertible MR coil compression. Magn Reson Imaging 2024; 108:116-128. [PMID: 38325727 DOI: 10.1016/j.mri.2024.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/27/2024] [Accepted: 01/27/2024] [Indexed: 02/09/2024]
Abstract
To improve the efficiency of multi-coil data compression and recover the compressed image reversibly, increasing the possibility of applying the proposed method to medical scenarios. A deep learning algorithm is employed for MR coil compression in the presented work. The approach introduces a variable augmentation network for invertible coil compression (VAN-ICC). This network utilizes the inherent reversibility of normalizing flow-based models. The aim is to enhance the readability of the sentence and clearly convey the key components of the algorithm. By applying the variable augmentation technology to image/k-space variables from multi-coils, VAN-ICC trains the invertible network by finding an invertible and bijective function, which can map the original data to the compressed counterpart and vice versa. Experiments conducted on both fully-sampled and under-sampled data verified the effectiveness and flexibility of VAN-ICC. Quantitative and qualitative comparisons with traditional non-deep learning-based approaches demonstrated that VAN-ICC carries much higher compression effects. The proposed method trains the invertible network by finding an invertible and bijective function, which improves the defects of traditional coil compression method by utilizing inherent reversibility of normalizing flow-based models. In addition, the application of variable augmentation technology ensures the implementation of reversible networks. In short, VAN-ICC offered a competitive advantage over other traditional coil compression algorithms.
Collapse
Affiliation(s)
- Xianghao Liao
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
| | - Bin Huang
- Department of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China..
| |
Collapse
|
2
|
Chang Y, Zhang J, Pham HA, Lyu J, Li Z. Interpretable Dimension Reduction for MRI Channel Suppression. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1456-1459. [PMID: 36085960 DOI: 10.1109/embc48229.2022.9871474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Channel suppression can reduce the redundant information in multiple channel receiver coils and accelerate reconstruction speed to meet real-time imaging requirements. The principal component analysis has been used for channel suppression, but it is difficult to be interpreted because all channels contribute to principal components. Furthermore, the importance of interpretability in machine learning has recently attracted increasing attention in radiology. To improve the interpretability of PCA-based channel suppression, a sparse PCA method is proposed to reduce the most coils' loadings to be zero. Channel suppression is formulated as solving a nonlinear eigenvalue problem using the inverse power method instead of the direct matrix decomposition. Experimental results of in vivo data show that the sparse PCA-based channel suppression not only improves the interpretability with sparse channels, but also improves reconstruction quality compared to the standard PCA-based reconstruction with the similar reconstruction time.
Collapse
|
3
|
Inam O, Qureshi M, Laraib Z, Akram H, Omer H. GPU accelerated Cartesian GRAPPA reconstruction using CUDA. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 337:107175. [PMID: 35259611 DOI: 10.1016/j.jmr.2022.107175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 02/10/2022] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE GRAPPA (Generalized Auto-calibrating Partially Parallel Acquisition) is an advanced parallel MRI reconstruction method (pMRI) that enables under-sampled data acquisition with multiple receiver coils to reduce the MRI scan time and reconstructs artifact free image from the acquired under-sampled data. However, the reduction in MRI scan time comes at the expense of long reconstruction time. It is because the GRAPPA reconstruction time shows exponential growth with increasing number of receiver coils. Consequently, the conventional CPU platforms may not adhere to the requirements of fast data processing for MR image reconstruction. METHODS Graphics Processing Units (GPUs) have recently emerged as a viable commodity hardware to reduce the reconstruction time of pMRI methods. This paper presents a novel GPU based implementation of GRAPPA using custom built CUDA kernels, to meet the rising demands of fast MRI processing. The proposed framework exploits intrinsic parallelism in the calibration and synthesis phases of GRAPPA reconstruction process, aiming to achieve high speed MR image reconstruction for various GRAPPA configuration settings using different number of receiver coils, auto-calibration signals (ACS), sizes of GRAPPA kernel and acceleration factors. In-vivo experiments (using 8, 12 and 30 receiver coils) are performed to compare the performance of the proposed GPU accelerated GRAPPA with the CPU based GRAPPA extensions and GPU counterpart. RESULTS The results indicate that the proposed method achieves up to ≈47.8× , ≈17× and ≈3.8× speed up gains over multicore CPU (single thread), multicore CPU (8 thread) and Gadgetron (GPU based GRAPPA) respectively, without compromising the reconstruction accuracy. CONCLUSIONS The proposed method reduces the GRAPPA reconstruction time by employing the calibration phase (GRAPPA weights estimation) and synthesis phase (interpolation) on GPU. Our study shows that the proposed GPU based parallel framework for GRAPPA reconstruction provides a solution for high-speed image reconstruction while maintaining the quality of the reconstructed images.
