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Soustelle L, Troalen T, Hertanu A, Ranjeva JP, Guye M, Varma G, Alsop DC, Duhamel G, Girard OM. Quantitative magnetization transfer MRI unbiased by on-resonance saturation and dipolar order contributions. Magn Reson Med 2023. [PMID: 37154400 DOI: 10.1002/mrm.29678] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/22/2023] [Accepted: 04/01/2023] [Indexed: 05/10/2023]
Abstract
PURPOSE To demonstrate the bias in quantitative MT (qMT) measures introduced by the presence of dipolar order and on-resonance saturation (ONRS) effects using magnetization transfer (MT) spoiled gradient-recalled (SPGR) acquisitions, and propose changes to the acquisition and analysis strategies to remove these biases. METHODS The proposed framework consists of SPGR sequences prepared with simultaneous dual-offset frequency-saturation pulses to cancel out dipolar order and associated relaxation (T1D ) effects in Z-spectrum acquisitions, and a matched quantitative MT (qMT) mathematical model that includes ONRS effects of readout pulses. Variable flip angle and MT data were fitted jointly to simultaneously estimate qMT parameters (macromolecular proton fraction [MPF], T2,f , T2,b , R, and free pool T1 ). This framework is compared with standard qMT and investigated in terms of reproducibility, and then further developed to follow a joint single-point qMT methodology for combined estimation of MPF and T1 . RESULTS Bland-Altman analyses demonstrated a systematic underestimation of MPF (-2.5% and -1.3%, on average, in white and gray matter, respectively) and overestimation of T1 (47.1 ms and 38.6 ms, on average, in white and gray matter, respectively) if both ONRS and dipolar order effects are ignored. Reproducibility of the proposed framework is excellent (ΔMPF = -0.03% and ΔT1 = -19.0 ms). The single-point methodology yielded consistent MPF and T1 values with respective maximum relative average bias of -0.15% and -3.5 ms found in white matter. CONCLUSION The influence of acquisition strategy and matched mathematical model with regard to ONRS and dipolar order effects in qMT-SPGR frameworks has been investigated. The proposed framework holds promise for improved accuracy with reproducibility.
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Affiliation(s)
- Lucas Soustelle
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | | | - Andreea Hertanu
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Jean-Philippe Ranjeva
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Gopal Varma
- Division of MR Research, Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - David C Alsop
- Division of MR Research, Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Guillaume Duhamel
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Olivier M Girard
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
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Zhao C, Shao X, Shou Q, Ma SJ, Gokyar S, Graf C, Stollberger R, Wang DJJ. Whole-Cerebrum distortion-free three-dimensional pseudo-Continuous Arterial Spin Labeling at 7T. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.24.23289051. [PMID: 37163115 PMCID: PMC10168494 DOI: 10.1101/2023.04.24.23289051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Fulfilling potentials of ultrahigh field for pseudo-Continuous Arterial Spin Labeling (pCASL) has been hampered by B1/B0 inhomogeneities that affect pCASL labeling, background suppression (BS), and the readout sequence. This study aimed to present a whole-cerebrum distortion-free three-dimensional (3D) pCASL sequence at 7T by optimizing pCASL labeling parameters, BS pulses, and an accelerated Turbo-FLASH (TFL) readout. A new set of pCASL labeling parameters (Gave=0.4mT/m, Gratio=14.67) was proposed to avoid interferences in bottom slices while achieving robust labeling efficiency (LE). An OPTIM BS pulse was designed based on the range of B1/B0 inhomogeneities at 7T. A 3D TFL readout with 2D-CAIPIRINHA undersampling (R=2×2) and centric ordering was developed, and the number of segments (Nseg) and flip angle (FA) were varied in simulation to achieve the optimal trade-off between SNR and spatial blurring. In-vivo experiments were performed on 19 subjects. The results showed that the new set of labeling parameters effectively achieved whole-cerebrum coverage by eliminating interferences in bottom slices while maintaining a high LE. The OPTIM BS pulse achieved 33.3% higher perfusion signal in gray matter (GM) than the original BS pulse with a cost of 4.8-fold SAR. Incorporating a moderate FA (8 ° ) and Nseg (2), whole-cerebrum 3D TFL-pCASL imaging was achieved with a 2×2×4 mm 3 resolution without distortion and susceptibility artifacts compared to 3D GRASE-pCASL. In addition, 3D TFL-pCASL showed a good to excellent test-retest repeatability and potential of higher resolution (2 mm isotropic). The proposed technique also significantly improved SNR when compared to the same sequence at 3T and simultaneous multislice TFL-pCASL at 7T. By combining a new set of labeling parameters, OPTIM BS pulse, and accelerated 3D TFL readout, we achieved high resolution pCASL at 7T with whole-cerebrum coverage, detailed perfusion and anatomical information without distortion, and sufficient SNR.
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Affiliation(s)
- Chenyang Zhao
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California
| | - Xingfeng Shao
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California
| | - Qinyang Shou
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California
| | - Samantha J. Ma
- Siemens Medical Solutions USA, Los Angeles, California, USA
| | - Sayim Gokyar
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California
| | - Christina Graf
- Institute of Biomedical Imaging, Graz University of Technology
| | | | - Danny JJ Wang
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California
- Department of Neurology, Keck School of Medicine, University of Southern California
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53
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Zhou Z, Li Q, Liao C, Cao X, Liang H, Chen Q, Pu R, Ye H, Tong Q, He H, Zhong J. Optimized three-dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping. Hum Brain Mapp 2023; 44:2209-2223. [PMID: 36629336 PMCID: PMC10028641 DOI: 10.1002/hbm.26203] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/12/2022] [Accepted: 01/01/2023] [Indexed: 01/12/2023] Open
Abstract
Quantitative assessment of brain myelination has gained attention for both research and diagnosis of neurological diseases. However, conventional pulse sequences cannot directly acquire the myelin-proton signals due to its extremely short T2 and T2* values. To obtain the myelin-proton signals, dedicated short T2 acquisition techniques, such as ultrashort echo time (UTE) imaging, have been introduced. However, it remains challenging to isolate the myelin-proton signals from tissues with longer T2. In this article, we extended our previous two-dimensional ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) with dual-echo acquisition to three dimensional (3D). Given a relatively low proton density (PD) of myelin-proton, we utilized Cramér-Rao Lower Bound to encode myelin-proton with the maximal SNR efficiency for optimizing the MR fingerprinting design, in order to improve the sensitivity of the sequence to myelin-proton. In addition, with a second echo of approximately 3 ms, myelin-water component can be also captured. A myelin-tissue (myelin-proton and myelin-water) fraction mapping can be thus calculated. The optimized 3D UTE-MRF with dual-echo acquisition is tested in simulations, physical phantom and in vivo studies of both healthy subjects and multiple sclerosis patients. The results suggest that the rapidly decayed myelin-proton and myelin-water signal can be depicted with UTE signals of our method at clinically relevant resolution (1.8 mm isotropic) in 15 min. With its good sensitivity to myelin loss in multiple sclerosis patients demonstrated, our method for the whole brain myelin-tissue fraction mapping in clinical friendly scan time has the potential for routine clinical imaging.
