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Levac B, Kumar S, Jalal A, Tamir JI. Accelerated motion correction with deep generative diffusion models. Magn Reson Med 2024; 92:853-868. [PMID: 38688874 DOI: 10.1002/mrm.30082] [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: 09/18/2023] [Revised: 01/02/2024] [Accepted: 02/23/2024] [Indexed: 05/02/2024]
Abstract
PURPOSE The aim of this work is to develop a method to solve the ill-posed inverse problem of accelerated image reconstruction while correcting forward model imperfections in the context of subject motion during MRI examinations. METHODS The proposed solution uses a Bayesian framework based on deep generative diffusion models to jointly estimate a motion-free image and rigid motion estimates from subsampled and motion-corrupt two-dimensional (2D) k-space data. RESULTS We demonstrate the ability to reconstruct motion-free images from accelerated two-dimensional (2D) Cartesian and non-Cartesian scans without any external reference signal. We show that our method improves over existing correction techniques on both simulated and prospectively accelerated data. CONCLUSION We propose a flexible framework for retrospective motion correction of accelerated MRI based on deep generative diffusion models, with potential application to other forward model corruptions.
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Affiliation(s)
- Brett Levac
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, USA
| | - Sidharth Kumar
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, USA
| | - Ajil Jalal
- Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California, USA
| | - Jonathan I Tamir
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, USA
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Morales MA, Ghanbari F, Nakamori S, Assana S, Amyar A, Yoon S, Rodriguez J, Maron MS, Rowin EJ, Kim J, Judd RM, Weinsaft JW, Nezafat R. Deformation-encoding Deep Learning Transformer for High-Frame-Rate Cardiac Cine MRI. Radiol Cardiothorac Imaging 2024; 6:e230177. [PMID: 38722232 DOI: 10.1148/ryct.230177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
Purpose To develop a deep learning model for increasing cardiac cine frame rate while maintaining spatial resolution and scan time. Materials and Methods A transformer-based model was trained and tested on a retrospective sample of cine images from 5840 patients (mean age, 55 years ± 19 [SD]; 3527 male patients) referred for clinical cardiac MRI from 2003 to 2021 at nine centers; images were acquired using 1.5- and 3-T scanners from three vendors. Data from three centers were used for training and testing (4:1 ratio). The remaining data were used for external testing. Cines with downsampled frame rates were restored using linear, bicubic, and model-based interpolation. The root mean square error between interpolated and original cine images was modeled using ordinary least squares regression. In a prospective study of 49 participants referred for clinical cardiac MRI (mean age, 56 years ± 13; 25 male participants) and 12 healthy participants (mean age, 51 years ± 16; eight male participants), the model was applied to cines acquired at 25 frames per second (fps), thereby doubling the frame rate, and these interpolated cines were compared with actual 50-fps cines. The preference of two readers based on perceived temporal smoothness and image quality was evaluated using a noninferiority margin of 10%. Results The model generated artifact-free interpolated images. Ordinary least squares regression analysis accounting for vendor and field strength showed lower error (P < .001) with model-based interpolation compared with linear and bicubic interpolation in internal and external test sets. The highest proportion of reader choices was "no preference" (84 of 122) between actual and interpolated 50-fps cines. The 90% CI for the difference between reader proportions favoring collected (15 of 122) and interpolated (23 of 122) high-frame-rate cines was -0.01 to 0.14, indicating noninferiority. Conclusion A transformer-based deep learning model increased cardiac cine frame rates while preserving both spatial resolution and scan time, resulting in images with quality comparable to that of images obtained at actual high frame rates. Keywords: Functional MRI, Heart, Cardiac, Deep Learning, High Frame Rate Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Manuel A Morales
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
| | - Fahime Ghanbari
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
| | - Shiro Nakamori
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
| | - Salah Assana
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
| | - Amine Amyar
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
| | - Siyeop Yoon
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
| | - Jennifer Rodriguez
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
| | - Martin S Maron
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
| | - Ethan J Rowin
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
| | - Jiwon Kim
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
| | - Robert M Judd
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
| | - Jonathan W Weinsaft
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
| | - Reza Nezafat
- From the Cardiovascular Medicine Division, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (M.A.M., F.G., S.N., S.A., A.A., S.Y., J.R., R.N.); Division of Cardiology, Department of Medicine, Tufts Medical Center, Boston, Mass (M.S.M., E.J.R.); Division of Cardiology, Weill Cornell Medicine, New York, NY (J.K., J.W.W.); and Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC (R.M.J.)
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Munoz C, Fotaki A, Botnar RM, Prieto C. Latest Advances in Image Acceleration: All Dimensions are Fair Game. J Magn Reson Imaging 2023; 57:387-402. [PMID: 36205716 PMCID: PMC10092100 DOI: 10.1002/jmri.28462] [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: 06/16/2022] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 01/20/2023] Open
Abstract
Magnetic resonance imaging (MRI) is a versatile modality that can generate high-resolution images with a variety of tissue contrasts. However, MRI is a slow technique and requires long acquisition times, which increase with higher temporal and spatial resolution and/or when multiple contrasts and large volumetric coverage is required. In order to speedup MR data acquisition, several approaches have been introduced in the literature. Most of these techniques acquire less data than required and exploit intrinsic redundancies in the MR images to recover the information that was not sampled. This article presents a review of MR acquisition and reconstruction methods that have exploited redundancies in the temporal, spatial, and contrast/parametric dimensions to accelerate image data acquisition, focusing on cardiac and abdominal MR imaging applications. The review describes how each of these dimensions has been separately exploited for speeding up MR acquisition to then discuss more advanced techniques where multiple dimensions are exploited together for further reducing scan times. Finally, future directions for multidimensional image acceleration and remaining technical challenges are discussed. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: 1.
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Affiliation(s)
- Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
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Ahmed AH, Zou Q, Nagpal P, Jacob M. Dynamic Imaging Using Deep Bi-Linear Unsupervised Representation (DEBLUR). IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2693-2703. [PMID: 35436187 PMCID: PMC9744437 DOI: 10.1109/tmi.2022.3168559] [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] [Indexed: 06/14/2023]
Abstract
Bilinear models such as low-rank and dictionary methods, which decompose dynamic data to spatial and temporal factor matrices are powerful and memory-efficient tools for the recovery of dynamic MRI data. Current bilinear methods rely on sparsity and energy compaction priors on the factor matrices to regularize the recovery. Motivated by deep image prior, we introduce a novel bilinear model, whose factor matrices are generated using convolutional neural networks (CNNs). The CNN parameters, and equivalently the factors, are learned from the undersampled data of the specific subject. Unlike current unrolled deep learning methods that require the storage of all the time frames in the dataset, the proposed approach only requires the storage of the factors or compressed representation; this approach allows the direct use of this scheme to large-scale dynamic applications, including free breathing cardiac MRI considered in this work. To reduce the run time and to improve performance, we initialize the CNN parameters using existing factor methods. We use sparsity regularization of the network parameters to minimize the overfitting of the network to measurement noise. Our experiments on free-breathing and ungated cardiac cine data acquired using a navigated golden-angle gradient-echo radial sequence show the ability of our method to provide reduced spatial blurring as compared to classical bilinear methods as well as a recent unsupervised deep-learning approach.
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Li P, Chen J, Nan D, Zou J, Lin D, Hu Y. Motion-Aligned 4D-MRI Reconstruction using Higher Degree Total Variation and Locally Low-Rank Regularization. Magn Reson Imaging 2022; 93:97-107. [DOI: 10.1016/j.mri.2022.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/29/2022] [Accepted: 08/02/2022] [Indexed: 11/25/2022]
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Demirel OB, Yaman B, Moeller S, Weingartner S, Akcakaya M. Signal-Intensity Informed Multi-Coil MRI Encoding Operator for Improved Physics-Guided Deep Learning Reconstruction of Dynamic Contrast-Enhanced MRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1472-1476. [PMID: 36086262 DOI: 10.1109/embc48229.2022.9871668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Dynamic contrast enhanced (DCE) MRI acquires a series of images following the administration of a contrast agent, and plays an important clinical role in diagnosing various diseases. DCE MRI typically necessitates rapid imaging to provide sufficient spatio-temporal resolution and coverage. Conventional MRI acceleration techniques exhibit limited image quality at such high acceleration rates. Recently, deep learning (DL) methods have gained interest for improving highly-accelerated MRI. However, DCE MRI series show substantial variations in SNR and contrast across images. This hinders the quality and generalizability of DL methods, when applied across time frames. In this study, we propose signal intensity informed multi-coil MRI encoding operator for improved DL reconstruction of DCE MRI. The output of the corresponding inverse problem for this forward operator leads to more uniform contrast across time frames, since the proposed operator captures signal intensity variations across time frames while not altering the coil sensitivities. Our results in perfusion cardiac MRI show that high-quality images are reconstructed at very high acceleration rates, with substantial improvement over existing methods.