Collapse
Affiliation(s)
- Omair Inam
- Medical Image Processing Research Group (MIPRG), Department of Electrical & Computer Engineering, COMSATS University Islamabad, Pakistan.
| | - Mahmood Qureshi
- Medical Image Processing Research Group (MIPRG), Department of Electrical & Computer Engineering, COMSATS University Islamabad, Pakistan.
| | - Zoia Laraib
- Medical Image Processing Research Group (MIPRG), Department of Electrical & Computer Engineering, COMSATS University Islamabad, Pakistan
| | - Hamza Akram
- Medical Image Processing Research Group (MIPRG), Department of Electrical & Computer Engineering, COMSATS University Islamabad, Pakistan
| | - Hammad Omer
- Medical Image Processing Research Group (MIPRG), Department of Electrical & Computer Engineering, COMSATS University Islamabad, Pakistan.
| |
Collapse
|
4
|
Vafay Eslahi S, Ji J. Accelerated positive contrast MRI of interventional devices using parallel compressed sensing imaging. Magn Reson Imaging 2019; 60:130-136. [PMID: 31028791 DOI: 10.1016/j.mri.2019.04.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 04/04/2019] [Accepted: 04/06/2019] [Indexed: 11/24/2022]
Abstract
Susceptibility-based magnetic resonance imaging (MRI) method can image small MR-compatible devices with positive contrast. However, the relatively long data acquisition time required by the method hinders its practical applications. This study presents a parallel compressive sensing technique with a modified fast spin echo to accelerate data acquisition for the susceptibility-based positive contrast MRI. The method integrates the generalized autocalibrating partially parallel acquisitions and the compressive sensing techniques in the reconstruction algorithm. MR imaging data acquired from several phantoms containing interventional devices such as biopsy needles, stent, and brachytherapy seeds, used for validating the proposed technique. The results show that it can speed up data acquisition by a factor of about five while preserving the quality of the positive contrast images.
Collapse
Affiliation(s)
- Samira Vafay Eslahi
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Jim Ji
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
| |
Collapse
|
5
|
A Feasibility Study of Geometric-Decomposition Coil Compression in MRI Radial Acquisitions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:7685208. [PMID: 28659993 PMCID: PMC5474278 DOI: 10.1155/2017/7685208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 05/10/2017] [Indexed: 12/03/2022]
Abstract
Receiver arrays with a large number of coil elements are becoming progressively available because of their increased signal-to-noise ratio (SNR) and enhanced parallel imaging performance. However, longer reconstruction time and intensive computational cost have become significant concerns as the number of channels increases, especially in some iterative reconstructions. Coil compression can effectively solve this problem by linearly combining the raw data from multiple coils into fewer virtual coils. In this work, geometric-decomposition coil compression (GCC) is applied to radial sampling (both linear-angle and golden-angle patterns are discussed) for better compression. GCC, which is different from directly compressing in k-space, is performed separately in each spatial location along the fully sampled directions, then followed by an additional alignment step to guarantee the smoothness of the virtual coil sensitivities. Both numerical simulation data and in vivo data were tested. Experimental results demonstrated that the GCC algorithm can achieve higher SNR and lower normalized root mean squared error values than the conventional principal component analysis approach in radial acquisitions.
Collapse
|
6
|
Campbell-Washburn AE, Faranesh AZ, Lederman RJ, Hansen MS. Magnetic Resonance Sequences and Rapid Acquisition for MR-Guided Interventions. Magn Reson Imaging Clin N Am 2015; 23:669-79. [PMID: 26499283 DOI: 10.1016/j.mric.2015.05.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Interventional MR uses rapid imaging to guide diagnostic and therapeutic procedures. One of the attractions of MR-guidance is the abundance of inherent contrast mechanisms available. Dynamic procedural guidance with real-time imaging has pushed the limits of MR technology, demanding rapid acquisition and reconstruction paired with interactive control and device visualization. This article reviews the technical aspects of real-time MR sequences that enable MR-guided interventions.