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Affiliation(s)
- Zihan Zhou
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qing Li
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- MR Collaborations, Siemens Healthineers Ltd, Shanghai, China
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Hui Liang
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Quan Chen
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Run Pu
- Neusoft Medical Systems, Shanghai, China
| | - Huihui Ye
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qiqi Tong
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
- School of Physics, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
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Wicaksono KP, Fushimi Y, Nakajima S, Sakata A, Okuchi S, Hinoda T, Oshima S, Otani S, Tagawa H, Urushibata Y, Nakamoto Y. Accuracy, repeatability, and reproducibility of T 1 and T 2 relaxation times measurement by 3D magnetic resonance fingerprinting with different dictionary resolutions. Eur Radiol 2023; 33:2895-2904. [PMID: 36422648 PMCID: PMC10017611 DOI: 10.1007/s00330-022-09244-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 08/29/2022] [Accepted: 10/14/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To assess the accuracy, repeatability, and reproducibility of T1 and T2 relaxation time measurements by three-dimensional magnetic resonance fingerprinting (3D MRF) using various dictionary resolutions. METHODS The ISMRM/NIST phantom was scanned daily for 10 days in two 3 T MR scanners using a 3D MRF sequence reconstructed using four dictionaries with varying step sizes and one dictionary with wider ranges. Thirty-nine healthy volunteers were enrolled: 20 subjects underwent whole-brain MRF scans in both scanners and the rest in one scanner. ROI/VOI analyses were performed on phantom and brain MRF maps. Accuracy, repeatability, and reproducibility metrics were calculated. RESULTS In the phantom study, all dictionaries showed high T1 linearity to the reference values (R2 > 0.99), repeatability (CV < 3%), and reproducibility (CV < 3%) with lower linearity (R2 > 0.98), repeatability (CV < 6%), and reproducibility (CV ≤ 4%) for T2 measurement. The volunteer study demonstrated high T1 reproducibility of within-subject CV (wCV) < 4% by all dictionaries with the same ranges, both in the brain parenchyma and CSF. Yet, reproducibility was moderate for T2 measurement (wCV < 8%). In CSF measurement, dictionaries with a smaller range showed a seemingly better reproducibility (T1, wCV 3%; T2, wCV 8%) than the much wider range dictionary (T1, wCV 5%; T2, wCV 13%). Truncated CSF relaxometry values were evident in smaller range dictionaries. CONCLUSIONS The accuracy, repeatability, and reproducibility of 3D MRF across various dictionary resolutions were high for T1 and moderate for T2 measurements. A lower-resolution dictionary with a well-defined range may be adequate, thus significantly reducing the computational load. KEY POINTS • A lower-resolution dictionary with a well-defined range may be sufficient for 3D MRF reconstruction. • CSF relaxation times might be underestimated due to truncation by the upper dictionary range. • Dictionary with a higher upper range might be advisable, especially for CSF evaluation and elderly subjects whose perivascular spaces are more prominent.
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Affiliation(s)
- Krishna Pandu Wicaksono
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan.
| | - Satoshi Nakajima
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Akihiko Sakata
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Sachi Okuchi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Takuya Hinoda
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Sonoko Oshima
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Sayo Otani
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Hiroshi Tagawa
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | | | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
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Rahbek S, Schakel T, Mahmood F, Madsen KH, Philippens MEP, Hanson LG. Optimized flip angle schemes for the split acquisition of fast spin-echo signals (SPLICE) sequence and application to diffusion-weighted imaging. Magn Reson Med 2023; 89:1469-1480. [PMID: 36420920 PMCID: PMC10099388 DOI: 10.1002/mrm.29545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 10/21/2022] [Accepted: 11/14/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE The diffusion-weighted SPLICE (split acquisition of fast spin-echo signals) sequence employs split-echo rapid acquisition with relaxation enhancement (RARE) readout to provide images almost free of geometric distortions. However, due to the varying T 2 $$ {}_2 $$ -weighting during k-space traversal, SPLICE suffers from blurring. This work extends a method for controlling the spatial point spread function (PSF) while optimizing the signal-to-noise ratio (SNR) achieved by adjusting the flip angles in the refocusing pulse train of SPLICE. METHODS An algorithm based on extended phase graph (EPG) simulations optimizes the flip angles by maximizing SNR for a flexibly chosen predefined target PSF that describes the desired k-space density weighting and spatial resolution. An optimized flip angle scheme and a corresponding post-processing correction filter which together achieve the target PSF was tested by healthy subject brain imaging using a clinical 1.5 T scanner. RESULTS Brain images showed a clear and consistent improvement over those obtained with a standard constant flip angle scheme. SNR was increased and apparent diffusion coefficient estimates were more accurate. For a modified Hann k-space weighting example, considerable benefits resulted from acquisition weighting by flip angle control. CONCLUSION The presented flexible method for optimizing SPLICE flip angle schemes offers improved MR image quality of geometrically accurate diffusion-weighted images that makes the sequence a strong candidate for radiotherapy planning or stereotactic surgery.
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Affiliation(s)
- Sofie Rahbek
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Tim Schakel
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | - Faisal Mahmood
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark.,Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Kristoffer H Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Hvidovre, Denmark.,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | | | - Lars G Hanson
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Hvidovre, Denmark
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Menon RG, Sharafi A, Muccio M, Smith T, Kister I, Ge Y, Regatte RR. Three-dimensional multi-parameter brain mapping using MR fingerprinting. RESEARCH SQUARE 2023:rs.3.rs-2675278. [PMID: 36993561 PMCID: PMC10055680 DOI: 10.21203/rs.3.rs-2675278/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
The purpose of this study was to develop and test a 3D multi-parameter MR fingerprinting (MRF) method for brain imaging applications. The subject cohort included 5 healthy volunteers, repeatability tests done on 2 healthy volunteers and tested on two multiple sclerosis (MS) patients. A 3D-MRF imaging technique capable of quantifying T1, T2 and T1ρ was used. The imaging sequence was tested in standardized phantoms and 3D-MRF brain imaging with multiple shots (1, 2 and 4) in healthy human volunteers and MS patients. Quantitative parametric maps for T1, T2, T1ρ, were generated. Mean gray matter (GM) and white matter (WM) ROIs were compared for each mapping technique, Bland-Altman plots and intra-class correlation coefficient (ICC) were used to assess repeatability and Student T-tests were used to compare results in MS patients. Standardized phantom studies demonstrated excellent agreement with reference T1/T2/T1ρ mapping techniques. This study demonstrates that the 3D-MRF technique is able to simultaneously quantify T1, T2 and T1ρ for tissue property characterization in a clinically feasible scan time. This multi-parametric approach offers increased potential to detect and differentiate brain lesions and to better test imaging biomarker hypotheses for several neurological diseases, including MS.
