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Martinez JA, Yu VY, Tringale KR, Otazo R, Cohen O. Phase-sensitive deep reconstruction method for rapid multiparametric MR fingerprinting and quantitative susceptibility mapping in the brain. Magn Reson Imaging 2024; 109:147-157. [PMID: 38513790 PMCID: PMC11042874 DOI: 10.1016/j.mri.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 03/23/2024]
Abstract
INTRODUCTION This study explores the potential of Magnetic Resonance Fingerprinting (MRF) with a novel Phase-Sensitivity Deep Reconstruction Network (PS-DRONE) for simultaneous quantification of T1, T2, Proton Density, B1+, phase and quantitative susceptibility mapping (QSM). METHODS Data were acquired at 3 T in vitro and in vivo using an optimized EPI-based MRF sequence. Phantom experiments were conducted using a standardized phantom for T1 and T2 maps and a custom-made agar-based gadolinium phantom for B1 and QSM maps. In vivo experiments included five healthy volunteers and one patient diagnosed with brain metastasis. PSDRONE maps were compared to reference maps obtained through standard imaging sequences. RESULTS Total scan time was 2 min for 32 slices and a resolution of [1 mm, 1 mm, 4.5 mm]. The reconstruction of T1, T2, Proton Density, B1+ and phase maps were reconstructed within 1 s. In the phantoms, PS-DRONE analysis presented accurate and strongly correlated T1 and T2 maps (r = 0.99) compared to the reference maps. B1 maps from PS-DRONE showed slightly higher values, though still correlated (r = 0.6) with the reference. QSM values showed a small bias but were strongly correlated (r = 0.99) with reference data. In the in vivo analysis, PS-DRONE-derived T1 and T2 values for gray and white matter matched reference values in healthy volunteers. PS-DRONE B1 and QSM maps showed strong correlations with reference values. CONCLUSION The PS-DRONE network enables concurrent acquisition of T1, T2, PD, B1+, phase and QSM maps, within 2 min of acquisition time and 1 s of reconstruction time.
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Affiliation(s)
- Jessica A Martinez
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA.
| | - Victoria Y Yu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Kathryn R Tringale
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
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Siddiq S, Murray V, Tyagi N, Borman P, Gui C, Crane C, Wu C, Otazo R. MR signature matching (MRSIGMA) implementation for true real-time free-breathing volumetric imaging with sub-200 ms latency on an MR-Linac. Magn Reson Med 2024. [PMID: 38576131 DOI: 10.1002/mrm.30097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/20/2024] [Accepted: 03/14/2024] [Indexed: 04/06/2024]
Abstract
PURPOSE Develop a true real-time implementation of MR signature matching (MRSIGMA) for free-breathing 3D MRI with sub-200 ms latency on the Elekta Unity 1.5T MR-Linac. METHODS MRSIGMA was implemented on an external computer with a network connection to the MR-Linac. Stack-of-stars with partial kz sampling was used to accelerate data acquisition and ReconSocket was employed for simultaneous data transmission. Movienet network computed the 4D MRI motion dictionary and correlation analysis was used for signature matching. A programmable 4D MRI phantom was utilized to evaluate MRSIGMA with respect to a ground-truth translational motion reference. In vivo validation was performed on patients with pancreatic cancer, where 15 patients were employed to train Movienet and 7 patients to test the real-time implementation of MRSIGMA. Dice coefficients between real-time MRSIGMA and a retrospectively computed 4D reference were used to evaluate motion tracking performance. RESULTS Motion dictionary was computed in under 5 s. Signature acquisition and matching presented 173 ms latency on the phantom and 193 ms on patients. MRSIGMA presented a mean error of 1.3-1.6 mm for all phantom experiments, which was below the 2 mm acquisition resolution along the motion direction. The Dice coefficient over time between MRSIGMA and reference contours was 0.88 ± 0.02 (GTV), 0.87 ± 0.02(duodenum-stomach), and 0.78 ± 0.02(small bowel), demonstrating high motion tracking performance for both tumor and organs at risk. CONCLUSION The real-time implementation of MRSIGMA enabled true real-time free-breathing 3D MRI with sub-200 ms imaging latency on a clinical MR-Linac system, which can be used for treatment monitoring, adaptive radiotherapy and dose accumulation mapping in tumors affected by respiratory motion.
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Affiliation(s)
- Saad Siddiq
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Victor Murray
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Pim Borman
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Chengcheng Gui
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Christopher Crane
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Can Wu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Murray V, Siddiq S, Crane C, El Homsi M, Kim TH, Wu C, Otazo R. Movienet: Deep space-time-coil reconstruction network without k-space data consistency for fast motion-resolved 4D MRI. Magn Reson Med 2024; 91:600-614. [PMID: 37849064 PMCID: PMC10842259 DOI: 10.1002/mrm.29892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/19/2023]
Abstract
PURPOSE To develop a novel deep learning approach for 4D-MRI reconstruction, named Movienet, which exploits space-time-coil correlations and motion preservation instead of k-space data consistency, to accelerate the acquisition of golden-angle radial data and enable subsecond reconstruction times in dynamic MRI. METHODS Movienet uses a U-net architecture with modified residual learning blocks that operate entirely in the image domain to remove aliasing artifacts and reconstruct an unaliased motion-resolved 4D image. Motion preservation is enforced by sorting the input image and reference for training in a linear motion order from expiration to inspiration. The input image was collected with a lower scan time than the reference XD-GRASP image used for training. Movienet is demonstrated for motion-resolved 4D MRI and motion-resistant 3D MRI of abdominal tumors on a therapeutic 1.5T MR-Linac (1.5-fold acquisition acceleration) and diagnostic 3T MRI scanners (2-fold and 2.25-fold acquisition acceleration for 4D and 3D, respectively). Image quality was evaluated quantitatively and qualitatively by expert clinical readers. RESULTS The reconstruction time of Movienet was 0.69 s (4 motion states) and 0.75 s (10 motion states), which is substantially lower than iterative XD-GRASP and unrolled reconstruction networks. Movienet enables faster acquisition than XD-GRASP with similar overall image quality and improved suppression of streaking artifacts. CONCLUSION Movienet accelerates data acquisition with respect to compressed sensing and reconstructs 4D images in less than 1 s, which would enable an efficient implementation of 4D MRI in a clinical setting for fast motion-resistant 3D anatomical imaging or motion-resolved 4D imaging.
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Affiliation(s)
- Victor Murray
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Syed Siddiq
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Christopher Crane
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Tae-Hyung Kim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Can Wu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Alus O, El Homsi M, Golia Pernicka JS, Rodriguez L, Mazaheri Y, Kee Y, Petkovska I, Otazo R. Convolutional network denoising for acceleration of multi-shot diffusion MRI. Magn Reson Imaging 2024; 105:108-113. [PMID: 37820978 DOI: 10.1016/j.mri.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/04/2023] [Accepted: 10/07/2023] [Indexed: 10/13/2023]
Abstract
Multi-shot echo planar imaging is a promising technique to reduce geometric distortions and increase spatial resolution in diffusion-weighted MRI (DWI), at the expense of increased scan time. Moreover, performing DWI in the body requires multiple repetitions to obtain sufficient signal-to-noise ratio, which further increases the scan time. This work proposes to reduce the number of repetitions and perform denoising of high b-value images using a convolutional network denoising trained on single-shot DWI to accelerate the acquisition of multi-shot DWI. Convolutional network denoising is demonstrated to accelerate the acquisition of 2-shot DWI by a factor of 4 compared to the clinical standard on patients with rectal cancer. Image quality was evaluated using qualitative scores from expert body radiologists between accelerated and non-accelerated acquisition. Additionally, the effect of convolutional network denoising on each image quality score was analyzed using a Wilcoxon signed-rank test. Convolutional network denoising would enable to increase the number of shots without increasing scan time for significant geometric artifact reduction and spatial resolution increase.
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Affiliation(s)
- Or Alus
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Lee Rodriguez
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Youngwook Kee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Kang M, Behr GG, Jafari R, Gambarin M, Otazo R, Kee Y. Free-breathing high isotropic resolution quantitative susceptibility mapping (QSM) of liver using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction. Magn Reson Med 2023; 90:1844-1858. [PMID: 37392413 PMCID: PMC10529485 DOI: 10.1002/mrm.29779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 05/15/2023] [Accepted: 06/06/2023] [Indexed: 07/03/2023]
Abstract
PURPOSE To enable free-breathing and high isotropic resolution liver quantitative susceptibility mapping (QSM) using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction. METHODS Using 3D multi-echo UTE cones MRI, a respiratory motion was estimated from the k-space center of the imaging data. After sorting the k-space data with estimated motion, respiratory motion state-resolved reconstruction was performed for multi-echo data followed by nonlinear least-squares fitting for proton density fat fraction (PDFF),R 2 * $$ {\mathrm{R}}_2^{\ast } $$ , and fat-corrected B0 field maps. PDFF and B0 field maps were subsequently used for QSM reconstruction. The proposed method was compared with motion-averaged (gridding) reconstruction and conventional 3D multi-echo Cartesian MRI in moving gadolinium phantom and in vivo studies. Region of interest (ROI)-based linear regression analysis was performed on these methods to investigate correlations between gadolinium concentration and QSM in the phantom study and betweenR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and QSM in in vivo study. RESULTS Cones with motion-resolved reconstruction showed sharper image quality compared to motion-averaged reconstruction with a substantial reduction of motion artifacts in both moving phantom and in vivo studies. For ROI-based linear regression analysis of the phantom study, susceptibility values from cones with motion-resolved reconstruction (QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.31 × gadolinium mM + $$ \times {\mathrm{gadolinium}}_{\mathrm{mM}}+ $$ 0.05,R 2 $$ {R}^2 $$ = 0.999) and Cartesian without motion (QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.32× gadolinium mM + $$ \times {\mathrm{gadolinium}}_{\mathrm{mM}}+ $$ 0.04,R 2 $$ {R}^2 $$ = 1.000) showed linear relationships with gadolinium concentrations and showed good agreement with each other. For in vivo, motion-resolved reconstruction showed higher goodness of fit (QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.00261 × R 2 s - 1 * - $$ \times {\mathrm{R}}_{2_{{\mathrm{s}}^{-1}}}^{\ast }- $$ 0.524,R 2 $$ {R}^2 $$ = 0.977) compared to motion-averaged reconstruction (QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.0021 × R 2 s - 1 * - $$ \times {\mathrm{R}}_{2_{{\mathrm{s}}^{-1}}}^{\ast }- $$ 0.572,R 2 $$ {R}^2 $$ = 0.723) in ROI-based linear regression analysis betweenR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and QSM. CONCLUSION Feasibility of free-breathing liver QSM was demonstrated with motion-resolved 3D multi-echo UTE cones MRI, achieving high isotropic resolution currently unachievable in conventional Cartesian MRI.
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Affiliation(s)
- MungSoo Kang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Gerald G. Behr
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ramin Jafari
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Maya Gambarin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Youngwook Kee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Cohen O, Otazo R. Global deep learning optimization of chemical exchange saturation transfer magnetic resonance fingerprinting acquisition schedule. NMR Biomed 2023; 36:e4954. [PMID: 37070221 PMCID: PMC10896067 DOI: 10.1002/nbm.4954] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/10/2023] [Accepted: 04/11/2023] [Indexed: 05/06/2023]
Abstract
Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging technique but suffers from long scan times and complicated processing. CEST was recently combined with magnetic resonance fingerprinting (MRF) to address these shortcomings. However, the CEST-MRF signal depends on multiple acquisition and tissue parameters so selecting an optimal acquisition schedule is challenging. In this work, we propose a novel dual-network deep learning framework to optimize the CEST-MRF acquisition schedule. The quality of the optimized schedule was assessed in a digital brain phantom and compared with alternate deep learning optimization approaches. The effect of schedule length on the reconstruction error was also investigated. A healthy subject was scanned with optimized and random schedules and with a conventional CEST sequence for comparison. The optimized schedule was also tested in a subject with metastatic renal cell carcinoma. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter (WM) and grey matter (GM). The optimized schedule was 12% shorter but yielded equal or lower normalized root mean square error for all parameters. The proposed optimization also provided a lower error compared with alternate methodologies. Longer schedules generally yielded lower error. In vivo maps obtained with the optimized schedule showed reduced noise and improved delineation of GM and WM. CEST curves synthesized from the optimized parameters were highly correlated (r = 0.99) with measured conventional CEST. The mean concordance correlation coefficient in WM/GM for all tissue parameters was 0.990/0.978 for the optimized schedule but only 0.979/0.975 for the random schedule. The proposed schedule optimization is widely applicable to MRF pulse sequences and provides accurate and reproducible tissue maps with reduced noise at a shorter scan time than a randomly generated schedule.
