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Munoz C, Fotaki A, Hua A, Hajhosseiny R, Kunze KP, Ismail TF, Neji R, Pushparajah K, Botnar RM, Prieto C. Simultaneous Highly Efficient Contrast-Free Lumen and Vessel Wall MR Imaging for Anatomical Assessment of Aortic Disease. J Magn Reson Imaging 2023; 58:1110-1122. [PMID: 36757267 PMCID: PMC10946808 DOI: 10.1002/jmri.28613] [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: 09/20/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Bright-blood lumen and black-blood vessel wall imaging are required for the comprehensive assessment of aortic disease. These images are usually acquired separately, resulting in long examinations and potential misregistration between images. PURPOSE To characterize the performance of an accelerated and respiratory motion-compensated three-dimensional (3D) cardiac MRI technique for simultaneous contrast-free aortic lumen and vessel wall imaging with an interleaved T2 and inversion recovery prepared sequence (iT2Prep-BOOST). STUDY TYPE Prospective. POPULATION A total of 30 consecutive patients with aortopathy referred for a clinically indicated cardiac MRI examination (9 females, mean age ± standard deviation: 32 ± 12 years). FIELD STRENGTH/SEQUENCE 1.5-T; bright-blood MR angiography (diaphragmatic navigator-gated T2-prepared 3D balanced steady-state free precession [bSSFP], T2Prep-bSSFP), breath-held black-blood two-dimensional (2D) half acquisition single-shot turbo spin echo (HASTE), and 3D bSSFP iT2Prep-BOOST. ASSESSMENT iT2Prep-BOOST bright-blood images were compared to T2prep-bSSFP images in terms of aortic vessel dimensions, lumen-to-myocardium contrast ratio (CR), and image quality (diagnostic confidence, vessel sharpness and presence of artifacts, assessed by three cardiologists on a 4-point scale, 1: nondiagnostic to 4: excellent). The iT2Prep-BOOST black-blood images were compared to 2D HASTE images for quantification of wall thickness. A visual comparison between computed tomography (CT) and iT2Prep-BOOST was performed in a patient with chronic aortic dissection. STATISTICAL TESTS Paired t-tests, Wilcoxon signed-rank tests, intraclass correlation coefficient (ICC), Bland-Altman analysis. A P value < 0.05 was considered statistically significant. RESULTS Bright-blood iT2Prep-BOOST resulted in significantly improved image quality (mean ± standard deviation 3.8 ± 0.5 vs. 3.3 ± 0.8) and CR (2.9 ± 0.8 vs. 1.8 ± 0.5) compared with T2Prep-bSSFP, with a shorter scan time (7.8 ± 1.7 minutes vs. 12.9 ± 3.4 minutes) while providing a complementary 3D black-blood image. Aortic lumen diameter and vessel wall thickness measurements in bright-blood and black-blood images were in good agreement with T2Prep-bSSFP and HASTE images (<0.02 cm and <0.005 cm bias, respectively) and good intrareader (ICC > 0.96) and interreader (ICC > 0.94) agreement was observed for all measurements. DATA CONCLUSION iT2Prep-BOOST might enable time-efficient simultaneous bright- and black-blood aortic imaging, with improved image quality compared to T2Prep-bSSFP and HASTE imaging, and comparable measurements for aortic wall and lumen dimensions. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Camila Munoz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Alina Hua
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Karl P. Kunze
- MR Research CollaborationsSiemens Healthcare LimitedFrimleyUK
| | - Tevfik F. Ismail
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- MR Research CollaborationsSiemens Healthcare LimitedFrimleyUK
| | - Kuberan Pushparajah
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - René M. Botnar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Escuela de Ingeniería, Pontificia Universidad Católica de ChileSantiagoChile
- Instituto de Ingeniería Biológica y Médica, Pontificia Universidad Católica de ChileSantiagoChile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTHSantiagoChile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Escuela de Ingeniería, Pontificia Universidad Católica de ChileSantiagoChile
- Instituto de Ingeniería Biológica y Médica, Pontificia Universidad Católica de ChileSantiagoChile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTHSantiagoChile
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Esfahani EE. Isotropic multichannel total variation framework for joint reconstruction of multicontrast parallel MRI. J Med Imaging (Bellingham) 2022; 9:013502. [PMID: 35187198 PMCID: PMC8849322 DOI: 10.1117/1.jmi.9.1.013502] [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: 07/21/2021] [Accepted: 01/25/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: To develop a synergistic image reconstruction framework that exploits multicontrast (MC), multicoil, and compressed sensing (CS) redundancies in magnetic resonance imaging (MRI). Approach: CS, MC acquisition, and parallel imaging (PI) have been individually well developed, but the combination of the three has not been equally well studied, much less the potential benefits of isotropy within such a setting. Inspired by total variation theory, we introduce an isotropic MC image regularizer and attain its full potential by integrating it into compressed MC multicoil MRI. A convex optimization problem is posed to model the new variational framework and a first-order algorithm is developed to solve the problem. Results: It turns out that the proposed isotropic regularizer outperforms many of the state-of-the-art reconstruction methods not only in terms of rotation-invariance preservation of symmetrical features, but also in suppressing noise or streaking artifacts, which are normally encountered in PI methods at aggressive undersampling rates. Moreover, the new framework significantly prevents intercontrast leakage of contrast-specific details, which seems to be a difficult situation to handle for some variational and low-rank MC reconstruction approaches. Conclusions: The new framework is a viable option for image reconstruction in fast protocols of MC parallel MRI, potentially reducing patient discomfort in otherwise long and time-consuming scans.