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Affiliation(s)
| | | | | | - Tyler Smith
- New York University Grossman School of Medicine
| | - Ilya Kister
- New York University Grossman School of Medicine
| | - Yulin Ge
- New York University Grossman School of Medicine
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Dokumacı AS, Aitken FR, Sedlacik J, Bridgen P, Tomi‐Tricot R, Mooiweer R, Vecchiato K, Wilkinson T, Casella C, Giles S, Hajnal JV, Malik SJ, O'Muircheartaigh J, Carmichael DW. Simultaneous Optimization of MP2RAGE T 1 -weighted (UNI) and FLuid And White matter Suppression (FLAWS) brain images at 7T using Extended Phase Graph (EPG) Simulations. Magn Reson Med 2023; 89:937-950. [PMID: 36352772 PMCID: PMC10100108 DOI: 10.1002/mrm.29479] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/16/2022] [Accepted: 09/16/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE The MP2RAGE sequence is typically optimized for either T1 -weighted uniform image (UNI) or gray matter-dominant fluid and white matter suppression (FLAWS) contrast images. Here, the purpose was to optimize an MP2RAGE protocol at 7 Tesla to provide UNI and FLAWS images simultaneously in a clinically applicable acquisition time at <0.7 mm isotropic resolution. METHODS Using the extended phase graph formalism, the signal evolution of the MP2RAGE sequence was simulated incorporating T2 relaxation, diffusion, RF spoiling, and B1 + variability. Flip angles and TI were optimized at different TRs (TRMP2RAGE ) to produce an optimal contrast-to-noise ratio for UNI and FLAWS images. Simulation results were validated by comparison to MP2RAGE brain scans of 5 healthy subjects, and a final protocol at TRMP2RAGE = 4000 ms was applied in 19 subjects aged 8-62 years with and without epilepsy. RESULTS FLAWS contrast images could be obtained while maintaining >85% of the optimal UNI contrast-to-noise ratio. Using TI1 /TI2 /TRMP2RAGE of 650/2280/4000 ms, 6/8 partial Fourier in the inner phase-encoding direction, and GRAPPA factor = 4 in the other, images with 0.65 mm isotropic resolution were produced in <7.5 min. The contrast-to-noise ratio was around 20% smaller at TRMP2RAGE = 4000 ms compared to that at TRMP2RAGE = 5000 ms; however, the 20% shorter duration makes TRMP2RAGE = 4000 ms a good candidate for clinical applications example, pediatrics. CONCLUSION FLAWS and UNI images could be obtained in a single scan with 0.65 mm isotropic resolution, providing a set of high-contrast images and full brain coverage in a clinically applicable scan time. Images with excellent anatomical detail were demonstrated over a wide age range using the optimized parameter set.
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Affiliation(s)
- Ayşe Sıla Dokumacı
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Fraser R. Aitken
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Jan Sedlacik
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- Radiology DepartmentGreat Ormond Street Hospital for ChildrenLondonUnited Kingdom
| | - Pip Bridgen
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Raphael Tomi‐Tricot
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUnited Kingdom
| | - Ronald Mooiweer
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUnited Kingdom
| | - Katy Vecchiato
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- Department of Forensic and Neurodevelopmental SciencesInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging Sciences, King's College LondonLondonUnited Kingdom
| | - Tom Wilkinson
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Chiara Casella
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging Sciences, King's College LondonLondonUnited Kingdom
| | - Sharon Giles
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Joseph V. Hajnal
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Shaihan J. Malik
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
| | - Jonathan O'Muircheartaigh
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
- Department of Forensic and Neurodevelopmental SciencesInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging Sciences, King's College LondonLondonUnited Kingdom
- MRC Centre for Neurodevelopmental Disorders, King's College LondonLondonUnited Kingdom
| | - David W. Carmichael
- Biomedical Engineering DepartmentSchool of Biomedical Engineering and Imaging Sciences, King's College London
LondonUnited Kingdom
- London Collaborative Ultra high field System (LoCUS)LondonUnited Kingdom
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Kent JL, Dragonu I, Valkovič L, Hess AT. Rapid 3D absolute B 1 + mapping using a sandwiched train presaturated TurboFLASH sequence at 7 T for the brain and heart. Magn Reson Med 2023; 89:964-976. [PMID: 36336893 PMCID: PMC10099228 DOI: 10.1002/mrm.29497] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 09/22/2022] [Accepted: 09/29/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE To shorten the acquisition time of magnetization-prepared absolute transmit field (B1 + ) mapping known as presaturation TurboFLASH, or satTFL, to enable single breath-hold whole-heart 3D B1 + mapping. METHODS SatTFL is modified to remove the delay between the reference and prepared images (typically 5 T1 ), with matching transmit configurations for excitation and preparation RF pulses. The new method, called Sandwich, is evaluated as a 3D sequence, measuring whole-brain and gated whole-heart B1 + maps in a single breath-hold. We evaluate the sensitivity to B1 + and T1 using numerical Bloch, extended phase graph, and Monte Carlo simulations. Phantom and in vivo images were acquired in both the brain and heart using an 8-channel transmit 7 Tesla MRI system to support the simulations. A segmented satTFL with a short readout train was used as a reference. RESULTS The method significantly reduces acquisition times of 3D measurements from 360 s to 20 s, in the brain, while simultaneously reducing bias in the measured B1 + due to T1 and magnetization history. The mean coefficient of variation was reduced by 81% for T1 s of 0.5-3 s compared to conventional satTFL. In vivo, the reproducibility coefficient for flip angles in the range 0-130° was 4.5° for satTFL and 4.7° for our scheme, significantly smaller than for a short TR satTFL sequence, which was 12°. The 3D sequence measured B1 + maps of the whole thorax in 26 heartbeats. CONCLUSION Our adaptations enable faster B1 + mapping, with minimal T1 sensitivity and lower sensitivity to magnetization history, enabling single breath-hold whole-heart absolute B1 + mapping.