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Affiliation(s)
- Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Wu C, Murray V, Siddiq SS, Tyagi N, Reyngold M, Crane C, Otazo R. Real-time 4D MRI using MR signature matching (MRSIGMA) on a 1.5T MR-Linac system. Phys Med Biol 2023; 68:10.1088/1361-6560/acf3cc. [PMID: 37619588 PMCID: PMC10513779 DOI: 10.1088/1361-6560/acf3cc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/24/2023] [Indexed: 08/26/2023]
Abstract
Objective. To develop real-time 4D MRI using MR signature matching (MRSIGMA) for volumetric motion imaging in patients with pancreatic cancer on a 1.5T MR-Linac system.Approach. Two consecutive MRI scans with 3D golden-angle radial stack-of-stars acquisitions were performed on ten patients with inoperable pancreatic cancer. The complete first scan (905 angles) was used to compute a 4D motion dictionary including ten pairs of 3D motion images and signatures. The second scan was used for real-time imaging, where each angle (275 ms) was processed separately to match it to one of the dictionary entries. The complete second scan was also used to compute a 4D reference to assess motion tracking performance.Dicecoefficients of the gross tumor volume (GTV) and two organs-at-risk (duodenum-stomach and small bowel) were calculated between signature matching and reference. In addition, volume changes, displacements, center of mass shifts, andDicescores over time were calculated to characterize motion.Main results. Total imaging latency of MRSIGMA (acquisition + matching) was less than 300 ms. TheDicecoefficients were 0.87 ± 0.06 (GTV), 0.86 ± 0.05 (duodenum-stomach), and 0.85 ± 0.05 (small bowel), which indicate high accuracy (high mean value) and low uncertainty (low standard deviation) of MRSIGMA for real-time motion tracking. The center of mass shift was 3.1 ± 2.0 mm (GTV), 5.3 ± 3.0 mm (duodenum-stomach), and 3.4 ± 1.5 mm (small bowel). TheDicescores over time (0.97 ± [0.01-0.03]) were similarly high for MRSIGMA and reference scans in all the three contours.Significance. This work demonstrates the feasibility of real-time 4D MRI using MRSIGMA for volumetric motion tracking on a 1.5T MR-Linac system. The high accuracy and low uncertainty of real-time MRSIGMA is an essential step towards continuous treatment adaptation of tumors affected by real-time respiratory motion and could ultimately improve treatment safety by optimizing ablative dose delivery near gastrointestinal organs.
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Affiliation(s)
- Can Wu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Victor Murray
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Syed S. Siddiq
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Marsha Reyngold
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Christopher Crane
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
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Yu VY, Otazo R, Wu C, Subashi E, Baumann M, Koken P, Doneva M, Mazurkewitz P, Shasha D, Zelefsky M, Cervino L, Cohen O. Quantitative longitudinal mapping of radiation-treated prostate cancer using MR fingerprinting with radial acquisition and subspace reconstruction. Magn Reson Imaging 2023; 101:25-34. [PMID: 37015305 PMCID: PMC10623548 DOI: 10.1016/j.mri.2023.03.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 03/29/2023] [Indexed: 04/06/2023]
Abstract
MR fingerprinting (MRF) enables fast multiparametric quantitative imaging with a single acquisition and has been shown to improve diagnosis of prostate cancer. However, most prostate MRF studies were performed with spiral acquisitions that are sensitive to B0 inhomogeneities and consequent blurring. In this work, a radial MRF acquisition with a novel subspace reconstruction technique was developed to enable fast T1/T2 mapping in the prostate in under 4 min. The subspace reconstruction exploits the extensive temporal correlations in the MRF dictionary to pre-compute a low dimensional space for the solution and thus reduce the number of radial spokes to accelerate the acquisition. Iterative reconstruction with the subspace model and additional regularization of the signal representation in the subspace is performed to minimize the number of spokes and maintain matching quality and SNR. Reconstruction accuracy was assessed using the ISMRM NIST phantom. In-vivo validation was performed on two healthy subjects and two prostate cancer patients undergoing radiation therapy. The longitudinal repeatability was quantified using the concordance correlation coefficient (CCC) in one of the healthy subjects by repeated scans over 1 year. One prostate cancer patient was scanned at three time points, before initiating therapy and following brachytherapy and external beam radiation. Changes in the T1/T2 maps obtained with the proposed method were quantified. The prostate, peripheral and transitional zones, and visible dominant lesion were delineated for each study, and the statistics and distribution of the quantitative mapping values were analyzed. Significant image quality improvements compared with standard reconstruction methods were obtained with the proposed subspace reconstruction method. A notable decrease in the spread of the T1/T2 values without biasing the estimated mean values was observed with the subspace reconstruction and agreed with reported literature values. The subspace reconstruction enabled visualization of small differences in T1/T2 values in the tumor region within the peripheral zone. Longitudinal imaging of a volunteer subject yielded CCC of 0.89 for MRF T1, and 0.81 for MRF T2 in the prostate gland. Longitudinal imaging of the prostate patient confirmed the feasibility of capturing radiation treatment related changes. This work is a proof-of-concept for a high resolution and fast quantitative mapping using golden-angle radial MRF combined with a subspace reconstruction technique for longitudinal treatment response assessment in subjects undergoing radiation treatment.
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Affiliation(s)
- Victoria Y Yu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Can Wu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ergys Subashi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Peter Koken
- Philips Research, MR Research, Hamburg, Germany
| | | | | | - Daniel Shasha
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael Zelefsky
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Laura Cervino
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Chekhonin IV, Cohen O, Otazo R, Young RJ, Holodny AI, Pronin IN. Magnetic resonance relaxometry in quantitative imaging of brain gliomas: A literature review. Neuroradiol J 2023:19714009231173100. [PMID: 37133228 DOI: 10.1177/19714009231173100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023] Open
Abstract
Magnetic resonance (MR) relaxometry is a quantitative imaging method that measures tissue relaxation properties. This review discusses the state of the art of clinical proton MR relaxometry for glial brain tumors. Current MR relaxometry technology also includes MR fingerprinting and synthetic MRI, which solve the inefficiencies and challenges of earlier techniques. Despite mixed results regarding its capability for brain tumor differential diagnosis, there is growing evidence that MR relaxometry can differentiate between gliomas and metastases and between glioma grades. Studies of the peritumoral zones have demonstrated their heterogeneity and possible directions of tumor infiltration. In addition, relaxometry offers T2* mapping that can define areas of tissue hypoxia not discriminated by perfusion assessment. Studies of tumor therapy response have demonstrated an association between survival and progression terms and dynamics of native and contrast-enhanced tumor relaxometric profiles. In conclusion, MR relaxometry is a promising technique for glial tumor diagnosis, particularly in association with neuropathological studies and other imaging techniques.
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Affiliation(s)
- Ivan V Chekhonin
- Federal State Autonomous Institution N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
- Federal State Budgetary Institution V.P. Serbsky National Medical Research Centre for Psychiatry and Narcology of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
| | - Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
- Department of Neuroscience, Weill Cornell Graduate School of the Medical Sciences, New York, NY, USA
| | - Igor N Pronin
- Federal State Autonomous Institution N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
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Mohammadi M, Kaye EA, Alus O, Kee Y, Golia Pernicka JS, El Homsi M, Petkovska I, Otazo R. Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network. Bioengineering (Basel) 2023; 10:bioengineering10030359. [PMID: 36978750 PMCID: PMC10045764 DOI: 10.3390/bioengineering10030359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/28/2023] [Accepted: 03/09/2023] [Indexed: 03/16/2023] Open
Abstract
This work presents a deep-learning-based denoising technique to accelerate the acquisition of high b-value diffusion-weighted MRI for rectal cancer. A denoising convolutional neural network (DCNN) with a combined L1–L2 loss function was developed to denoise high b-value diffusion-weighted MRI data acquired with fewer repetitions (NEX: number of excitations) using the low b-value image as an anatomical guide. DCNN was trained using 85 datasets acquired on patients with rectal cancer and tested on 20 different datasets with NEX = 1, 2, and 4, corresponding to acceleration factors of 16, 8, and 4, respectively. Image quality was assessed qualitatively by expert body radiologists. Reader 1 scored similar overall image quality between denoised images with NEX = 1 and NEX = 2, which were slightly lower than the reference. Reader 2 scored similar quality between NEX = 1 and the reference, while better quality for NEX = 2. Denoised images with fourfold acceleration (NEX = 4) received even higher scores than the reference, which is due in part to the effect of gas-related motion in the rectum, which affects longer acquisitions. The proposed deep learning denoising technique can enable eightfold acceleration with similar image quality (average image quality = 2.8 ± 0.5) and fourfold acceleration with higher image quality (3.0 ± 0.6) than the clinical standard (2.5 ± 0.8) for improved diagnosis of rectal cancer.
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Affiliation(s)
- Mohaddese Mohammadi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Elena A. Kaye
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Or Alus
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Youngwook Kee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | | | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Correspondence:
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11
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Jafari R, Do RKG, LaGratta MD, Fung M, Bayram E, Cashen T, Otazo R. GRASPNET: Fast spatiotemporal deep learning reconstruction of golden-angle radial data for free-breathing dynamic contrast-enhanced magnetic resonance imaging. NMR Biomed 2023; 36:e4861. [PMID: 36305619 PMCID: PMC9898111 DOI: 10.1002/nbm.4861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 10/23/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
The purpose of the current study was to develop a deep learning technique called Golden-angle RAdial Sparse Parallel Network (GRASPnet) for fast reconstruction of dynamic contrast-enhanced 4D MRI acquired with golden-angle radial k-space trajectories. GRASPnet operates in the image-time space and does not use explicit data consistency to minimize the reconstruction time. Three different network architectures were developed: (1) GRASPnet-2D: 2D convolutional kernels (x,y) and coil and contrast dimensions collapsed into a single combined dimension; (2) GRASPnet-3D: 3D kernels (x,y,t); and (3) GRASPnet-2D + time: two 3D kernels to first exploit spatial correlations (x,y,1) followed by temporal correlations (1,1,t). The networks were trained using iterative GRASP reconstruction as the reference. Free-breathing 3D abdominal imaging with contrast injection was performed on 33 patients with liver lesions using a T1-weighted golden-angle stack-of-stars pulse sequence. Ten datasets were used for testing. The three GRASPnet architectures were compared with iterative GRASP results using quantitative and qualitative analysis, including impressions from two body radiologists. The three GRASPnet techniques reduced the reconstruction time to about 13 s with similar results with respect to iterative GRASP. Among the GRASPnet techniques, GRASPnet-2D + time compared favorably in the quantitative analysis. Spatiotemporal deep learning enables reconstruction of dynamic 4D contrast-enhanced images in a few seconds, which would facilitate translation to clinical practice of compressed sensing methods that are currently limited by long reconstruction times.
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Affiliation(s)
- Ramin Jafari
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | | | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
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12
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Wu C, Krishnamoorthy G, Yu V, Subashi E, Rimner A, Otazo R. 4D lung MRI with high-isotropic-resolution using half-spoke (UTE) and full-spoke 3D radial acquisition and temporal compressed sensing reconstruction. Phys Med Biol 2023; 68. [PMID: 36535035 DOI: 10.1088/1361-6560/acace6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 12/19/2022] [Indexed: 12/23/2022]
Abstract
Objective. To develop a respiratory motion-resolved four-dimensional (4D) magnetic resonance imaging (MRI) technique with high-isotropic-resolution (1.1 mm) using 3D radial sampling, camera-based respiratory motion sensing, and temporal compressed sensing reconstruction for lung cancer imaging.Approach. Free-breathing half- and full-spoke 3D golden-angle radial acquisitions were performed on eight healthy volunteers and eight patients with lung tumors of varying size. A back-and-forth k-space ordering between consecutive interleaves of the 3D radial acquisition was performed to minimize eddy current-related artifacts. Data were sorted into respiratory motion states using camera-based motion navigation and 4D images were reconstructed using temporal compressed sensing to reduce scan time. Normalized sharpness indices of the diaphragm, apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (CNR) of the lung tumor (patients only), liver, and aortic arch were compared between half- and full-spoke 4D MRI images to evaluate the impact of respiratory motion and image contrast on 4D MRI image quality. Respiration-induced changes in lung volumes and center of mass shifts were compared between half- and full-spoke 4D MRI measurements. In addition, the motion measurements from 4D MRI and the same-day 4D CT were presented in one of the lung tumor patients.Main results. Half-spoke 4D MRI provides better visualization of the lung parenchyma, while full-spoke 4D MRI presents sharper diaphragm images and higher aSNR and CNR in the lung tumor, liver, and aortic arch. Lung volume changes and center of mass shifts measured by half- and full-spoke 4D MRI were not statistically different. For the patient with 4D MRI and same-day 4D CT, lung volume changes and center of mass shifts were generally comparable.Significance. This work demonstrates the feasibility of a motion-resolved 4D MRI technique with high-isotropic-resolution using 3D radial acquisition, camera-based respiratory motion sensing, and temporal compressed sensing reconstruction for treatment planning and motion monitoring in radiotherapy of lung cancer.
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Affiliation(s)
- Can Wu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | | | - Victoria Yu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Ergys Subashi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
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13
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Cohen O, Yu VY, Tringale KR, Young RJ, Perlman O, Farrar CT, Otazo R. CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction. Magn Reson Med 2023; 89:233-249. [PMID: 36128888 PMCID: PMC9617776 DOI: 10.1002/mrm.29448] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 08/09/2022] [Accepted: 08/19/2022] [Indexed: 01/21/2023]
Abstract
PURPOSE To develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction. METHODS A CEST-MRF pulse sequence originally designed for animal imaging was modified to conform to hardware limits on clinical scanners while keeping scan time under 2 min. Quantitative MRF reconstruction was performed using a deep reconstruction network (DRONE) to yield the water relaxation and chemical exchange parameters. The feasibility of the six parameter DRONE reconstruction was tested in simulations using a digital brain phantom. A healthy subject was scanned with the CEST-MRF sequence, conventional MRF and CEST sequences for comparison. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter and gray matter. The clinical utility of CEST-MRF was demonstrated on four patients with brain metastases in comparison to standard clinical imaging sequences. Tumors were segmented into edema, solid core, and necrotic core regions and the CEST-MRF values compared to the contra-lateral side. RESULTS DRONE reconstruction of the digital phantom yielded a normalized RMS error of ≤7% for all parameters. The CEST-MRF parameters were in good agreement with those from conventional MRF and CEST sequences and previous studies. The mean concordance correlation coefficient for all six parameters was 0.98 ± 0.01 in white matter and 0.98 ± 0.02 in gray matter. The CEST-MRF values in nearly all tumor regions were significantly different (P = 0.05) from each other and the contra-lateral side. CONCLUSION Combination of EPI readout and deep learning reconstruction enabled fast, accurate and reproducible CEST-MRF in brain tumors.