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McElroy S, Kunze KP, Nazir MS, Speier P, Stäb D, Villa ADM, Yazdani M, Vergani V, Roujol S, Neji R, Chiribiri A. Simultaneous multi-slice steady-state free precession myocardial perfusion with iterative reconstruction and integrated motion compensation. Eur J Radiol 2022; 151:110286. [PMID: 35452953 PMCID: PMC9941714 DOI: 10.1016/j.ejrad.2022.110286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/09/2022] [Accepted: 03/23/2022] [Indexed: 11/22/2022]
Abstract
PURPOSE Simultaneous multi-slice (SMS) balanced steady-state free precession (bSSFP) acquisition and iterative reconstruction can provide high spatial resolution and coverage for cardiac magnetic resonance (CMR) perfusion. However, respiratory motion remains a challenge for iterative reconstruction techniques employing temporal regularisation. The aim of this study is to evaluate an iterative reconstruction with integrated motion compensation for SMS-bSSFP first-pass myocardial stress perfusion in the presence of respiratory motion. METHODS Thirty-one patients with suspected coronary artery disease were prospectively recruited and imaged at 1.5 T. A SMS-bSSFP prototype myocardial perfusion sequence was acquired at stress in all patients. All datasets were reconstructed using an iterative reconstruction with temporal regularisation, once with and once without motion compensation (MC and NMC, respectively). Three readers scored each dataset in terms of: image quality (1:poor; 4:excellent), motion/blurring (1:severe motion/blurring; 3:no motion/blurring), and diagnostic confidence (1:poor confidence; 3:high confidence). Quantitative assessment of sharpness was performed. The number of uncorrupted first-pass dynamics was measured on the NMC datasets to classify patients into 'suboptimal breath-hold (BH)' and 'good BH' groups. RESULTS Compared across all cases, MC performed better than NMC in terms of image quality (3.5 ± 0.5 vs. 3.0 ± 0.8, P = 0.002), motion/blurring (2.9 ± 0.1 vs. 2.2 ± 0.8, P < 0.001), diagnostic confidence (2.9 ± 0.1 vs. 2.3 ± 0.7, P < 0.001) and sharpness index (0.34 ± 0.05 vs. 0.31 ± 0.06, P < 0.001). Fourteen patients with a suboptimal BH were identified. For the suboptimal BH group, MC performed better than NMC in terms of image quality (3.8 ± 0.4 vs. 2.6 ± 0.8, P < 0.001), motion/blurring (3.0 ± 0.1 vs. 1.6 ± 0.7, P < 0.001), diagnostic confidence (3.0 ± 0.1 vs. 1.9 ± 0.7, P < 0.001) and sharpness index (0.34 ± 0.05 vs. 0.30 ± 0.06, P = 0.004). For the good BH group, sharpness index was higher for MC than NMC (0.34 ± 0.06 vs 0.31 ± 0.07, P = 0.03), while there were no significant differences observed for the other three metrics assessed (P > 0.11). There were no significant differences between suboptimal BH MC and good BH MC for any of the reported metrics (P > 0.06). CONCLUSIONS Integrated motion compensation significantly reduces motion/blurring and improves image quality, diagnostic confidence and sharpness index of SMS-bSSFP perfusion with iterative reconstruction in the presence of motion.
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Affiliation(s)
- Sarah McElroy
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom.
| | - Karl P Kunze
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Muhummad Sohaib Nazir
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Peter Speier
- Cardiovascular Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Daniel Stäb
- MR Research Collaborations, Siemens Healthcare Limited, Melbourne, Australia
| | - Adriana D M Villa
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Momina Yazdani
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Vittoria Vergani
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Radhouene Neji
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom.
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Tourais J, Scannell CM, Schneider T, Alskaf E, Crawley R, Bosio F, Sanchez-Gonzalez J, Doneva M, Schülke C, Meineke J, Keupp J, Smink J, Breeuwer M, Chiribiri A, Henningsson M, Correia T. High-Resolution Free-Breathing Quantitative First-Pass Perfusion Cardiac MR Using Dual-Echo Dixon With Spatio-Temporal Acceleration. Front Cardiovasc Med 2022; 9:884221. [PMID: 35571164 PMCID: PMC9099052 DOI: 10.3389/fcvm.2022.884221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/04/2022] [Indexed: 11/21/2022] Open
Abstract
Introduction To develop and test the feasibility of free-breathing (FB), high-resolution quantitative first-pass perfusion cardiac MR (FPP-CMR) using dual-echo Dixon (FOSTERS; Fat-water separation for mOtion-corrected Spatio-TEmporally accelerated myocardial peRfuSion). Materials and Methods FOSTERS was performed in FB using a dual-saturation single-bolus acquisition with dual-echo Dixon and a dynamically variable Cartesian k-t undersampling (8-fold) approach, with low-rank and sparsity constrained reconstruction, to achieve high-resolution FPP-CMR images. FOSTERS also included automatic in-plane motion estimation and T2* correction to obtain quantitative myocardial blood flow (MBF) maps. High-resolution (1.6 x 1.6 mm2) FB FOSTERS was evaluated in eleven patients, during rest, against standard-resolution (2.6 x 2.6 mm2) 2-fold SENSE-accelerated breath-hold (BH) FPP-CMR. In addition, MBF was computed for FOSTERS and spatial wavelet-based compressed sensing (CS) reconstruction. Two cardiologists scored the image quality (IQ) of FOSTERS, CS, and standard BH FPP-CMR images using a 4-point scale (1–4, non-diagnostic – fully diagnostic). Results FOSTERS produced high-quality images without dark-rim and with reduced motion-related artifacts, using an 8x accelerated FB acquisition. FOSTERS and standard BH FPP-CMR exhibited excellent IQ with an average score of 3.5 ± 0.6 and 3.4 ± 0.6 (no statistical difference, p > 0.05), respectively. CS images exhibited severe artifacts and high levels of noise, resulting in an average IQ score of 2.9 ± 0.5. MBF values obtained with FOSTERS presented a lower variance than those obtained with CS. Discussion FOSTERS enabled high-resolution FB FPP-CMR with MBF quantification. Combining motion correction with a low-rank and sparsity-constrained reconstruction results in excellent image quality.
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Affiliation(s)
- Joao Tourais
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of MR R&D – Clinical Science, Philips Healthcare, Best, Netherlands
- Department of Imaging Physics, Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
| | - Cian M. Scannell
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Ebraham Alskaf
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Richard Crawley
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Filippo Bosio
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | | | | | | | | | - Jouke Smink
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of MR R&D – Clinical Science, Philips Healthcare, Best, Netherlands
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Markus Henningsson
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linkoping University, Linkoping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linkoping University, Linkoping, Sweden
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Centre for Marine Sciences (CCMAR), Faro, Portugal
- *Correspondence: Teresa Correia
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Yang J, Küstner T, Hu P, Liò P, Qi H. End-to-End Deep Learning of Non-rigid Groupwise Registration and Reconstruction of Dynamic MRI. Front Cardiovasc Med 2022; 9:880186. [PMID: 35571217 PMCID: PMC9095964 DOI: 10.3389/fcvm.2022.880186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 04/08/2022] [Indexed: 12/03/2022] Open
Abstract
Temporal correlation has been exploited for accelerated dynamic MRI reconstruction. Some methods have modeled inter-frame motion into the reconstruction process to produce temporally aligned image series and higher reconstruction quality. However, traditional motion-compensated approaches requiring iterative optimization of registration and reconstruction are time-consuming, while most deep learning-based methods neglect motion in the reconstruction process. We propose an unrolled deep learning framework with each iteration consisting of a groupwise diffeomorphic registration network (GRN) and a motion-augmented reconstruction network. Specifically, the whole dynamic sequence is registered at once to an implicit template which is used to generate a new set of dynamic images to efficiently exploit the full temporal information of the acquired data via the GRN. The generated dynamic sequence is then incorporated into the reconstruction network to augment the reconstruction performance. The registration and reconstruction networks are optimized in an end-to-end fashion for simultaneous motion estimation and reconstruction of dynamic images. The effectiveness of the proposed method is validated in highly accelerated cardiac cine MRI by comparing with other state-of-the-art approaches.
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Affiliation(s)
- Junwei Yang
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
- The School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
| | - Peng Hu
- The School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Pietro Liò
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Haikun Qi
- The School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
- *Correspondence: Haikun Qi
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Ding Z, Cheng Z, She H, Liu B, Yin Y, Du YP. Dynamic pulmonary MRI using motion-state weighted motion-compensation (MostMoCo) reconstruction with ultrashort TE: A structural and functional study. Magn Reson Med 2022; 88:224-238. [PMID: 35388914 DOI: 10.1002/mrm.29204] [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: 09/27/2021] [Revised: 12/24/2021] [Accepted: 02/01/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE To improve the quality of structural images and the quantification of ventilation in free-breathing dynamic pulmonary MRI. METHODS A 3D radial ultrashort TE (UTE) sequence with superior-inferior navigators was used to acquire pulmonary data during free breathing. All acquired data were binned into different motion states according to the respiratory signal extracted from superior-inferior navigators. Motion-resolved images were reconstructed using eXtra-Dimensional (XD) UTE reconstruction. The initial motion fields were generated by registering images at each motion state to other motion states in motion-resolved images. A motion-state weighted motion-compensation (MostMoCo) reconstruction algorithm was proposed to reconstruct the dynamic UTE images. This technique, termed as MostMoCo-UTE, was compared with XD-UTE and iterative motion-compensation (iMoCo) on a porcine lung and 10 subjects. RESULTS MostMoCo reconstruction provides higher peak SNR (37.0 vs. 35.4 and 34.2) and structural similarity (0.964 vs. 0.931 and 0.947) compared to XD-UTE and iMoCo in the porcine lung experiment. Higher apparent SNR and contrast-to-noise ratio are achieved using MostMoCo in the human experiment. MostMoCo reconstruction better preserves the temporal variations of signal intensity of parenchyma compared to iMoCo, shows reduced random noise and improved sharpness of anatomical structures compared to XD-UTE. In the porcine lung experiment, the quantification of ventilation using MostMoCo images is more accurate than that using XD-UTE and iMoCo images. CONCLUSION The proposed MostMoCo-UTE provides improved quality of structural images and quantification of ventilation for free-breathing pulmonary MRI. It has the potential for the detection of structural and functional disorders of the lung in clinical settings.