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Affiliation(s)
- Erfan Ebrahim Esfahani
- Independent Researcher, Tehran, Iran,Address all correspondence to Erfan Ebrahim Esfahani,
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Tivnan M, Wang W, Stayman JW. A prototype spatial-spectral CT system for material decomposition with energy-integrating detectors. Med Phys 2021; 48:6401-6411. [PMID: 33964021 DOI: 10.1002/mp.14930] [Citation(s) in RCA: 3] [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: 10/20/2020] [Revised: 04/12/2021] [Accepted: 04/12/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE Spectral CT has great potential for a variety of clinical applications due to improved tissue and material discrimination over conventional single-energy CT. Many clinical and preclinical spectral CT systems have two spectral channels enabling dual-energy CT. Strategies include split filtration, dual-layer detectors, photon-counting detectors, and kVp switching. The motivation for this work is the development of an x-ray source spectral modulation device with three or more spectral channels to enable high-sensitivity multi-material decomposition with energy-integrating detectors. MATERIALS AND METHODS We present spatial-spectral filters which are a new x-ray source modulation technology with the potential for additional channel diversity. The filtering device consists of an array of K-edge materials which divide the x-ray beam into spectrally varied beamlets. This design allows for an arbitrary number of spectral channels-trading off spatial and spectral information. We use a one-step model-based material decomposition (MBMD) algorithm to iteratively estimate material density images directly from the spatial-spectral CT data. In this work, we present a prototype spatial-spectral filter integrated with an x-ray CT test bench. The filter is composed of an array of tin, erbium, tantalum, and lead filter tiles which spatially modulate the system spectral sensitivity pattern. In a simulation study, we investigate the particular problem of mis-calibration between the data acquisition and the reconstruction model. With an understanding of the required model accuracy, we present a spectral calibration method to estimate the critical model parameters. To demonstrate feasibility of the spatial-spectral filter with a calibrated beamlet model, we collected a spatial-spectral CT scan of a multicontrast-enhanced phantom containing water, iodine, and gadolinium solutions. RESULTS With simulations, we show that material decomposition is possible with spatial-spectral-filtered CT data, and we demonstrate the importance of a well-calibrated physical model. We find a 50% increase in error for focal spot model mismatch of 0.27mm and gap width model mismatch of 16 mμ. With physical results, we demonstrate that the calibrated system model is in close agreement with the measured data, and that the reconstructed material density images match the ground truth concentrations for the multicontrast phantom. Empirical results indicate gadolinium density estimation had an error of 17-58% mostly due to a systematic constant bias of 0.30-0.60 mg/ml. Water density estimates are within 1% and iodine estimates are within 10% of ground truth. CONCLUSION These preliminary results demonstrate the potential of spatial-spectral filters to enable multicontrast imaging. Moreover, this device is compatible with energy-integrating detectors and so provides a feasible modification to enable spectral CT imaging with existing single-energy systems.