Collapse
Affiliation(s)
- Adrienne E Campbell-Washburn
- Cardiovascular and Pulmonary Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B1D416, Bethesda, MD 20892, USA.
| | - Anthony Z Faranesh
- Cardiovascular and Pulmonary Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 2C713, Bethesda, MD 20892, USA
| | - Robert J Lederman
- Cardiovascular and Pulmonary Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 2C713, Bethesda, MD 20892, USA
| | - Michael S Hansen
- Cardiovascular and Pulmonary Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B1D416, Bethesda, MD 20892, USA
| |
Collapse
|
7
|
Lyu J, Chang Y, Ying L. Fast GRAPPA reconstruction with random projection. Magn Reson Med 2014; 74:71-80. [PMID: 25043333 DOI: 10.1002/mrm.25373] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2014] [Revised: 06/25/2014] [Accepted: 06/26/2014] [Indexed: 11/09/2022]
Abstract
PURPOSE To address the issue of computational complexity in generalized autocalibrating partially parallel acquisition (GRAPPA) when several calibration data are used. METHOD GRAPPA requires fully sampled data for accurate calibration with increasing data needed for higher reduction factors to maintain accuracy, which leads to longer computational time, especially in a three-dimensional (3D) setting and with higher channel count coils. Channel reduction methods have been developed to address this issue when massive array coils are used. In this study, the complexity problem was addressed from a different prospective. Instead of compressing to fewer channels, we propose the use of random projections to reduce the dimension of the linear equation in the calibration phase. The equivalence before and after the reduction is supported by the Johnson-Lindenstrauss lemma. The proposed random projection method can be integrated with channel reduction sequentially for even higher computational efficiency. RESULTS Experimental results show that GRAPPA with random projection can achieve comparable image quality with much less computational time when compared with conventional GRAPPA without random projection. CONCLUSION The proposed random projection method is able to reduce the computational time of GRAPPA, especially in a 3D setting, without compromising the image quality, or to improve the reconstruction quality by allowing more data for calibration when the computational time is a limiting factor. Magn Reson Med 74:71-80, 2015. © 2014 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Jingyuan Lyu
- Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, New York, USA
| | - Yuchou Chang
- Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Leslie Ying
- Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, New York, USA
| |
Collapse
|
8
|
Beatty PJ, Chang S, Holmes JH, Wang K, Brau ACS, Reeder SB, Brittain JH. Design of k-space channel combination kernels and integration with parallel imaging. Magn Reson Med 2013; 71:2139-54. [PMID: 23943602 DOI: 10.1002/mrm.24883] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2012] [Revised: 06/14/2013] [Accepted: 06/20/2013] [Indexed: 11/11/2022]
Abstract
PURPOSE In this work, a new method is described for producing local k-space channel combination kernels using a small amount of low-resolution multichannel calibration data. Additionally, this work describes how these channel combination kernels can be combined with local k-space unaliasing kernels produced by the calibration phase of parallel imaging methods such as GRAPPA, PARS and ARC. METHODS Experiments were conducted to evaluate both the image quality and computational efficiency of the proposed method compared to a channel-by-channel parallel imaging approach with image-space sum-of-squares channel combination. RESULTS Results indicate comparable image quality overall, with some very minor differences seen in reduced field-of-view imaging. It was demonstrated that this method enables a speed up in computation time on the order of 3-16X for 32-channel data sets. CONCLUSION The proposed method enables high quality channel combination to occur earlier in the reconstruction pipeline, reducing computational and memory requirements for image reconstruction.