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Affiliation(s)
- Zekang Ding
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Huajun She
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Bei Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yongfang Yin
- Department of Radiology, People's Hospital of Jilin Province, Changchun, China
| | - Yiping P Du
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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11
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Ismail TF, Strugnell W, Coletti C, Božić-Iven M, Weingärtner S, Hammernik K, Correia T, Küstner T. Cardiac MR: From Theory to Practice. Front Cardiovasc Med 2022; 9:826283. [PMID: 35310962 PMCID: PMC8927633 DOI: 10.3389/fcvm.2022.826283] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/17/2022] [Indexed: 01/10/2023] Open
Abstract
Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.
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Affiliation(s)
- Tevfik F. Ismail
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Cardiology Department, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Wendy Strugnell
- Queensland X-Ray, Mater Hospital Brisbane, Brisbane, QLD, Australia
| | - Chiara Coletti
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
| | - Maša Božić-Iven
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
- Computer Assisted Clinical Medicine, Heidelberg University, Mannheim, Germany
| | | | - Kerstin Hammernik
- Lab for AI in Medicine, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, United Kingdom
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Centre of Marine Sciences, Faro, Portugal
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
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12
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Kustner T, Pan J, Qi H, Cruz G, Gilliam C, Blu T, Yang B, Gatidis S, Botnar R, Prieto C. LAPNet: Non-Rigid Registration Derived in k-Space for Magnetic Resonance Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3686-3697. [PMID: 34242163 DOI: 10.1109/tmi.2021.3096131] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been proposed to compensate for these types of motion during thoracic scans, relying on accurate motion estimation from undersampled motion-resolved reconstruction. A particular interest and challenge lie in the derivation of reliable non-rigid motion fields from the undersampled motion-resolved data. Motion estimation is usually formulated in image space via diffusion, parametric-spline, or optical flow methods. However, image-based registration can be impaired by remaining aliasing artifacts due to the undersampled motion-resolved reconstruction. In this work, we describe a formalism to perform non-rigid registration directly in the sampled Fourier space, i.e. k-space. We propose a deep-learning based approach to perform fast and accurate non-rigid registration from the undersampled k-space data. The basic working principle originates from the Local All-Pass (LAP) technique, a recently introduced optical flow-based registration. The proposed LAPNet is compared against traditional and deep learning image-based registrations and tested on fully-sampled and highly-accelerated (with two undersampling strategies) 3D respiratory motion-resolved MR images in a cohort of 40 patients with suspected liver or lung metastases and 25 healthy subjects. The proposed LAPNet provided consistent and superior performance to image-based approaches throughout different sampling trajectories and acceleration factors.
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13
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Ferrazzi G, McElroy S, Neji R, Kunze KP, Nazir MS, Speier P, Stäb D, Forman C, Razavi R, Chiribiri A, Roujol S. All-systolic first-pass myocardial rest perfusion at a long saturation time using simultaneous multi-slice imaging and compressed sensing acceleration. Magn Reson Med 2021; 86:663-676. [PMID: 33749026 PMCID: PMC7611406 DOI: 10.1002/mrm.28712] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 12/17/2020] [Accepted: 01/11/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE To enable all-systolic first-pass rest myocardial perfusion with long saturation times. To investigate the change in perfusion contrast and dark rim artefacts through simulations and surrogate measurements. METHODS Simulations were employed to investigate optimal saturation time for myocardium-perfusion defect contrast and blood-to-myocardium signal ratios. Two saturation recovery blocks with long/short saturation times (LTS/STS) were employed to image 3 slices at end-systole and diastole. Simultaneous multi-slice balanced steady state free precession imaging and compressed sensing acceleration were combined. The sequence was compared to a 3 slice-by-slice clinical protocol in 10 patients. Quantitative assessment of myocardium-peak pre contrast and blood-to-myocardium signal ratios, as well as qualitative assessment of perceived SNR, image quality, blurring, and dark rim artefacts, were performed. RESULTS Simulations showed that with a bolus of 0.075 mmol/kg, a LTS of 240-470 ms led to a relative increase in myocardium-perfusion defect contrast of 34% ± 9%-28% ± 27% than a STS = 120 ms, while reducing blood-to-myocardium signal ratio by 18% ± 10%-32% ± 14% at peak myocardium. With a bolus of 0.05 mmol/kg, LTS was 320-570 ms with an increase in myocardium-perfusion defect contrast of 63% ± 13%-62% ± 29%. Across patients, LTS led to an average increase in myocardium-peak pre contrast of 59% (P < .001) at peak myocardium and a lower blood-to-myocardium signal ratio of 47% (P < .001) and 15% (P < .001) at peak blood/myocardium. LTS had improved motion robustness (P = .002), image quality (P < .001), and decreased dark rim artefacts (P = .008) than the clinical protocol. CONCLUSION All-systolic rest perfusion can be achieved by combining simultaneous multi-slice and compressed sensing acceleration, enabling 3-slice cardiac coverage with reduced motion and dark rim artefacts. Numerical simulations indicate that myocardium-perfusion defect contrast increases at LTS.
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Affiliation(s)
- Giulio Ferrazzi
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
- IRCCS San Camillo Hospital, Venice, Italy
| | - Sarah McElroy
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Karl P. Kunze
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Muhummad Sohaib Nazir
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Peter Speier
- Cardiovascular MR predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Daniel Stäb
- MR Research Collaborations, Siemens Healthcare Limited, Melbourne, Australia
| | - Christoph Forman
- Cardiovascular MR predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
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14
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Godino-Moya A, Menchón-Lara RM, Martín-Fernández M, Prieto C, Alberola-López C. Elastic AlignedSENSE for Dynamic MR Reconstruction: A Proof of Concept in Cardiac Cine. ENTROPY 2021; 23:e23050555. [PMID: 33947089 PMCID: PMC8145958 DOI: 10.3390/e23050555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 11/25/2022]
Abstract
Numerous methods in the extensive literature on magnetic resonance imaging (MRI) reconstruction exploit temporal redundancy to accelerate cardiac cine. Some of them include motion compensation, which involves high computational costs and long runtimes. In this work, we proposed a method—elastic alignedSENSE (EAS)—for the direct reconstruction of a motion-free image plus a set of nonrigid deformations to reconstruct a 2D cardiac sequence. The feasibility of the proposed approach was tested in 2D Cartesian and golden radial multi-coil breath-hold cardiac cine acquisitions. The proposed approach was compared against parallel imaging compressed sense (sPICS) and group-wise motion corrected compressed sense (GWCS) reconstructions. EAS provides better results on objective measures with considerable less runtime when an acceleration factor is higher than 10×. Subjective assessment of an expert, however, invited proposing the combination of EAS and GWCS as a preferable alternative to GWCS or EAS in isolation.
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Affiliation(s)
- Alejandro Godino-Moya
- Laboratorio de Procesado de Imagen, E.T.S.I. Telecomunicación, Universidad de Valladolid, Paseo Belén 15, 47011 Valladolid, Spain; (R.-M.M.-L.); (M.M.-F.); (C.A.-L.)
- Correspondence:
| | - Rosa-María Menchón-Lara
- Laboratorio de Procesado de Imagen, E.T.S.I. Telecomunicación, Universidad de Valladolid, Paseo Belén 15, 47011 Valladolid, Spain; (R.-M.M.-L.); (M.M.-F.); (C.A.-L.)
| | - Marcos Martín-Fernández
- Laboratorio de Procesado de Imagen, E.T.S.I. Telecomunicación, Universidad de Valladolid, Paseo Belén 15, 47011 Valladolid, Spain; (R.-M.M.-L.); (M.M.-F.); (C.A.-L.)
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK;
- School of Engineering, Pontificia Universidad Catolica de Chile, Santiago 4860, Chile
| | - Carlos Alberola-López
- Laboratorio de Procesado de Imagen, E.T.S.I. Telecomunicación, Universidad de Valladolid, Paseo Belén 15, 47011 Valladolid, Spain; (R.-M.M.-L.); (M.M.-F.); (C.A.-L.)
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15
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Corona V, Aviles-Rivero A, Debroux N, Le Guyader C, Schönlieb CB. Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution. Med Image Anal 2020; 68:101941. [PMID: 33385698 DOI: 10.1016/j.media.2020.101941] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 11/27/2020] [Accepted: 12/07/2020] [Indexed: 11/27/2022]
Abstract
Motion degradation is a central problem in Magnetic Resonance Imaging (MRI). This work addresses the problem of how to obtain higher quality, super-resolved motion-free reconstructions from highly undersampled MRI data. In this work, we present for the first time a variational multi-task framework that allows joining three relevant tasks in MRI: reconstruction, registration and super-resolution. Our framework takes a set of multiple undersampled MR acquisitions corrupted by motion into a novel multi-task optimisation model, which is composed of an L2 fidelity term that allows sharing representation between tasks, super-resolution foundations and hyperelastic deformations to model biological tissue behaviors. We demonstrate that this combination yields significant improvements over sequential models and other bi-task methods. Our results exhibit fine details and compensate for motion producing sharp and highly textured images compared to state of the art methods while keeping low CPU time. Our improvements are appraised on both clinical assessment and statistical analysis.