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Affiliation(s)
- Matthew Tivnan
- Johns Hopkins University, 720 Rutland Ave., Traylor Building 605, Baltimore, MD, 21205, USA
| | - Wenying Wang
- Johns Hopkins University, 720 Rutland Ave., Traylor Building 605, Baltimore, MD, 21205, USA
| | - J Webster Stayman
- Johns Hopkins University, 720 Rutland Ave., Traylor Building 605, Baltimore, MD, 21205, USA
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Nejad-Davarani SP, Zakariaei N, Chen Y, Haacke EM, Hurst NJ, Salim Siddiqui M, Schultz LR, Snyder JM, Walbert T, Glide-Hurst CK. Rapid multicontrast brain imaging on a 0.35T MR-linac. Med Phys 2020; 47:4064-4076. [PMID: 32434276 DOI: 10.1002/mp.14251] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [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: 12/10/2019] [Revised: 04/03/2020] [Accepted: 05/13/2020] [Indexed: 12/27/2022] Open
Abstract
PURPOSE Magnetic resonance-guided radiation therapy (MRgRT) has shown great promise for localization and real-time tumor monitoring. However, to date, quantitative imaging has been limited for low field MRgRT. This work benchmarks quantitative T1, R2*, and Proton Density (PD)mapping in a phantom on a 0.35 T MR-linac and implements a novel acquisition method, STrategically Acquired Gradient Echo (STAGE). To further validate STAGE in a clinical setting, a pilot study was undertaken in a cohort of brain tumor patients to elucidate opportunities for longitudinal functional imaging with an MR-linac in the brain. METHODS STAGE (two triple-echo gradient echo (GRE) acquisitions) was optimized for a 0.35T low-field MR-linac. Simulations were performed to choose two flip angles to optimize signal-to-noise ratio (SNR) and T1-mapping precision. Tradeoffs between SNR, scan time, and spatial resolution for whole-brain coverage were evaluated in healthy volunteers. Data were inputted into a STAGE processing pipeline to yield four qualitative images (T1-weighted, enhanced T1-weighted, proton-density (PD) weighted, and simulated FLuid-Attenuated Inversion Recovery (sFLAIR)), and three quantitative datasets (T1, PD, and R2*). A benchmarking ISMRM/NIST phantom consisting of vials with variable NiCl2 and MnCl2 concentrations was scanned using variable flip angles (VFA) (2-60 degrees) and inversion recovery (IR) methods at 0.35 T. STAGE and VFA T1 values of vials were compared to IR T1 values. As measures of agreement with reference values and repeatability, relative error (RE) and coefficient of variability (CV) were calculated, respectively, for quantitative MR values within the phantom vials (spheres). To demonstrate feasibility, longitudinal STAGE data (pretreatment, weekly, and ~ 2 months post-treatment) were acquired in an IRB-approved pilot study of brain tumor cases via the generation of temporal and differential quantitative MRI maps. RESULTS In the phantom, RE of measured VFA T1 and STAGE relative to IR reference values were 7.0 ± 2.5% and 9.5 ± 2.2% respectively. RE for the PD vials was 8.1 ± 6.8% and CV for phantom R2* measurements was 10.1 ± 9.9%. Simulations and volunteer experiments yielded final STAGE parameters of FA = 50°/10°, 1 × 1 × 3 mm3 resolution, TR = 40 ms, TE = 5/20/34 ms in 10 min (64 slices). In the pilot study of brain tumor patients, differential maps for R2* and T1 maps were sensitive to local tumor changes and appeared similar to 3 T follow-up MRI datasets. CONCLUSION Quantitative T1, R2*, and PD mapping are promising at 0.35 T agreeing well with reference data. STAGE phantom data offer quantitative representations comparable to traditional methods in a fraction of the acquisition time. Initial feasibility of implementing STAGE at 0.35 T in a patient brain tumor cohort suggests that detectable changes can be observed over time. With confirmation in a larger cohort, results may be implemented to identify areas of recurrence and facilitate adaptive radiation therapy.
