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Zhang C, Piccini D, Demirel OB, Bonanno G, Roy CW, Yaman B, Moeller S, Shenoy C, Stuber M, Akçakaya M. Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01157-8. [PMID: 38743377 DOI: 10.1007/s10334-024-01157-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/19/2024] [Accepted: 03/13/2024] [Indexed: 05/16/2024]
Abstract
OBJECT To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability. MATERIALS AND METHODS While PG-DL has emerged as a powerful image reconstruction method, its application to large-scale 3D non-Cartesian MRI is hindered by hardware limitations and limited availability of training data. We combine several recent advances in deep learning and MRI reconstruction to tackle the former challenge, and we further propose a 2.5D reconstruction using 2D convolutional neural networks, which treat 3D volumes as batches of 2D images to train the network with a limited amount of training data. Both 3D and 2.5D variants of the PG-DL networks were compared to conventional methods for high-resolution 3D kooshball coronary MRI. RESULTS Proposed PG-DL reconstructions of 3D non-Cartesian coronary MRI with 3D and 2.5D processing outperformed all conventional methods both quantitatively and qualitatively in terms of image assessment by an experienced cardiologist. The 2.5D variant further improved vessel sharpness compared to 3D processing, and scored higher in terms of qualitative image quality. DISCUSSION PG-DL reconstruction of large-scale 3D non-Cartesian MRI without compromising image size or network complexity is achieved, and the proposed 2.5D processing enables high-quality reconstruction with limited training data.
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Affiliation(s)
- Chi Zhang
- Electrical and Computer Engineering, University of Minnesota, 200 Union Street S.E., Minneapolis, MN, 55455, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Davide Piccini
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International, Lausanne, Switzerland
| | - Omer Burak Demirel
- Electrical and Computer Engineering, University of Minnesota, 200 Union Street S.E., Minneapolis, MN, 55455, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Gabriele Bonanno
- Advanced Clinical Imaging Technology, Siemens Healthineers International, Lausanne, Switzerland
| | - Christopher W Roy
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Burhaneddin Yaman
- Electrical and Computer Engineering, University of Minnesota, 200 Union Street S.E., Minneapolis, MN, 55455, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Steen Moeller
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Chetan Shenoy
- Department of Medicine (Cardiology), University of Minnesota, Minneapolis, MN, 55455, USA
| | - Matthias Stuber
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Center for Biomedical Imaging, Lausanne, Switzerland
| | - Mehmet Akçakaya
- Electrical and Computer Engineering, University of Minnesota, 200 Union Street S.E., Minneapolis, MN, 55455, USA.
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA.
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Athertya JS, Lo J, Chen X, Shin SH, Malhi BS, Jerban S, Ji Y, Sedaghat S, Yoshioka H, Du J, Guma M, Chang EY, Ma Y. High contrast cartilaginous endplate imaging in spine using three dimensional dual-inversion recovery prepared ultrashort echo time (3D DIR-UTE) sequence. Skeletal Radiol 2024; 53:881-890. [PMID: 37935923 PMCID: PMC10973042 DOI: 10.1007/s00256-023-04503-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 10/14/2023] [Accepted: 10/29/2023] [Indexed: 11/09/2023]
Abstract
PURPOSE To investigate the feasibility and application of a novel imaging technique, a three-dimensional dual adiabatic inversion recovery prepared ultrashort echo time (3D DIR-UTE) sequence, for high contrast assessment of cartilaginous endplate (CEP) imaging with head-to-head comparisons between other UTE imaging techniques. METHOD The DIR-UTE sequence employs two narrow-band adiabatic full passage (AFP) pulses to suppress signals from long T2 water (e.g., nucleus pulposus (NP)) and bone marrow fat (BMF) independently, followed by multispoke UTE acquisition to detect signals from the CEP with short T2 relaxation times. The DIR-UTE sequence, in addition to three other UTE sequences namely, an IR-prepared and fat-saturated UTE (IR-FS-UTE), a T1-weighted and fat-saturated UTE sequence (T1w-FS-UTE), and a fat-saturated UTE (FS-UTE) was used for MR imaging on a 3 T scanner to image six asymptomatic volunteers, six patients with low back pain, as well as a human cadaveric specimen. The contrast-to-noise ratio of the CEP relative to the adjacent structures-specifically the NP and BMF-was then compared from the acquired images across the different UTE sequences. RESULTS For asymptomatic volunteers, the DIR-UTE sequence showed significantly higher contrast-to-noise ratio values between the CEP and BMF (CNRCEP-BMF) (19.9 ± 3.0) and between the CEP and NP (CNRCEP-NP) (23.1 ± 1.7) compared to IR-FS-UTE (CNRCEP-BMF: 17.3 ± 1.2 and CNRCEP-NP: 19.1 ± 1.8), T1w-FS-UTE (CNRCEP-BMF: 9.0 ± 2.7 and CNRCEP-NP: 10.4 ± 3.5), and FS-UTE (CNRCEP-BMF: 7.7 ± 2.2 and CNRCEP-NP: 5.8 ± 2.4) for asymptomatic volunteers (all P-values < 0.001). For the spine sample and patients with low back pain, the DIR-UTE technique detected abnormalities such as irregularities and focal defects in the CEP regions. CONCLUSION The 3D DIR-UTE sequence is able to provide high-contrast volumetric CEP imaging for human spines on a clinical 3 T scanner.
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Affiliation(s)
- Jiyo S Athertya
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - James Lo
- Department of Radiology, University of California San Diego, San Diego, CA, USA
- Department of Bioengineering, University of California San Diego, San Diego, CA, USA
| | - Xiaojun Chen
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Soo Hyun Shin
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | | | - Saeed Jerban
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Yang Ji
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Sam Sedaghat
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Hiroshi Yoshioka
- Department of Radiological Sciences, University of California Irvine, Irvine, CA, USA
| | - Jiang Du
- Department of Radiology, University of California San Diego, San Diego, CA, USA
- Department of Bioengineering, University of California San Diego, San Diego, CA, USA
- Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Monica Guma
- Department of Medicine, University of California San Diego, San Diego, CA, USA
- Medicine Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Eric Y Chang
- Department of Radiology, University of California San Diego, San Diego, CA, USA
- Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Yajun Ma
- Department of Radiology, University of California San Diego, San Diego, CA, USA.
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Oscanoa JA, Ong F, Iyer SS, Li Z, Sandino CM, Ozturkler B, Ennis DB, Pilanci M, Vasanawala SS. Coil sketching for computationally efficient MR iterative reconstruction. Magn Reson Med 2024; 91:784-802. [PMID: 37848365 DOI: 10.1002/mrm.29883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/23/2023] [Accepted: 09/17/2023] [Indexed: 10/19/2023]
Abstract
PURPOSE Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially for three-dimensional (3D) non-Cartesian acquisitions. One common approach is to reduce the number of coil channels actively used during reconstruction as in coil compression. While effective for Cartesian imaging, coil compression inherently loses signal energy, producing shading artifacts that compromise image quality for 3D non-Cartesian imaging. We propose coil sketching, a general and versatile method for computationally-efficient iterative MR image reconstruction. THEORY AND METHODS We based our method on randomized sketching algorithms, a type of large-scale optimization algorithms well established in the fields of machine learning and big data analysis. We adapt the sketching theory to the MRI reconstruction problem via a structured sketching matrix that, similar to coil compression, considers high-energy virtual coils obtained from principal component analysis. But, unlike coil compression, it also considers random linear combinations of the remaining low-energy coils, effectively leveraging information from all coils. RESULTS First, we performed ablation experiments to validate the sketching matrix design on both Cartesian and non-Cartesian datasets. The resulting design yielded both improved computatioanal efficiency and preserved signal-to-noise ratio (SNR) as measured by the inverse g-factor. Then, we verified the efficacy of our approach on high-dimensional non-Cartesian 3D cones datasets, where coil sketching yielded up to three-fold faster reconstructions with equivalent image quality. CONCLUSION Coil sketching is a general and versatile reconstruction framework for computationally fast and memory-efficient reconstruction.
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Affiliation(s)
- Julio A Oscanoa
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Frank Ong
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Siddharth S Iyer
- Department of Electrical Engineering and Computer Science, Massachussetts Institute of Technology, Cambridge, Massachussetts, USA
| | - Zhitao Li
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Christopher M Sandino
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Batu Ozturkler
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Daniel B Ennis
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Mert Pilanci
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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Mao A, Flassbeck S, Gultekin C, Assländer J. Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI. ARXIV 2023:arXiv:2305.00326v2. [PMID: 37961734 PMCID: PMC10635289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
We extend the traditional framework for estimating subspace bases that maximize the preserved signal energy to additionally preserve the Cramér-Rao bound (CRB) of the biophysical parameters and, ultimately, improve accuracy and precision in the quantitative maps. To this end, we introduce an approximate compressed CRB based on orthogonalized versions of the signal's derivatives with respect to the model parameters. This approximation permits singular value decomposition (SVD)-based minimization of both the CRB and signal losses during compression. Compared to the traditional SVD approach, the proposed method better preserves the CRB across all biophysical parameters with negligible cost to the preserved signal energy, leading to reduced bias and variance of the parameter estimates in simulation. In vivo, improved accuracy and precision are observed in two quantitative neuroimaging applications, permitting the use of smaller basis sizes in subspace reconstruction and offering significant computational savings.