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Affiliation(s)
- James L Kent
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Ladislav Valkovič
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), University of Oxford, Oxford, UK.,Department of Imaging Methods, Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Aaron T Hess
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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Fuderer M, van der Heide O, Liu H, van den Berg CAT, Sbrizzi A. Efficient performance analysis and optimization of transient-state sequences for multiparametric magnetic resonance imaging. NMR IN BIOMEDICINE 2023; 36:e4864. [PMID: 36321222 PMCID: PMC10078474 DOI: 10.1002/nbm.4864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/11/2022] [Accepted: 10/30/2022] [Indexed: 06/16/2023]
Abstract
In transient-state multiparametric MRI sequences such as Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT), MR fingerprinting, or hybrid-state imaging, the flip angle pattern of the RF excitation varies over the sequence. This gives considerable freedom to choose an optimal pattern of flip angles. For pragmatic reasons, most optimization methodologies choose for a single-voxel approach (i.e., without taking the spatial encoding scheme into account). Particularly in MR-STAT, the context of spatial encoding is important. In the current study, we present a methodology, called BLock Analysis of a K-space-domain Jacobian (BLAKJac), which is sufficiently fast to optimize a sequence in the context of a predetermined phase-encoding pattern. Based on MR-STAT acquisitions and reconstructions, we show that sequences optimized using BLAKJac are more reliable in terms of actually achieved precision than conventional single-voxel-optimized sequences. In addition, BLAKJac provides analytical tools that give insights into the performance of the sequence in a very limited computation time. Our experiments are based on MR-STAT, but the theory is equally valid for other transient-state multiparametric methods.
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Affiliation(s)
- Miha Fuderer
- Radiotherapy, Imaging DivisionUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Oscar van der Heide
- Radiotherapy, Imaging DivisionUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Hongyan Liu
- Radiotherapy, Imaging DivisionUniversity Medical Center UtrechtUtrechtthe Netherlands
| | | | - Alessandro Sbrizzi
- Radiotherapy, Imaging DivisionUniversity Medical Center UtrechtUtrechtthe Netherlands
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60
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Amirrajab S, Khalil YA, Lorenz C, Weese J, Pluim J, Breeuwer M. A Framework for Simulating Cardiac MR Images With Varying Anatomy and Contrast. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:726-738. [PMID: 36260571 DOI: 10.1109/tmi.2022.3215798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
One of the limiting factors for the development and adoption of novel deep-learning (DL) based medical image analysis methods is the scarcity of labeled medical images. Medical image simulation and synthesis can provide solutions by generating ample training data with corresponding ground truth labels. Despite recent advances, generated images demonstrate limited realism and diversity. In this work, we develop a flexible framework for simulating cardiac magnetic resonance (MR) images with variable anatomical and imaging characteristics for the purpose of creating a diversified virtual population. We advance previous works on both cardiac MR image simulation and anatomical modeling to increase the realism in terms of both image appearance and underlying anatomy. To diversify the generated images, we define parameters: 1)to alter the anatomy, 2) to assign MR tissue properties to various tissue types, and 3) to manipulate the image contrast via acquisition parameters. The proposed framework is optimized to generate a substantial number of cardiac MR images with ground truth labels suitable for downstream supervised tasks. A database of virtual subjects is simulated and its usefulness for aiding a DL segmentation method is evaluated. Our experiments show that training completely with simulated images can perform comparable with a model trained with real images for heart cavity segmentation in mid-ventricular slices. Moreover, such data can be used in addition to classical augmentation for boosting the performance when training data is limited, particularly by increasing the contrast and anatomical variation, leading to better regularization and generalization. The database is publicly available at https://osf.io/bkzhm/ and the simulation code will be available at https://github.com/sinaamirrajab/CMRI.
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Taso M, Munsch F, Girard OM, Duhamel G, Alsop DC, Varma G. Fast-spin-echo versus rapid gradient-echo for 3D magnetization-prepared acquisitions: Application to inhomogeneous magnetization transfer. Magn Reson Med 2023; 89:550-564. [PMID: 36306334 PMCID: PMC10848167 DOI: 10.1002/mrm.29461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 08/23/2022] [Accepted: 08/26/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To evaluate the benefits of fast spin echo (FSE) imaging over rapid gradient-echo (RAGE) for magnetization-prepared inhomogeneous magnetization transfer (ihMT) imaging. METHODS A 3D FSE sequence was modified to include an ihMT preparation (ihMT-FSE) with an optional CSF suppression based on an inversion-recovery (ihMT-FLAIR). After numeric simulations assessing SNR benefits of FSE and the potential impact of an additional inversion-recovery, ihMT-RAGE, ihMT-FSE, and ihMT-FLAIR sequences were compared in a group of six healthy volunteers, evaluating image quality, thermal, and physiological noise as well as quantification using an ihMT saturation (ihMTsat) approach. A preliminary exploration in the cervical spinal cord was also conducted in a group of three healthy volunteers. RESULTS Several fold improvements in thermal SNR were observed with ihMT-FSE in agreement with numerical simulations. However, we observed significantly higher physiological noise in ihMT-FSE compared to ihMT-RAGE that was mitigated in ihMT-FLAIR, which provided the best total SNR (+74% and +49% compared to ihMT-RAGE in the white and gray matter, P ≤ 0.004). IhMTsat quantification was successful in all cases with strong correlation between all sequences (r2 > 0.75). Early experiments showed potential for spinal cord imaging. CONCLUSIONS FSE generally offers higher SNR compared to gradient-echo based acquisitions for magnetization-prepared contrasts as illustrated here in the case of ihMT. However, physiological noise has a significant effect, but an inversion-recovery-based CSF suppression was shown to be efficient in mitigating effects of CSF motion.
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Affiliation(s)
- Manuel Taso
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Fanny Munsch
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | | | | | - David C. Alsop
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Gopal Varma
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
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Barbieri M, Chaudhari AS, Moran CJ, Gold GE, Hargreaves BA, Kogan F. A method for measuring B 0 field inhomogeneity using quantitative double-echo in steady-state. Magn Reson Med 2023; 89:577-593. [PMID: 36161727 PMCID: PMC9712261 DOI: 10.1002/mrm.29465] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To develop and validate a method forB 0 $$ {B}_0 $$ mapping for knee imaging using the quantitative Double-Echo in Steady-State (qDESS) exploiting the phase difference (Δ θ $$ \Delta \theta $$ ) between the two echoes acquired. Contrary to a two-gradient-echo (2-GRE) method,Δ θ $$ \Delta \theta $$ depends only on the first echo time. METHODS Bloch simulations were applied to investigate robustness to noise of the proposed methodology and all imaging studies were validated with phantoms and in vivo simultaneous bilateral knee acquisitions. Two phantoms and five healthy subjects were scanned using qDESS, water saturation shift referencing (WASSR), and multi-GRE sequences.Δ B 0 $$ \Delta {B}_0 $$ maps were calculated with the qDESS and the 2-GRE methods and compared against those obtained with WASSR. The comparison was quantitatively assessed exploiting pixel-wise difference maps, Bland-Altman (BA) analysis, and Lin's concordance coefficient (ρ c $$ {\rho}_c $$ ). For in vivo subjects, the comparison was assessed in cartilage using average values in six subregions. RESULTS The proposed method for measuringΔ B 0 $$ \Delta {B}_0 $$ inhomogeneities from a qDESS acquisition providedΔ B 0 $$ \Delta {B}_0 $$ maps that were in good agreement with those obtained using WASSR.Δ B 0 $$ \Delta {B}_0 $$ ρ c $$ {\rho}_c $$ values were≥ $$ \ge $$ 0.98 and 0.90 in phantoms and in vivo, respectively. The agreement between qDESS and WASSR was comparable to that of a 2-GRE method. CONCLUSION The proposed method may allow B0 correction for qDESST 2 $$ {T}_2 $$ mapping using an inherently co-registeredΔ B 0 $$ \Delta {B}_0 $$ map without requiring an additional B0 measurement sequence. More generally, the method may help shorten knee imaging protocols that require an auxiliaryΔ B 0 $$ \Delta {B}_0 $$ map by exploiting a qDESS acquisition that also providesT 2 $$ {T}_2 $$ measurements and high-quality morphological imaging.