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Affiliation(s)
- Veronica Corona
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK.
| | | | - Noémie Debroux
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont-Ferrand, France
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16
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Aviles-Rivero AI, Debroux N, Williams G, Graves MJ, Schönlieb CB. Compressed sensing plus motion (CS + M): A new perspective for improving undersampled MR image reconstruction. Med Image Anal 2020; 68:101933. [PMID: 33341495 DOI: 10.1016/j.media.2020.101933] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 11/23/2020] [Accepted: 11/27/2020] [Indexed: 10/22/2022]
Abstract
We address the problem of reconstructing high quality images from undersampled MRI data. This is a challenging task due to the highly ill-posed nature of the problem. In particular, in dynamic MRI scans, the interaction between the target structure and the physical motion affects the acquired measurements leading to blurring artefacts and loss of fine details. In this work, we propose a framework for dynamic MRI reconstruction framed under a new multi-task optimisation model called Compressed Sensing Plus Motion (CS + M). Firstly, we propose a single optimisation problem that simultaneously computes the MRI reconstruction and the physical motion. Secondly, we show our model can be efficiently solved by breaking it up into two computationally tractable problems. The potentials and generalisation capabilities of our approach are demonstrated in different clinical applications including cardiac cine, cardiac perfusion and brain perfusion imaging. We show, through numerical experiments, that the proposed scheme reduces blurring artefacts, and preserves the target shape and fine details in the reconstruction. We also report the highest quality reconstruction under high undersampling rates in comparison to several state of the art techniques.
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Affiliation(s)
| | - Noémie Debroux
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, France
| | - Guy Williams
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, UK
| | - Martin J Graves
- Department of Radiology, Cambridge University Hospitals, University of Cambridge, UK
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17
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Mooiweer R, Neji R, McElroy S, Nazir MS, Razavi R, Chiribiri A, Roujol S. A fast navigator (fastNAV) for prospective respiratory motion correction in first-pass myocardial perfusion imaging. Magn Reson Med 2020; 85:2661-2671. [PMID: 33270946 PMCID: PMC7898590 DOI: 10.1002/mrm.28617] [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: 08/10/2020] [Revised: 11/05/2020] [Accepted: 11/05/2020] [Indexed: 11/10/2022]
Abstract
PURPOSE To develop and evaluate a fast respiratory navigator (fastNAV) for cardiac MR perfusion imaging with subject-specific prospective slice tracking. METHODS A fastNAV was developed for dynamic contrast-enhanced cardiac MR perfusion imaging by combining spatially nonselective saturation with slice-selective tip-up and slice-selective excitation pulses. The excitation slice was angulated from the tip-up slice in the transverse plane to overlap only in the right hemidiaphragm for suppression of signal outside the right hemidiaphragm. A calibration scan was developed to enable the estimation of subject-specific tracking factors. Perfusion imaging using subject-specific fastNAV-based slice tracking was then compared to a conventional sequence (ie, without slice tracking) in 10 patients under free-breathing conditions. Respiratory motion in perfusion images was quantitatively assessed by measuring the average overlap of the left ventricle across images (avDice, 0:no overlap/1:perfect overlap) and the average displacement of the center of mass of the left ventricle (avCoM). Image quality was subjectively assessed using a 4-point scoring system (1: poor, 4: excellent). RESULTS The fastNAV calibration was successfully performed in all subjects (average tracking factor of 0.46 ± 0.13, R = 0.94 ± 0.03). Prospective motion correction using fastNAV led to higher avDice (0.94 ± 0.02 vs. 0.90 ± 0.03, P < .001) and reduced avCoM (4.03 ± 0.84 vs. 5.22 ± 1.22, P < .001). There were no statistically significant differences between the 2 sequences in terms of image quality (both sequences: median = 3 and interquartile range = 3-4, P = 1). CONCLUSION fastNAV enables fast and robust right hemidiaphragm motion tracking in a perfusion sequence. In combination with subject-specific slice tracking, fastNAV reduces the effect of respiratory motion during free-breathing cardiac MR perfusion imaging.
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Affiliation(s)
- Ronald Mooiweer
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom.,MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Sarah McElroy
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Muhummad Sohaib Nazir
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
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18
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McElroy S, Ferrazzi G, Nazir MS, Kunze KP, Neji R, Speier P, Stäb D, Forman C, Razavi R, Chiribiri A, Roujol S. Combined simultaneous multislice bSSFP and compressed sensing for first-pass myocardial perfusion at 1.5 T with high spatial resolution and coverage. Magn Reson Med 2020; 84:3103-3116. [PMID: 32530064 PMCID: PMC7611375 DOI: 10.1002/mrm.28345] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 05/14/2020] [Accepted: 05/14/2020] [Indexed: 02/02/2023]
Abstract
PURPOSE To implement and evaluate a pseudorandom undersampling scheme for combined simultaneous multislice (SMS) balanced SSFP (bSSFP) and compressed-sensing (CS) reconstruction to enable myocardial perfusion imaging with high spatial resolution and coverage at 1.5 T. METHODS A prospective pseudorandom undersampling scheme that is compatible with SMS-bSSFP phase-cycling requirements and CS was developed. The SMS-bSSFP CS with pseudorandom and linear undersampling schemes were compared in a phantom. A high-resolution (1.4 × 1.4 mm2 ) six-slice SMS-bSSFP CS perfusion sequence was compared with a conventional (1.9 × 1.9 mm2 ) three-slice sequence in 10 patients. Qualitative assessment of image quality, perceived SNR, and number of diagnostic segments and quantitative measurements of sharpness, upslope index, and contrast ratio were performed. RESULTS In phantom experiments, pseudorandom undersampling resulted in residual artifact (RMS error) reduction by a factor of 7 compared with linear undersampling. In vivo, the proposed sequence demonstrated higher perceived SNR (2.9 ± 0.3 vs. 2.2 ± 0.6, P = .04), improved sharpness (0.35 ± 0.03 vs. 0.32 ± 0.05, P = .01), and a higher number of diagnostic segments (100% vs. 94%, P = .03) compared with the conventional sequence. There were no significant differences between the sequences in terms of image quality (2.5 ± 0.4 vs. 2.8 ± 0.2, P = .08), upslope index (0.11 ± 0.02 vs. 0.10 ± 0.01, P = .3), or contrast ratio (3.28 ± 0.35 vs. 3.36 ± 0.43, P = .7). CONCLUSION A pseudorandom k-space undersampling compatible with SMS-bSSFP and CS reconstruction has been developed and enables cardiac MR perfusion imaging with increased spatial resolution and myocardial coverage, increased number of diagnostic segments and perceived SNR, and no difference in image quality, upslope index, and contrast ratio.
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Affiliation(s)
- Sarah McElroy
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Giulio Ferrazzi
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Muhummad Sohaib Nazir
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Karl P. Kunze
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Peter Speier
- Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany
| | - Daniel Stäb
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia
| | | | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
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19
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Muehlberg F, Stoetzner A, Forman C, Schmidt M, Riazy L, Dieringer M, der Geest RV, Schwenke C, Schulz-Menger J. Comparability of compressed sensing-based gradient echo perfusion sequence SPARSE and conventional gradient echo sequence in assessment of myocardial ischemia. Eur J Radiol 2020; 131:109213. [PMID: 32846332 DOI: 10.1016/j.ejrad.2020.109213] [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: 03/31/2020] [Revised: 07/08/2020] [Accepted: 08/03/2020] [Indexed: 11/24/2022]
Abstract
PURPOSE Stress perfusion imaging plays a major role in non-invasive detection of coronary artery disease. We compared a compressed sensing-based and a conventional gradient echo perfusion sequence with regard to image quality and diagnostic performance. METHOD Patients sent for coronary angiography due to pathologic stress perfusion CMR were recruited. All patients underwent two adenosine stress CMR using conventional TurboFLASH and prototype SPARSE sequence as well as quantitative coronary angiography with fractional flow reserve (FFR) within 6 weeks. Coronary angiography was considered gold standard with FFR < 0.75 or visual stenosis >90 % for identification of myocardial ischemia. Diagnostic performance of perfusion imaging was assessed in basal, mid-ventricular and apical slices by quantification of myocardial perfusion reserve (MPR) analysis utilizing the signal upslope method and a deconvolution technique using the fermi function model. RESULTS 23 patients with mean age of 69.6 ± 8.9 years were enrolled. 46 % were female. Image quality was similar in conventional TurboFLASH sequence and SPARSE sequence (2.9 ± 0.5 vs 3.1 ± 0.7, p = 0,06). SPARSE sequence showed higher contrast-to-noise ratio (52.1 ± 27.4 vs 40.5 ± 17.6, p < 0.01) and signal-to-noise ratio (15.6 ± 6.2 vs 13.2 ± 4.2, p < 0.01) than TurboFLASH sequence. Dark-rim artifacts occurred less often with SPARSE (9 % of segments) than with TurboFLASH (23 %). In visual assessment of perfusion defects, SPARSE sequence detected less false-positive perfusion defects (n = 1) than TurboFLASH sequence (n = 3). Quantitative perfusion analysis on segment basis showed equal detection of perfusion defects for TurboFLASH and SPARSE with both upslope MPR analysis (TurboFLASH 0.88 ± 0.18; SPARSE 0.77 ± 0.26; p = 0.06) and fermi function model (TurboFLASH 0.85 ± 0.24; SPARSE 0.76 ± 0.30; p = 0.13). CONCLUSIONS Compressed sensing perfusion imaging using SPARSE sequence allows reliable detection of myocardial ischemia.