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Affiliation(s)
- Andrew Mao
- Center for Biomedical Imaging, NYU School of Medicine, New York, NY 10016
| | | | - Cem Gultekin
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10012
| | - Jakob Assländer
- Center for Biomedical Imaging, NYU School of Medicine, New York, NY 10016
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Qu B, Zhang Z, Chen Y, Qian C, Kang T, Lin J, Chen L, Wu Z, Wang J, Zheng G, Qu X. A convergence analysis for projected fast iterative soft-thresholding algorithm under radial sampling MRI. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2023; 351:107425. [PMID: 37060889 DOI: 10.1016/j.jmr.2023.107425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 02/27/2023] [Accepted: 03/17/2023] [Indexed: 05/29/2023]
Abstract
Radial sampling is a fast magnetic resonance imaging technique. Further imaging acceleration can be achieved with undersampling but how to reconstruct a clear image with fast algorithm is still challenging. Previous work has shown the advantage of removing undersampling image artifacts using the tight-frame sparse reconstruction model. This model was further solved with a projected fast iterative soft-thresholding algorithm (pFISTA). However, the convergence of this algorithm under radial sampling has not been clearly set up. In this work, the authors derived a theoretical convergence condition for this algorithm. This condition was approximated by estimating the maximal eigenvalue of reconstruction operators through the power iteration. Based on the condition, an optimal step size was further suggested to allow the fastest convergence. Verifications were made on the prospective in vivo data of static brain imaging and dynamic contrast-enhanced liver imaging, demonstrating that the recommended parameter allowed fast convergence in radial MRI.
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Affiliation(s)
- Biao Qu
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China
| | - Zuwen Zhang
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Yewei Chen
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Chen Qian
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Taishan Kang
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Jianzhong Lin
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Lihua Chen
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | | | | | - Gaofeng Zheng
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China.
| | - Xiaobo Qu
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
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Koundinyan SP, Baron CA, Malavé MO, Ong F, Addy NO, Cheng JY, Yang PC, Hu BS, Nishimura DG. High-resolution, respiratory-resolved coronary MRA using a Phyllotaxis-reordered variable-density 3D cones trajectory. Magn Reson Imaging 2023; 98:140-148. [PMID: 36646397 PMCID: PMC9991864 DOI: 10.1016/j.mri.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 01/10/2023] [Accepted: 01/10/2023] [Indexed: 01/15/2023]
Abstract
PURPOSE To develop a respiratory-resolved motion-compensation method for free-breathing, high-resolution coronary magnetic resonance angiography (CMRA) using a 3D cones trajectory. METHODS To achieve respiratory-resolved 0.98 mm resolution images in a clinically relevant scan time, we undersample the imaging data with a variable-density 3D cones trajectory. For retrospective motion compensation, translational estimates from 3D image-based navigators (3D iNAVs) are used to bin the imaging data into four phases from end-expiration to end-inspiration. To ensure pseudo-random undersampling within each respiratory phase, we devise a phyllotaxis readout ordering scheme mindful of eddy current artifacts in steady state free precession imaging. Following binning, residual 3D translational motion within each phase is computed using the 3D iNAVs and corrected for in the imaging data. The noise-like aliasing characteristic of the combined phyllotaxis and cones sampling pattern is leveraged in a compressed sensing reconstruction with spatial and temporal regularization to reduce aliasing in each of the respiratory phases. RESULTS In initial studies of six subjects, respiratory motion compensation using the proposed method yields improved image quality compared to non-respiratory-resolved approaches with no motion correction and with 3D translational correction. Qualitative assessment by two cardiologists and quantitative evaluation with the image edge profile acutance metric indicate the superior sharpness of coronary segments reconstructed with the proposed method (P < 0.01). CONCLUSION We have demonstrated a new method for free-breathing, high-resolution CMRA based on a variable-density 3D cones trajectory with modified phyllotaxis ordering and respiratory-resolved motion compensation with 3D iNAVs.
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Affiliation(s)
| | - Corey A Baron
- Medical Biophysics, Western University, London, Ontario, Canada
| | - Mario O Malavé
- Electrical Engineering, Stanford University, Stanford, CA, United States
| | - Frank Ong
- Electrical Engineering, Stanford University, Stanford, CA, United States
| | - Nii Okai Addy
- Electrical Engineering, Stanford University, Stanford, CA, United States
| | - Joseph Y Cheng
- Electrical Engineering, Stanford University, Stanford, CA, United States; Radiology, Stanford University, Stanford, CA, United States
| | - Phillip C Yang
- Cardiovascular Medicine, Stanford University, Stanford, CA, United States
| | - Bob S Hu
- Electrical Engineering, Stanford University, Stanford, CA, United States; Cardiology, Palo Alto Medical Foundation, Palo Alto, CA, United States
| | - Dwight G Nishimura
- Electrical Engineering, Stanford University, Stanford, CA, United States.
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Iyer SS, Schauman SS, Sandino CM, Yurt M, Cao X, Liao C, Ruengchaijatuporn N, Chatnuntawech I, Tong E, Setsompop K. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.28.534431. [PMID: 37034586 PMCID: PMC10081201 DOI: 10.1101/2023.03.28.534431] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Introduction Spatio-temporal MRI methods enable whole-brain multi-parametric mapping at ultra-fast acquisition times through efficient k-space encoding, but can have very long reconstruction times, which limit their integration into clinical practice. Deep learning (DL) is a promising approach to accelerate reconstruction, but can be computationally intensive to train and deploy due to the large dimensionality of spatio-temporal MRI. DL methods also need large training data sets and can produce results that don't match the acquired data if data consistency is not enforced. The aim of this project is to reduce reconstruction time using DL whilst simultaneously limiting the risk of deep learning induced hallucinations, all with modest hardware requirements. Methods Deep Learning Initialized Compressed Sensing (Deli-CS) is proposed to reduce the reconstruction time of iterative reconstructions by "kick-starting" the iterative reconstruction with a DL generated starting point. The proposed framework is applied to volumetric multi-axis spiral projection MRF that achieves whole-brain T1 and T2 mapping at 1-mm isotropic resolution for a 2-minute acquisition. First, the traditional reconstruction is optimized from over two hours to less than 40 minutes while using more than 90% less RAM and only 4.7 GB GPU memory, by using a memory-efficient GPU implementation. The Deli-CS framework is then implemented and evaluated against the above reconstruction. Results Deli-CS achieves comparable reconstruction quality with 50% fewer iterations bringing the full reconstruction time to 20 minutes. Conclusion Deli-CS reduces the reconstruction time of subspace reconstruction of volumetric spatio-temporal acquisitions by providing a warm start to the iterative reconstruction algorithm.
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Affiliation(s)
- Siddharth S. Iyer
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, MA, USA
- Department of Radiology, Stanford University, CA, USA
| | | | | | - Mahmut Yurt
- Department of Electrical Engineering, Stanford University, CA, USA
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, CA, USA
| | - Congyu Liao
- Department of Radiology, Stanford University, CA, USA
| | - Natthanan Ruengchaijatuporn
- Center of Excellence in Computational Molecular Biology, Chulalongkorn University, Bangkok, Thailand
- Center for Artificial Intelligence in Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Itthi Chatnuntawech
- National Nanotechnology Center, National Science and Technology Development Agency, Pathum Thani, Thailand
| | | | - Kawin Setsompop
- Department of Radiology, Stanford University, CA, USA
- Department of Electrical Engineering, Stanford University, CA, USA
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Optimization in the space domain for density compensation with the nonuniform FFT. Magn Reson Imaging 2023; 100:102-111. [PMID: 36934830 DOI: 10.1016/j.mri.2023.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 03/02/2023] [Accepted: 03/12/2023] [Indexed: 03/19/2023]
Abstract
The non-uniform Discrete Fourier Transform algorithm has shown great utility for reconstructing images from non-uniformly spaced samples in the Fourier domain in several imaging modalities. Due to the non-uniform spacing, some correction for the variable density of the samples must be made. Common methods for generating density compensation values are either sub-optimal or only consider a finite set of points in the optimization. This manuscript presents an algorithm for generating density compensation values from a set of Fourier samples that takes into account the point spread function over an entire rectangular region in the image domain. We show that the reconstructed images using the density compensation values of this method are of superior quality when compared to other standard methods. Results are shown with a numerical phantom and with magnetic resonance images of the abdomen and the knee.
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Athertya JS, Akers J, Sedaghat S, Wei Z, Moazamian D, Dwek S, Thu M, Jang H. Detection of iron oxide nanoparticle (IONP)-labeled stem cells using quantitative ultrashort echo time imaging: a feasibility study. Quant Imaging Med Surg 2023; 13:585-597. [PMID: 36819276 PMCID: PMC9929408 DOI: 10.21037/qims-22-654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 11/17/2022] [Indexed: 01/12/2023]
Abstract
Background In this study, we investigated the feasibility of quantitative ultrashort echo time (qUTE) magnetic resonance (MR) imaging techniques in the detection and quantification of iron oxide nanoparticle (IONP)-labeled stem cells. Methods A stem cell phantom containing multiple layers of unlabeled or labeled stem cells with different densities was prepared. The phantom was imaged with quantitative UTE (qUTE) MR techniques [i.e., UTE-T1 mapping, UTE-T2* mapping, and UTE-based quantitative susceptibility mapping (UTE-QSM)] as well as with a clinical T2 mapping sequence on a 3T clinical MR system. For T1 mapping, a variable flip angle (VFA) method based on actual flip angle imaging (AFI) technique was utilized. For T2* mapping and UTE-QSM, multiple images with variable, interleaved echo times including UTE images and gradient recalled echo (GRE) images were used. For UTE-QSM, the phase information from the multi-echo images was utilized and processed using a QSM framework based on the morphology-enabled dipole inversion (MEDI) algorithm. The qUTE techniques were also evaluated in an ex vivo experiment with a mouse injected with IONP-labeled stem cells. Results In the phantom experiment, the parameters estimated with qUTE techniques showed high linearity with respect to the density of IONP-labeled stem cells (R2>0.99), while the clinical T2 parameter showed impaired linearity (R2=0.87). In the ex vivo mouse experiment, UTE-T2* mapping and UTE-QSM showed feasibility in the detection of injected stem cells with high contrast, whereas UTE-T1 and UTE-T2* showed limited detection. Overall, UTE-QSM demonstrated the best contrast of all, with other methods being subjected more to a confounding factor due to different magnetic susceptibilities of various types of neighboring tissues, which creates inhomogeneous contrast that behaves similar to IONP. Conclusions In this study, we evaluated the feasibility of a series of qUTE imaging techniques as well as conventional T2 mapping for the detection of IONP-labeled stem cells in vitro and ex vivo. UTE-QSM performed superior amongst other qUTE techniques as well as conventional T2 mapping in detecting stem cells with high contrast.