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Affiliation(s)
- Marco Barbieri
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
| | - Akshay S. Chaudhari
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
- Department of Biomedical Data Science, Stanford University, Stanford, CA, U.S.A
| | | | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
- Department of Bioengineering, Stanford University, Stanford, CA, U.S.A
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
- Department of Bioengineering, Stanford University, Stanford, CA, U.S.A
- Department of Electrical Engineering, Stanford University, Stanford, CA, U.S.A
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
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Fotaki A, Velasco C, Prieto C, Botnar RM. Quantitative MRI in cardiometabolic disease: From conventional cardiac and liver tissue mapping techniques to multi-parametric approaches. Front Cardiovasc Med 2023; 9:991383. [PMID: 36756640 PMCID: PMC9899858 DOI: 10.3389/fcvm.2022.991383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 12/29/2022] [Indexed: 01/24/2023] Open
Abstract
Cardiometabolic disease refers to the spectrum of chronic conditions that include diabetes, hypertension, atheromatosis, non-alcoholic fatty liver disease, and their long-term impact on cardiovascular health. Histological studies have confirmed several modifications at the tissue level in cardiometabolic disease. Recently, quantitative MR methods have enabled non-invasive myocardial and liver tissue characterization. MR relaxation mapping techniques such as T1, T1ρ, T2 and T2* provide a pixel-by-pixel representation of the corresponding tissue specific relaxation times, which have been shown to correlate with fibrosis, altered tissue perfusion, oedema and iron levels. Proton density fat fraction mapping approaches allow measurement of lipid tissue in the organ of interest. Several studies have demonstrated their utility as early diagnostic biomarkers and their potential to bear prognostic implications. Conventionally, the quantification of these parameters by MRI relies on the acquisition of sequential scans, encoding and mapping only one parameter per scan. However, this methodology is time inefficient and suffers from the confounding effects of the relaxation parameters in each single map, limiting wider clinical and research applications. To address these limitations, several novel approaches have been proposed that encode multiple tissue parameters simultaneously, providing co-registered multiparametric information of the tissues of interest. This review aims to describe the multi-faceted myocardial and hepatic tissue alterations in cardiometabolic disease and to motivate the application of relaxometry and proton-density cardiac and liver tissue mapping techniques. Current approaches in myocardial and liver tissue characterization as well as latest technical developments in multiparametric quantitative MRI are included. Limitations and challenges of these novel approaches, and recommendations to facilitate clinical validation are also discussed.
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Affiliation(s)
- Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom,*Correspondence: Anastasia Fotaki,
| | - Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom,School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile,Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile,Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom,School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile,Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile,Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
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64
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Roberts NT, Tamada D, Muslu Y, Hernando D, Reeder SB. Confounder-corrected T 1 mapping in the liver through simultaneous estimation of T 1 , PDFF, R 2 * , and B 1 + in a single breath-hold acquisition. Magn Reson Med 2023; 89:2186-2203. [PMID: 36656152 PMCID: PMC10139739 DOI: 10.1002/mrm.29590] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 12/23/2022] [Accepted: 01/01/2023] [Indexed: 01/20/2023]
Abstract
PURPOSE Quantitative volumetric T1 mapping in the liver has the potential to aid in the detection, diagnosis, and quantification of liver fibrosis, inflammation, and spatially resolved liver function. However, accurate measurement of hepatic T1 is confounded by the presence of fat and inhomogeneous B 1 + $$ {B}_1^{+} $$ excitation. Furthermore, scan time constraints related to respiratory motion require tradeoffs of reduced volumetric coverage and/or increased acquisition time. This work presents a novel 3D acquisition and estimation method for confounder-corrected T1 measurement over the entire liver within a single breath-hold through simultaneous estimation of T1 , fat and B 1 + $$ {B}_1^{+} $$ . THEORY AND METHODS The proposed method combines chemical shift encoded MRI and variable flip angle MRI with a B 1 + $$ {B}_1^{+} $$ mapping technique to enable confounder-corrected T1 mapping. The method was evaluated theoretically and demonstrated in both phantom and in vivo acquisitions at 1.5 and 3.0T. At 1.5T, the method was evaluated both pre- and post- contrast enhancement in healthy volunteers. RESULTS The proposed method demonstrated excellent linear agreement with reference inversion-recovery spin-echo based T1 in phantom acquisitions at both 1.5 and 3.0T, with minimal bias (5.2 and 45 ms, respectively) over T1 ranging from 200-1200 ms. In vivo results were in general agreement with reference saturation-recovery based 2D T1 maps (SMART1 Map, GE Healthcare). CONCLUSION The proposed 3D T1 mapping method accounts for fat and B 1 + $$ {B}_1^{+} $$ confounders through simultaneous estimation of T1 , B 1 + $$ {B}_1^{+} $$ , PDFF and R 2 * $$ {R}_2^{\ast } $$ . It demonstrates strong linear agreement with reference T1 measurements, with low bias and high precision, and can achieve full liver coverage in a single breath-hold.