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Affiliation(s)
- Ouri Cohen
- Department of Medical PhysicsMemorial Sloan Kettering Cancer Center
New YorkNew YorkUSA
| | - Victoria Y. Yu
- Department of Medical PhysicsMemorial Sloan Kettering Cancer Center
New YorkNew YorkUSA
| | - Kathryn R. Tringale
- Department of Radiation OncologyMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Robert J. Young
- Department of RadiologyMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Or Perlman
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolCharlestownMassachusettsUSA
- Department of Biomedical EngineeringTel Aviv UniversityTel AvivIsrael
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
| | - Christian T. Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Ricardo Otazo
- Department of Medical PhysicsMemorial Sloan Kettering Cancer Center
New YorkNew YorkUSA
- Department of RadiologyMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
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14
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Subashi E, Feng L, Liu Y, Robertson S, Segars P, Driehuys B, Kelsey CR, Yin FF, Otazo R, Cai J. View-sharing for 4D magnetic resonance imaging with randomized projection-encoding enables improvements of respiratory motion imaging for treatment planning in abdominothoracic radiotherapy. Phys Imaging Radiat Oncol 2023; 25:100409. [PMID: 36655213 PMCID: PMC9841273 DOI: 10.1016/j.phro.2022.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023] Open
Abstract
Background and Purpose The accuracy and precision of radiation therapy are dependent on the characterization of organ-at-risk and target motion. This work aims to demonstrate a 4D magnetic resonance imaging (MRI) method for improving spatial and temporal resolution in respiratory motion imaging for treatment planning in abdominothoracic radiotherapy. Materials and Methods The spatial and temporal resolution of phase-resolved respiratory imaging is improved by considering a novel sampling function based on quasi-random projection-encoding and peripheral k-space view-sharing. The respiratory signal is determined directly from k-space, obviating the need for an external surrogate marker. The average breathing curve is used to optimize spatial resolution and temporal blurring by limiting the extent of data sharing in the Fourier domain. Improvements in image quality are characterized by evaluating changes in signal-to-noise ratio (SNR), resolution, target detection, and level of artifact. The method is validated in simulations, in a dynamic phantom, and in-vivo imaging. Results Sharing of high-frequency k-space data, driven by the average breathing curve, improves spatial resolution and reduces artifacts. Although equal sharing of k-space data improves resolution and SNR in stationary features, phases with large temporal changes accumulate significant artifacts due to averaging of high frequency features. In the absence of view-sharing, no averaging and detection artifacts are observed while spatial resolution is degraded. Conclusions The use of a quasi-random sampling function, with view-sharing driven by the average breathing curve, provides a feasible method for self-navigated 4D-MRI at improved spatial resolution.
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Affiliation(s)
- Ergys Subashi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Li Feng
- Biomedical Engineering and Imaging Institute, Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Yilin Liu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Scott Robertson
- Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, United States
- Department of Radiology, Duke University Medical Center, Durham, NC, United States
| | - Paul Segars
- Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, United States
- Department of Radiology, Duke University Medical Center, Durham, NC, United States
| | - Bastiaan Driehuys
- Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, United States
- Department of Radiology, Duke University Medical Center, Durham, NC, United States
| | - Christopher R Kelsey
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, United States
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
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15
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Mazaheri Y, Kim N, Lakhman Y, Jafari R, Vargas A, Otazo R. Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters. NMR Biomed 2022; 35:e4718. [PMID: 35226774 PMCID: PMC9203940 DOI: 10.1002/nbm.4718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/17/2022] [Accepted: 02/22/2022] [Indexed: 06/14/2023]
Abstract
The aim of this work is to develop a data-driven quantitative dynamic contrast-enhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with user-defined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation. Computer simulations were performed to determine the accuracy (expressed as percentage error [PE]) and precision of the estimated parameters. PK parameters (volume transfer constant [Ktrans ], fractional volume of the extravascular extracellular space [ve ], and blood plasma volume fraction [vp ]) and normalized root-mean-square error [nRMSE] (%) of the fitting errors for the tumor contrast kinetic data were measured both with population-averaged and data-driven AIFs. On patient data, the Wilcoxon signed-rank test was performed to compare nRMSE. Simulations demonstrated that GRASP image reconstruction with a temporal resolution of 1 s/frame for AIF estimation and 5 s/frame for PK analysis resulted in an absolute PE of less than 5% in the estimation of Ktrans and ve , and less than 11% in the estimation of vp . The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK parameters from a single acquisition resulting in data-driven AIFs and voxelwise PK parametric maps.
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Affiliation(s)
- Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nathanael Kim
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Yulia Lakhman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ramin Jafari
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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16
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Kim N, Tringale KR, Crane C, Tyagi N, Otazo R. MR SIGnature MAtching (MRSIGMA) with retrospective self-evaluation for real-time volumetric motion imaging. Phys Med Biol 2021; 66. [PMID: 34619666 DOI: 10.1088/1361-6560/ac2dd2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 10/07/2021] [Indexed: 11/11/2022]
Abstract
Objective. MR SIGnature MAtching (MRSIGMA) is a real-time volumetric MRI technique to image tumor and organs at risk motion in real-time for radiotherapy applications, where a dictionary of high-resolution 3D motion states and associated motion signatures are computed first during offline training and real-time 3D imaging is performed afterwards using fast signature-only acquisition and signature matching. However, the lack of a reference image with similar spatial resolution and temporal resolution introduces significant challenges forin vivovalidation.Approach. This work proposes a retrospective self-validation for MRSIGMA, where the same data used for real-time imaging are used to create a non-real-time reference for comparison. MRSIGMA with self-validation is tested in patients with liver tumors using quantitative metrics defined on the tumor and nearby organs-at-risk structures. The dice coefficient between contours defined on the real-time MRSIGMA and non-real-time reference was used to assess motion imaging performance.Main Results. Total latency (including signature acquisition and signature matching) was between 250 and 314 ms, which is sufficient for organs affected by respiratory motion. Mean ± standard deviation dice coefficient over time was 0.74 ± 0.03 for patients imaged without contrast agent and 0.87 ± 0.03 for patients imaged with contrast agent, which demonstrated high-performance real-time motion imaging.Signficance. MRSIGMA with self-evaluation provides a means to perform real-time volumetric MRI for organ motion tracking with quantitative performance measures.
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Affiliation(s)
- Nathanael Kim
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Kathryn R Tringale
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Christopher Crane
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
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17
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Bogner W, Otazo R, Henning A. Accelerated MR spectroscopic imaging-a review of current and emerging techniques. NMR Biomed 2021; 34:e4314. [PMID: 32399974 PMCID: PMC8244067 DOI: 10.1002/nbm.4314] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 03/24/2020] [Accepted: 03/30/2020] [Indexed: 05/14/2023]
Abstract
Over more than 30 years in vivo MR spectroscopic imaging (MRSI) has undergone an enormous evolution from theoretical concepts in the early 1980s to the robust imaging technique that it is today. The development of both fast and efficient sampling and reconstruction techniques has played a fundamental role in this process. State-of-the-art MRSI has grown from a slow purely phase-encoded acquisition technique to a method that today combines the benefits of different acceleration techniques. These include shortening of repetition times, spatial-spectral encoding, undersampling of k-space and time domain, and use of spatial-spectral prior knowledge in the reconstruction. In this way in vivo MRSI has considerably advanced in terms of spatial coverage, spatial resolution, acquisition speed, artifact suppression, number of detectable metabolites and quantification precision. Acceleration not only has been the enabling factor in high-resolution whole-brain 1 H-MRSI, but today is also common in non-proton MRSI (31 P, 2 H and 13 C) and applied in many different organs. In this process, MRSI techniques had to constantly adapt, but have also benefitted from the significant increase of magnetic field strength boosting the signal-to-noise ratio along with high gradient fidelity and high-density receive arrays. In combination with recent trends in image reconstruction and much improved computation power, these advances led to a number of novel developments with respect to MRSI acceleration. Today MRSI allows for non-invasive and non-ionizing mapping of the spatial distribution of various metabolites' tissue concentrations in animals or humans, is applied for clinical diagnostics and has been established as an important tool for neuro-scientific and metabolism research. This review highlights the developments of the last five years and puts them into the context of earlier MRSI acceleration techniques. In addition to 1 H-MRSI it also includes other relevant nuclei and is not limited to certain body regions or specific applications.
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Affiliation(s)
- Wolfgang Bogner
- High‐Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Ricardo Otazo
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew York, New YorkUSA
| | - Anke Henning
- Max Planck Institute for Biological CyberneticsTübingenGermany
- Advanced Imaging Research Center, UT Southwestern Medical CenterDallasTexasUSA
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18
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Jenabi M, Young RJ, Moreno R, Gene M, Cho N, Otazo R, Holodny AI, Peck KK. Multiband diffusion tensor imaging for presurgical mapping of motor and language pathways in patients with brain tumors. J Neuroimaging 2021; 31:784-795. [PMID: 33817896 DOI: 10.1111/jon.12859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND PURPOSE Assessment of the essential white matter fibers of arcuate fasciculus and corticospinal tract (CST), required for preoperative planning in brain tumor patients, relies on the reliability of diffusion tensor imaging (DTI). The recent development of multiband DTI (mb-DTI) based on simultaneous multislice excitation could maintain the overall quality of tractography while not exceeding standard clinical care time. To address this potential, we performed quantitative analyses to evaluate tractography results of arcuate fasciculus and CST acquired by mb-DTI in brain tumor patients. METHODS We retrospectively analyzed 44 patients with brain lesions who underwent presurgical single-shot DTI (s-DTI) and mb-DTI. We measured DTI parameters: fractional anisotropy (FA) and mean diffusivity (MD [mm2 s-1 ]) in whole brain and tumor regions; and the tractography parameters: fiber FA, MD (mm2 s-1 ), volume (mm3 ), and length (mm) in the whole brain, arcuate fasciculus, and CST. Additionally, three neuroradiologists performed a blinded visual assessment comparing s-DTI with mb-DTI. RESULTS The mb-DTI showed higher mean FA and lower MD (r > .95, p < .002) in whole brain and tumor regions of interest; slightly higher fiber FA, volume, and length; and slightly lower fiber MD in whole brain, arcuate fasciculus, and CST than in s-DTI. These differences were significant for fiber FA in all tracts; length (mm) in arcuate fasciculus; and fiber MD (mm2 s-1 ) and volume (mm3 ) in all patients with tumor involved in the arcuate fasciculus, CST, and whole brain tracts (p = .001). Visual assessment demonstrated that both techniques produced visually similar tracts. CONCLUSIONS This study demonstrated the clinical potential and significant advantages of preoperative mb-DTI in brain tumor patients.
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Affiliation(s)
- Mehrnaz Jenabi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Raquel Moreno
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Madeleine Gene
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nicholas Cho
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.,Department of Neuroscience, Weill-Cornell Graduate School of the Medical Sciences, New York, New York, USA
| | - Kyung K Peck
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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19
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Otazo R, Lambin P, Pignol JP, Ladd ME, Schlemmer HP, Baumann M, Hricak H. MRI-guided Radiation Therapy: An Emerging Paradigm in Adaptive Radiation Oncology. Radiology 2020; 298:248-260. [PMID: 33350894 DOI: 10.1148/radiol.2020202747] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Radiation therapy (RT) continues to be one of the mainstays of cancer treatment. Considerable efforts have been recently devoted to integrating MRI into clinical RT planning and monitoring. This integration, known as MRI-guided RT, has been motivated by the superior soft-tissue contrast, organ motion visualization, and ability to monitor tumor and tissue physiologic changes provided by MRI compared with CT. Offline MRI is already used for treatment planning at many institutions. Furthermore, MRI-guided linear accelerator systems, allowing use of MRI during treatment, enable improved adaptation to anatomic changes between RT fractions compared with CT guidance. Efforts are underway to develop real-time MRI-guided intrafraction adaptive RT of tumors affected by motion and MRI-derived biomarkers to monitor treatment response and potentially adapt treatment to physiologic changes. These developments in MRI guidance provide the basis for a paradigm change in treatment planning, monitoring, and adaptation. Key challenges to advancing MRI-guided RT include real-time volumetric anatomic imaging, addressing image distortion because of magnetic field inhomogeneities, reproducible quantitative imaging across different MRI systems, and biologic validation of quantitative imaging. This review describes emerging innovations in offline and online MRI-guided RT, exciting opportunities they offer for advancing research and clinical care, hurdles to be overcome, and the need for multidisciplinary collaboration.