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Affiliation(s)
- Jiyo S. Athertya
- Department of Radiology, University of California, San Diego, San Diego, CA, USA
| | | | - Sam Sedaghat
- Department of Radiology, University of California, San Diego, San Diego, CA, USA
| | - Zhao Wei
- Department of Radiology, University of California, San Diego, San Diego, CA, USA
| | - Dina Moazamian
- Department of Radiology, University of California, San Diego, San Diego, CA, USA
| | - Sophia Dwek
- Department of Radiology, University of California, San Diego, San Diego, CA, USA
| | - Mya Thu
- VisiCELL Medical Inc., San Diego, CA, USA
| | - Hyungseok Jang
- Department of Radiology, University of California, San Diego, San Diego, CA, USA
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10
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Dubovan PI, Baron CA. Model-based determination of the synchronization delay between MRI and trajectory data. Magn Reson Med 2023; 89:721-728. [PMID: 36161333 DOI: 10.1002/mrm.29460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/26/2022] [Accepted: 08/27/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Real-time monitoring of dynamic magnetic fields has recently become a commercially available option for measuring MRI k-space trajectories and magnetic fields induced by eddy currents in real time. However, for accurate image reconstructions, sub-microsecond synchronization between the MRI data and field dynamics (ie, k-space trajectory plus other spatially varying fields) is required. In this work, we introduce a new model-based algorithm to automatically perform this synchronization using only the MRI data and field dynamics. METHODS The algorithm works by enforcing consistency among the MRI data, field dynamics, and receiver sensitivity profiles by iteratively alternating between convex optimizations for (a) the image and (b) the synchronization delay. A healthy human subject was scanned at 7 T using a transmit-receive coil with integrated field probes using both single-shot spiral and EPI, and reconstructions with various synchronization delays were compared with the result of the proposed algorithm. The accuracy of the algorithm was also investigated using simulations, in which the acquisition delays for simulated acquisitions were determined using the proposed algorithm and compared with the known ground truth. RESULTS In the in vivo scans, the proposed algorithm minimized artifacts related to synchronization delay for both spiral and EPI acquisitions, and the computation time required was less than 30 s. The simulations demonstrated accuracy to within tens of nanoseconds. CONCLUSIONS The proposed algorithm can automatically determine synchronization delays between MRI data and field dynamics measured using a field probe system.
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Affiliation(s)
- Paul Ioan Dubovan
- Department of Medical Biophysics, Western University, London, Ontario, Canada.,Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Corey Allan Baron
- Department of Medical Biophysics, Western University, London, Ontario, Canada.,Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada
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11
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Blumenthal M, Luo G, Schilling M, Holme HCM, Uecker M. Deep, deep learning with BART. Magn Reson Med 2023; 89:678-693. [PMID: 36254526 PMCID: PMC10898647 DOI: 10.1002/mrm.29485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/26/2022] [Accepted: 09/20/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To develop a deep-learning-based image reconstruction framework for reproducible research in MRI. METHODS The BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing. In this work, BART was extended by a nonlinear operator framework that provides automatic differentiation to allow computation of gradients. Existing MRI-specific operators of BART, such as the nonuniform fast Fourier transform, are directly integrated into this framework and are complemented by common building blocks used in neural networks. To evaluate the use of the framework for advanced deep-learning-based reconstruction, two state-of-the-art unrolled reconstruction networks, namely the Variational Network and MoDL, were implemented. RESULTS State-of-the-art deep image-reconstruction networks can be constructed and trained using BART's gradient-based optimization algorithms. The BART implementation achieves a similar performance in terms of training time and reconstruction quality compared to the original implementations based on TensorFlow. CONCLUSION By integrating nonlinear operators and neural networks into BART, we provide a general framework for deep-learning-based reconstruction in MRI.
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Affiliation(s)
- Moritz Blumenthal
- Institute for Diagnostic and Interventional Radiology,
University Medical Center Göttingen, Göttingen, Germany
| | - Guanxiong Luo
- Institute for Diagnostic and Interventional Radiology,
University Medical Center Göttingen, Göttingen, Germany
| | - Martin Schilling
- Institute for Diagnostic and Interventional Radiology,
University Medical Center Göttingen, Göttingen, Germany
| | | | - Martin Uecker
- Institute for Diagnostic and Interventional Radiology,
University Medical Center Göttingen, Göttingen, Germany
- Institute of Biomedical Imaging, Graz University of
Technology, Graz, Austria
- German Centre for Cardiovascular Research (DZHK),Partner
Site Göttingen, Göttingen, Germany
- Cluster of Excellence “Multiscale Bioimaging: from
Molecular Machines to Networks of Excitable Cells” (MBExC), University of
Göttingen, Germany
- BioTechMed-Graz, Graz, Austria
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12
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Phair A, Cruz G, Qi H, Botnar RM, Prieto C. Free-running 3D whole-heart T 1 and T 2 mapping and cine MRI using low-rank reconstruction with non-rigid cardiac motion correction. Magn Reson Med 2023; 89:217-232. [PMID: 36198014 PMCID: PMC9828568 DOI: 10.1002/mrm.29449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/14/2022] [Accepted: 08/18/2022] [Indexed: 01/12/2023]
Abstract
PURPOSE To introduce non-rigid cardiac motion correction into a novel free-running framework for the simultaneous acquisition of 3D whole-heart myocardial <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images, enabling a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>∼</mml:mo></mml:mrow> <mml:annotation>$$ \sim $$</mml:annotation></mml:semantics> </mml:math> 3-min scan. METHODS Data were acquired using a free-running 3D golden-angle radial readout interleaved with inversion recovery and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> -preparation pulses. After correction for translational respiratory motion, non-rigid cardiac-motion-corrected reconstruction with dictionary-based low-rank compression and patch-based regularization enabled 3D whole-heart <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> mapping at any given cardiac phase as well as whole-heart cardiac cine imaging. The framework was validated and compared with established methods in 11 healthy subjects. RESULTS Good quality 3D <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images were reconstructed for all subjects. Septal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> values using the proposed approach ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>1200</mml:mn> <mml:mo>±</mml:mo> <mml:mn>50</mml:mn></mml:mrow> <mml:annotation>$$ 1200\pm 50 $$</mml:annotation></mml:semantics> </mml:math> ms) were higher than those from a 2D MOLLI sequence ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>1063</mml:mn> <mml:mo>±</mml:mo> <mml:mn>33</mml:mn></mml:mrow> <mml:annotation>$$ 1063\pm 33 $$</mml:annotation></mml:semantics> </mml:math> ms), which is known to underestimate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> , while <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> values from the proposed approach ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>51</mml:mn> <mml:mo>±</mml:mo> <mml:mn>4</mml:mn></mml:mrow> <mml:annotation>$$ 51\pm 4 $$</mml:annotation></mml:semantics> </mml:math> ms) were in good agreement with those from a 2D GraSE sequence ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>51</mml:mn> <mml:mo>±</mml:mo> <mml:mn>2</mml:mn></mml:mrow> <mml:annotation>$$ 51\pm 2 $$</mml:annotation></mml:semantics> </mml:math> ms). CONCLUSION The proposed technique provides 3D <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images with isotropic spatial resolution in a single <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>∼</mml:mo></mml:mrow> <mml:annotation>$$ \sim $$</mml:annotation></mml:semantics> </mml:math> 3.3-min scan.
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Affiliation(s)
- Andrew Phair
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Gastão Cruz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Haikun Qi
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
| | - René M. Botnar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK,Instituto de Ingeniería Biológica y MédicaPontificia Universidad Católica de ChileSantiagoChile,Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile,Millennium Institute for Intelligent Healthcare EngineeringSantiagoChile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK,Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile,Millennium Institute for Intelligent Healthcare EngineeringSantiagoChile
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13
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Demirel OB, Yaman B, Shenoy C, Moeller S, Weingärtner S, Akçakaya M. Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR. Magn Reson Med 2023; 89:308-321. [PMID: 36128896 PMCID: PMC9617789 DOI: 10.1002/mrm.29453] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 07/21/2022] [Accepted: 08/21/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE To develop a physics-guided deep learning (PG-DL) reconstruction strategy based on a signal intensity informed multi-coil (SIIM) encoding operator for highly-accelerated simultaneous multislice (SMS) myocardial perfusion cardiac MRI (CMR). METHODS First-pass perfusion CMR acquires highly-accelerated images with dynamically varying signal intensity/SNR following the administration of a gadolinium-based contrast agent. Thus, using PG-DL reconstruction with a conventional multi-coil encoding operator leads to analogous signal intensity variations across different time-frames at the network output, creating difficulties in generalization for varying SNR levels. We propose to use a SIIM encoding operator to capture the signal intensity/SNR variations across time-frames in a reformulated encoding operator. This leads to a more uniform/flat contrast at the output of the PG-DL network, facilitating generalizability across time-frames. PG-DL reconstruction with the proposed SIIM encoding operator is compared to PG-DL with conventional encoding operator, split slice-GRAPPA, locally low-rank (LLR) regularized reconstruction, low-rank plus sparse (L + S) reconstruction, and regularized ROCK-SPIRiT. RESULTS Results on highly accelerated free-breathing first pass myocardial perfusion CMR at three-fold SMS and four-fold in-plane acceleration show that the proposed method improves upon the reconstruction methods use for comparison. Substantial noise reduction is achieved compared to split slice-GRAPPA, and aliasing artifacts reduction compared to LLR regularized reconstruction, L + S reconstruction and PG-DL with conventional encoding. Furthermore, a qualitative reader study indicated that proposed method outperformed all methods. CONCLUSION PG-DL reconstruction with the proposed SIIM encoding operator improves generalization across different time-frames /SNRs in highly accelerated perfusion CMR.