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Affiliation(s)
- Nathan T Roberts
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Electrical and Computer Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Daiki Tamada
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Yavuz Muslu
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Biomedical Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Diego Hernando
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Electrical and Computer Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Biomedical Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Medical Physics, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Scott B Reeder
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Biomedical Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Medical Physics, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Medicine, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Emergency Medicine, University of Wisconsin - Madison, Madison, Wisconsin, USA
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Vegezzi E, Cortese A, Bergsland N, Mussinelli R, Paoletti M, Solazzo F, Currò R, Ascagni L, Callegari I, Quartesan I, Lozza A, Deligianni X, Santini F, Marchioni E, Cosentino G, Alfonsi E, Tassorelli C, Bastianello S, Merlini G, Palladini G, Obici L, Pichiecchio A. Muscle quantitative MRI as a novel biomarker in hereditary transthyretin amyloidosis with polyneuropathy: a cross-sectional study. J Neurol 2023; 270:328-339. [PMID: 36064814 DOI: 10.1007/s00415-022-11336-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND The development of reproducible and sensitive outcome measures has been challenging in hereditary transthyretin (ATTRv) amyloidosis. Recently, quantification of intramuscular fat by magnetic resonance imaging (MRI) has proven as a sensitive marker in patients with other genetic neuropathies. The aim of this study was to investigate the role of muscle quantitative MRI (qMRI) as an outcome measure in ATTRv. METHODS Calf- and thigh-centered multi-echo T2-weighted spin-echo and gradient-echo sequences were obtained in patients with ATTRv amyloidosis with polyneuropathy (n = 24) and healthy controls (n = 12). Water T2 (wT2) and fat fraction (FF) were calculated. Neurological assessment was performed in all ATTRv subjects. Quantitative MRI parameters were correlated with clinical and neurophysiological measures of disease severity. RESULTS Quantitative imaging revealed significantly higher FF in lower limb muscles in patients with ATTRv amyloidosis compared to controls. In addition, wT2 was significantly higher in ATTRv patients. There was prominent involvement of the posterior compartment of the thighs. Noticeably, FF and wT2 did not exhibit a length-dependent pattern in ATTRv patients. MRI biomarkers correlated with previously validated clinical outcome measures, Polyneuropathy Disability scoring system, Neuropathy Impairment Score (NIS) and NIS-lower limb, and neurophysiological parameters of axonal damage regardless of age, sex, treatment and TTR mutation. CONCLUSIONS Muscle qMRI revealed significant difference between ATTRv and healthy controls. MRI biomarkers showed high correlation with clinical and neurophysiological measures of disease severity making qMRI as a promising tool to be further investigated in longitudinal studies to assess its role at monitoring onset, progression, and therapy efficacy for future clinical trials on this treatable condition.
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Affiliation(s)
- Elisa Vegezzi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Neuroncology and Neuroinflammation Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Andrea Cortese
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy. .,Department of Neuromuscular Disease, UCL Queen Square Institute of Neurology and The National Hospital for Neurology and Neurosurgery, London, UK.
| | - Niels Bergsland
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.,IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Roberta Mussinelli
- Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy
| | - Matteo Paoletti
- Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Francesca Solazzo
- Specialization School in Occupational Medicine, University of Pavia, Pavia, Italy
| | - Riccardo Currò
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Department of Neuromuscular Disease, UCL Queen Square Institute of Neurology and The National Hospital for Neurology and Neurosurgery, London, UK
| | - Lucia Ascagni
- Neuroscience Department, Meyer Children's University Hospital, University of Florence, Florence, Italy
| | - Ilaria Callegari
- Department of Biomedicine, University Hospital Basel, University of Basel, Hebelstrasse 20, 4031, Basel, Switzerland
| | - Ilaria Quartesan
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Alessandro Lozza
- Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy
| | - Xeni Deligianni
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland.,Department of Biomedical Engineering, Basel Muscle MRI Group, University of Basel, Allschwil, Switzerland
| | - Francesco Santini
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland.,Department of Biomedical Engineering, Basel Muscle MRI Group, University of Basel, Allschwil, Switzerland
| | - Enrico Marchioni
- Neuroncology and Neuroinflammation Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Giuseppe Cosentino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Translational Neurophysiology Research Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Enrico Alfonsi
- Translational Neurophysiology Research Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Cristina Tassorelli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Stefano Bastianello
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Giampaolo Merlini
- Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy.,Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Giovanni Palladini
- Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy.,Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Laura Obici
- Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy
| | - Anna Pichiecchio
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy
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Colelli G, Barzaghi L, Paoletti M, Monforte M, Bergsland N, Manco G, Deligianni X, Santini F, Ricci E, Tasca G, Mira A, Figini S, Pichiecchio A. Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease. Front Neurol 2023; 14:1105276. [PMID: 36908599 PMCID: PMC9999017 DOI: 10.3389/fneur.2023.1105276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/30/2023] [Indexed: 03/14/2023] Open
Abstract
Purpose Quantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms. Methods Twenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows (WF1, WF2, WF3), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles. Results The combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about ± 5 pp for FF and ± 1.8 ms for wT2. Conclusion This pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence.
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Affiliation(s)
- Giulia Colelli
- Department of Mathematics, University of Pavia, Pavia, Italy.,Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy.,INFN, Group of Pavia, Pavia, Italy
| | - Leonardo Barzaghi
- Department of Mathematics, University of Pavia, Pavia, Italy.,Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Matteo Paoletti
- Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Mauro Monforte
- UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Niels Bergsland
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, University of Buffalo, The State University of New York, Buffalo, NY, United States.,IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Giulia Manco
- Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Xeni Deligianni
- Department of Radiology, University Hospital Basel, Basel, Switzerland.,Basel Muscle MRI, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Francesco Santini
- Department of Radiology, University Hospital Basel, Basel, Switzerland.,Basel Muscle MRI, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Enzo Ricci
- UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giorgio Tasca
- UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trusts, Newcastle upon Tyne, United Kingdom
| | - Antonietta Mira
- Data Science Lab, Università della Svizzera italiana, Lugano, Switzerland.,Department of Science and High Technology, University of Insubria, Como, Italy
| | - Silvia Figini
- Department of Political and Social Sciences, University of Pavia, Pavia, Italy.,BioData Science Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Anna Pichiecchio
- Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
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Gatefait CGF, Ellison SLR, Nyangoma S, Schmitter S, Kolbitsch C. Optimisation of data acquisition towards continuous cardiac Magnetic Resonance Fingerprinting applications. Phys Med 2023; 105:102514. [PMID: 36608390 DOI: 10.1016/j.ejmp.2022.102514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 11/10/2022] [Accepted: 12/12/2022] [Indexed: 01/06/2023] Open
Abstract
PURPOSE Assess and optimise acquisition parameters for continuous cardiac Magnetic Resonance Fingerprinting (MRF). METHODS Different acquisition schemes (flip angle amplitude, lobe size, T2-preparation pulses) for cardiac MRF were assessed in simulations and phantom and demonstrated in one healthy volunteer. Three different experimental designs were evaluated using central composite and fractional factorial designs. Relative errors for T1 and T2 were calculated for a wide range of realistic T1 and T2 value combinations. The effect of different designs on the accuracy of T1 and T2 was assessed using response surface modelling and Cohen's f calculations. RESULTS Larger flip angle amplitudes lead to an improvement of T2 accuracy and precision for simulations and phantom experiments. Similar effects could also be shown qualitatively in in-vivo scans. Accuracy and precision of T1 were robust to different design parameters with improved values for faster flip angle variation. Cohen's f showed that T2-preparation pulses influence the accuracy of T2. The number of pulses used is the most important parameter. Without T2-preparation pulses, RMSE were 3.0 ± 8.09 % for T1 and 16.24 ± 14.47 % for T2. Using those pulses reduced the RMSE to 2.3 ± 8.4 % for T1 and 14.11 ± 13.46 % for T2. Nonetheless, even if the improvement is significant, RMSE are still too high for reliable quantification. CONCLUSION In contrast to previous study using triggered MRF sequences using < 30° flip angles, large flip angle amplitudes led to better results for continuous cardiac MRF sequences. T2-preparation pulse can improve the accuracy of T2 estimation but lead to longer scan times.