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Affiliation(s)
- Ricardo Otazo
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Philippe Lambin
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Jean-Philippe Pignol
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Mark E Ladd
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Heinz-Peter Schlemmer
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Michael Baumann
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Hedvig Hricak
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
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Kaye EA, Aherne EA, Duzgol C, Häggström I, Kobler E, Mazaheri Y, Fung MM, Zhang Z, Otazo R, Vargas HA, Akin O. Accelerating Prostate Diffusion-weighted MRI Using a Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study. Radiol Artif Intell 2020; 2:e200007. [PMID: 33033804 DOI: 10.1148/ryai.2020200007] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 04/29/2020] [Accepted: 05/06/2020] [Indexed: 02/02/2023]
Abstract
PURPOSE To investigate the feasibility of accelerating prostate diffusion-weighted imaging (DWI) by reducing the number of acquired averages and denoising the resulting image using a proposed guided denoising convolutional neural network (DnCNN). MATERIALS AND METHODS Raw data from the prostate DWI scans were retrospectively gathered between July 2018 and July 2019 from six single-vendor MRI scanners. There were 103 datasets used for training (median age, 64 years; interquartile range [IQR], 11), 15 for validation (median age, 68 years; IQR, 12), and 37 for testing (median age, 64 years; IQR, 12). High b-value diffusion-weighted (hb DW) data were reconstructed into noisy images using two averages and reference images using all 16 averages. A conventional DnCNN was modified into a guided DnCNN, which uses the low b-value DW image as a guidance input. Quantitative and qualitative reader evaluations were performed on the denoised hb DW images. A cumulative link mixed regression model was used to compare the readers' scores. The agreement between the apparent diffusion coefficient (ADC) maps (denoised vs reference) was analyzed using Bland-Altman analysis. RESULTS Compared with the original DnCNN, the guided DnCNN produced denoised hb DW images with higher peak signal-to-noise ratio (32.79 ± 3.64 [standard deviation] vs 33.74 ± 3.64), higher structural similarity index (0.92 ± 0.05 vs 0.93 ± 0.04), and lower normalized mean square error (3.9% ± 10 vs 1.6% ± 1.5) (P < .001 for all). Compared with the reference images, the denoised images received higher image quality scores from the readers (P < .0001). The ADC values based on the denoised hb DW images were in good agreement with the reference ADC values (mean ADC difference ranged from -0.04 to 0.02 × 10-3 mm2/sec). CONCLUSION Accelerating prostate DWI by reducing the number of acquired averages and denoising the resulting image using the proposed guided DnCNN is technically feasible. Supplemental material is available for this article. © RSNA, 2020.
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Affiliation(s)
- Elena A Kaye
- Departments of Medical Physics (E.A.K., I.H., Y.M., R.O.), Radiology (E.A.A., C.D., R.O., H.A.V., O.A.), and Epidemiology and Biostatistics (Z.Z.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Room S1212B, New York, NY 10065; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (E.K.); and MR Applications & Workflow Team, GE Healthcare, Chicago, Ill (M.M.F.)
| | - Emily A Aherne
- Departments of Medical Physics (E.A.K., I.H., Y.M., R.O.), Radiology (E.A.A., C.D., R.O., H.A.V., O.A.), and Epidemiology and Biostatistics (Z.Z.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Room S1212B, New York, NY 10065; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (E.K.); and MR Applications & Workflow Team, GE Healthcare, Chicago, Ill (M.M.F.)
| | - Cihan Duzgol
- Departments of Medical Physics (E.A.K., I.H., Y.M., R.O.), Radiology (E.A.A., C.D., R.O., H.A.V., O.A.), and Epidemiology and Biostatistics (Z.Z.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Room S1212B, New York, NY 10065; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (E.K.); and MR Applications & Workflow Team, GE Healthcare, Chicago, Ill (M.M.F.)
| | - Ida Häggström
- Departments of Medical Physics (E.A.K., I.H., Y.M., R.O.), Radiology (E.A.A., C.D., R.O., H.A.V., O.A.), and Epidemiology and Biostatistics (Z.Z.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Room S1212B, New York, NY 10065; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (E.K.); and MR Applications & Workflow Team, GE Healthcare, Chicago, Ill (M.M.F.)
| | - Erich Kobler
- Departments of Medical Physics (E.A.K., I.H., Y.M., R.O.), Radiology (E.A.A., C.D., R.O., H.A.V., O.A.), and Epidemiology and Biostatistics (Z.Z.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Room S1212B, New York, NY 10065; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (E.K.); and MR Applications & Workflow Team, GE Healthcare, Chicago, Ill (M.M.F.)
| | - Yousef Mazaheri
- Departments of Medical Physics (E.A.K., I.H., Y.M., R.O.), Radiology (E.A.A., C.D., R.O., H.A.V., O.A.), and Epidemiology and Biostatistics (Z.Z.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Room S1212B, New York, NY 10065; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (E.K.); and MR Applications & Workflow Team, GE Healthcare, Chicago, Ill (M.M.F.)
| | - Maggie M Fung
- Departments of Medical Physics (E.A.K., I.H., Y.M., R.O.), Radiology (E.A.A., C.D., R.O., H.A.V., O.A.), and Epidemiology and Biostatistics (Z.Z.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Room S1212B, New York, NY 10065; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (E.K.); and MR Applications & Workflow Team, GE Healthcare, Chicago, Ill (M.M.F.)
| | - Zhigang Zhang
- Departments of Medical Physics (E.A.K., I.H., Y.M., R.O.), Radiology (E.A.A., C.D., R.O., H.A.V., O.A.), and Epidemiology and Biostatistics (Z.Z.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Room S1212B, New York, NY 10065; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (E.K.); and MR Applications & Workflow Team, GE Healthcare, Chicago, Ill (M.M.F.)
| | - Ricardo Otazo
- Departments of Medical Physics (E.A.K., I.H., Y.M., R.O.), Radiology (E.A.A., C.D., R.O., H.A.V., O.A.), and Epidemiology and Biostatistics (Z.Z.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Room S1212B, New York, NY 10065; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (E.K.); and MR Applications & Workflow Team, GE Healthcare, Chicago, Ill (M.M.F.)
| | - Hebert A Vargas
- Departments of Medical Physics (E.A.K., I.H., Y.M., R.O.), Radiology (E.A.A., C.D., R.O., H.A.V., O.A.), and Epidemiology and Biostatistics (Z.Z.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Room S1212B, New York, NY 10065; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (E.K.); and MR Applications & Workflow Team, GE Healthcare, Chicago, Ill (M.M.F.)
| | - Oguz Akin
- Departments of Medical Physics (E.A.K., I.H., Y.M., R.O.), Radiology (E.A.A., C.D., R.O., H.A.V., O.A.), and Epidemiology and Biostatistics (Z.Z.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Room S1212B, New York, NY 10065; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (E.K.); and MR Applications & Workflow Team, GE Healthcare, Chicago, Ill (M.M.F.)
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Feng L, Tyagi N, Otazo R. MRSIGMA: Magnetic Resonance SIGnature MAtching for real-time volumetric imaging. Magn Reson Med 2020; 84:1280-1292. [PMID: 32086858 DOI: 10.1002/mrm.28200] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Revised: 12/13/2019] [Accepted: 01/16/2020] [Indexed: 12/25/2022]
Abstract
PURPOSE To propose a real-time 3D MRI technique called MR SIGnature MAtching (MRSIGMA) for high-resolution volumetric imaging and motion tracking with very low imaging latency. METHODS MRSIGMA consists of two steps: (1) offline learning of a database of possible 3D motion states and corresponding motion signature ranges and (2) online matching of new motion signatures acquired in real time with prelearned motion states. Specifically, the offline learning step (non-real-time) reconstructs motion-resolved 4D images representing different motion states and assigns a unique motion range to each state. The online matching step (real-time) acquires motion signatures only and selects one of the prelearned 3D motion states for each newly acquired signature, which generates 3D images efficiently in real time. The MRSIGMA technique was evaluated on 15 golden-angle stack-of-stars liver data sets, and the performance of respiratory motion tracking with the online-generated real-time 3D MRI was compared with the corresponding 2D projections acquired in real time. RESULTS The total latency of generating each 3D image during online matching was about 300 ms, including acquisition of the motion signature data (~138 ms) and corresponding matching process (~150 ms). Linear correlation assessment suggested excellent correlation (R2 = 0.948) between motion displacement measured from the online-generated real-time 3D images and the 2D real-time projections. CONCLUSION This proof-of-concept study demonstrates the feasibility of MRSIGMA for high-resolution real-time volumetric imaging, which shifts the acquisition and reconstruction burden to an offline learning step and leaves fast online matching for online imaging with very low imaging latency. The MRSIGMA technique can potentially be used for real-time motion tracking in MRI-guided radiation therapy.
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Affiliation(s)
- Li Feng
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Biomedical Engineering and Imaging Institute, Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Zibetti MVW, Sharafi A, Otazo R, Regatte RR. Accelerated mono- and biexponential 3D-T1ρ relaxation mapping of knee cartilage using golden angle radial acquisitions and compressed sensing. Magn Reson Med 2019; 83:1291-1309. [PMID: 31626381 DOI: 10.1002/mrm.28019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 09/05/2019] [Accepted: 09/06/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE To use golden-angle radial sampling and compressed sensing (CS) for accelerating mono- and biexponential 3D spin-lattice relaxation time in the rotating frame (T1ρ ) mapping of knee cartilage. METHODS Golden-angle radial stack-of-stars and Cartesian 3D-T1ρ -weighted knee cartilage datasets (n = 12) were retrospectively undersampled by acceleration factors (AFs) 2-10. CS-based reconstruction using 8 different sparsifying transforms were compared for mono- and biexponential T1ρ -mapping of knee cartilage, including spatio-temporal finite differences, wavelets, dictionary from principal component analysis, and exponential decay models, and also low rank and low rank plus sparse models (L+S). Complex-valued fitting was used and Marchenko-Pastur principal component analysis filtering also tested. RESULTS Most CS methods performed well for an AF of 2, with relative median normalized absolute deviation below 10% for monoexponential and biexponential mapping. For monoexponential mapping, radial sampling obtained a median normalized absolute deviation below 10% up to AF of 10, while Cartesian obtained this level of error only up to AF of 4. Radial sampling was also better with biexponential T1ρ mapping, with median normalized absolute deviation below 10% up to AF of 6. CONCLUSION Golden-angle radial acquisitions combined with CS outperformed Cartesian acquisitions for 3D-T1ρ mapping of knee cartilage, being it is a good alternative to Cartesian sampling for reducing scan time and/or improving image and mapping quality. The methods exponential decay models, spatio-temporal finite differences, and low rank obtained the best results for radial sampling patterns.
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Affiliation(s)
- Marcelo V W Zibetti
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Azadeh Sharafi
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Ravinder R Regatte
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
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Muckley MJ, Chen B, Vahle T, O'Donnell T, Knoll F, Sodickson AD, Sodickson DK, Otazo R. Image reconstruction for interrupted-beam x-ray CT on diagnostic clinical scanners. Phys Med Biol 2019; 64:155007. [PMID: 31258151 DOI: 10.1088/1361-6560/ab2df1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Low-dose x-ray CT is a major research area with high clinical impact. Compressed sensing using view-based sparse sampling and sparsity-promoting regularization has shown promise in simulations, but these methods can be difficult to implement on diagnostic clinical CT scanners since the x-ray beam cannot be switched on and off rapidly enough. An alternative to view-based sparse sampling is interrupted-beam sparse sampling. SparseCT is a recently-proposed interrupted-beam scheme that achieves sparse sampling by blocking a portion of the beam using a multislit collimator (MSC). The use of an MSC necessitates a number of modifications to the standard compressed sensing reconstruction pipeline. In particular, we find that SparseCT reconstruction is feasible within a model-based image reconstruction framework that incorporates data fidelity weighting to consider penumbra effects and source jittering to consider the effect of partial source obstruction. Here, we present these modifications and demonstrate their application in simulations and real-world prototype scans. In simulations compared to conventional low-dose acquisitions, SparseCT is able to achieve smaller normalized root-mean square differences and higher structural similarity measures on two reduction factors. In prototype experiments, we successfully apply our reconstruction modifications and maintain image resolution at quarter-dose reduction level. The SparseCT design requires only small hardware modifications to current diagnostic clinical scanners, opening up new possibilities for CT dose reduction.
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Affiliation(s)
- Matthew J Muckley
- New York University School of Medicine, New York, NY, United States of America
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Chen B, Kobler E, Muckley MJ, Sodickson AD, O'Donnell T, Flohr T, Schmidt B, Sodickson DK, Otazo R. SparseCT: System concept and design of multislit collimators. Med Phys 2019; 46:2589-2599. [PMID: 30980728 DOI: 10.1002/mp.13544] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 02/28/2019] [Accepted: 04/04/2019] [Indexed: 12/11/2022] Open
Abstract
PURPOSE SparseCT, an undersampling scheme for compressed sensing (CS) computed tomography (CT), has been proposed to reduce radiation dose by acquiring undersampled projection data from clinical CT scanners (Koesters et al. in, SparseCT: Interrupted-Beam Acquisition and Sparse Reconstruction for Radiation Dose Reduction; 2017). SparseCT partially blocks the x-ray beam with a multislit collimator (MSC) to perform a multidimensional undersampling along the view and detector row dimensions. SparseCT undersamples the projection data within each view and moves the MSC along the z-direction during gantry rotation to change the undersampling pattern. It enables reconstruction of images from undersampled data using CS algorithms. The purpose of this work is to design the spacing and width of the MSC slits and the MSC motion patterns based on beam separation, undersampling efficiency, and image quality. The development and testing of a SparseCT prototype with the designed MSC will be described in a following paper. METHODS We chose a few initial MSC designs based on the guidance from two metrics: beam separation and undersampling efficiency. Both beam separation and undersampling efficiency were measured from numerically simulated photon distribution with MSC taken into consideration. Beam separation measures the separation between x-ray beams from consecutive slits, taking into account penumbra effects on both sides of each slit. Undersampling efficiency measures the dose-weighted similarity between penumbra undersampling and binary undersampling, in other words, the effective contribution of the incident dose to the signal to noise ratio of the projection data. We then compared the initially chosen MSC designs in terms of their reconstruction image quality. SparseCT projections were simulated from fully sampled patient projection data according to the MSC design and motion pattern, reconstructed iteratively using a sparsity-enforcing penalized weighted least squares cost function with ordered subsets/momentum algorithm, and compared visually and quantitatively. RESULTS Simulated photon distributions indicate that the size of the penumbra is dominated by the size of the focal spot. Therefore, a wider MSC slit and a smaller focal spot lead to increased beam separation and undersampling efficiency. For fourfold undersampling with a 1.2 mm focal spot, a minimum MSC slit width of three detector rows (projected to the detector surface) is needed for beam separation; for threefold undersampling, a minimum slit width of four detector rows is needed. Simulations of SparseCT projection and reconstruction indicate that the motion pattern of the MSC does not have a visible impact on image quality. An MSC slit width of three or four detector rows yields similar image quality. CONCLUSION The MSC is the key component of the SparseCT method. Simulations of MSC designs incorporating x-ray beam penumbra effects showed that for threefold and fourfold dose reductions, an MSC slit width of four detector rows provided reasonable beam separation, undersampling efficiency, and image quality.