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Affiliation(s)
- Omer Burak Demirel
- Department of Electrical and Computer EngineeringUniversity of MinnesotaMinneapolisMinnesotaUSA,Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Burhaneddin Yaman
- Department of Electrical and Computer EngineeringUniversity of MinnesotaMinneapolisMinnesotaUSA,Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Chetan Shenoy
- Department of Medicine (Cardiology)University of MinnesotaMinneapolisMinnesotaUSA
| | - Steen Moeller
- Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | | | - Mehmet Akçakaya
- Department of Electrical and Computer EngineeringUniversity of MinnesotaMinneapolisMinnesotaUSA,Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
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14
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Athertya JS, Ma Y, Masoud Afsahi A, Lombardi AF, Moazamian D, Jerban S, Sedaghat S, Jang H. Accelerated Quantitative 3D UTE-Cones Imaging Using Compressed Sensing. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197459. [PMID: 36236557 PMCID: PMC9573555 DOI: 10.3390/s22197459] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/27/2022] [Accepted: 09/29/2022] [Indexed: 05/11/2023]
Abstract
In this study, the feasibility of accelerated quantitative Ultrashort Echo Time Cones (qUTE-Cones) imaging with compressed sensing (CS) reconstruction is investigated. qUTE-Cones sequences for variable flip angle-based UTE T1 mapping, UTE adiabatic T1ρ mapping, and UTE quantitative magnetization transfer modeling of macromolecular fraction (MMF) were implemented on a clinical 3T MR system. Twenty healthy volunteers were recruited and underwent whole-knee MRI using qUTE-Cones sequences. The k-space data were retrospectively undersampled with different undersampling rates. The undersampled qUTE-Cones data were reconstructed using both zero-filling and CS reconstruction. Using CS-reconstructed UTE images, various parameters were estimated in 10 different regions of interests (ROIs) in tendons, ligaments, menisci, and cartilage. Structural similarity, percentage error, and Pearson’s correlation were calculated to assess the performance. Dramatically reduced streaking artifacts and improved SSIM were observed in UTE images from CS reconstruction. A mean SSIM of ~0.90 was achieved for all CS-reconstructed images. Percentage errors between fully sampled and undersampled CS-reconstructed images were below 5% for up to 50% undersampling (i.e., 2× acceleration). High linear correlation was observed (>0.95) for all qUTE parameters estimated in all subjects. CS-based reconstruction combined with efficient Cones trajectory is expected to achieve a clinically feasible scan time for qUTE imaging.
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15
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Chen J, Pal P, Ahrens ET. Enhanced detection of paramagnetic fluorine-19 magnetic resonance imaging agents using zero echo time sequence and compressed sensing. NMR IN BIOMEDICINE 2022; 35:e4725. [PMID: 35262991 PMCID: PMC10655826 DOI: 10.1002/nbm.4725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 02/25/2022] [Accepted: 03/04/2022] [Indexed: 06/14/2023]
Abstract
Fluorine-19 (19 F) magnetic resonance imaging (MRI) is an emerging technique offering specific detection of labeled cells in vivo. Lengthy acquisition times and modest signal-to-noise ratio (SNR) makes three-dimensional spin-density-weighted 19 F imaging challenging. Recent advances in tracer paramagnetic metallo-perfluorocarbon (MPFC) nanoemulsion probes have shown multifold SNR improvements due to an accelerated 19 F T1 relaxation rate and a commensurate gain in imaging speed and averages. However, 19 F T2 -reduction and increased linewidth limit the amount of metal additive in MPFC probes, thus constraining the ultimate SNR. To overcome these barriers, we describe a compressed sampling (CS) scheme, implemented using a "zero" echo time (ZTE) sequence, with data reconstructed via a sparsity-promoting algorithm. Our CS-ZTE scheme acquires k-space data using an undersampled spherical radial pattern and signal averaging. Image reconstruction employs off-the-shelf sparse solvers to solve a joint total variation and l 1 -norm regularized least square problem. To evaluate CS-ZTE, we performed simulations and acquired 19 F MRI data at 11.7 T in phantoms and mice receiving MPFC-labeled dendritic cells. For MPFC-labeled cells in vivo, we show SNR gains of ~6.3 × with 8-fold undersampling. We show that this enhancement is due to three mechanisms including undersampling and commensurate increase in signal averaging in a fixed scan time, denoising attributes from the CS algorithm, and paramagnetic reduction of T1 . Importantly, 19 F image intensity analyses yield accurate estimates of absolute quantification of 19 F spins. Overall, the CS-ZTE method using MPFC probes achieves ultrafast imaging, a substantial boost in detection sensitivity, accurate 19 F spin quantification, and minimal image artifacts.
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Affiliation(s)
- Jiawen Chen
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA
| | - Piya Pal
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA
| | - Eric T. Ahrens
- Department of Radiology, University of California San Diego, La Jolla, California, USA
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16
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Zhang J, Han L, Sun J, Wang Z, Xu W, Chu Y, Xia L, Jiang M. Compressed sensing based dynamic MR image reconstruction by using 3D-total generalized variation and tensor decomposition: k-t TGV-TD. BMC Med Imaging 2022; 22:101. [PMID: 35624425 PMCID: PMC9137209 DOI: 10.1186/s12880-022-00826-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 05/18/2022] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is a promising technique to accelerate dynamic cardiac MR imaging (DCMRI). For DCMRI, the CS-MRI usually exploits image signal sparsity and low-rank property to reconstruct dynamic images from the undersampled k-space data. In this paper, a novel CS algorithm is investigated to improve dynamic cardiac MR image reconstruction quality under the condition of minimizing the k-space recording. METHODS The sparse representation of 3D cardiac magnetic resonance data is implemented by synergistically integrating 3D total generalized variation (3D-TGV) algorithm and high order singular value decomposition (HOSVD) based Tensor Decomposition, termed k-t TGV-TD method. In the proposed method, the low rank structure of the 3D dynamic cardiac MR data is performed with the HOSVD method, and the localized image sparsity is achieved by the 3D-TGV method. Moreover, the Fast Composite Splitting Algorithm (FCSA) method, combining the variable splitting with operator splitting techniques, is employed to solve the low-rank and sparse problem. Two different cardiac MR datasets (cardiac perfusion and cine MR datasets) are used to evaluate the performance of the proposed method. RESULTS Compared with the state-of-art methods, such as k-t SLR, 3D-TGV, HOSVD based tensor decomposition and low-rank plus sparse method, the proposed k-t TGV-TD method can offer improved reconstruction accuracy in terms of higher peak SNR (PSNR) and structural similarity index (SSIM). The proposed k-t TGV-TD method can achieve significantly better and stable reconstruction results than state-of-the-art methods in terms of both PSNR and SSIM, especially for cardiac perfusion MR dataset. CONCLUSIONS This work proved that the k-t TGV-TD method was an effective sparse representation way for DCMRI, which was capable of significantly improving the reconstruction accuracy with different acceleration factors.
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Affiliation(s)
- Jucheng Zhang
- Department of Clinical Engineering, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310019, People's Republic of China
| | - Lulu Han
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, People's Republic of China.,Zhejiang Aerospace HengJia Data Technology Co., Ltd., Jiaxing, People's Republic of China
| | - Jianzhong Sun
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Zhikang Wang
- Department of Clinical Engineering, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310019, People's Republic of China
| | - Wenlong Xu
- Department of Biomedical Engineering, China Jiliang University, Hangzhou, 310018, People's Republic of China
| | - Yonghua Chu
- Department of Clinical Engineering, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310019, People's Republic of China
| | - Ling Xia
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Mingfeng Jiang
- School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, People's Republic of China.
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17
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Ljungberg E, Wood TC, Solana AB, Williams SCR, Barker GJ, Wiesinger F. Motion corrected silent ZTE neuroimaging. Magn Reson Med 2022; 88:195-210. [PMID: 35381110 PMCID: PMC9321117 DOI: 10.1002/mrm.29201] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/16/2022] [Accepted: 01/28/2022] [Indexed: 11/11/2022]
Abstract
Purpose To develop self‐navigated motion correction for 3D silent zero echo time (ZTE) based neuroimaging and characterize its performance for different types of head motion. Methods The proposed method termed MERLIN (Motion Estimation & Retrospective correction Leveraging Interleaved Navigators) achieves self‐navigation by using interleaved 3D phyllotaxis k‐space sampling. Low resolution navigator images are reconstructed continuously throughout the ZTE acquisition using a sliding window and co‐registered in image space relative to a fixed reference position. Rigid body motion corrections are then applied retrospectively to the k‐space trajectory and raw data and reconstructed into a final, high‐resolution ZTE image. Results MERLIN demonstrated successful and consistent motion correction for magnetization prepared ZTE images for a range of different instructed motion paradigms. The acoustic noise response of the self‐navigated phyllotaxis trajectory was found to be only slightly above ambient noise levels (<4 dBA). Conclusion Silent ZTE imaging combined with MERLIN addresses two major challenges intrinsic to MRI (i.e., subject motion and acoustic noise) in a synergistic and integrated manner without increase in scan time and thereby forms a versatile and powerful framework for clinical and research MR neuroimaging applications.
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Affiliation(s)
- Emil Ljungberg
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Tobias C Wood
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Steven C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Florian Wiesinger
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,GE Healthcare, Munich, Germany
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18
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Chen Z, Chen Y, Xie Y, Li D, Christodoulou AG. Data-Consistent non-Cartesian deep subspace learning for efficient dynamic MR image reconstruction. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2022; 2022:10.1109/isbi52829.2022.9761497. [PMID: 35572068 PMCID: PMC9104888 DOI: 10.1109/isbi52829.2022.9761497] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Non-Cartesian sampling with subspace-constrained image reconstruction is a popular approach to dynamic MRI, but slow iterative reconstruction limits its clinical application. Data-consistent (DC) deep learning can accelerate reconstruction with good image quality, but has not been formulated for non-Cartesian subspace imaging. In this study, we propose a DC non-Cartesian deep subspace learning framework for fast, accurate dynamic MR image reconstruction. Four novel DC formulations are developed and evaluated: two gradient decent approaches, a directly solved approach, and a conjugate gradient approach. We applied a U-Net model with and without DC layers to reconstruct T1-weighted images for cardiac MR Multitasking (an advanced multidimensional imaging method), comparing our results to the iteratively reconstructed reference. Experimental results show that the proposed framework significantly improves reconstruction accuracy over the U-Net model without DC, while significantly accelerating the reconstruction over conventional iterative reconstruction.