Collapse
Affiliation(s)
- Philip J Beatty
- Global Applied Science Laboratory, GE Healthcare, Toronto, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | | | | | | | | | | | | |
Collapse
|
9
|
Zhang T, Pauly JM, Vasanawala SS, Lustig M. Coil compression for accelerated imaging with Cartesian sampling. Magn Reson Med 2012; 69:571-82. [PMID: 22488589 DOI: 10.1002/mrm.24267] [Citation(s) in RCA: 165] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2011] [Revised: 02/10/2012] [Accepted: 02/29/2012] [Indexed: 11/09/2022]
Abstract
MRI using receiver arrays with many coil elements can provide high signal-to-noise ratio and increase parallel imaging acceleration. At the same time, the growing number of elements results in larger datasets and more computation in the reconstruction. This is of particular concern in 3D acquisitions and in iterative reconstructions. Coil compression algorithms are effective in mitigating this problem by compressing data from many channels into fewer virtual coils. In Cartesian sampling there often are fully sampled k-space dimensions. In this work, a new coil compression technique for Cartesian sampling is presented that exploits the spatially varying coil sensitivities in these nonsubsampled dimensions for better compression and computation reduction. Instead of directly compressing in k-space, coil compression is performed separately for each spatial location along the fully sampled directions, followed by an additional alignment process that guarantees the smoothness of the virtual coil sensitivities. This important step provides compatibility with autocalibrating parallel imaging techniques. Its performance is not susceptible to artifacts caused by a tight imaging field-of-view. High quality compression of in vivo 3D data from a 32 channel pediatric coil into six virtual coils is demonstrated.
Collapse
Affiliation(s)
- Tao Zhang
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, California, USA.
| | | | | | | |
Collapse
|
10
|
Chang Y, Liang D, Ying L. Nonlinear GRAPPA: a kernel approach to parallel MRI reconstruction. Magn Reson Med 2011; 68:730-40. [PMID: 22161975 DOI: 10.1002/mrm.23279] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2011] [Revised: 10/04/2011] [Accepted: 10/10/2011] [Indexed: 11/06/2022]
Abstract
GRAPPA linearly combines the undersampled k-space signals to estimate the missing k-space signals where the coefficients are obtained by fitting to some auto-calibration signals (ACS) sampled with Nyquist rate based on the shift-invariant property. At high acceleration factors, GRAPPA reconstruction can suffer from a high level of noise even with a large number of auto-calibration signals. In this work, we propose a nonlinear method to improve GRAPPA. The method is based on the so-called kernel method which is widely used in machine learning. Specifically, the undersampled k-space signals are mapped through a nonlinear transform to a high-dimensional feature space, and then linearly combined to reconstruct the missing k-space data. The linear combination coefficients are also obtained through fitting to the ACS data but in the new feature space. The procedure is equivalent to adding many virtual channels in reconstruction. A polynomial kernel with explicit mapping functions is investigated in this work. Experimental results using phantom and in vivo data demonstrate that the proposed nonlinear GRAPPA method can significantly improve the reconstruction quality over GRAPPA and its state-of-the-art derivatives.
Collapse
Affiliation(s)
- Yuchou Chang
- Department of Electrical Engineering and Computer Science, University of Wisconsin, Milwaukee, Wisconsin 53211, USA
| | | | | |
Collapse
|
11
|
Feng S, Ji J. Parallel magnetic resonance imaging using localized receive arrays with sinc interpolation (PILARS). Magn Reson Med 2011; 67:1114-9. [PMID: 21858866 DOI: 10.1002/mrm.23079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2011] [Revised: 05/07/2011] [Accepted: 06/13/2011] [Indexed: 11/08/2022]
Abstract
Large arrays with localized coil sensitivity make it possible to use parallel imaging to significantly accelerate MR imaging speed. However, the need for auto calibration signals limits the actual acceleration factors achievable with large arrays. This paper presents a novel method for parallel imaging with large arrays. The method uses Sinc kernels for k-space data interpolation that only requires one phase parameter to be estimated using a small size of calibration signals. Simulations based on synthetic array data and phantom experiments show that the new method can achieve higher actual acceleration factors with comparable reconstruction quality.
Collapse
Affiliation(s)
- Shuo Feng
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | | |
Collapse
|
12
|
Huang F, Lin W, Duensing GR, Reykowski A. A hybrid method for more efficient channel-by-channel reconstruction with many channels. Magn Reson Med 2011; 67:835-43. [DOI: 10.1002/mrm.23048] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2010] [Revised: 03/14/2011] [Accepted: 05/23/2011] [Indexed: 11/07/2022]
|