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Affiliation(s)
- Baiyu Chen
- Department of Radiology, NYU School of Medicine, New York, NY, 10016, USA
| | - Erich Kobler
- Institute of Computer Graphics and Vision, Graz University of Technology, Graz, 8010, Austria
| | - Matthew J Muckley
- Department of Radiology, NYU School of Medicine, New York, NY, 10016, USA
| | - Aaron D Sodickson
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | | | | | | | - Daniel K Sodickson
- Department of Radiology, NYU School of Medicine, New York, NY, 10016, USA
| | - Ricardo Otazo
- Department of Radiology, NYU School of Medicine, New York, NY, 10016, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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Prabhu V, Rosenkrantz AB, Otazo R, Sodickson DK, Kang SK. Population net benefit of prostate MRI with high spatiotemporal resolution contrast-enhanced imaging: A decision curve analysis. J Magn Reson Imaging 2019; 49:1400-1408. [DOI: 10.1002/jmri.26318] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 08/10/2018] [Accepted: 08/13/2018] [Indexed: 12/29/2022] Open
Affiliation(s)
- Vinay Prabhu
- Department of Radiology; NYU School of Medicine; New York New York USA
| | | | - Ricardo Otazo
- Department of Medical Physics; Memorial Sloan Kettering Cancer Center; New York USA
| | | | - Stella K. Kang
- Department of Radiology; NYU School of Medicine; New York New York USA
- Department of Population Health; NYU School of Medicine; New York New York USA
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Zhang J, Feng L, Otazo R, Kim SG. Rapid dynamic contrast-enhanced MRI for small animals at 7T using 3D ultra-short echo time and golden-angle radial sparse parallel MRI. Magn Reson Med 2019; 81:140-152. [PMID: 30058079 PMCID: PMC6258350 DOI: 10.1002/mrm.27357] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 04/02/2018] [Accepted: 04/22/2018] [Indexed: 01/18/2023]
Abstract
PURPOSE To develop a rapid dynamic contrast-enhanced MRI method with high spatial and temporal resolution for small-animal imaging at 7 Tesla. METHODS An ultra-short echo time (UTE) pulse sequence using a 3D golden-angle radial sampling was implemented to achieve isotropic spatial resolution with flexible temporal resolution. Continuously acquired radial spokes were grouped into subsets for image reconstruction using a multicoil compressed sensing approach (Golden-angle RAdial Sparse Parallel; GRASP). The proposed 3D-UTE-GRASP method with high temporal and spatial resolutions was tested using 7 mice with GL261 intracranial glioma models. RESULTS Iterative reconstruction with different temporal resolutions and regularization factors λ showed that, in all cases, the cost function decreased to less than 2.5% of its starting value within 20 iterations. The difference between the time-intensity curves of 3D-UTE-GRASP and nonuniform fast Fourier transform (NUFFT) images was minimal when λ was 1% of the maximum signal intensity of the initial NUFFT images. The 3D isotropic images were used to generate pharmacokinetic parameter maps to show the detailed images of the tumor characteristics in 3D and also to show longitudinal changes during tumor growth. CONCLUSION This feasibility study demonstrated that the proposed 3D-UTE-GRASP method can be used for effective measurement of the 3D spatial heterogeneity of tumor pharmacokinetic parameters.
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Affiliation(s)
- Jin Zhang
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, United States
| | - Li Feng
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, United States
| | - Ricardo Otazo
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, United States
| | - Sungheon Gene Kim
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, United States
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Zibetti MVW, Baboli R, Chang G, Otazo R, Regatte RR. Rapid compositional mapping of knee cartilage with compressed sensing MRI. J Magn Reson Imaging 2018; 48:1185-1198. [PMID: 30295344 PMCID: PMC6231228 DOI: 10.1002/jmri.26274] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 07/12/2018] [Indexed: 12/15/2022] Open
Abstract
More than a decade after the introduction of compressed sensing (CS) in MRI, researchers are still working on ways to translate it into different research and clinical applications. The greatest advantage of CS in MRI is the reduced amount of k-space data needed to reconstruct images, which can be exploited to reduce scan time or to improve spatial resolution and volumetric coverage. Efficient data acquisition using CS is extremely important for compositional mapping of the musculoskeletal system in general and knee cartilage mapping techniques in particular. High-resolution quantitative information about tissue biochemical composition could be obtained in just a few minutes using CS MRI. However, in order to make this goal a reality, some issues still need to be addressed. In this article we review the current state of the art of CS methods for rapid compositional mapping of knee cartilage. Specifically, data acquisition strategies, image reconstruction algorithms, and data fitting models are discussed. Different CS studies for T2 and T1ρ mapping of knee cartilage are reviewed, with illustrative results. Future directions, opportunities, and challenges of rapid compositional mapping techniques are also discussed. Level of Evidence: 4 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2018;47:1185-1198.
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Affiliation(s)
- Marcelo V W Zibetti
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Rahman Baboli
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Gregory Chang
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Ricardo Otazo
- Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Ravinder R Regatte
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
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Feng L, Delacoste J, Smith D, Weissbrot J, Flagg E, Moore WH, Girvin F, Raad R, Bhattacharji P, Stoffel D, Piccini D, Stuber M, Sodickson DK, Otazo R, Chandarana H. Simultaneous Evaluation of Lung Anatomy and Ventilation Using 4D Respiratory-Motion-Resolved Ultrashort Echo Time Sparse MRI. J Magn Reson Imaging 2018; 49:411-422. [PMID: 30252989 DOI: 10.1002/jmri.26245] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 06/14/2018] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Computed tomography (CT) and spirometry are the current standard methods for assessing lung anatomy and pulmonary ventilation, respectively. However, CT provides limited ventilation information and spirometry only provides global measures of lung ventilation. Thus, a method that can enable simultaneous examination of lung anatomy and ventilation is of clinical interest. PURPOSE To develop and test a 4D respiratory-resolved sparse lung MRI (XD-UTE: eXtra-Dimensional Ultrashort TE imaging) approach for simultaneous evaluation of lung anatomy and pulmonary ventilation. STUDY TYPE Prospective. POPULATION In all, 23 subjects (11 volunteers and 12 patients, mean age = 63.6 ± 8.4). FIELD STRENGTH/SEQUENCE 3T MR; a prototype 3D golden-angle radial UTE sequence, a Cartesian breath-hold volumetric-interpolated examination (BH-VIBE) sequence. ASSESSMENT All subjects were scanned using the 3D golden-angle radial UTE sequence during normal breathing. Ten subjects underwent an additional scan during alternating normal and deep breathing. Respiratory-motion-resolved sparse reconstruction was performed for all the acquired data to generate dynamic normal-breathing or deep-breathing image series. For comparison, BH-VIBE was performed in 12 subjects. Lung images were visually scored by three experienced chest radiologists and were analyzed by two observers who segmented the left and right lung to derive ventilation parameters in comparison with spirometry. STATISTICAL TESTS Nonparametric paired two-tailed Wilcoxon signed-rank test; intraclass correlation coefficient, Pearson correlation coefficient. RESULTS XD-UTE achieved significantly improved image quality compared both with Cartesian BH-VIBE and radial reconstruction without motion compensation (P < 0.05). The global ventilation parameters (a sum of the left and right lung measures) were in good correlation with spirometry in the same subjects (correlation coefficient = 0.724). There were excellent correlations between the results obtained by two observers (intraclass correlation coefficient ranged from 0.8855-0.9995). DATA CONCLUSION Simultaneous evaluation of lung anatomy and ventilation using XD-UTE is demonstrated, which have shown good potential for improved diagnosis and management of patients with heterogeneous lung diseases. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:411-422.
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Affiliation(s)
- Li Feng
- Center for Advanced Imaging Innovation and Research (CAIR), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jean Delacoste
- Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - David Smith
- Center for Advanced Imaging Innovation and Research (CAIR), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Joseph Weissbrot
- Center for Advanced Imaging Innovation and Research (CAIR), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Eric Flagg
- Center for Advanced Imaging Innovation and Research (CAIR), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - William H Moore
- Center for Advanced Imaging Innovation and Research (CAIR), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Francis Girvin
- Center for Advanced Imaging Innovation and Research (CAIR), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Roy Raad
- Center for Advanced Imaging Innovation and Research (CAIR), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Priya Bhattacharji
- Center for Advanced Imaging Innovation and Research (CAIR), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - David Stoffel
- Center for Advanced Imaging Innovation and Research (CAIR), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Davide Piccini
- Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Matthias Stuber
- Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAIR), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Center for Advanced Imaging Innovation and Research (CAIR), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAIR), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
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Zibetti MVW, Sharafi A, Otazo R, Regatte RR. Compressed sensing acceleration of biexponential 3D-T 1ρ relaxation mapping of knee cartilage. Magn Reson Med 2018; 81:863-880. [PMID: 30230588 DOI: 10.1002/mrm.27416] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 05/23/2018] [Accepted: 06/01/2018] [Indexed: 12/14/2022]
Abstract
PURPOSE Use compressed sensing (CS) for 3D biexponential spin-lattice relaxation time in the rotating frame (T1ρ ) mapping of knee cartilage, reducing the total scan time and maintaining the quality of estimated biexponential T1ρ parameters (short and long relaxation times and corresponding fractions) comparable to fully sampled scans. METHODS Fully sampled 3D-T1ρ -weighted data sets were retrospectively undersampled by factors 2-10. CS reconstruction using 12 different sparsifying transforms were compared for biexponential T1ρ -mapping of knee cartilage, including temporal and spatial wavelets and finite differences, dictionary from principal component analysis (PCA), k-means singular value decomposition (K-SVD), exponential decay models, and also low rank and low rank plus sparse models. Synthetic phantom (N = 6) and in vivo human knee cartilage data sets (N = 7) were included in the experiments. Spatial filtering before biexponential T1ρ parameter estimation was also tested. RESULTS Most CS methods performed satisfactorily for an acceleration factor (AF) of 2, with relative median normalized absolute deviation (MNAD) around 10%. Some sparsifying transforms, such as low rank with spatial finite difference (L + S SFD), spatiotemporal finite difference (STFD), and exponential dictionaries (EXP) significantly improved this performance, reaching MNAD below 15% with AF up to 10, when spatial filtering was used. CONCLUSION Accelerating biexponential 3D-T1ρ mapping of knee cartilage with CS is feasible. The best results were obtained by STFD, EXP, and L + S SFD regularizers combined with spatial prefiltering. These 3 CS methods performed satisfactorily on synthetic phantom as well as in vivo knee cartilage for AFs up to 10, with median error below 15%.
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Affiliation(s)
- Marceo V W Zibetti
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Azadeh Sharafi
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Ricardo Otazo
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Ravinder R Regatte
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
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Sigmund EE, Baete SH, Patel K, Wang D, Stoffel D, Otazo R, Parasoglou P, Bencardino J. Spatially resolved kinetics of skeletal muscle exercise response and recovery with multiple echo diffusion tensor imaging (MEDITI): a feasibility study. MAGMA 2018; 31:599-608. [PMID: 29761414 DOI: 10.1007/s10334-018-0686-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 03/26/2018] [Accepted: 04/23/2018] [Indexed: 12/20/2022]
Abstract
OBJECTIVES We describe measurement of skeletal muscle kinetics with multiple echo diffusion tensor imaging (MEDITI). This approach allows characterization of the microstructural dynamics in healthy and pathologic muscle. MATERIALS AND METHODS In a Siemens 3-T Skyra scanner, MEDITI was used to collect dynamic DTI with a combination of rapid diffusion encoding, radial imaging, and compressed sensing reconstruction in a multi-compartment agarose gel rotation phantom and within in vivo calf muscle. An MR-compatible ergometer (Ergospect Trispect) was employed to enable in-scanner plantar flexion exercise. In a HIPAA-compliant study with written informed consent, post-exercise recovery of DTI metrics was quantified in eight volunteers. Exercise response of DTI metrics was compared with that of T2-weighted imaging and characterized by a gamma variate model. RESULTS Phantom results show quantification of diffusivities in each compartment over its full dynamic rotation. In vivo calf imaging results indicate larger radial than axial exercise response and recovery in the plantar flexion-challenged gastrocnemius medialis (fractional response: nT2w = 0.385 ± 0.244, nMD = 0.163 ± 0.130, nλ1 = 0.110 ± 0.093, nλrad = 0.303 ± 0.185). Diffusion and T2-weighted response magnitudes were correlated (e.g., r = 0.792, p = 0.019 for nMD vs. nT2w). CONCLUSION We have demonstrated the feasibility of MEDITI for capturing spatially resolved diffusion tensor data in dynamic systems including post-exercise skeletal muscle recovery following in-scanner plantar flexion.