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Affiliation(s)
- Zihao Chen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
- Department of Bioengineering, UCLA, Los Angeles, USA
| | - Yuhua Chen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
- Department of Bioengineering, UCLA, Los Angeles, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
- Department of Bioengineering, UCLA, Los Angeles, USA
| | - Anthony G Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
- Department of Bioengineering, UCLA, Los Angeles, USA
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19
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Gilbert KM, Dubovan PI, Gati JS, Menon RS, Baron CA. Integration of an RF coil and commercial field camera for ultrahigh-field MRI. Magn Reson Med 2021; 87:2551-2565. [PMID: 34932225 DOI: 10.1002/mrm.29130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/16/2021] [Accepted: 12/03/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE To develop an RF coil with an integrated commercial field camera for ultrahigh field (7T) neuroimaging. The RF coil would operate within a head-only gradient coil and be subject to the corresponding design constraints. The RF coil can thereafter be used for subject-specific correction of k-space trajectories-notably in gradient-sensitive sequences such as single-shot spiral imaging. METHODS The transmit and receive performance was evaluated before and after the integration of field probes, whereas field probes were evaluated when in an optimal configuration external to the coil and after their integration. Diffusion-weighted EPI and single-shot spiral acquisitions were employed to evaluate the efficacy of correcting higher order field perturbations and the consequent effect on image quality. RESULTS Field probes had a negligible effect on RF-coil performance, including the transmit efficiency, transmit uniformity, and mean SNR over the brain. Modest reductions in field-probe signal lifetimes were observed, caused primarily by nonidealities in the gradient and shim fields of the head-only gradient coil at the probe positions. The field-monitoring system could correct up to second-order field perturbations in single-shot spiral imaging. CONCLUSION The integrated RF coil and field camera was capable of concurrent-field monitoring within a 7T head-only scanner and facilitated the subsequent correction of k-space trajectories during spiral imaging.
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Affiliation(s)
- Kyle M Gilbert
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada.,Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Paul I Dubovan
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada.,Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Joseph S Gati
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada.,Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada.,Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Corey A Baron
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada.,Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
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20
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Franson D, Dupuis A, Gulani V, Griswold M, Seiberlich N. A System for Real-Time, Online Mixed-Reality Visualization of Cardiac Magnetic Resonance Images. J Imaging 2021; 7:jimaging7120274. [PMID: 34940741 PMCID: PMC8709155 DOI: 10.3390/jimaging7120274] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 12/07/2021] [Accepted: 12/09/2021] [Indexed: 11/16/2022] Open
Abstract
Image-guided cardiovascular interventions are rapidly evolving procedures that necessitate imaging systems capable of rapid data acquisition and low-latency image reconstruction and visualization. Compared to alternative modalities, Magnetic Resonance Imaging (MRI) is attractive for guidance in complex interventional settings thanks to excellent soft tissue contrast and large fields-of-view without exposure to ionizing radiation. However, most clinically deployed MRI sequences and visualization pipelines exhibit poor latency characteristics, and spatial integration of complex anatomy and device orientation can be challenging on conventional 2D displays. This work demonstrates a proof-of-concept system linking real-time cardiac MR image acquisition, online low-latency reconstruction, and a stereoscopic display to support further development in real-time MR-guided intervention. Data are acquired using an undersampled, radial trajectory and reconstructed via parallelized through-time radial generalized autocalibrating partially parallel acquisition (GRAPPA) implemented on graphics processing units. Images are rendered for display in a stereoscopic mixed-reality head-mounted display. The system is successfully tested by imaging standard cardiac views in healthy volunteers. Datasets comprised of one slice (46 ms), two slices (92 ms), and three slices (138 ms) are collected, with the acquisition time of each listed in parentheses. Images are displayed with latencies of 42 ms/frame or less for all three conditions. Volumetric data are acquired at one volume per heartbeat with acquisition times of 467 ms and 588 ms when 8 and 12 partitions are acquired, respectively. Volumes are displayed with a latency of 286 ms or less. The faster-than-acquisition latencies for both planar and volumetric display enable real-time 3D visualization of the heart.
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Affiliation(s)
- Dominique Franson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
- Correspondence: (D.F.); (A.D.)
| | - Andrew Dupuis
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
- Correspondence: (D.F.); (A.D.)
| | - Vikas Gulani
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (V.G.); (N.S.)
| | - Mark Griswold
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (V.G.); (N.S.)
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21
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Hong K, Schiffers F, DiCarlo AL, Rigsby CK, Haji-Valizadeh H, Lee DC, Markl M, Katsaggelos AK, Kim D. Accelerating compressed sensing reconstruction of subsampled radial k-space data using geometrically-derived density compensation. Phys Med Biol 2021; 66. [PMID: 34607316 DOI: 10.1088/1361-6560/ac2c9d] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 10/04/2021] [Indexed: 11/12/2022]
Abstract
Objective.To accelerate compressed sensing (CS) reconstruction of subsampled radial k-space data using a geometrically-derived density compensation function (gDCF) without significant loss in image quality.Approach.We developed a theoretical framework to calculate a gDCF based on Nyquist distance along the radial and circumferential directions of a discrete polar coordinate system. Our gDCF was compared against standard DCF (e.g. ramp filter) and another commonly used DCF (modified Shepp-Logan (SL) filter). The resulting image quality produced by each DCF was quantified using normalized root-mean-square-error (NRMSE), blur metric (1 = blurriest; 0 = sharpest), and structural similarity index (SSIM; 1 = perfect match; 0 = no match) compared with the reference. For filtered backprojection (FBP) of phantom data obtained at the Nyquist sampling rate, Cartesian k-space sampling was used as the reference. For CS reconstruction of subsampled cardiac magnetic resonance imaging datasets (real-time cardiac cine data with 11 projections per frame,n = 20 patients; cardiac perfusion data with 30 projections per frame,n = 19 patients), CS reconstruction without DCF was used as the reference.Main results.The NRMSE, SSIM, and blur metrics of the phantom data were good for all DCFs, confirming that our gDCF produces uniform densities at the upper limit (Nyquist). For CS reconstruction of subsampled real-time cine and cardiac perfusion datasets, the image quality metrics (SSIM, NRMSE) were significantly (p < 0.05) higher for our gDCF than other DCFs, and the reconstruction time was significantly (p < 0.05) faster for our gDCF (reference) than no DCF (11.9%-52.9% slower), standard DCF (23.9%-57.6% slower), and modified SL filter (13.5%-34.8% slower).Significance.The proposed gDCF accelerates CS reconstruction of subsampled radial k-space data without significant loss in image quality compared with no DCF as the reference.
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Affiliation(s)
- KyungPyo Hong
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Florian Schiffers
- Department of Computer Science, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, Illinois, United States of America
| | - Amanda L DiCarlo
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Cynthia K Rigsby
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.,Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, United States of America
| | - Hassan Haji-Valizadeh
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Daniel C Lee
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.,Department of Medicine, Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Michael Markl
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.,Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois, United States of America
| | - Aggelos K Katsaggelos
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.,Department of Computer Science, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, Illinois, United States of America.,Department of Electrical and Computer Engineering, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, Illinois, United States of America
| | - Daniel Kim
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.,Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois, United States of America
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22
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Dwork N, O'Connor D, Baron CA, Johnson EMI, Kerr AB, Pauly JM, Larson PEZ. Utilizing the Wavelet Transform's Structure in Compressed Sensing. SIGNAL, IMAGE AND VIDEO PROCESSING 2021; 15:1407-1414. [PMID: 34531930 PMCID: PMC8439112 DOI: 10.1007/s11760-021-01872-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/11/2021] [Accepted: 02/08/2021] [Indexed: 06/13/2023]
Abstract
Compressed sensing has empowered quality image reconstruction with fewer data samples than previously thought possible. These techniques rely on a sparsifying linear transformation. The Daubechies wavelet transform is commonly used for this purpose. In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result. After inclusion of this affine transformation, we modify the resulting optimization problem to comply with the form of the Basis Pursuit Denoising problem. Finally, we show theoretically that this yields a lower bound on the error of the reconstruction and present results where solving this modified problem yields images of higher quality for the same sampling patterns using both magnetic resonance and optical images.
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Affiliation(s)
- Nicholas Dwork
- University of California, San Francisco, Department of Radiology and Biomedical Imaging
| | - Daniel O'Connor
- Department of Mathematics and Statistics, University of San Francisco
| | | | | | - Adam B Kerr
- Stanford University, Center for Cognitive and Neurobiological Imaging
| | - John M Pauly
- Stanford University, Department of Electrical Engineering
| | - Peder E Z Larson
- University of California, San Francisco, Department of Radiology and Biomedical Imaging
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23
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Domain knowledge augmentation of parallel MR image reconstruction using deep learning. Comput Med Imaging Graph 2021; 92:101968. [PMID: 34390918 DOI: 10.1016/j.compmedimag.2021.101968] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 07/08/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
A deep learning (DL) method for accelerated magnetic resonance (MR) imaging is presented that incorporates domain knowledge of parallel MR imaging to augment the DL networks for accurate and stable image reconstruction. The proposed DL method employs a novel loss function consisting of a combination of mean absolute error, structural similarity, and sobel edge loss. The DL model takes both original measurements and images reconstructed by the parallel imaging method as inputs to the network. The accuracy of the proposed method was evaluated using two anatomical regions and six MRI contrasts and was compared with state-of-the-art parallel imaging and deep learning methods. The proposed method significantly outperformed the other methods for all the six different contrasts in terms of structural similarity, peak signal to noise ratio, and normalized mean squared error. The out-of-sample performance of the proposed method was assessed for a truly "unseen" case in a volunteer scan. The method produced images without any artificial features, often occurring in the DL image reconstruction methods. A stability analysis was performed by adding perturbations to the input, which demonstrated that the proposed method is robust and stable with respect to small structural changes, and different undersampling ratios. Comprehensive validation on large datasets demonstrated that incorporation of domain knowledge sufficiently regularizes the DL based image reconstruction and produces accurate and stable image enhancement.