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Affiliation(s)
- Fabian Muehlberg
- HELIOS Hospital Berlin-Buch, Department of Cardiology and Nephrology, Lindenberger Weg 80, 13125 Berlin, Germany.
| | - Arthur Stoetzner
- HELIOS Hospital Berlin-Buch, Department of Cardiology and Nephrology, Lindenberger Weg 80, 13125 Berlin, Germany.
| | - Christoph Forman
- Siemens Healthineers, Diagnostic Imaging, Magnetic Resonance, Allee am Röthelheimpark 2, 91052 Erlangen, Germany.
| | - Michaela Schmidt
- Siemens Healthineers, Diagnostic Imaging, Magnetic Resonance, Allee am Röthelheimpark 2, 91052 Erlangen, Germany.
| | - Leili Riazy
- HELIOS Hospital Berlin-Buch, Department of Cardiology and Nephrology, Lindenberger Weg 80, 13125 Berlin, Germany.
| | - Matthias Dieringer
- Siemens Healthineers, Diagnostic Imaging, Magnetic Resonance, Allee am Röthelheimpark 2, 91052 Erlangen, Germany.
| | - Rob van der Geest
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands.
| | - Carsten Schwenke
- SCO:SSiS Statistical Consulting, Karmeliterweg 42, 13465 Berlin, Germany.
| | - Jeanette Schulz-Menger
- HELIOS Hospital Berlin-Buch, Department of Cardiology and Nephrology, Lindenberger Weg 80, 13125 Berlin, Germany.
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Scannell CM, Correia T, Villa ADM, Schneider T, Lee J, Breeuwer M, Chiribiri A, Henningsson M. Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging. Sci Rep 2020; 10:12684. [PMID: 32728198 PMCID: PMC7392760 DOI: 10.1038/s41598-020-69747-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 07/15/2020] [Indexed: 11/08/2022] Open
Abstract
Dynamic contrast-enhanced quantitative first-pass perfusion using magnetic resonance imaging enables non-invasive objective assessment of myocardial ischemia without ionizing radiation. However, quantification of perfusion is challenging due to the non-linearity between the magnetic resonance signal intensity and contrast agent concentration. Furthermore, respiratory motion during data acquisition precludes quantification of perfusion. While motion correction techniques have been proposed, they have been hampered by the challenge of accounting for dramatic contrast changes during the bolus and long execution times. In this work we investigate the use of a novel free-breathing multi-echo Dixon technique for quantitative myocardial perfusion. The Dixon fat images, unaffected by the dynamic contrast-enhancement, are used to efficiently estimate rigid-body respiratory motion and the computed transformations are applied to the corresponding diagnostic water images. This is followed by a second non-linear correction step using the Dixon water images to remove residual motion. The proposed Dixon motion correction technique was compared to the state-of-the-art technique (spatiotemporal based registration). We demonstrate that the proposed method performs comparably to the state-of-the-art but is significantly faster to execute. Furthermore, the proposed technique can be used to correct for the decay of signal due to T2* effects to improve quantification and additionally, yields fat-free diagnostic images.
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Affiliation(s)
- Cian M Scannell
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Adriana D M Villa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | - Jack Lee
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Marcel Breeuwer
- Philips Healthcare, Best, The Netherlands
- Department of Biomedical Engineering, Medical Image Analysis Group, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Markus Henningsson
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
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Tian Y, Mendes J, Wilson B, Ross A, Ranjan R, DiBella E, Adluru G. Whole-heart, ungated, free-breathing, cardiac-phase-resolved myocardial perfusion MRI by using Continuous Radial Interleaved simultaneous Multi-slice acquisitions at sPoiled steady-state (CRIMP). Magn Reson Med 2020; 84:3071-3087. [PMID: 32492235 DOI: 10.1002/mrm.28337] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 04/28/2020] [Accepted: 05/01/2020] [Indexed: 11/09/2022]
Abstract
PURPOSE To develop a whole-heart, free-breathing, non-electrocardiograph (ECG)-gated, cardiac-phase-resolved myocardial perfusion MRI framework (CRIMP; Continuous Radial Interleaved simultaneous Multi-slice acquisitions at sPoiled steady-state) and test its quantification feasibility. METHODS CRIMP used interleaved radial simultaneous multi-slice (SMS) slice groups to cover the whole heart in 9 or 12 short-axis slices. The sequence continuously acquired data without magnetization preparation, ECG gating or breath-holding, and captured multiple cardiac phases. Images were reconstructed by a motion-compensated patch-based locally low-rank reconstruction. Bloch simulations were performed to study the signal-to-noise ratio/contrast-to-noise ratio (SNR/CNR) for CRIMP and to study the steady-state signal under motion. Seven patients were scanned with CRIMP at stress and rest to develop the sequence. One human and two dogs were scanned at rest with a dual-bolus method to test the quantification feasibility of CRIMP. The dual-bolus scans were performed using both CRIMP and an ungated radial SMS saturation recovery (SMS-SR) sequence with injection dose = 0.075 mmol/kg to compare the sequences in terms of SNR, cardiac phase resolution and quantitative myocardial blood flow (MBF). RESULTS Perfusion images with multiple cardiac phases in all image slices with a temporal resolution of 72 ms/frame were obtained. Simulations and in-vivo acquisitions showed CRIMP kept the inner slices in steady-state regardless of motion. CRIMP outperformed SMS-SR in slice coverage (9 over 6), SNR (mean 20% improvement), and provided cardiac phase resolution. CRIMP and SMS-SR sequences provided comparable MBF values (rest systolic CRIMP = 0.58 ± 0.07, SMS-SR = 0.61 ± 0.16). CONCLUSION CRIMP allows for whole-heart, cardiac-phase-resolved myocardial perfusion images without ECG-gating or breath-holding. The sequence can provide MBF if an accurate arterial input function is obtained separately.
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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, USA.,Department of Physics and Astronomy, University of Utah, Salt Lake City, Utah, USA
| | - Jason Mendes
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Brent Wilson
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Alexander Ross
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Ravi Ranjan
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Edward DiBella
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA.,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - Ganesh Adluru
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA.,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
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22
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Liu F, Li D, Jin X, Qiu W, Xia Q, Sun B. Dynamic cardiac MRI reconstruction using motion aligned locally low rank tensor (MALLRT). Magn Reson Imaging 2020; 66:104-115. [DOI: 10.1016/j.mri.2019.07.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Revised: 07/01/2019] [Accepted: 07/01/2019] [Indexed: 01/10/2023]
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Lingala SG, Guo Y, Bliesener Y, Zhu Y, Lebel RM, Law M, Nayak KS. Tracer kinetic models as temporal constraints during brain tumor DCE-MRI reconstruction. Med Phys 2019; 47:37-51. [PMID: 31663134 PMCID: PMC6980286 DOI: 10.1002/mp.13885] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 10/17/2019] [Accepted: 10/17/2019] [Indexed: 12/11/2022] Open
Abstract
Purpose To apply tracer kinetic models as temporal constraints during reconstruction of under‐sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI). Methods A library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under‐sampling experiments are performed on a brain tumor DCE digital reference object (DRO), and 12 brain tumor in‐vivo 3T datasets. The performances of the proposed under‐sampled reconstruction scheme and an existing compressed sensing‐based temporal finite‐difference (tFD) under‐sampled reconstruction were compared against the fully sampled inverse Fourier Transform‐based reconstruction. Results Simulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts‐Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO‐based experiments showed good fidelity in recovery of kinetic maps from 20‐fold under‐sampled data. In‐vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at under‐sampled reduction factors >= 20. Conclusions Tracer kinetic models can be applied as temporal constraints during brain tumor DCE‐MRI reconstruction. The proposed under‐sampled scheme resulted in model parameter estimates less biased with respect to conventional fully sampled DCE MRI reconstructions and parameter estimation. The approach is flexible, can use nonlinear kinetic models, and does not require tuning of regularization parameters.
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Affiliation(s)
- Sajan Goud Lingala
- Roy J Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
| | - Yi Guo
- Snap Inc., San Francisco, CA, USA
| | - Yannick Bliesener
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | | | - R Marc Lebel
- GE Healthcare Applied Sciences Laboratory, Calgary, Canada
| | - Meng Law
- Department of Neuroscience, Monash University, Melbourne, Australia
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
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24
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Zhao N, O'Connor D, Basarab A, Ruan D, Sheng K. Motion Compensated Dynamic MRI Reconstruction With Local Affine Optical Flow Estimation. IEEE Trans Biomed Eng 2019; 66:3050-3059. [PMID: 30794164 PMCID: PMC10919160 DOI: 10.1109/tbme.2019.2900037] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
This paper proposes a novel framework to reconstruct dynamic magnetic resonance imaging (DMRI) with motion compensation (MC). Specifically, by combining the intensity-based optical flow constraint with the traditional compressed sensing scheme, we are able to jointly reconstruct the DMRI sequences and estimate the interframe motion vectors. Then, the DMRI reconstruction can be refined through MC with the estimated motion field. By employing the coarse-to-fine multi-scale resolution strategy, we are able to update the motion field in different spatial scales. The estimated motion vectors need to be interpolated to the finest resolution scale to compensate the DMRI reconstruction. Moreover, the proposed framework is capable of handling a wide class of prior information (regularizations) for DMRI reconstruction, such as sparsity, low rank, and total variation. The formulated optimization problem is solved by a primal-dual algorithm with linesearch due to its efficiency when dealing with non-differentiable problems. Experiments on various DMRI datasets validate the reconstruction quality improvement using the proposed scheme in comparison to several state-of-the-art algorithms.
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25
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Mohsin YQ, Poddar S, Jacob M. Free-Breathing & Ungated Cardiac MRI Using Iterative SToRM (i-SToRM). IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2303-2313. [PMID: 30932835 PMCID: PMC7893810 DOI: 10.1109/tmi.2019.2908140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
We introduce a local manifold regularization approach to recover dynamic MRI data from highly undersampled measurements. The proposed scheme relies on the manifold structure of local image patches at the same spatial location in a free-breathing cardiac MRI dataset; this approach is a generalization of the SmooThness Regularization on Manifolds (SToRM) scheme that exploits the global manifold structure of images in the dataset. Since the manifold structure of the patches varies depending on the spatial location and is often considerably simpler than the global one, this approach significantly reduces the data demand, facilitating the recovery from shorter scans. Since the navigator-based estimation of manifold structure pursued in SToRM is not feasible in this setting, a reformulation of SToRM is introduced. Specifically, the regularization term of the cost function involves the sum of robust distances between images sub-patches in the dataset. The optimization algorithm alternates between updating the images and estimating the manifold structure of the image patches. The utility of the proposed scheme is demonstrated in the context of in-vivo prospective free-breathing cardiac CINE MRI imaging with multichannel acquisitions and simulated phantoms. The new framework facilitates a reduction in scan time, as compared to the SToRM strategy.