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Affiliation(s)
- E E Sigmund
- Department of Radiology, New York University Langone Health, New York, NY, USA. .,Center for Advanced Imaging and Innovation (CAI2R), New York University Langone Health, New York, NY, USA.
| | - S H Baete
- Department of Radiology, New York University Langone Health, New York, NY, USA.,Center for Advanced Imaging and Innovation (CAI2R), New York University Langone Health, New York, NY, USA
| | - K Patel
- Department of Radiology, New York University Langone Health, New York, NY, USA.,NYU Tandon School of Engineering, Brooklyn, NY, USA
| | - D Wang
- Department of Radiology, New York University Langone Health, New York, NY, USA.,NYU Tandon School of Engineering, Brooklyn, NY, USA
| | - D Stoffel
- Department of Radiology, New York University Langone Health, New York, NY, USA.,Center for Advanced Imaging and Innovation (CAI2R), New York University Langone Health, New York, NY, USA
| | - R Otazo
- Department of Radiology, New York University Langone Health, New York, NY, USA.,Center for Advanced Imaging and Innovation (CAI2R), New York University Langone Health, New York, NY, USA.,Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - P Parasoglou
- Department of Radiology, New York University Langone Health, New York, NY, USA.,Center for Advanced Imaging and Innovation (CAI2R), New York University Langone Health, New York, NY, USA
| | - J Bencardino
- Department of Radiology, New York University Langone Health, New York, NY, USA
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32
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Baete SH, Chen J, Lin YC, Wang X, Otazo R, Boada FE. Low Rank plus Sparse decomposition of ODFs for improved detection of group-level differences and variable correlations in white matter. Neuroimage 2018. [PMID: 29526742 DOI: 10.1016/j.neuroimage.2018.03.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
A novel approach is presented for group statistical analysis of diffusion weighted MRI datasets through voxelwise Orientation Distribution Functions (ODF). Recent advances in MRI acquisition make it possible to use high quality diffusion weighted protocols (multi-shell, large number of gradient directions) for routine in vivo study of white matter architecture. The dimensionality of these data sets is however often reduced to simplify statistical analysis. While these approaches may detect large group differences, they do not fully capitalize on all acquired image volumes. Incorporation of all available diffusion information in the analysis however risks biasing the outcome by outliers. Here we propose a statistical analysis method operating on the ODF, either the diffusion ODF or fiber ODF. To avoid outlier bias and reliably detect voxelwise group differences and correlations with demographic or behavioral variables, we apply the Low-Rank plus Sparse (L+S) matrix decomposition on the voxelwise ODFs which separates the sparse individual variability in the sparse matrix S whilst recovering the essential ODF features in the low-rank matrix L. We demonstrate the performance of this ODF L+S approach by replicating the established negative association between global white matter integrity and physical obesity in the Human Connectome dataset. The volume of positive findings p<0.01,227cm3, agrees with and expands on the volume found by TBSS (17 cm3), Connectivity based fixel enhancement (15 cm3) and Connectometry (212 cm3). In the same dataset we further localize the correlations of brain structure with neurocognitive measures such as fluid intelligence and episodic memory. The presented ODF L+S approach will aid in the full utilization of all acquired diffusion weightings leading to the detection of smaller group differences in clinically relevant settings as well as in neuroscience applications.
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Affiliation(s)
- Steven H Baete
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, 660 First Ave 4th Floor, New York, NY, 10016, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, 660 First Ave 4th Floor, New York, NY, 10016, USA.
| | - Jingyun Chen
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, 660 First Ave 4th Floor, New York, NY, 10016, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, 660 First Ave 4th Floor, New York, NY, 10016, USA; Dept. of Psychiatry, NYU School of Medicine, One Park Avenue, New York, NY, 10016, USA
| | - Ying-Chia Lin
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, 660 First Ave 4th Floor, New York, NY, 10016, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, 660 First Ave 4th Floor, New York, NY, 10016, USA
| | - Xiuyuan Wang
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, 660 First Ave 4th Floor, New York, NY, 10016, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, 660 First Ave 4th Floor, New York, NY, 10016, USA
| | - Ricardo Otazo
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, 660 First Ave 4th Floor, New York, NY, 10016, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, 660 First Ave 4th Floor, New York, NY, 10016, USA
| | - Fernando E Boada
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, 660 First Ave 4th Floor, New York, NY, 10016, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, 660 First Ave 4th Floor, New York, NY, 10016, USA
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Kesner A, Laforest R, Otazo R, Jennifer K, Pan T. Medical imaging data in the digital innovation age. Med Phys 2018; 45:e40-e52. [PMID: 29405298 DOI: 10.1002/mp.12794] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 11/21/2017] [Accepted: 01/08/2018] [Indexed: 12/17/2022] Open
Abstract
As we reflect on decades of exponential advancements in electronic innovation, we can see the field of medical imaging eclipsed by a new digital landscape - one that is inexpensive, fast, and powerful. This new paradigm presents new opportunities to innovate in both research and clinical settings. In this article, we review the current role of data: the common perceptions around its valuation and the infrastructure currently in place for data-driven innovation. Looking forward, we consider what has already been achieved using modern data capacities, the opportunities we have for further expansion in this area, and the obstacles we will need to transcend.
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Affiliation(s)
- Adam Kesner
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard Laforest
- Division of Radiological Sciences, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ricardo Otazo
- Department of Radiology, New York University Lagone Health, New York, NY, USA
| | - Kwak Jennifer
- Department of Radiology, University of Colorado, Denver, CO, USA
| | - Tinsu Pan
- Department of Imaging Physics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
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Zibetti MVW, Sharafi A, Otazo R, Regatte RR. Accelerating 3D-T 1ρ mapping of cartilage using compressed sensing with different sparse and low rank models. Magn Reson Med 2018; 80:1475-1491. [PMID: 29479738 DOI: 10.1002/mrm.27138] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 01/11/2018] [Accepted: 01/26/2018] [Indexed: 12/16/2022]
Abstract
PURPOSE To evaluate the feasibility of using compressed sensing (CS) to accelerate 3D-T1ρ mapping of cartilage and to reduce total scan times without degrading the estimation of T1ρ relaxation times. METHODS Fully sampled 3D-T1ρ datasets were retrospectively undersampled by factors 2-10. CS reconstruction using 12 different sparsifying transforms were compared, including finite differences, temporal and spatial wavelets, learned transforms using principal component analysis (PCA) and K-means singular value decomposition (K-SVD), explicit exponential models, low rank and low rank plus sparse models. Spatial filtering prior to T1ρ parameter estimation was also tested. Synthetic phantom (n = 6) and in vivo human knee cartilage datasets (n = 7) were included. RESULTS Most CS methods performed satisfactorily for an acceleration factor (AF) of 2, with relative T1ρ error lower than 4.5%. Some sparsifying transforms, such as spatiotemporal finite difference (STFD), exponential dictionaries (EXP) and low rank combined with spatial finite difference (L+S SFD) significantly improved this performance, reaching average relative T1ρ error below 6.5% on T1ρ relaxation times with AF up to 10, when spatial filtering was used before T1ρ fitting, at the expense of smoothing the T1ρ maps. The STFD achieved 5.1% error at AF = 10 with spatial filtering prior to T1ρ fitting. CONCLUSION Accelerating 3D-T1ρ mapping of cartilage with CS is feasible up to AF of 10 when using STFD, EXP or L+S SFD regularizers. These three best CS methods performed satisfactorily on synthetic phantom and in vivo knee cartilage for AFs up to 10, with T1ρ error of 6.5%.
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Affiliation(s)
- Marcelo V W Zibetti
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Azadeh Sharafi
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Ricardo Otazo
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Ravinder R Regatte
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
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Feng L, Huang C, Shanbhogue K, Sodickson DK, Chandarana H, Otazo R. RACER-GRASP: Respiratory-weighted, aortic contrast enhancement-guided and coil-unstreaking golden-angle radial sparse MRI. Magn Reson Med 2017; 80:77-89. [PMID: 29193260 DOI: 10.1002/mrm.27002] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 10/18/2017] [Accepted: 10/19/2017] [Indexed: 12/25/2022]
Abstract
PURPOSE To develop and evaluate a novel dynamic contrast-enhanced imaging technique called RACER-GRASP (Respiratory-weighted, Aortic Contrast Enhancement-guided and coil-unstReaking Golden-angle RAdial Sparse Parallel) MRI that extends GRASP to include automatic contrast bolus timing, respiratory motion compensation, and coil-weighted unstreaking for improved imaging performance in liver MRI. METHODS In RACER-GRASP, aortic contrast enhancement (ACE) guided k-space sorting and respiratory-weighted sparse reconstruction are performed using aortic contrast enhancement and respiratory motion signals extracted directly from the acquired data. Coil unstreaking aims to weight multicoil k-space according to streaking artifact level calculated for each individual coil during image reconstruction, so that coil elements containing a high level of streaking artifacts contribute less to the final results. Self-calibrating GRAPPA operator gridding was applied as a pre-reconstruction step to reduce computational burden in the subsequent iterative reconstruction. The RACER-GRASP technique was compared with standard GRASP reconstruction in a group of healthy volunteers and patients referred for clinical liver MR examination. RESULTS Compared with standard GRASP, RACER-GRASP significantly improved overall image quality (average score: 3.25 versus 3.85) and hepatic vessel sharpness/clarity (average score: 3.58 versus 4.0), and reduced residual streaking artifact level (average score: 3.23 versus 3.94) in different contrast phases. RACER-GRASP also enabled automatic timing of the arterial phases. CONCLUSIONS The aortic contrast enhancement-guided sorting, respiratory motion suppression and coil unstreaking introduced by RACER-GRASP improve upon the imaging performance of standard GRASP for free-breathing dynamic contrast-enhanced MRI of the liver. Magn Reson Med 80:77-89, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Chenchan Huang
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Krishna Shanbhogue
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
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Feng L, Coppo S, Piccini D, Yerly J, Lim RP, Masci PG, Stuber M, Sodickson DK, Otazo R. 5D whole-heart sparse MRI. Magn Reson Med 2017; 79:826-838. [PMID: 28497486 DOI: 10.1002/mrm.26745] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 04/09/2017] [Accepted: 04/11/2017] [Indexed: 01/18/2023]
Abstract
PURPOSE A 5D whole-heart sparse imaging framework is proposed for simultaneous assessment of myocardial function and high-resolution cardiac and respiratory motion-resolved whole-heart anatomy in a single continuous noncontrast MR scan. METHODS A non-electrocardiograph (ECG)-triggered 3D golden-angle radial balanced steady-state free precession sequence was used for data acquisition. The acquired 3D k-space data were sorted into a 5D dataset containing separated cardiac and respiratory dimensions using a self-extracted respiratory motion signal and a recorded ECG signal. Images were then reconstructed using XD-GRASP, a multidimensional compressed sensing technique exploiting correlations/sparsity along cardiac and respiratory dimensions. 5D whole-heart imaging was compared with respiratory motion-corrected 3D and 4D whole-heart imaging in nine volunteers for evaluation of the myocardium, great vessels, and coronary arteries. It was also compared with breath-held, ECG-gated 2D cardiac cine imaging for validation of cardiac function quantification. RESULTS 5D whole-heart images received systematic higher quality scores in the myocardium, great vessels and coronary arteries. Quantitative coronary sharpness and length were always better for the 5D images. Good agreement was obtained for quantification of cardiac function compared with 2D cine imaging. CONCLUSION 5D whole-heart sparse imaging represents a robust and promising framework for simplified comprehensive cardiac MRI without the need for breath-hold and motion correction. Magn Reson Med 79:826-838, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Simone Coppo
- Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Davide Piccini
- Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland
| | - Jerome Yerly
- Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Ruth P Lim
- Department of Radiology, Austin Health and The University of Melbourne, Melbourne, Victoria, Australia
| | - Pier Giorgio Masci
- Division of Cardiology and Cardiac MR Center, University Hospital (CHUV), Lausanne, Switzerland
| | - Matthias Stuber
- Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
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Abstract
While current state of the art MR-PET scanners enable simultaneous MR and PET measurements, the acquired data sets are still usually reconstructed separately. We propose a new multi-modality reconstruction framework using second order Total Generalized Variation (TGV) as a dedicated multi-channel regularization functional that jointly reconstructs images from both modalities. In this way, information about the underlying anatomy is shared during the image reconstruction process while unique differences are preserved. Results from numerical simulations and in-vivo experiments using a range of accelerated MR acquisitions and different MR image contrasts demonstrate improved PET image quality, resolution, and quantitative accuracy.
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Affiliation(s)
- Florian Knoll
- Bernard and Irene Schwartz Center for Biomedical Imaging, and the Center for Advanced Imaging Innovation and Research (CAIR), in the Department of Radiology at NYU School of Medicine, New York, NY, United States
| | - Martin Holler
- Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria. The Institute of Mathematics and Scientific Computing is a member of NAWI Graz (www.nawigraz.at) and BioTechMed Graz (www.biotechmed.at)
| | - Thomas Koesters
- Bernard and Irene Schwartz Center for Biomedical Imaging, and the Center for Advanced Imaging Innovation and Research (CAIR), in the Department of Radiology at NYU School of Medicine, New York, NY, United States
| | - Ricardo Otazo
- Bernard and Irene Schwartz Center for Biomedical Imaging, and the Center for Advanced Imaging Innovation and Research (CAIR), in the Department of Radiology at NYU School of Medicine, New York, NY, United States
| | - Kristian Bredies
- Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria. The Institute of Mathematics and Scientific Computing is a member of NAWI Graz (www.nawigraz.at) and BioTechMed Graz (www.biotechmed.at)
| | - Daniel K Sodickson
- Bernard and Irene Schwartz Center for Biomedical Imaging, and the Center for Advanced Imaging Innovation and Research (CAIR), in the Department of Radiology at NYU School of Medicine, New York, NY, United States
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Feng L, Benkert T, Block KT, Sodickson DK, Otazo R, Chandarana H. Compressed sensing for body MRI. J Magn Reson Imaging 2016; 45:966-987. [PMID: 27981664 DOI: 10.1002/jmri.25547] [Citation(s) in RCA: 172] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 10/25/2016] [Indexed: 12/18/2022] Open
Abstract
The introduction of compressed sensing for increasing imaging speed in magnetic resonance imaging (MRI) has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This article presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and nonlinear reconstruction. The second section reviews state-of-the-art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the article discusses current challenges and future opportunities. LEVEL OF EVIDENCE 5 J. Magn. Reson. Imaging 2017;45:966-987.