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24
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Bogner W, Otazo R, Henning A. Accelerated MR spectroscopic imaging-a review of current and emerging techniques. NMR IN BIOMEDICINE 2021; 34:e4314. [PMID: 32399974 PMCID: PMC8244067 DOI: 10.1002/nbm.4314] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 03/24/2020] [Accepted: 03/30/2020] [Indexed: 05/14/2023]
Abstract
Over more than 30 years in vivo MR spectroscopic imaging (MRSI) has undergone an enormous evolution from theoretical concepts in the early 1980s to the robust imaging technique that it is today. The development of both fast and efficient sampling and reconstruction techniques has played a fundamental role in this process. State-of-the-art MRSI has grown from a slow purely phase-encoded acquisition technique to a method that today combines the benefits of different acceleration techniques. These include shortening of repetition times, spatial-spectral encoding, undersampling of k-space and time domain, and use of spatial-spectral prior knowledge in the reconstruction. In this way in vivo MRSI has considerably advanced in terms of spatial coverage, spatial resolution, acquisition speed, artifact suppression, number of detectable metabolites and quantification precision. Acceleration not only has been the enabling factor in high-resolution whole-brain 1 H-MRSI, but today is also common in non-proton MRSI (31 P, 2 H and 13 C) and applied in many different organs. In this process, MRSI techniques had to constantly adapt, but have also benefitted from the significant increase of magnetic field strength boosting the signal-to-noise ratio along with high gradient fidelity and high-density receive arrays. In combination with recent trends in image reconstruction and much improved computation power, these advances led to a number of novel developments with respect to MRSI acceleration. Today MRSI allows for non-invasive and non-ionizing mapping of the spatial distribution of various metabolites' tissue concentrations in animals or humans, is applied for clinical diagnostics and has been established as an important tool for neuro-scientific and metabolism research. This review highlights the developments of the last five years and puts them into the context of earlier MRSI acceleration techniques. In addition to 1 H-MRSI it also includes other relevant nuclei and is not limited to certain body regions or specific applications.
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Affiliation(s)
- Wolfgang Bogner
- High‐Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Ricardo Otazo
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew York, New YorkUSA
| | - Anke Henning
- Max Planck Institute for Biological CyberneticsTübingenGermany
- Advanced Imaging Research Center, UT Southwestern Medical CenterDallasTexasUSA
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25
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Shan S, Li M, Li M, Tang F, Crozier S, Liu F. ReUINet: A fast GNL distortion correction approach on a 1.0 T MRI-Linac scanner. Med Phys 2021; 48:2991-3002. [PMID: 33763850 DOI: 10.1002/mp.14861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/17/2021] [Accepted: 03/19/2021] [Indexed: 11/12/2022] Open
Abstract
PURPOSE The hybrid system combining a magnetic resonance imaging (MRI) scanner with a linear accelerator (Linac) has become increasingly desirable for tumor treatment because of excellent soft tissue contrast and nonionizing radiation. However, image distortions caused by gradient nonlinearity (GNL) can have detrimental impacts on real-time radiotherapy using MRI-Linac systems, where accurate geometric information of tumors is essential. METHODS In this work, we proposed a deep convolutional neural network-based method to efficiently recover undistorted images (ReUINet) for real-time image guidance. The ReUINet, based on the encoder-decoder structure, was created to learn the relationship between the undistorted images and distorted images. The ReUINet was pretrained and tested on a publically available brain MR image dataset acquired from 23 volunteers. Then, transfer learning was adopted to implement the pretrained model (i.e., network with optimal weights) on the experimental three-dimensional (3D) grid phantom and in-vivo pelvis image datasets acquired from the 1.0 T Australian MRI-Linac system. RESULTS Evaluations on the phantom (768 slices) and pelvis data (88 slices) showed that the ReUINet achieved improvement over 15 times and 45 times on computational efficiency in comparison with standard interpolation and GNL-encoding methods, respectively. Moreover, qualitative and quantitative results demonstrated that the ReUINet provided better correction results than the standard interpolation method, and comparable performance compared to the GNL-encoding approach. CONCLUSIONS Validated by simulation and experimental results, the proposed ReUINet showed promise in obtaining accurate MR images for the implementation of real-time MRI-guided radiotherapy.
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Affiliation(s)
- Shanshan Shan
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia.,ACRF Image X Institute, School of Health Sciences, University of Sydney, Sydney, Australia
| | - Mao Li
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Mingyan Li
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Fangfang Tang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
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26
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Zhang Y, She H, Du YP. Dynamic MRI of the abdomen using parallel non-Cartesian convolutional recurrent neural networks. Magn Reson Med 2021; 86:964-973. [PMID: 33749023 DOI: 10.1002/mrm.28774] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/25/2021] [Accepted: 02/25/2021] [Indexed: 11/10/2022]
Abstract
PURPOSE To improve the image quality and reduce computational time for the reconstruction of undersampled non-Cartesian abdominal dynamic parallel MR data using the deep learning approach. METHODS An algorithm of parallel non-Cartesian convolutional recurrent neural networks (PNCRNNs) was developed to enable the use of the redundant information in both spatial and temporal domains, and achieve data fidelity for the reconstruction of non-Cartesian parallel MR data. The performance of PNCRNNs was evaluated for various acceleration rates, motion patterns, and imaging applications in comparison with that of the state-of-the-art algorithms of dynamic imaging, including extra-dimensional golden-angle radial sparse parallel MRI (XD-GRASP), low-rank plus sparse matrix decomposition (L+S), blind compressive sensing (BCS), and 3D convolutional neural networks (3D CNNs). RESULTS PNCRNNs increased the peak SNR of 9.07 dB compared with XD-GRASP, 9.26 dB compared with L+S, 3.48 dB compared with BCS, and 3.14 dB compared with 3D CNN at R = 16. The reconstruction time was 18 ms for each bin, which was two orders faster than that of XD-GRASP, L+S, and BCS. PNCRNNs provided good reconstruction for various motion patterns, k-space trajectories, and imaging applications. CONCLUSION The proposed PNCRNN provides substantial improvement of the image quality for dynamic golden-angle radial imaging of the abdomen in comparison with XD-GRASP, L+S, BCS, and 3D CNN. The reconstruction time of PNCRNN can be as fast as 50 bins per second, due to the use of the highly computational efficient Toeplitz approach.
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Affiliation(s)
- Yufei Zhang
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Huajun She
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yiping P Du
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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27
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Jaeger E, Sonnabend K, Schaarschmidt F, Maintz D, Weiss K, Bunck AC. Compressed-sensing accelerated 4D flow MRI of cerebrospinal fluid dynamics. Fluids Barriers CNS 2020; 17:43. [PMID: 32677977 PMCID: PMC7364783 DOI: 10.1186/s12987-020-00206-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 07/06/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND 4D flow magnetic resonance imaging (MRI) of CSF can make an important contribution to the understanding of hydrodynamic changes in various neurological diseases but remains limited in clinical application due to long acquisition times. The aim of this study was to evaluate the accuracy of compressed SENSE accelerated MRI measurements of the spinal CSF flow. METHODS In 20 healthy subjects 4D flow MRI of the CSF in the cervical spine was acquired using compressed sensitivity encoding [CSE, a combination of compressed sensing and parallel imaging (SENSE) provided by the manufacturer] with acceleration factors between 4 and 10. A conventional scan using SENSE was used as reference. Extracted parameters were peak velocity, absolute net flow, forward flow and backward flow. Bland-Altman analysis was performed to determine the scan-rescan reproducibility and the agreement between SENSE and compressed SENSE. Additionally, a time accumulated flow error was calculated. In one additional subject flow of the spinal canal at the level of the entire spinal cord was assessed. RESULTS Averaged acquisition times were 10:21 min (SENSE), 9:31 min (CSE4), 6:25 min (CSE6), 4:53 min (CSE8) and 3:51 min (CSE10). Acquisition of the CSF flow surrounding the entire spinal cord took 14:40 min. Bland-Altman analysis showed good agreement for peak velocity, but slight overestimations for absolute net flow, forward flow and backward flow (< 1 ml/min) in CSE4-8. Results of the accumulated flow error were similar for CSE4 to CSE8. CONCLUSION A quantitative analysis of acceleration factors CSE4-10 showed that CSE with an acceleration factor up to 6 is feasible. This allows a scan time reduction of 40% and enables the acquisition and analysis of the CSF flow dynamics surrounding the entire spinal cord within a clinically acceptable scan time.
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Affiliation(s)
- Elena Jaeger
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Street 62, 50937, Cologne, Germany
| | - Kristina Sonnabend
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Street 62, 50937, Cologne, Germany.
| | - Frank Schaarschmidt
- Institute of Cell Biology and Biophysics, Biostatistics Department, Leibniz University Hannover, Hannover, Germany
| | - David Maintz
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Street 62, 50937, Cologne, Germany
| | - Kilian Weiss
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Street 62, 50937, Cologne, Germany.,Philips GmbH, Hamburg, Germany
| | - Alexander C Bunck
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Street 62, 50937, Cologne, Germany
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28
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Kozak BM, Jaimes C, Kirsch J, Gee MS. MRI Techniques to Decrease Imaging Times in Children. Radiographics 2020; 40:485-502. [PMID: 32031912 DOI: 10.1148/rg.2020190112] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Long acquisition times can limit the use of MRI in pediatric patients, and the use of sedation or general anesthesia is frequently necessary to facilitate diagnostic examinations. The use of sedation or anesthesia has disadvantages including increased cost and imaging time and potential risks to the patient. Reductions in imaging time may decrease or eliminate the need for sedation or general anesthesia. Over the past decade, a number of imaging techniques that can decrease imaging time have become commercially available. These products have been used increasingly in clinical practice and include parallel imaging, simultaneous multisection imaging, radial k-space acquisition, compressed sensing MRI reconstruction, and automated protocol selection software. The underlying concepts, supporting data, current clinical applications, and available products for each of these strategies are reviewed in this article. In addition, emerging techniques that are still under investigation may provide further reductions in imaging time, including artificial intelligence-based reconstruction, gradient-controlled aliasing sampling and reconstruction, three-dimensional MR spectroscopy, and prospective motion correction. The preliminary results for these techniques are also discussed. ©RSNA, 2020 See discussion on this article by Greer and Vasanawala.