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26
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Scannell CM, Villa AD, Lee J. Robust Non-Rigid Motion Compensation of Free-Breathing Myocardial Perfusion MRI Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1812-1820. [PMID: 30716032 PMCID: PMC6699991 DOI: 10.1109/tmi.2019.2897044] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Kinetic parameter values, such as myocardial perfusion, can be quantified from dynamic contrast-enhanced magnetic resonance imaging data using tracer-kinetic modeling. However, respiratory motion affects the accuracy of this process. Motion compensation of the image series is difficult due to the rapid local signal enhancement caused by the passing of the gadolinium-based contrast agent. This contrast enhancement invalidates the assumptions of the (global) cost functions traditionally used in intensity-based registrations. The algorithms are unable to distinguish whether the differences in signal intensity between frames are caused by the spatial motion artifacts or the local contrast enhancement. In order to address this problem, a fully automated motion compensation scheme is proposed, which consists of two stages. The first of which uses robust principal component analysis (PCA) to separate the local signal enhancement from the baseline signal, before a refinement stage which uses the traditional PCA to construct a synthetic reference series that is free from motion but preserves the signal enhancement. Validation is performed on 18 subjects acquired in free-breathing and 5 clinical subjects acquired with a breath-hold. The validation assesses the visual quality, the temporal smoothness of tissue curves, and the clinically relevant quantitative perfusion values. The expert observers score the visual quality increased by a mean of 1.58/5 after motion compensation and improvement over the previously published methods. The proposed motion compensation scheme also leads to the improved quantitative performance of motion compensated free-breathing image series [30% reduction in the coefficient of variation across quantitative perfusion maps and 53% reduction in temporal variations (p < 0.001)].
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Huang J, Zhou G, Yu G. Orthogonal tensor dictionary learning for accelerated dynamic MRI. Med Biol Eng Comput 2019; 57:1933-1946. [PMID: 31254175 DOI: 10.1007/s11517-019-02005-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 06/13/2019] [Indexed: 11/25/2022]
Abstract
A direct application of the compressed sensing (CS) theory to dynamic magnetic resonance imaging (MRI) reconstruction needs vectorization or matricization of the dynamic MRI data, which is composed of a stack of 2D images and can be naturally regarded as a tensor. This 1D/2D model may destroy the inherent spatial structure property of the data. An alternative way to exploit the multidimensional structure in dynamic MRI is to employ tensor decomposition for dictionary learning, that is, learning multiple dictionaries along each dimension (mode) and sparsely representing the multidimensional data with respect to the Kronecker product of these dictionaries. In this work, we introduce a novel tensor dictionary learning method under an orthonormal constraint on the elementary matrix of the tensor dictionary for dynamic MRI reconstruction. The proposed algorithm alternates sparse coding, tensor dictionary learning, and updating reconstruction, and each corresponding subproblem is efficiently solved by a closed-form solution. Numerical experiments on phantom and synthetic data show significant improvements in reconstruction accuracy and computational efficiency obtained by the proposed scheme over the existing method that uses the 1D/2D model with overcomplete dictionary learning. Graphical abstract Fig. 1 Comparison between (a) the traditional method and (b) the proposed method based on dictionary learning for dynamic MRI reconstruction.
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Affiliation(s)
- Jinhong Huang
- School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China.
| | - Genjiao Zhou
- School of Science and Technology, Gannan Normal University, Ganzhou, China
| | - Gaohang Yu
- Department of Mathematics, School of Science, Hangzhou Dianzi University, Hangzhou, China
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28
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Taso M, Zhao L, Guidon A, Litwiller DV, Alsop DC. Volumetric abdominal perfusion measurement using a pseudo-randomly sampled 3D fast-spin-echo (FSE) arterial spin labeling (ASL) sequence and compressed sensing reconstruction. Magn Reson Med 2019; 82:680-692. [PMID: 30953396 DOI: 10.1002/mrm.27761] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 02/04/2019] [Accepted: 03/11/2019] [Indexed: 12/22/2022]
Abstract
PURPOSE To improve image quality and spatial coverage for abdominal perfusion imaging by implementing an arterial spin labeling (ASL) sequence that combines variable-density 3D fast-spin-echo (FSE) with Cartesian trajectory and compressed-sensing (CS) reconstruction. METHODS A volumetric FSE sequence was modified to include background-suppressed pseudo-continuous ASL labeling and to support variable-density (VD) Poisson-disk sampling for acceleration. We additionally explored the benefits of center oversampling and variable outer k-space sampling. Fourteen healthy volunteers were scanned on a 3T scanner to test acceleration factors as well as the various sampling schemes described under synchronized-breathing to limit motion issues. A CS reconstruction was implemented using the BART toolbox to reconstruct perfusion-weighted ASL volumes, assessing the impact of acceleration, different reconstruction, and sampling strategies on image quality. RESULTS CS acceleration is feasible with ASL, and a strong renal perfusion signal could be observed even at very high acceleration rates (≈15). We have shown that ASL k-space complex subtraction was desirable before CS reconstruction. Although averaging of multiple highly accelerated images helped to reduce artifacts from physiologic fluctuations, superior image quality was achieved by interleaving of different highly undersampled pseudo-random spatial sampling patterns and using 4D-CS reconstruction. Combination of these enhancements produces high-quality ASL volumes in under 5 min. CONCLUSIONS High-quality isotropic ASL abdominal perfusion volumes can be obtained in healthy volunteers with a VD-FSE and CS reconstruction. This lays the groundwork for future developments toward whole abdomen free-breathing non-contrast perfusion imaging.
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Affiliation(s)
- Manuel Taso
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Li Zhao
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Arnaud Guidon
- Global MR applications and Workflow, GE Healthcare, Boston, Massachusetts
| | - Daniel V Litwiller
- Global MR applications and Workflow, GE Healthcare, New York City, New York
| | - David C Alsop
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
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Sliding motion compensated low-rank plus sparse (SMC-LS) reconstruction for high spatiotemporal free-breathing liver 4D DCE-MRI. Magn Reson Imaging 2019; 58:56-66. [PMID: 30658071 DOI: 10.1016/j.mri.2019.01.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 12/06/2018] [Accepted: 01/12/2019] [Indexed: 02/03/2023]
Abstract
Liver dynamic contrast-enhanced MRI (DCE-MRI) requires high spatiotemporal resolution and large field of view to clearly visualize all relevant enhancement phases and detect early-stage liver lesions. The low-rank plus sparse (L + S) reconstruction outperforms standard sparsity-only-based reconstruction through separation of low-rank background component (L) and sparse dynamic components (S). However, the L + S decomposition is sensitive to respiratory motion so that image quality is compromised when breathing occurs during long time data acquisition. To enable high quality reconstruction for free-breathing liver 4D DCE-MRI, this paper presents a novel method called SMC-LS, which incorporates Sliding Motion Compensation into the standard L + S reconstruction. The global superior-inferior displacement of the internal abdominal organs is inferred directly from the undersampled raw data and then used to correct the breathing induced sliding motion which is the dominant component of respiratory motion. With sliding motion compensation, the reconstructed temporal frames are roughly registered before applying the standard L + S decomposition. The proposed method has been validated using free-breathing liver 4D MRI phantom data, free-breathing liver 4D DCE-MRI phantom data, and in vivo free breathing liver 4D MRI dataset. Results demonstrated that SMC-LS reconstruction can effectively reduce motion blurring artefacts and preserve both spatial structures and temporal variations at a sub-second temporal frame rate for free-breathing whole-liver 4D DCE-MRI.
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Weller DS, Wang L, Mugler JP, Meyer CH. Motion-compensated reconstruction of magnetic resonance images from undersampled data. Magn Reson Imaging 2019; 55:36-45. [PMID: 30213754 PMCID: PMC6242755 DOI: 10.1016/j.mri.2018.09.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 08/16/2018] [Accepted: 09/08/2018] [Indexed: 02/03/2023]
Abstract
Magnetic resonance imaging of patients who find difficulty lying still or holding their breath can be challenging. Unresolved intra-frame motion yields blurring artifacts and limits spatial resolution. To correct for intra-frame non-rigid motion, such as in pediatric body imaging, this paper describes a multi-scale technique for joint estimation of the motion occurring during the acquisition and of the desired uncorrupted image. This technique regularizes the motion coefficients to enforce invertibility and minimize numerical instability. This multi-scale approach takes advantage of variable-density sampling patterns used in accelerated imaging to resolve large motion from a coarse scale. The resulting method improves image quality for a set of two-dimensional reconstructions from data simulated with independently generated deformations, with statistically significant increases in both peak signal to error ratio and structural similarity index. These improvements are consistent across varying undersampling factors and severities of motion and take advantage of the variable density sampling pattern.