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Affiliation(s)
- Siamak P Nejad-Davarani
- Department of Radiation Oncology, Henry Ford Cancer Institute, 2799 West Grand Blvd., Detroit, MI, 48202, USA
| | - Niloufar Zakariaei
- Department of Radiation Oncology, Henry Ford Cancer Institute, 2799 West Grand Blvd., Detroit, MI, 48202, USA
| | - Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, 4201 St Antoine Blvd, 8C UHC, Detroit, MI, 48201, USA
| | - E Mark Haacke
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, 48201, USA.,The MRI Institute for Biomedical Research, 30200 Telegraph Rd, STE 104, Bingham Farms, Detroit, MI, 48025, USA
| | - Newton J Hurst
- Department of Radiation Oncology, Henry Ford Cancer Institute, 2799 West Grand Blvd., Detroit, MI, 48202, USA
| | - M Salim Siddiqui
- Department of Radiation Oncology, Henry Ford Cancer Institute, 2799 West Grand Blvd., Detroit, MI, 48202, USA
| | - Lonni R Schultz
- Department of Public Health Sciences, Henry Ford Health System, One Ford Place, Ste 3E, Detroit, MI, 48202, USA
| | - James M Snyder
- Departments of Neurosurgery and Neurology, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Tobias Walbert
- Departments of Neurosurgery and Neurology, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, Henry Ford Cancer Institute, 2799 West Grand Blvd., Detroit, MI, 48202, USA
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Hu Z, Christodoulou AG, Wang N, Shaw JL, Song SS, Maya MM, Ishimori ML, Forbess LJ, Xiao J, Bi X, Han F, Li D, Fan Z. Magnetic resonance multitasking for multidimensional assessment of cardiovascular system: Development and feasibility study on the thoracic aorta. Magn Reson Med 2020; 84:2376-2388. [PMID: 32301164 DOI: 10.1002/mrm.28275] [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] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 03/15/2020] [Accepted: 03/16/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE To develop an MR multitasking-based multidimensional assessment of cardiovascular system (MT-MACS) with electrocardiography-free and navigator-free data acquisition for a comprehensive evaluation of thoracic aortic diseases. METHODS The MT-MACS technique adopts a low-rank tensor image model with a cardiac time dimension for phase-resolved cine imaging and a T2 -prepared inversion-recovery dimension for multicontrast assessment. Twelve healthy subjects and 2 patients with thoracic aortic diseases were recruited for the study at 3 T, and both qualitative (image quality score) and quantitative (contrast-to-noise ratio between lumen and wall, lumen and wall area, and aortic strain index) analyses were performed in all healthy subjects. The overall image quality was scored based on a 4-point scale: 3, excellent; 2, good; 1, fair; and 0, poor. Statistical analysis was used to test the measurement agreement between MT-MACS and its corresponding 2D references. RESULTS The MT-MACS images reconstructed from acquisitions as short as 6 minutes demonstrated good or excellent image quality for bright-blood (2.58 ± 0.46), dark-blood (2.58 ± 0.50), and gray-blood (2.17 ± 0.53) contrast weightings, respectively. The contrast-to-noise ratios for the three weightings were 49.2 ± 12.8, 20.0 ± 5.8 and 2.8 ± 1.8, respectively. There were good agreements in the lumen and wall area (intraclass correlation coefficient = 0.993, P < .001 for lumen; intraclass correlation coefficient = 0.969, P < .001 for wall area) and strain (intraclass correlation coefficient = 0.947, P < .001) between MT-MACS and conventional 2D sequences. CONCLUSION The MT-MACS technique provides high-quality, multidimensional images for a comprehensive assessment of the thoracic aorta. Technical feasibility was demonstrated in healthy subjects and patients with thoracic aortic diseases. Further clinical validation is warranted.
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Affiliation(s)
- Zhehao Hu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.,Department of Bioengineering, University of California, Los Angeles, California
| | - Anthony G Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.,Department of Medicine, University of California, Los Angeles, California
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.,Department of Bioengineering, University of California, Los Angeles, California
| | - Jaime L Shaw
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Shlee S Song
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California
| | - Marcel M Maya
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California
| | - Mariko L Ishimori
- Department of Rheumatology, Cedars-Sinai Medical Center, Los Angeles, California
| | - Lindsy J Forbess
- Department of Rheumatology, Cedars-Sinai Medical Center, Los Angeles, California
| | - Jiayu Xiao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | | | - Fei Han
- Siemens Healthcare, Los Angeles, California
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.,Department of Bioengineering, University of California, Los Angeles, California.,Department of Medicine, University of California, Los Angeles, California
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.,Department of Bioengineering, University of California, Los Angeles, California.,Department of Medicine, University of California, Los Angeles, California
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