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Sharafi A, Zibetti MVW, Chang G, Cloos M, Regatte RR. 3D magnetic resonance fingerprinting for rapid simultaneous T1, T2, and T1ρ volumetric mapping of human articular cartilage at 3 T. NMR IN BIOMEDICINE 2022; 35:e4800. [PMID: 35815660 PMCID: PMC9669203 DOI: 10.1002/nbm.4800] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 06/28/2022] [Accepted: 07/08/2022] [Indexed: 05/25/2023]
Abstract
Quantitative MRI can detect early biochemical changes in cartilage; however, the conventional techniques only measure one parameter (e.g., T1 , T2 , and T1ρ ) at a time while also being comparatively slow. We implemented a 3D magnetic resonance fingerprinting (3D-MRF) technique for simultaneous, volumetric mapping of T1 , T2 , and T1ρ in knee articular cartilage in under 9 min. It is evaluated on 11 healthy volunteers (mean age: 53 ± 9 years), five mild knee osteoarthritis (OA) patients (Kellgren-Lawrence (KL) score: 2, mean age: 60 ± 4 years), and the National Institute of Standards and Technology (NIST)/International Society for Magnetic Resonance in Medicine (ISMRM) system phantom. Proton density image, and T1 , T2, T1ρ relaxation times, and B1 + were estimated in the NIST/ISMRM system phantom as well as in the human knee medial and lateral femur, medial and lateral tibia, and patellar cartilage. The repeatability and reproducibility of the proposed technique were assessed in the phantom using analysis of the Bland-Altman plots. The intrasubject repeatability was assessed with the coefficient of variation (CV) and root mean square CV (rmsCV). The Mann-Whitney U test was used to assess the difference between healthy subjects and mild knee OA patients. The Bland-Altman plots in the NIST/ISMRM phantom demonstrated an average difference of 0.001% ± 015%, 1.2% ± 7.1%, and 0.47% ± 3% between two scans from the same 3-T scanner (repeatability), and 0.002% ± 015%, 0.62% ± 10.5%, and 0.97% ± 14% between the scans acquired on two different 3-T scanners (reproducibility) for T1 , T2 , and T1ρ , respectively. The in vivo knee study showed excellent repeatability with rmsCV less than 1%, 2%, and 1% for T1 , T2 , and T1ρ , respectively. T1ρ relaxation time in the mild knee OA patients was significantly higher (p < 0.05) than in healthy subjects. The proposed 3D-MRF sequence is fast, reproducible, robust to B1 + inhomogeneity, and can simultaneously measure the T1 , T2 , T1ρ , and B1 + volumetric maps of the knee joint in a single scan within a clinically feasible scan time.
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Affiliation(s)
- Azadeh Sharafi
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Marcelo V. W. Zibetti
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Gregory Chang
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Martijn Cloos
- Center of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ravinder R. Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
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69
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Lee H, Ozturk B, Stringer MS, Koundal S, MacIntosh BJ, Rothman D, Benveniste H. Choroid plexus tissue perfusion and blood to CSF barrier function in rats measured with continuous arterial spin labeling. Neuroimage 2022; 261:119512. [PMID: 35882269 PMCID: PMC9969358 DOI: 10.1016/j.neuroimage.2022.119512] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/18/2022] [Accepted: 07/22/2022] [Indexed: 02/08/2023] Open
Abstract
The choroid plexus (ChP) of the cerebral ventricles is a source of cerebrospinal fluid (CSF) production and also plays a key role in immune surveillance at the level of blood-to-CSF-barrier (BCSFB). In this study, we quantify ChP blood perfusion and BCSFB mediated water exchange from arterial blood into ventricular CSF using non-invasive continuous arterial spin labelling magnetic resonance imaging (CASL-MRI). Systemic administration of anti-diuretic hormone (vasopressin) was used to validate BCSFB water flow as a metric of choroidal CSF secretory function. To further investigate the coupling between ChP blood perfusion and BCSFB water flow, we characterized the effects of two anesthetic regimens known to have large-scale differential effects on cerebral blood flow. For quantification of ChP blood perfusion a multi-compartment perfusion model was employed, and we discovered that partial volume correction improved measurement accuracy. Vasopressin significantly reduced both ChP blood perfusion and BCSFB water flow. ChP blood perfusion was significantly higher with pure isoflurane anesthesia (2-2.5%) when compared to a balanced anesthesia with dexmedetomidine and low-dose isoflurane (1.0 %), and significant correlation between ChP blood perfusion and BCSFB water flow was observed, however there was no significant difference in BCSFB water flow. In summary, here we introduce a non-invasive, robust, and spatially resolved in vivo imaging platform to quantify ChP blood perfusion as well as BCSFB water flow which can be applied to study coupling of these two key parameters in future clinical translational studies.
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Affiliation(s)
- Hedok Lee
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA.
| | - Burhan Ozturk
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
| | - Michael S Stringer
- Brain Research Imaging Centre and UK Dementia Research Institute, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Sunil Koundal
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
| | - Bradley J MacIntosh
- Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Douglas Rothman
- Departments of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Helene Benveniste
- Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA
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70
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Liu H, van der Heide O, Mandija S, van den Berg CAT, Sbrizzi A. Acceleration Strategies for MR-STAT: Achieving High-Resolution Reconstructions on a Desktop PC Within 3 Minutes. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2681-2692. [PMID: 35436186 DOI: 10.1109/tmi.2022.3168436] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
MR-STAT is an emerging quantitative magnetic resonance imaging technique which aims at obtaining multi-parametric tissue parameter maps from single short scans. It describes the relationship between the spatial-domain tissue parameters and the time-domain measured signal by using a comprehensive, volumetric forward model. The MR-STAT reconstruction solves a large-scale nonlinear problem, thus is very computationally challenging. In previous work, MR-STAT reconstruction using Cartesian readout data was accelerated by approximating the Hessian matrix with sparse, banded blocks, and can be done on high performance CPU clusters with tens of minutes. In the current work, we propose an accelerated Cartesian MR-STAT algorithm incorporating two different strategies: firstly, a neural network is trained as a fast surrogate to learn the magnetization signal not only in the full time-domain but also in the compressed low-rank domain; secondly, based on the surrogate model, the Cartesian MR-STAT problem is re-formulated and split into smaller sub-problems by the alternating direction method of multipliers. The proposed method substantially reduces the computational requirements for runtime and memory. Simulated and in-vivo balanced MR-STAT experiments show similar reconstruction results using the proposed algorithm compared to the previous sparse Hessian method, and the reconstruction times are at least 40 times shorter. Incorporating sensitivity encoding and regularization terms is straightforward, and allows for better image quality with a negligible increase in reconstruction time. The proposed algorithm could reconstruct both balanced and gradient-spoiled in-vivo data within 3 minutes on a desktop PC, and could thereby facilitate the translation of MR-STAT in clinical settings.