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Affiliation(s)
| | - Luonan Wang
- University of Virginia, Charlottesville, VA 22904, USA.
| | - John P Mugler
- University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
| | - Craig H Meyer
- University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
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Tolouee A, Alirezaie J, Babyn P. Motion-compensated data decomposition algorithm to accelerate dynamic cardiac MRI. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2017; 31:33-47. [PMID: 28569375 DOI: 10.1007/s10334-017-0628-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 05/19/2017] [Accepted: 05/20/2017] [Indexed: 10/19/2022]
Abstract
OBJECTIVES In dynamic cardiac magnetic resonance imaging (MRI), the spatiotemporal resolution is often limited by low imaging speed. Compressed sensing (CS) theory can be applied to improve imaging speed and spatiotemporal resolution. The combination of compressed sensing and low-rank matrix completion represents an attractive means to further increase imaging speed. By extending prior work, a Motion-Compensated Data Decomposition (MCDD) algorithm is proposed to improve the performance of CS for accelerated dynamic cardiac MRI. MATERIALS AND METHODS The process of MCDD can be described as follows: first, we decompose the dynamic images into a low-rank (L) and a sparse component (S). The L component includes periodic motion in the background, since it is highly correlated among frames, and the S component corresponds to respiratory motion. A motion-estimation/motion-compensation (ME-MC) algorithm is then applied to the low-rank component to reconstruct a cardiac motion compensated dynamic cardiac MRI. RESULTS With validations on the numerical phantom and in vivo cardiac MRI data, we demonstrate the utility of the proposed scheme in significantly improving compressed sensing reconstructions by minimizing motion artifacts. The proposed method achieves higher PSNR and lower MSE and HFEN for medium to high acceleration factors. CONCLUSION The proposed method is observed to yield reconstructions with minimal spatiotemporal blurring and motion artifacts in comparison to the existing state-of-the-art methods.
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Affiliation(s)
- Azar Tolouee
- Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B2K3, Canada
| | - Javad Alirezaie
- Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B2K3, Canada.
| | - Paul Babyn
- Department of Medical Imaging, University of Saskatchewan and Saskatoon Health Region, Saskatoon, SK, Canada
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32
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Kamesh Iyer S, Tasdizen T, Likhite D, DiBella E. Split Bregman multicoil accelerated reconstruction technique: A new framework for rapid reconstruction of cardiac perfusion MRI. Med Phys 2016; 43:1969. [PMID: 27036592 DOI: 10.1118/1.4943643] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Rapid reconstruction of undersampled multicoil MRI data with iterative constrained reconstruction method is a challenge. The authors sought to develop a new substitution based variable splitting algorithm for faster reconstruction of multicoil cardiac perfusion MRI data. METHODS The new method, split Bregman multicoil accelerated reconstruction technique (SMART), uses a combination of split Bregman based variable splitting and iterative reweighting techniques to achieve fast convergence. Total variation constraints are used along the spatial and temporal dimensions. The method is tested on nine ECG-gated dog perfusion datasets, acquired with a 30-ray golden ratio radial sampling pattern and ten ungated human perfusion datasets, acquired with a 24-ray golden ratio radial sampling pattern. Image quality and reconstruction speed are evaluated and compared to a gradient descent (GD) implementation and to multicoil k-t SLR, a reconstruction technique that uses a combination of sparsity and low rank constraints. RESULTS Comparisons based on blur metric and visual inspection showed that SMART images had lower blur and better texture as compared to the GD implementation. On average, the GD based images had an ∼18% higher blur metric as compared to SMART images. Reconstruction of dynamic contrast enhanced (DCE) cardiac perfusion images using the SMART method was ∼6 times faster than standard gradient descent methods. k-t SLR and SMART produced images with comparable image quality, though SMART was ∼6.8 times faster than k-t SLR. CONCLUSIONS The SMART method is a promising approach to reconstruct good quality multicoil images from undersampled DCE cardiac perfusion data rapidly.
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Affiliation(s)
- Srikant Kamesh Iyer
- Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112; Scientific Computing and Imaging Institute (SCI), University of Utah, Salt Lake City, Utah 84112; and UCAIR, Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah 84108
| | - Tolga Tasdizen
- Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112 and Scientific Computing and Imaging Institute (SCI), University of Utah, Salt Lake City, Utah 84112
| | - Devavrat Likhite
- Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112 and UCAIR, Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah 84108
| | - Edward DiBella
- UCAIR, Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah 84108
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Zhao B, Setsompop K, Ye H, Cauley SF, Wald LL. Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1812-23. [PMID: 26915119 PMCID: PMC5271418 DOI: 10.1109/tmi.2016.2531640] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple MR tissue parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization.
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Pang J, Chen Y, Fan Z, Nguyen C, Yang Q, Xie Y, Li D. High efficiency coronary MR angiography with nonrigid cardiac motion correction. Magn Reson Med 2016; 76:1345-1353. [PMID: 27455164 DOI: 10.1002/mrm.26332] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 06/09/2016] [Accepted: 06/15/2016] [Indexed: 11/05/2022]
Abstract
PURPOSE To improve the coronary visualization quality of four-dimensional (4D) coronary MR angiography (MRA) through cardiac motion correction and iterative reconstruction. METHODS A contrast-enhanced, spoiled gradient echo sequence with 3D radial trajectory and self-gating was used for 4D coronary MRA data acquisition at 3 Tesla. A whole-heart 16-phase cine series was reconstructed with respiratory motion correction. Nonrigid registration was performed between the identified quiescent phases and a reference. The motion information of all included phases was then used along with the corresponding k-space data to iteratively reconstruct the final image. Healthy volunteer studies (N = 13) were conducted to compare the proposed method with the conventional strategy, which accepts data from a single, contiguous window out of the original 16-phase data. Apparent signal-to-noise ratio (aSNR) and coronary sharpness were used as the image quality metrics. RESULTS The proposed method significantly improved aSNR (11.89 ± 3.76 to 13.97 ± 5.21; P = 0.005) and scan efficiency (18.8% ± 6.0% to 40.9% ± 9.7%; P < 0.001), compared with the conventional strategy. Sharpness of left main (P = 0.002), proximal (P = 0.04), and middle (P = 0.02) right coronary artery, and proximal left anterior descending (P = 0.04) was also significantly improved. CONCLUSION The proposed cardiac motion-corrected reconstruction significantly improved the achievable quality of coronary visualization from 4D coronary MRA. Magn Reson Med 76:1345-1353, 2016. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Jianing Pang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Yuhua Chen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Computer and Information Science, University of Pennsylvania, Philadelphia, Pennyslvania, USA
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Christopher Nguyen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Qi Yang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. .,Bioengineering, University of California, Los Angeles, California, USA.
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Yang ACY, Kretzler M, Sudarski S, Gulani V, Seiberlich N. Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption. Invest Radiol 2016; 51:349-64. [PMID: 27003227 PMCID: PMC4948115 DOI: 10.1097/rli.0000000000000274] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.
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Affiliation(s)
- Alice Chieh-Yu Yang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
| | - Madison Kretzler
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, USA
| | - Sonja Sudarski
- Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim - Heidelberg University, Heidelberg, Germany
| | - Vikas Gulani
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
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Adluru G, Gur Y, Chen L, Feinberg D, Anderson J, DiBella EVR. MRI reconstruction of multi-image acquisitions using a rank regularizer with data reordering. Med Phys 2016; 42:4734-44. [PMID: 26233201 DOI: 10.1118/1.4926777] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
PURPOSE To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions. METHODS Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data. RESULTS Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively. CONCLUSIONS The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions.
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Affiliation(s)
- Ganesh Adluru
- UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108
| | - Yaniv Gur
- IBM Almaden Research Center, San Jose, California 95120
| | - Liyong Chen
- Advanced MRI Technologies, Sebastpool, California, 95472
| | - David Feinberg
- Advanced MRI Technologies, Sebastpool, California, 95472
| | - Jeffrey Anderson
- UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108
| | - Edward V R DiBella
- UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108 and Department of Bioengineering, University of Utah, Salt Lake City, Utah 84112
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Mohsin YQ, Lingala SG, DiBella E, Jacob M. Accelerated dynamic MRI using patch regularization for implicit motion compensation. Magn Reson Med 2016; 77:1238-1248. [PMID: 27091812 DOI: 10.1002/mrm.26215] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Revised: 02/19/2016] [Accepted: 02/22/2016] [Indexed: 12/17/2022]
Abstract
PURPOSE To introduce a fast algorithm for motion-compensated accelerated dynamic MRI. METHODS An efficient patch smoothness regularization scheme, which implicitly compensates for inter-frame motion, is introduced to recover dynamic MRI data from highly undersampled measurements. The regularization prior is a sum of distances between each rectangular patch in the dataset with other patches in the dataset using a saturating distance metric. Unlike current motion estimation and motion compensation (ME-MC) methods, the proposed scheme does not require reference frames or complex motion models. The proposed algorithm, which alternates between inter-patch shrinkage step and conjugate gradient algorithm, is considerably more computationally efficient than ME-MC methods. The reconstructions obtained using the proposed algorithm is compared against state-of-the-art methods. RESULTS The proposed method is observed to yield reconstructions with minimal spatiotemporal blurring and motion artifacts. In comparison to the existing state-of-the-art ME-MC methods, PRICE provides comparable or even better image quality with faster reconstruction times (approximately nine times faster). CONCLUSION The presented scheme enables computationally efficient and effective motion-compensated reconstruction in a variety of applications with large inter-frame motion and contrast changes. This algorithm could be seen as an alternative over the current state-of-the-art ME-MC schemes that are computationally expensive. Magn Reson Med 77:1238-1248, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Yasir Q Mohsin
- Department of Electrical and Computer Engineering, the University of Iowa, Iowa, USA
| | - Sajan Goud Lingala
- Department of Electrical Engineering, University of Southern California, California, USA
| | - Edward DiBella
- Department of Radiology, the University of Utah, Utah, USA
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, the University of Iowa, Iowa, USA
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A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift. Int J Biomed Imaging 2016; 2016:9416435. [PMID: 27066068 PMCID: PMC4811091 DOI: 10.1155/2016/9416435] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 02/23/2016] [Indexed: 12/05/2022] Open
Abstract
Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use. Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) random shift. Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches.