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Affiliation(s)
- Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Thomas Benkert
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Kai Tobias Block
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
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Tautz L, Feng L, Otazo R, Hennemuth A, Axel L. Cardiac function analysis with cardiorespiratory-synchronized CMR. J Cardiovasc Magn Reson 2016. [PMCID: PMC5032361 DOI: 10.1186/1532-429x-18-s1-w24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Heacock L, Gao Y, Heller SL, Melsaether AN, Babb JS, Block TK, Otazo R, Kim SG, Moy L. Comparison of conventional DCE-MRI and a novel golden-angle radial multicoil compressed sensing method for the evaluation of breast lesion conspicuity. J Magn Reson Imaging 2016; 45:1746-1752. [PMID: 27859874 DOI: 10.1002/jmri.25530] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 10/10/2016] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To compare a novel multicoil compressed sensing technique with flexible temporal resolution, golden-angle radial sparse parallel (GRASP), to conventional fat-suppressed spoiled three-dimensional (3D) gradient-echo (volumetric interpolated breath-hold examination, VIBE) MRI in evaluating the conspicuity of benign and malignant breast lesions. MATERIALS AND METHODS Between March and August 2015, 121 women (24-84 years; mean, 49.7 years) with 180 biopsy-proven benign and malignant lesions were imaged consecutively at 3.0 Tesla in a dynamic contrast-enhanced (DCE) MRI exam using sagittal T1-weighted fat-suppressed 3D VIBE in this Health Insurance Portability and Accountability Act-compliant, retrospective study. Subjects underwent MRI-guided breast biopsy (mean, 13 days [1-95 days]) using GRASP DCE-MRI, a fat-suppressed radial "stack-of-stars" 3D FLASH sequence with golden-angle ordering. Three readers independently evaluated breast lesions on both sequences. Statistical analysis included mixed models with generalized estimating equations, kappa-weighted coefficients and Fisher's exact test. RESULTS All lesions demonstrated good conspicuity on VIBE and GRASP sequences (4.28 ± 0.81 versus 3.65 ± 1.22), with no significant difference in lesion detection (P = 0.248). VIBE had slightly higher lesion conspicuity than GRASP for all lesions, with VIBE 12.6% (0.63/5.0) more conspicuous (P < 0.001). Masses and nonmass enhancement (NME) were more conspicuous on VIBE (P < 0.001), with a larger difference for NME (14.2% versus 9.4% more conspicuous). Malignant lesions were more conspicuous than benign lesions (P < 0.001) on both sequences. CONCLUSION GRASP DCE-MRI, a multicoil compressed sensing technique with high spatial resolution and flexible temporal resolution, has near-comparable performance to conventional VIBE imaging for breast lesion evaluation. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;45:1746-1752.
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Affiliation(s)
- Laura Heacock
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Yiming Gao
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Samantha L Heller
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Amy N Melsaether
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - James S Babb
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Tobias K Block
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Sungheon G Kim
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
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Otazo R, Nittka M, Bruno M, Raithel E, Geppert C, Gyftopoulos S, Recht M, Rybak L. Sparse-SEMAC: rapid and improved SEMAC metal implant imaging using SPARSE-SENSE acceleration. Magn Reson Med 2016; 78:79-87. [PMID: 27454003 DOI: 10.1002/mrm.26342] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Revised: 05/27/2016] [Accepted: 06/21/2016] [Indexed: 02/03/2023]
Abstract
PURPOSE To develop an accelerated SEMAC metal implant MRI technique (Sparse-SEMAC) with reduced scan time and improved metal distortion correction. METHODS Sparse-SEMAC jointly exploits the inherent sparsity along the additional phase-encoding dimension and multicoil encoding capabilities to significantly accelerate data acquisition. A prototype pulse sequence with pseudorandom ky -kz undersampling and an inline image reconstruction was developed for integration in clinical studies. Three patients with hip implants were imaged using the proposed Sparse-SEMAC with eight-fold acceleration and compared with the standard-SEMAC technique used in clinical studies (three-fold GRAPPA acceleration). Measurements were performed with SEMAC-encoding steps (SES) = 15 for Sparse-SEMAC and SES = 9 for Standard-SEMAC using high spatial resolution Proton Density (PD) and lower-resolution STIR acquisitions. Two expert musculoskeletal (MSK) radiologists performed a consensus reading to score image-quality parameters. RESULTS Sparse-SEMAC enables up to eight-fold acceleration of data acquisition that results in two-fold scan time reductions, compared with Standard-SEMAC, with improved metal artifact correction for patients with hip implants without degrading spatial resolution. CONCLUSION The high acceleration enabled by Sparse-SEMAC would enable clinically feasible examination times with improved correction of metal distortion. Magn Reson Med 78:79-87, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Ricardo Otazo
- Department of Radiology, NYU School of Medicine, New York, New York, USA
| | | | - Mary Bruno
- Department of Radiology, NYU School of Medicine, New York, New York, USA
| | | | | | | | - Michael Recht
- Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Leon Rybak
- Department of Radiology, NYU School of Medicine, New York, New York, USA
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Abstract
Heart disease is a worldwide public health problem; assessment of cardiac function is an important part of the diagnosis and management of heart disease. MRI of the heart can provide clinically useful information on cardiac function, although it is still not routinely used in clinical practice, in part because of limited imaging speed. New accelerated methods for performing cardiovascular MRI (CMR) have the potential to provide both increased imaging speed and robustness to CMR, as well as access to increased functional information. In this review, we will briefly discuss the main methods currently employed to accelerate CMR methods, such as parallel imaging, k-t undersampling and compressed sensing, as well as new approaches that extend the idea of compressed sensing and exploit sparsity to provide richer information of potential use in clinical practice.
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Affiliation(s)
- Leon Axel
- Department of Radiology, NYU School of Medicine, New York, NY, USA
| | - Ricardo Otazo
- Department of Radiology, NYU School of Medicine, New York, NY, USA
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Piccini D, Feng L, Bonanno G, Coppo S, Yerly J, Lim RP, Schwitter J, Sodickson DK, Otazo R, Stuber M. Four-dimensional respiratory motion-resolved whole heart coronary MR angiography. Magn Reson Med 2016; 77:1473-1484. [PMID: 27052418 DOI: 10.1002/mrm.26221] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 01/25/2016] [Accepted: 02/24/2016] [Indexed: 12/27/2022]
Abstract
PURPOSE Free-breathing whole-heart coronary MR angiography (MRA) commonly uses navigators to gate respiratory motion, resulting in lengthy and unpredictable acquisition times. Conversely, self-navigation has 100% scan efficiency, but requires motion correction over a broad range of respiratory displacements, which may introduce image artifacts. We propose replacing navigators and self-navigation with a respiratory motion-resolved reconstruction approach. METHODS Using a respiratory signal extracted directly from the imaging data, individual signal-readouts are binned according to their respiratory states. The resultant series of undersampled images are reconstructed using an extradimensional golden-angle radial sparse parallel imaging (XD-GRASP) algorithm, which exploits sparsity along the respiratory dimension. Whole-heart coronary MRA was performed in 11 volunteers and four patients with the proposed methodology. Image quality was compared with that obtained with one-dimensional respiratory self-navigation. RESULTS Respiratory-resolved reconstruction effectively suppressed respiratory motion artifacts. The quality score for XD-GRASP reconstructions was greater than or equal to self-navigation in 80/88 coronary segments, reaching diagnostic quality in 61/88 segments versus 41/88. Coronary sharpness and length were always superior for the respiratory-resolved datasets, reaching statistical significance (P < 0.05) in most cases. CONCLUSION XD-GRASP represents an attractive alternative for handling respiratory motion in free-breathing whole heart MRI and provides an effective alternative to self-navigation. Magn Reson Med 77:1473-1484, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Davide Piccini
- Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland.,Department of Radiology, University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Li Feng
- Center for Advanced Imaging Innovation and Research, and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Gabriele Bonanno
- Department of Radiology, University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Simone Coppo
- Department of Radiology, University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jérôme Yerly
- Department of Radiology, University Hospital and University of Lausanne, Lausanne, Switzerland.,Center for Biomedical Imaging, Lausanne, Switzerland
| | - Ruth P Lim
- Department of Radiology, Austin Health and The University of Melbourne, Melbourne, Victoria, Australia
| | - Juerg Schwitter
- Division of Cardiology and Cardiac MR Center, University Hospital of Lausanne, Lausanne, Switzerland
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research, and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Center for Advanced Imaging Innovation and Research, and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Matthias Stuber
- Department of Radiology, University Hospital and University of Lausanne, Lausanne, Switzerland.,Center for Biomedical Imaging, Lausanne, Switzerland
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Chandarana H, Doshi AM, Shanbhogue A, Babb JS, Bruno MT, Zhao T, Raithel E, Zenge MO, Li G, Otazo R. Three-dimensional MR Cholangiopancreatography in a Breath Hold with Sparsity-based Reconstruction of Highly Undersampled Data. Radiology 2016; 280:585-94. [PMID: 26982678 DOI: 10.1148/radiol.2016151935] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To develop a three-dimensional breath-hold (BH) magnetic resonance (MR) cholangiopancreatographic protocol with sampling perfection with application-optimized contrast using different flip-angle evolutions (SPACE) acquisition and sparsity-based iterative reconstruction (SPARSE) of prospectively sampled 5% k-space data and to compare the results with conventional respiratory-triggered (RT) acquisition. Materials and Methods This HIPAA-compliant prospective study was institutional review board approved. Twenty-nine patients underwent conventional RT SPACE and BH-accelerated SPACE acquisition with 5% k-space sampling at 3 T. Spatial resolution and other parameters were matched when possible. BH SPACE images were reconstructed by enforcing joint multicoil sparsity in the wavelet domain (SPARSE-SPACE). Two board-certified radiologists independently evaluated BH SPARSE-SPACE and RT SPACE images for image quality parameters in the pancreatic duct and common bile duct by using a five-point scale. The Wilcoxon signed-rank test was used to compare BH SPARSE-SPACE and RT SPACE images. Results Acquisition time for BH SPARSE-SPACE was 20 seconds, which was significantly (P < .001) shorter than that for RT SPACE (mean ± standard deviation, 338.8 sec ± 69.1). Overall image quality scores were higher for BH SPARSE-SPACE than for RT SPACE images for both readers for the proximal, middle, and distal pancreatic duct, but the difference was not statistically significant (P > .05). For reader 1, distal common bile duct scores were significantly higher with BH SPARSE-SPACE acquisition (P = .036). More patients had acceptable or better overall image quality (scores ≥ 3) with BH SPARSE-SPACE than with RT SPACE acquisition, respectively, for the proximal (23 of 29 [79%] vs 22 of 29 [76%]), middle (22 of 29 [76%] vs 18 of 29 [62%]), and distal (20 of 29 [69%] vs 13 of 29 [45%]) pancreatic duct and the proximal (25 of 28 [89%] vs 22 of 28 [79%]) and distal (25 of 28 [89%] vs 24 of 28 [86%]) common bile duct. Conclusion BH SPARSE-SPACE showed similar or superior image quality for the pancreatic and common duct compared with that of RT SPACE despite 17-fold shorter acquisition time. (©) RSNA, 2016.
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Affiliation(s)
- Hersh Chandarana
- From the Center for Advanced Imaging Innovation and Research (CAI2R) (H.C., J.S.B., R.O.) and Bernard and Irene Schwartz Center for Biomedical Imaging (H.C., A.M.D., A.S., J.S.B., M.T.B., R.O.), Department of Radiology, New York University School of Medicine, 660 First Ave, New York, NY 10016; Siemens Healthcare, New York, NY (T.Z., M.O.Z.); Siemens Healthcare, Erlangen, Germany (E.R.); and Department of Radiology, Section of Medical Physics, Freiburg University Medical Center, Freiburg, Germany (G.L.)
| | - Ankur M Doshi
- From the Center for Advanced Imaging Innovation and Research (CAI2R) (H.C., J.S.B., R.O.) and Bernard and Irene Schwartz Center for Biomedical Imaging (H.C., A.M.D., A.S., J.S.B., M.T.B., R.O.), Department of Radiology, New York University School of Medicine, 660 First Ave, New York, NY 10016; Siemens Healthcare, New York, NY (T.Z., M.O.Z.); Siemens Healthcare, Erlangen, Germany (E.R.); and Department of Radiology, Section of Medical Physics, Freiburg University Medical Center, Freiburg, Germany (G.L.)
| | - Alampady Shanbhogue
- From the Center for Advanced Imaging Innovation and Research (CAI2R) (H.C., J.S.B., R.O.) and Bernard and Irene Schwartz Center for Biomedical Imaging (H.C., A.M.D., A.S., J.S.B., M.T.B., R.O.), Department of Radiology, New York University School of Medicine, 660 First Ave, New York, NY 10016; Siemens Healthcare, New York, NY (T.Z., M.O.Z.); Siemens Healthcare, Erlangen, Germany (E.R.); and Department of Radiology, Section of Medical Physics, Freiburg University Medical Center, Freiburg, Germany (G.L.)
| | - James S Babb
- From the Center for Advanced Imaging Innovation and Research (CAI2R) (H.C., J.S.B., R.O.) and Bernard and Irene Schwartz Center for Biomedical Imaging (H.C., A.M.D., A.S., J.S.B., M.T.B., R.O.), Department of Radiology, New York University School of Medicine, 660 First Ave, New York, NY 10016; Siemens Healthcare, New York, NY (T.Z., M.O.Z.); Siemens Healthcare, Erlangen, Germany (E.R.); and Department of Radiology, Section of Medical Physics, Freiburg University Medical Center, Freiburg, Germany (G.L.)