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Affiliation(s)
- Benjamin M Kozak
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Founders 210, Boston, MA 02114 (B.M.K., J.K., M.S.G.); Department of Radiology, Harvard Medical School, Boston, Mass (B.M.K., C.J., J.K., M.S.G.); and Department of Radiology, Boston Children's Hospital, Boston, Mass (C.J.)
| | - Camilo Jaimes
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Founders 210, Boston, MA 02114 (B.M.K., J.K., M.S.G.); Department of Radiology, Harvard Medical School, Boston, Mass (B.M.K., C.J., J.K., M.S.G.); and Department of Radiology, Boston Children's Hospital, Boston, Mass (C.J.)
| | - John Kirsch
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Founders 210, Boston, MA 02114 (B.M.K., J.K., M.S.G.); Department of Radiology, Harvard Medical School, Boston, Mass (B.M.K., C.J., J.K., M.S.G.); and Department of Radiology, Boston Children's Hospital, Boston, Mass (C.J.)
| | - Michael S Gee
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Founders 210, Boston, MA 02114 (B.M.K., J.K., M.S.G.); Department of Radiology, Harvard Medical School, Boston, Mass (B.M.K., C.J., J.K., M.S.G.); and Department of Radiology, Boston Children's Hospital, Boston, Mass (C.J.)
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29
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Malavé MO, Baron CA, Koundinyan SP, Sandino CM, Ong F, Cheng JY, Nishimura DG. Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model. Magn Reson Med 2020; 84:800-812. [PMID: 32011021 DOI: 10.1002/mrm.28177] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 12/04/2019] [Accepted: 12/27/2019] [Indexed: 12/28/2022]
Abstract
PURPOSE To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in coronary magnetic resonance angiography (CMRA). METHODS An end-to-end unrolled network is trained to reconstruct beat-to-beat 3D iNAVs acquired during a CMRA sequence. The unrolled model incorporates a nonuniform FFT operator in TensorFlow to perform the data-consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm. The training set includes 6,000 3D iNAVs acquired from 7 different subjects and 11 scans using a variable-density (VD) cones trajectory. For testing, 3D iNAVs from 4 additional subjects are reconstructed using the unrolled model. To validate reconstruction accuracy, global and localized motion estimates from DL model-based 3D iNAVs are compared with those extracted from 3D iNAVs reconstructed with l 1 -ESPIRiT. Then, the high-resolution coronary MRA images motion corrected with autofocusing using the l 1 -ESPIRiT and DL model-based 3D iNAVs are assessed for differences. RESULTS 3D iNAVs reconstructed using the DL model-based approach and conventional l 1 -ESPIRiT generate similar global and localized motion estimates and provide equivalent coronary image quality. Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of l 1 -ESPIRiT (20× and 3× speed increases, respectively). CONCLUSIONS We have developed a deep neural network architecture to reconstruct undersampled 3D non-Cartesian VD cones iNAVs. Our approach decreases reconstruction time for 3D iNAVs, while preserving the accuracy of nonrigid motion information offered by them for correction.
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Affiliation(s)
- Mario O Malavé
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Corey A Baron
- Department of Medical Biophysics, Western University, London, ON, Canada
| | - Srivathsan P Koundinyan
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Christopher M Sandino
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Frank Ong
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Joseph Y Cheng
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.,Department of Radiology, Stanford University, Stanford, CA
| | - Dwight G Nishimura
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
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30
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Menchón-Lara RM, Simmross-Wattenberg F, Casaseca-de-la-Higuera P, Martín-Fernández M, Alberola-López C. Reconstruction techniques for cardiac cine MRI. Insights Imaging 2019; 10:100. [PMID: 31549235 PMCID: PMC6757088 DOI: 10.1186/s13244-019-0754-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 05/17/2019] [Indexed: 12/17/2022] Open
Abstract
The present survey describes the state-of-the-art techniques for dynamic cardiac magnetic resonance image reconstruction. Additionally, clinical relevance, main challenges, and future trends of this image modality are outlined. Thus, this paper aims to provide a general vision about cine MRI as the standard procedure in functional evaluation of the heart, focusing on technical methodologies.
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Affiliation(s)
- Rosa-María Menchón-Lara
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain.
| | - Federico Simmross-Wattenberg
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain
| | - Pablo Casaseca-de-la-Higuera
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain
| | - Marcos Martín-Fernández
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain
| | - Carlos Alberola-López
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain
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Yuan J, Wong OL, Zhou Y, Chueng KY, Yu SK. A fast volumetric 4D-MRI with sub-second frame rate for abdominal motion monitoring and characterization in MRI-guided radiotherapy. Quant Imaging Med Surg 2019; 9:1303-1314. [PMID: 31448215 DOI: 10.21037/qims.2019.06.23] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background To propose a fast volumetric 4D-MRI based on 3D pulse sequence acquisition for abdominal motion monitoring and characterization in MRI-guided radiotherapy (MRgRT). Methods A 3D spoiled gradient echo sequence volumetric interpolated breath-hold examination (VIBE) [repetition time/echo time (TR/TE) =0.53/1.57 ms, flip-angle =5°, receiver bandwidth (RBW) =1,400 Hz/voxel] based 4D-MRI acquisition, accelerated by 4-fold controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA), named CAIPIRINHA-VIBE 4D-MRI, was implemented on a 1.5T MRI simulator (MR-sim) and applied for abdominal imaging of nine healthy volunteers under free breathing. One hundred and forty-four dynamics of the entire abdomen volume (56 slices), in total 8,064 (144×56) images with a voxel size of 2.7×2.7×4.0 mm3, were acquired in 89 s for 4D-MRI. This CAIPIRINHA-VIBE 4D-MRI was qualitatively compared with a 2D half-Fourier acquisition single-shot turbo spin-echo (2D-HASTE) based 4D-MRI. The motions of liver dome, kidney and spleen were analyzed using the CAIPIRINHA-VIBE 4D-MRI data. The kidney motion was quantitatively characterized in terms of motion range and the correlations between left and right kidneys. Results CAIPIRINHA-VIBE 4D-MRI was successfully conducted in all subjects. CAIPIRINHA-VIBE 4D-MRI exhibited much higher effective volumetric temporal resolution (0.615 vs. ~5 s/volume) and better reconstructed volume consistency than 2D-HASTE 4D-MRI. CAIPIRINHA-VIBE 4D-MRI was able to characterize the respiratory motion of abdominal organs simultaneously in three orthogonal directions, and could potentially be used for whole abdomen deformable motion tracking. Renal motion range was most pronounced in superior-inferior (SI) direction (L: 10.03±2.65 mm; R: 10.38±2.80 mm), significantly larger (P<0.001) than that in anterior-posterior (AP) and the least in left-right (LR) directions. Right kidney had significantly larger mobility (4.18±2.19 vs. 2.32±1.34 mm, P=0.045) than left kidney in AP, but not in LR and SI directions. The Pearson correlation coefficients r between left and right kidney motion were 0.5063 (P=0.164), 0.6624 (P=0.052) and 0.5752 (P=0.105) in LR, AP and SI correspondingly. The correlation of renal motion in SI and AP was found significant in right kidney (r=0.843, P=0.004) but not in left kidney (r=0.467, P=0.205). Conclusions A fast volumetric 4D-MRI was implemented for abdominal motion monitoring in MRgRT. A sub-second volumetric temporal resolution of 0.615 s, covering the entire abdomen, was demonstrated for respiratory motion monitoring and characterization. This technique holds potentials for MRgRT applications.
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Affiliation(s)
- Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Oi Lei Wong
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Kin Yin Chueng
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Siu Ki Yu
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, China
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Tian Y, Mendes J, Pedgaonkar A, Ibrahim M, Jensen L, Schroeder JD, Wilson B, DiBella EVR, Adluru G. Feasibility of multiple-view myocardial perfusion MRI using radial simultaneous multi-slice acquisitions. PLoS One 2019; 14:e0211738. [PMID: 30742641 PMCID: PMC6370206 DOI: 10.1371/journal.pone.0211738] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 01/18/2019] [Indexed: 11/18/2022] Open
Abstract
Purpose Dynamic contrast enhanced MRI of the heart typically acquires 2–4 short-axis (SA) slices to detect and characterize coronary artery disease. This acquisition scheme is limited by incomplete coverage of the left ventricle. We studied the feasibility of using radial simultaneous multi-slice (SMS) technique to achieve SA, 2-chamber and/or 4-chamber long-axis (2CH LA and/or 4CH LA) coverage with and without electrocardiography (ECG) gating using a motion-robust reconstruction framework. Methods 12 subjects were scanned at rest and/or stress, free breathing, with or without ECG gating. Multiple sets of radial SMS k-space were acquired within each cardiac cycle, and each SMS set sampled 3 parallel slices that were either SA, 2CH LA, or 4CH LA slices. The radial data was interpolated onto Cartesian space using an SMS GRAPPA operator gridding method. Self-gating and respiratory states binning of the data were done. The binning information as well as a pixel tracking spatiotemporal constrained reconstruction method were applied to obtain motion-robust image reconstructions. Reconstructions with and without the pixel tracking method were compared for signal-to-noise ratio and contrast-to-noise ratio. Results Full coverage of the heart (at least 3 SA and 3 LA slices) during the first pass of contrast at every heartbeat was achieved by using the radial SMS acquisition. The proposed pixel tracking reconstruction improves the average SNR and CNR by 21% and 30% respectively, and reduces temporal blurring for both gated and ungated acquisitions. Conclusion Acquiring simultaneous multi-slice SA, 2CH LA and/or 4CH LA myocardial perfusion images in every heartbeat is feasible in both gated and ungated acquisitions. This can add confidence when detecting and characterizing coronary artery disease by revealing ischemia in different views, and by providing apical coverage that is improved relative to SA slices alone. The proposed pixel tracking framework improves the reconstruction while adding little computational cost.