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Rank CM, Heußer T, Buzan MTA, Wetscherek A, Freitag MT, Dinkel J, Kachelrieß M. 4D respiratory motion-compensated image reconstruction of free-breathing radial MR data with very high undersampling. Magn Reson Med 2016; 77:1170-1183. [PMID: 26991911 DOI: 10.1002/mrm.26206] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2015] [Revised: 02/16/2016] [Accepted: 02/16/2016] [Indexed: 11/10/2022]
Abstract
PURPOSE To develop four-dimensional (4D) respiratory time-resolved MRI based on free-breathing acquisition of radial MR data with very high undersampling. METHODS We propose the 4D joint motion-compensated high-dimensional total variation (4D joint MoCo-HDTV) algorithm, which alternates between motion-compensated image reconstruction and artifact-robust motion estimation at multiple resolution levels. The algorithm is applied to radial MR data of the thorax and upper abdomen of 12 free-breathing subjects with acquisition times between 37 and 41 s and undersampling factors of 16.8. Resulting images are compared with compressed sensing-based 4D motion-adaptive spatio-temporal regularization (MASTeR) and 4D high-dimensional total variation (HDTV) reconstructions. RESULTS For all subjects, 4D joint MoCo-HDTV achieves higher similarity in terms of normalized mutual information and cross-correlation than 4D MASTeR and 4D HDTV when compared with reference 4D gated gridding reconstructions with 8.4 ± 1.1 times longer acquisition times. In a qualitative assessment of artifact level and image sharpness by two radiologists, 4D joint MoCo-HDTV reveals higher scores (P < 0.05) than 4D HDTV and 4D MASTeR at the same undersampling factor and the reference 4D gated gridding reconstructions, respectively. CONCLUSIONS 4D joint MoCo-HDTV enables time-resolved image reconstruction of free-breathing radial MR data with undersampling factors of 16.8 while achieving low-streak artifact levels and high image sharpness. Magn Reson Med 77:1170-1183, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Christopher M Rank
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Thorsten Heußer
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Maria T A Buzan
- Department of Pneumology, Iuliu Hatieganu University of Medicine and Pharmacy, Hasdeu Str. 6, 400371, Cluj-Napoca, Romania.,Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at Heidelberg University Hospital, Amalienstr. 5, 69126, Heidelberg, Germany.,Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany
| | - Andreas Wetscherek
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Martin T Freitag
- Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Julien Dinkel
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at Heidelberg University Hospital, Amalienstr. 5, 69126, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Im Neuenheimer Feld 430, 69120, Heidelberg, Germany.,Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Marc Kachelrieß
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
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Royuela-del-Val J, Cordero-Grande L, Simmross-Wattenberg F, Martín-Fernández M, Alberola-López C. Jacobian weighted temporal total variation for motion compensated compressed sensing reconstruction of dynamic MRI. Magn Reson Med 2016; 77:1208-1215. [DOI: 10.1002/mrm.26198] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Revised: 02/10/2016] [Accepted: 02/12/2016] [Indexed: 11/11/2022]
Affiliation(s)
| | - Lucilio Cordero-Grande
- Division of Imaging Sciences and Biomedical Engineering, Centre for the Developing Brain; King's College London; London UK
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41
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Miao X, Lingala SG, Guo Y, Jao T, Usman M, Prieto C, Nayak KS. Accelerated cardiac cine MRI using locally low rank and finite difference constraints. Magn Reson Imaging 2016; 34:707-714. [PMID: 26968142 DOI: 10.1016/j.mri.2016.03.007] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Revised: 02/14/2016] [Accepted: 03/03/2016] [Indexed: 10/22/2022]
Abstract
PURPOSE To evaluate the potential value of combining multiple constraints for highly accelerated cardiac cine MRI. METHODS A locally low rank (LLR) constraint and a temporal finite difference (FD) constraint were combined to reconstruct cardiac cine data from highly undersampled measurements. Retrospectively undersampled 2D Cartesian reconstructions were quantitatively evaluated against fully-sampled data using normalized root mean square error, structural similarity index (SSIM) and high frequency error norm (HFEN). This method was also applied to 2D golden-angle radial real-time imaging to facilitate single breath-hold whole-heart cine (12 short-axis slices, 9-13s single breath hold). Reconstruction was compared against state-of-the-art constrained reconstruction methods: LLR, FD, and k-t SLR. RESULTS At 10 to 60 spokes/frame, LLR+FD better preserved fine structures and depicted myocardial motion with reduced spatio-temporal blurring in comparison to existing methods. LLR yielded higher SSIM ranking than FD; FD had higher HFEN ranking than LLR. LLR+FD combined the complimentary advantages of the two, and ranked the highest in all metrics for all retrospective undersampled cases. Single breath-hold multi-slice cardiac cine with prospective undersampling was enabled with in-plane spatio-temporal resolutions of 2×2mm(2) and 40ms. CONCLUSION Highly accelerated cardiac cine is enabled by the combination of 2D undersampling and the synergistic use of LLR and FD constraints.
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Affiliation(s)
- Xin Miao
- Department of Biomedical Engineering, University of Southern California, Los Angeles, USA.
| | - Sajan Goud Lingala
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, USA
| | - Yi Guo
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, USA
| | - Terrence Jao
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, USA
| | - Muhammad Usman
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Claudia Prieto
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Krishna S Nayak
- Department of Biomedical Engineering, University of Southern California, Los Angeles, USA; Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, USA
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Odille F, Menini A, Escanyé JM, Vuissoz PA, Marie PY, Beaumont M, Felblinger J. Joint Reconstruction of Multiple Images and Motion in MRI: Application to Free-Breathing Myocardial T₂Quantification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:197-207. [PMID: 26259015 DOI: 10.1109/tmi.2015.2463088] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Exploiting redundancies between multiple images of an MRI examination can be formalized as the joint reconstruction of these images. The anatomy is preserved indeed so that specific constraints can be implemented (e.g. most of the features or spatial gradients should be in the same place in all these images) and only the contrast changes from one image to another need to be encoded. The application of this concept is particularly challenging in cardiovascular and body imaging due to the complex organ deformations, especially with the patient breathing. In this study a joint optimization framework is proposed for reconstructing multiple MR images together with a nonrigid motion model. The motion model takes into account both intra-image and inter-image motion and therefore can correct for most ghosting/blurring artifacts and misregistration between images. The framework was validated with free-breathing myocardial T2 mapping experiments from nine heart transplant patients at 1.5 T. Results showed improved image quality and excellent image alignment with the multi-image reconstruction compared to the independent reconstruction of each image. Segment-wise myocardial T2 values were in good agreement with the reference values obtained from multiple breath-holds (62.5 ± 11.1 ms against 62.2 ± 11.2 ms which was not significant with p=0.49).
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Zhang Y, Dong Z, Phillips P, Wang S, Ji G, Yang J. Exponential Wavelet Iterative Shrinkage Thresholding Algorithm for compressed sensing magnetic resonance imaging. Inf Sci (N Y) 2015. [DOI: 10.1016/j.ins.2015.06.017] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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44
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Royuela-del-Val J, Cordero-Grande L, Simmross-Wattenberg F, Martín-Fernández M, Alberola-López C. Nonrigid groupwise registration for motion estimation and compensation in compressed sensing reconstruction of breath-hold cardiac cine MRI. Magn Reson Med 2015; 75:1525-36. [DOI: 10.1002/mrm.25733] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Revised: 03/19/2015] [Accepted: 03/22/2015] [Indexed: 11/06/2022]
Affiliation(s)
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain; Division of Imaging Sciences and Biomedical Engineering; King's College London; London UK
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45
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Bhave S, Lingala SG, Johnson CP, Magnotta VA, Jacob M. Accelerated whole-brain multi-parameter mapping using blind compressed sensing. Magn Reson Med 2015; 75:1175-86. [PMID: 25850952 DOI: 10.1002/mrm.25722] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2014] [Revised: 02/22/2015] [Accepted: 03/12/2015] [Indexed: 01/16/2023]
Abstract
PURPOSE To introduce a blind compressed sensing (BCS) framework to accelerate multi-parameter MR mapping, and demonstrate its feasibility in high-resolution, whole-brain T1ρ and T2 mapping. METHODS BCS models the evolution of magnetization at every pixel as a sparse linear combination of bases in a dictionary. Unlike compressed sensing, the dictionary and the sparse coefficients are jointly estimated from undersampled data. Large number of non-orthogonal bases in BCS accounts for more complex signals than low rank representations. The low degree of freedom of BCS, attributed to sparse coefficients, translates to fewer artifacts at high acceleration factors (R). RESULTS From 2D retrospective undersampling experiments, the mean square errors in T1ρ and T2 maps were observed to be within 0.1% up to R = 10. BCS was observed to be more robust to patient-specific motion as compared to other compressed sensing schemes and resulted in minimal degradation of parameter maps in the presence of motion. Our results suggested that BCS can provide an acceleration factor of 8 in prospective 3D imaging with reasonable reconstructions. CONCLUSION BCS considerably reduces scan time for multiparameter mapping of the whole brain with minimal artifacts, and is more robust to motion-induced signal changes compared to current compressed sensing and principal component analysis-based techniques.
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Affiliation(s)
- Sampada Bhave
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa, USA
| | - Sajan Goud Lingala
- Department of Electrical Engineering, University of Southern California, California, USA
| | | | | | - Mathews Jacob
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa, USA
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