| | - Mary T Bruno
- From the Center for Advanced Imaging Innovation and Research (CAI2R) (H.C., J.S.B., R.O.) and Bernard and Irene Schwartz Center for Biomedical Imaging (H.C., A.M.D., A.S., J.S.B., M.T.B., R.O.), Department of Radiology, New York University School of Medicine, 660 First Ave, New York, NY 10016; Siemens Healthcare, New York, NY (T.Z., M.O.Z.); Siemens Healthcare, Erlangen, Germany (E.R.); and Department of Radiology, Section of Medical Physics, Freiburg University Medical Center, Freiburg, Germany (G.L.)
| | - Tiejun Zhao
- From the Center for Advanced Imaging Innovation and Research (CAI2R) (H.C., J.S.B., R.O.) and Bernard and Irene Schwartz Center for Biomedical Imaging (H.C., A.M.D., A.S., J.S.B., M.T.B., R.O.), Department of Radiology, New York University School of Medicine, 660 First Ave, New York, NY 10016; Siemens Healthcare, New York, NY (T.Z., M.O.Z.); Siemens Healthcare, Erlangen, Germany (E.R.); and Department of Radiology, Section of Medical Physics, Freiburg University Medical Center, Freiburg, Germany (G.L.)
| | - Esther Raithel
- From the Center for Advanced Imaging Innovation and Research (CAI2R) (H.C., J.S.B., R.O.) and Bernard and Irene Schwartz Center for Biomedical Imaging (H.C., A.M.D., A.S., J.S.B., M.T.B., R.O.), Department of Radiology, New York University School of Medicine, 660 First Ave, New York, NY 10016; Siemens Healthcare, New York, NY (T.Z., M.O.Z.); Siemens Healthcare, Erlangen, Germany (E.R.); and Department of Radiology, Section of Medical Physics, Freiburg University Medical Center, Freiburg, Germany (G.L.)
| | - Michael O Zenge
- From the Center for Advanced Imaging Innovation and Research (CAI2R) (H.C., J.S.B., R.O.) and Bernard and Irene Schwartz Center for Biomedical Imaging (H.C., A.M.D., A.S., J.S.B., M.T.B., R.O.), Department of Radiology, New York University School of Medicine, 660 First Ave, New York, NY 10016; Siemens Healthcare, New York, NY (T.Z., M.O.Z.); Siemens Healthcare, Erlangen, Germany (E.R.); and Department of Radiology, Section of Medical Physics, Freiburg University Medical Center, Freiburg, Germany (G.L.)
| | - Guobin Li
- From the Center for Advanced Imaging Innovation and Research (CAI2R) (H.C., J.S.B., R.O.) and Bernard and Irene Schwartz Center for Biomedical Imaging (H.C., A.M.D., A.S., J.S.B., M.T.B., R.O.), Department of Radiology, New York University School of Medicine, 660 First Ave, New York, NY 10016; Siemens Healthcare, New York, NY (T.Z., M.O.Z.); Siemens Healthcare, Erlangen, Germany (E.R.); and Department of Radiology, Section of Medical Physics, Freiburg University Medical Center, Freiburg, Germany (G.L.)
| | - Ricardo Otazo
- From the Center for Advanced Imaging Innovation and Research (CAI2R) (H.C., J.S.B., R.O.) and Bernard and Irene Schwartz Center for Biomedical Imaging (H.C., A.M.D., A.S., J.S.B., M.T.B., R.O.), Department of Radiology, New York University School of Medicine, 660 First Ave, New York, NY 10016; Siemens Healthcare, New York, NY (T.Z., M.O.Z.); Siemens Healthcare, Erlangen, Germany (E.R.); and Department of Radiology, Section of Medical Physics, Freiburg University Medical Center, Freiburg, Germany (G.L.)
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Feng L, Axel L, Chandarana H, Block KT, Sodickson DK, Otazo R. XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing. Magn Reson Med 2016; 75:775-88. [PMID: 25809847 PMCID: PMC4583338 DOI: 10.1002/mrm.25665] [Citation(s) in RCA: 374] [Impact Index Per Article: 46.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2014] [Revised: 01/15/2015] [Accepted: 02/01/2015] [Indexed: 11/11/2022]
Abstract
PURPOSE To develop a novel framework for free-breathing MRI called XD-GRASP, which sorts dynamic data into extra motion-state dimensions using the self-navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing. METHODS Radial k-space data are continuously acquired using the golden-angle sampling scheme and sorted into multiple motion-states based on respiratory and/or cardiac motion signals derived directly from the data. The resulting undersampled multidimensional dataset is reconstructed using a compressed sensing approach that exploits sparsity along the new dynamic dimensions. The performance of XD-GRASP is demonstrated for free-breathing three-dimensional (3D) abdominal imaging, two-dimensional (2D) cardiac cine imaging and 3D dynamic contrast-enhanced (DCE) MRI of the liver, comparing against reconstructions without motion sorting in both healthy volunteers and patients. RESULTS XD-GRASP separates respiratory motion from cardiac motion in cardiac imaging, and respiratory motion from contrast enhancement in liver DCE-MRI, which improves image quality and reduces motion-blurring artifacts. CONCLUSION XD-GRASP represents a new use of sparsity for motion compensation and a novel way to handle motions in the context of a continuous acquisition paradigm. Instead of removing or correcting motion, extra motion-state dimensions are reconstructed, which improves image quality and also offers new physiological information of potential clinical value.
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Affiliation(s)
- Li Feng
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
| | - Leon Axel
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Kai Tobias Block
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Daniel K. Sodickson
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
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Piccini D, Feng L, Bonanno G, Coppo S, Yerly J, Lim RP, Schwitter J, Sodickson DK, Otazo R, Stuber M. Free-breathing 3D whole-heart coronary mra using respiratory motion-resolved sparse reconstruction. J Cardiovasc Magn Reson 2016. [PMCID: PMC5032211 DOI: 10.1186/1532-429x-18-s1-o105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Knoll F, Koesters T, Otazo R, Block T, Feng L, Vunckx K, Faul D, Nuyts J, Boada F, Sodickson DK. Joint reconstruction of simultaneously acquired MR-PET data with multi sensor compressed sensing based on a joint sparsity constraint. EJNMMI Phys 2015; 1:A26. [PMID: 26501612 PMCID: PMC4545956 DOI: 10.1186/2197-7364-1-s1-a26] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Florian Knoll
- Bernard & Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Thomas Koesters
- Bernard & Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Bernard & Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Tobias Block
- Bernard & Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Li Feng
- Bernard & Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
| | | | - David Faul
- Siemens Medical Solutions USA, New York, USA
| | - Johan Nuyts
- Department of Nuclear Medicine, KU Leuven, Leuven, Belgium
| | - Fernando Boada
- Bernard & Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Daniel K Sodickson
- Bernard & Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
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Kim SG, Feng L, Grimm R, Freed M, Block KT, Sodickson DK, Moy L, Otazo R. Influence of temporal regularization and radial undersampling factor on compressed sensing reconstruction in dynamic contrast enhanced MRI of the breast. J Magn Reson Imaging 2015; 43:261-9. [PMID: 26032976 DOI: 10.1002/jmri.24961] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Accepted: 05/15/2015] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND To evaluate the influence of temporal sparsity regularization and radial undersampling on compressed sensing reconstruction of dynamic contrast-enhanced (DCE) MRI, using the iterative Golden-angle RAdial Sparse Parallel (iGRASP) MRI technique in the setting of breast cancer evaluation. METHODS DCE-MRI examinations of the breast (n = 7) were conducted using iGRASP at 3 Tesla. Images were reconstructed with five different radial undersampling schemes corresponding to temporal resolutions between 2 and 13.4 s/frame and with four different weights for temporal sparsity regularization (λ = 0.1, 0.5, 2, and 6 times of noise level). Image similarity to time-averaged reference images was assessed by two breast radiologists and using quantitative metrics. Temporal similarity was measured in terms of wash-in slope and contrast kinetic model parameters. RESULTS iGRASP images reconstructed with λ = 2 and 5.1 s/frame had significantly (P < 0.05) higher similarity to time-averaged reference images than the images with other reconstruction parameters (mutual information (MI) >5%), in agreement with the assessment of two breast radiologists. Higher undersampling (temporal resolution < 5.1 s/frame) required stronger temporal sparsity regularization (λ ≥ 2) to remove streaking aliasing artifacts (MI > 23% between λ = 2 and 0.5). The difference between the kinetic-model transfer rates of benign and malignant groups decreased as temporal resolution decreased (82% between 2 and 13.4 s/frame). CONCLUSION This study demonstrates objective spatial and temporal similarity measures can be used to assess the influence of sparsity constraint and undersampling in compressed sensing DCE-MRI and also shows that the iGRASP method provides the flexibility of optimizing these reconstruction parameters in the postprocessing stage using the same acquired data.
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Affiliation(s)
- Sungheon G Kim
- Center for Advanced Imaging and Innovation and Research (CAI2R) and.,Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine New York, New York, USA
| | - Li Feng
- Center for Advanced Imaging and Innovation and Research (CAI2R) and.,Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine New York, New York, USA.,Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine New York, New York, USA
| | - Robert Grimm
- Pattern Recognition Lab, FAU Erlangen-Nuremberg, Erlangen, Germany.,Siemens AG Healthcare MR, Erlangen, Germany
| | - Melanie Freed
- Center for Advanced Imaging and Innovation and Research (CAI2R) and.,Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine New York, New York, USA
| | - Kai Tobias Block
- Center for Advanced Imaging and Innovation and Research (CAI2R) and.,Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine New York, New York, USA
| | - Daniel K Sodickson
- Center for Advanced Imaging and Innovation and Research (CAI2R) and.,Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine New York, New York, USA
| | - Linda Moy
- Center for Advanced Imaging and Innovation and Research (CAI2R) and.,Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine New York, New York, USA
| | - Ricardo Otazo
- Center for Advanced Imaging and Innovation and Research (CAI2R) and.,Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine New York, New York, USA
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Cao Z, Oh S, Otazo R, Sica CT, Griswold MA, Collins CM. Complex difference constrained compressed sensing reconstruction for accelerated PRF thermometry with application to MRI-induced RF heating. Magn Reson Med 2015; 73:1420-31. [PMID: 24753099 PMCID: PMC4205230 DOI: 10.1002/mrm.25255] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2013] [Revised: 03/24/2014] [Accepted: 03/25/2014] [Indexed: 01/01/2023]
Abstract
PURPOSE Introduce a novel compressed sensing reconstruction method to accelerate proton resonance frequency shift temperature imaging for MRI-induced radiofrequency heating evaluation. METHODS A compressed sensing approach that exploits sparsity of the complex difference between postheating and baseline images is proposed to accelerate proton resonance frequency temperature mapping. The method exploits the intra-image and inter-image correlations to promote sparsity and remove shared aliasing artifacts. Validations were performed on simulations and retrospectively undersampled data acquired in ex vivo and in vivo studies by comparing performance with previously published techniques. RESULTS The proposed complex difference constrained compressed sensing reconstruction method improved the reconstruction of smooth and local proton resonance frequency temperature change images compared to various available reconstruction methods in a simulation study, a retrospective study with heating of a human forearm in vivo, and a retrospective study with heating of a sample of beef ex vivo. CONCLUSION Complex difference based compressed sensing with utilization of a fully sampled baseline image improves the reconstruction accuracy for accelerated proton resonance frequency thermometry. It can be used to improve the volumetric coverage and temporal resolution in evaluation of radiofrequency heating due to MRI, and may help facilitate and validate temperature-based methods for safety assurance.
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Affiliation(s)
- Zhipeng Cao
- Department of Bioengineering, The Pennsylvania State University, Hershey, Pennsylvania, USA; Department of Radiology, The Pennsylvania State University, Hershey, Pennsylvania, USA
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Otazo R, Candès E, Sodickson DK. Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magn Reson Med 2015; 73:1125-36. [PMID: 24760724 PMCID: PMC4207853 DOI: 10.1002/mrm.25240] [Citation(s) in RCA: 287] [Impact Index Per Article: 31.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2013] [Revised: 02/19/2014] [Accepted: 03/16/2014] [Indexed: 11/10/2022]
Abstract
PURPOSE To apply the low-rank plus sparse (L+S) matrix decomposition model to reconstruct undersampled dynamic MRI as a superposition of background and dynamic components in various problems of clinical interest. THEORY AND METHODS The L+S model is natural to represent dynamic MRI data. Incoherence between k-t space (acquisition) and the singular vectors of L and the sparse domain of S is required to reconstruct undersampled data. Incoherence between L and S is required for robust separation of background and dynamic components. Multicoil L+S reconstruction is formulated using a convex optimization approach, where the nuclear norm is used to enforce low rank in L and the l1 norm is used to enforce sparsity in S. Feasibility of the L+S reconstruction was tested in several dynamic MRI experiments with true acceleration, including cardiac perfusion, cardiac cine, time-resolved angiography, and abdominal and breast perfusion using Cartesian and radial sampling. RESULTS The L+S model increased compressibility of dynamic MRI data and thus enabled high-acceleration factors. The inherent background separation improved background suppression performance compared to conventional data subtraction, which is sensitive to motion. CONCLUSION The high acceleration and background separation enabled by L+S promises to enhance spatial and temporal resolution and to enable background suppression without the need of subtraction or modeling.
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Affiliation(s)
- Ricardo Otazo
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Emmanuel Candès
- Departments of Mathematics and Statistics, Stanford University, Stanford, CA, USA
| | - Daniel K. Sodickson
- Department of Radiology, New York University School of Medicine, New York, NY, USA
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