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Affiliation(s)
- Ye Tian
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
- Department of Physics and Astronomy, University of Utah, Salt Lake City, Utah, United States of America
| | - Jason Mendes
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Apoorva Pedgaonkar
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Mark Ibrahim
- Division of Cardiology, University of Utah, Salt Lake City, Utah, United States of America
| | - Leif Jensen
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Joyce D. Schroeder
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Brent Wilson
- Division of Cardiology, University of Utah, Salt Lake City, Utah, United States of America
| | - Edward V. R. DiBella
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
| | - Ganesh Adluru
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States of America
- * E-mail:
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Weller DS, Salerno M, Meyer CH. Content-aware compressive magnetic resonance image reconstruction. Magn Reson Imaging 2018; 52:118-130. [PMID: 29935257 PMCID: PMC6102097 DOI: 10.1016/j.mri.2018.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2018] [Revised: 06/11/2018] [Accepted: 06/13/2018] [Indexed: 11/25/2022]
Abstract
This paper describes an adaptive approach to regularizing model-based reconstructions in magnetic resonance imaging to account for local structure or image content. In conjunction with common models like wavelet and total variation sparsity, this content-aware regularization avoids oversmoothing or compromising image features while suppressing noise and incoherent aliasing from accelerated imaging. To evaluate this regularization approach, the experiments reconstruct images from single- and multi-channel, Cartesian and non-Cartesian, brain and cardiac data. These reconstructions combine common analysis-form regularizers and autocalibrating parallel imaging (when applicable). In most cases, the results show widespread improvement in structural similarity and peak-signal-to-error ratio relative to the fully sampled images. These results suggest that this content-aware regularization can preserve local image structures such as edges while providing denoising power superior to sparsity-promoting or sparsity-reweighted regularization.
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Affiliation(s)
| | - Michael Salerno
- University of Virginia, Charlottesville, VA 22904, USA; University of Virginia Health System, Charlottesville, VA 22908, USA.
| | - Craig H Meyer
- University of Virginia, Charlottesville, VA 22904, USA.
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Shen D, Edelman RR, Robinson JD, Haji-Valizadeh H, Messina M, Giri S, Koktzoglou I, Rigsby CK, Kim D. Single-Shot Coronary Quiescent-Interval Slice-Selective Magnetic Resonance Angiography Using Compressed Sensing: A Feasibility Study in Patients With Congenital Heart Disease. J Comput Assist Tomogr 2018; 42:739-746. [PMID: 29958198 PMCID: PMC6138547 DOI: 10.1097/rct.0000000000000760] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
OBJECTIVE The aim of this study was to determine whether it is feasible to visualize the coronary origins in patients with congenital heart disease (CHD) using single-shot coronary quiescent-interval slice-selective (QISS) magnetic resonance angiography (MRA) with compressed sensing (CS). METHODS This retrospective study leveraged a parent study, which aimed to compare breath-hold, 2.1-fold accelerated, 2-shot coronary QISS MRA and clinical standard contrast-enhanced (CE) MRA in 14 patients with CHD (mean age, 17.0 ± 8.6 years, 6 females and 8 males). We evaluated the feasibility of single-shot coronary QISS MRA by retrospectively undersampling the 2-shot data set by an additional factor of 2, performing CS reconstruction, and comparing the retrospectively derived single-shot QISS MRA to 2-shot coronary QISS MRA and clinical standard CE MRA. For quantitative analysis, structural similarity index and normalized root mean square error were calculated. For qualitative analysis, 2 experienced readers scored the conspicuity of coronary origins on a 5-point Likert scale (1 = nondiagnostic, 2 = poor, 3 = clinically acceptable, 4 = good, 5 = excellent). RESULTS Compared with 2-shot QISS, single-shot QISS produced normalized root mean square error of 5.8% ± 0.8% and structural similarity index of 95.4% ± 1.6%, suggesting high data fidelity by CS reconstruction. Compared with the mean conspicuity scores for clinical CE MRA (4.2 ± 0.5 and 4.1 ± 0.6 for right and left coronary origins, respectively), the mean conspicuity scores were not significantly different (P > 0.3) for 2-shot QISS (4.4 ± 0.9 and 4.2 ± 1.1, respectively) and single-shot QISS with CS (4.3 ± 1.1 and 3.8 ± 1.3, respectively) and deemed clinically acceptable to good (scores ≥3.0). CONCLUSIONS This study shows that it is feasible to visualize the coronary origins in patients with CHD with clinically acceptable to good image quality using single-shot coronary QISS MRA with CS.
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Affiliation(s)
- Daming Shen
- Biomedical Engineering, Northwestern University, Evanston, Illinois, United States
- Department of Radiology, Northwestern University, Chicago, Illinois, United States
| | - Robert R. Edelman
- Department of Radiology, Northwestern University, Chicago, Illinois, United States
- Department of Radiology, NorthShore University HealthSystem, Evanston, Illinois, United States
| | - Joshua D. Robinson
- Division of Pediatric Cardiology, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, United States
| | - Hassan Haji-Valizadeh
- Biomedical Engineering, Northwestern University, Evanston, Illinois, United States
- Department of Radiology, Northwestern University, Chicago, Illinois, United States
| | - Marci Messina
- Department of Medical Imaging, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, United States
| | - Shivraman Giri
- Cardiovascular MR R&D, Siemens Healthineers, Chicago, Illinois, United States
| | - Ioannis Koktzoglou
- Department of Radiology, NorthShore University HealthSystem, Evanston, Illinois, United States
- Department of Radiology, University of Chicago Pritzker School of Medicine, Chicago, Illinois, United States
| | - Cynthia K. Rigsby
- Department of Radiology, Northwestern University, Chicago, Illinois, United States
- Department of Medical Imaging, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, United States
| | - Daniel Kim
- Department of Radiology, Northwestern University, Chicago, Illinois, United States
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35
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Fast, free-breathing and motion-minimized techniques for pediatric body magnetic resonance imaging. Pediatr Radiol 2018; 48:1197-1208. [PMID: 30078042 DOI: 10.1007/s00247-018-4116-x] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 01/26/2018] [Accepted: 03/11/2018] [Indexed: 12/26/2022]
Abstract
Magnetic resonance imaging (MRI) is the preferred imaging modality in children with complex medical issues. Patient motion and respiration remain major challenges in pediatric abdominal MRI. Young children ages 3 months to 6 years are unable to cooperate or perform breath-holding and frequently require deep sedation or general anesthesia to undergo MRI. Given the growing concerns associated with the use of sedation and anesthesia as well as the adverse impact on workflow, developing and implementing fast and motion-resistant MRI sequences are of great interest. Fast sequences such as single-shot fast spin echo and balanced steady-state free precession are useful as quick anatomical surveys on routine abdominal MRI. The widespread utilization of parallel imaging and sequences with radial k-space sampling has contributed to decreasing scan time and improving image quality, respectively. Newer strategies including compressed sensing, simultaneous multi-slice acquisition, and hybrid approaches hold the prospect of faster image acquisition that could significantly reduce the need for sedation in this vulnerable population and decrease the time of anesthesia in cases where it is indicated.
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Jimenez JE, Strigel RM, Johnson KM, Henze Bancroft LC, Reeder SB, Block WF. Feasibility of high spatiotemporal resolution for an abbreviated 3D radial breast MRI protocol. Magn Reson Med 2018; 80:1452-1466. [PMID: 29446125 DOI: 10.1002/mrm.27137] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 01/24/2018] [Accepted: 01/25/2018] [Indexed: 12/11/2022]
Abstract
PURPOSE To develop a volumetric imaging technique with 0.8-mm isotropic resolution and 10-s/volume rate to detect and analyze breast lesions in a bilateral, dynamic, contrast-enhanced MRI exam. METHODS A local low-rank temporal reconstruction approach that also uses parallel imaging and spatial compressed sensing was designed to create rapid volumetric frame rates during a contrast-enhanced breast exam (vastly undersampled isotropic projection [VIPR] spatial compressed sensing with temporal local low-rank [STELLR]). The dynamic-enhanced data are subtracted in k-space from static mask data to increase sparsity for the local low-rank approach to maximize temporal resolution. A T1 -weighted 3D radial trajectory (VIPR iterative decomposition with echo asymmetry and least squares estimation [IDEAL]) was modified to meet the data acquisition requirements of the STELLR approach. Additionally, the unsubtracted enhanced data are reconstructed using compressed sensing and IDEAL to provide high-resolution fat/water separation. The feasibility of the approach and the dual reconstruction methodology is demonstrated using a 16-channel breast coil and a 3T MR scanner in 6 patients. RESULTS The STELLR temporal performance of subtracted data matched the expected temporal perfusion enhancement pattern in small and large vascular structures. Differential enhancement within heterogeneous lesions is demonstrated with corroboration from a basic reconstruction using a strict 10-second temporal footprint. Rapid acquisition, reliable fat suppression, and high spatiotemporal resolution are presented, despite significant data undersampling. CONCLUSION The STELLR reconstruction approach of 3D radial sampling with mask subtraction provides a high-performance imaging technique for characterizing enhancing structures within the breast. It is capable of maintaining temporal fidelity, while visualizing breast lesions with high detail over a large FOV to include both breasts.
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Affiliation(s)
- Jorge E Jimenez
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin
| | - Roberta M Strigel
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin.,Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,Carbone Cancer Center, University of Wisconsin, Madison, Wisconsin
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin
| | - Leah C Henze Bancroft
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Scott B Reeder
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin.,Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,Department of Biomedical Engineering, University of Wisconsin, Madison, Wisconsin.,Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Walter F Block
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin.,Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,Department of Biomedical Engineering, University of Wisconsin, Madison, Wisconsin
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