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71
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Ma L, Wu J, Yang Q, Zhou Z, He H, Bao J, Bao L, Wang X, Zhang P, Zhong J, Cai C, Cai S, Chen Z. Single-shot multi-parametric mapping based on multiple overlapping-echo detachment (MOLED) imaging. Neuroimage 2022; 263:119645. [PMID: 36155244 DOI: 10.1016/j.neuroimage.2022.119645] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 09/21/2022] [Accepted: 09/21/2022] [Indexed: 11/29/2022] Open
Abstract
Multi-parametric quantitative magnetic resonance imaging (mqMRI) allows the characterization of multiple tissue properties non-invasively and has shown great potential to enhance the sensitivity of MRI measurements. However, real-time mqMRI during dynamic physiological processes or general motions remains challenging. To overcome this bottleneck, we propose a novel mqMRI technique based on multiple overlapping-echo detachment (MOLED) imaging, termed MQMOLED, to enable mqMRI in a single shot. In the data acquisition of MQMOLED, multiple MR echo signals with different multi-parametric weightings and phase modulations are generated and acquired in the same k-space. The k-space data is Fourier transformed and fed into a well-trained neural network for the reconstruction of multi-parametric maps. We demonstrated the accuracy and repeatability of MQMOLED in simultaneous mapping apparent proton density (APD) and any two parameters among T2, T2*, and apparent diffusion coefficient (ADC) in 130-170 ms. The abundant information delivered by the multiple overlapping-echo signals in MQMOLED makes the technique potentially robust to system imperfections, such as inhomogeneity of static magnetic field or radiofrequency field. Benefitting from the single-shot feature, MQMOLED exhibits a strong motion tolerance to the continuous movements of subjects. For the first time, it captured the synchronous changes of ADC, T2, and T1-weighted APD in contrast-enhanced perfusion imaging on patients with brain tumors, providing additional information about vascular density to the hemodynamic parametric maps. We expect that MQMOLED would promote the development of mqMRI technology and greatly benefit the applications of mqMRI, including therapeutics and analysis of metabolic/functional processes.
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Affiliation(s)
- Lingceng Ma
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Jian Wu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Qinqin Yang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Zihan Zhou
- The Center for Brain Imaging Science and Technology, The Collaborative Innovation Center for Diagnosis and The Treatment of Infectious Diseases, Zhejiang University, Hangzhou 310027, China
| | - Hongjian He
- The Center for Brain Imaging Science and Technology, The Collaborative Innovation Center for Diagnosis and The Treatment of Infectious Diseases, Zhejiang University, Hangzhou 310027, China
| | - Jianfeng Bao
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - Lijun Bao
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Xiaoyin Wang
- The Center for Brain Imaging Science and Technology, The Collaborative Innovation Center for Diagnosis and The Treatment of Infectious Diseases, Zhejiang University, Hangzhou 310027, China
| | - Pujie Zhang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Jianhui Zhong
- The Center for Brain Imaging Science and Technology, The Collaborative Innovation Center for Diagnosis and The Treatment of Infectious Diseases, Zhejiang University, Hangzhou 310027, China; Department of Imaging Sciences, University of Rochester, Rochester, NY 14642, USA
| | - Congbo Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
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Velasco C, Fletcher TJ, Botnar RM, Prieto C. Artificial intelligence in cardiac magnetic resonance fingerprinting. Front Cardiovasc Med 2022; 9:1009131. [PMID: 36204566 PMCID: PMC9530662 DOI: 10.3389/fcvm.2022.1009131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.
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Affiliation(s)
- Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- *Correspondence: Carlos Velasco
| | - Thomas J. Fletcher
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
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73
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Nunez-Gonzalez L, Nagtegaal MA, Poot DHJ, de Bresser J, van Osch MJP, Hernandez-Tamames JA, Vos FM. Accuracy and repeatability of joint sparsity multi-component estimation in MR Fingerprinting. Neuroimage 2022; 263:119638. [PMID: 36122685 DOI: 10.1016/j.neuroimage.2022.119638] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/11/2022] [Accepted: 08/31/2022] [Indexed: 11/30/2022] Open
Abstract
MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estimation for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmentations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL- and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray matter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter.
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Affiliation(s)
- L Nunez-Gonzalez
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.
| | - M A Nagtegaal
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - D H J Poot
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - J de Bresser
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - M J P van Osch
- C.J. Gorter Center for MRI, Radiology Department, Leiden University Medical Center, Leiden, the Netherlands
| | - J A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands; Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - F M Vos
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands; Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
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Wang Y, van Gelderen P, de Zwart JA, Özbay PS, Mandelkow H, Picchioni D, Duyn JH. Cerebrovascular activity is a major factor in the cerebrospinal fluid flow dynamics. Neuroimage 2022; 258:119362. [PMID: 35688316 PMCID: PMC9271599 DOI: 10.1016/j.neuroimage.2022.119362] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/02/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022] Open
Abstract
Cerebrospinal fluid (CSF) provides physical protection to the central nervous system as well as an essential homeostatic environment for the normal functioning of neurons. Additionally, it has been proposed that the pulsatile movement of CSF may assist in glymphatic clearance of brain metabolic waste products implicated in neurodegeneration. In awake humans, CSF flow dynamics are thought to be driven primarily by cerebral blood volume fluctuations resulting from a number of mechanisms, including a passive vascular response to blood pressure variations associated with cardiac and respiratory cycles. Recent research has shown that mechanisms that rely on the action of vascular smooth muscle cells ("cerebrovascular activity") such as neuronal activity, changes in intravascular CO2, and autonomic activation from the brainstem, may lead to CSF pulsations as well. Nevertheless, the relative contribution of these mechanisms to CSF flow remains unclear. To investigate this further, we developed an MRI approach capable of disentangling and quantifying CSF flow components of different time scales associated with these mechanisms. This approach was evaluated on human control subjects (n = 12) performing intermittent voluntary deep inspirations, by determining peak flow velocities and displaced volumes between these mechanisms in the fourth ventricle. We found that peak flow velocities were similar between the different mechanisms, while displaced volumes per cycle were about a magnitude larger for deep inspirations. CSF flow velocity peaked at around 10.4 s (range 7.1-14.8 s, n = 12) following deep inspiration, consistent with known cerebrovascular activation delays for this autonomic challenge. These findings point to an important role of cerebrovascular activity in the genesis of CSF pulsations. Other regulatory triggers for cerebral blood flow such as autonomic arousal and orthostatic challenges may create major CSF pulsatile movement as well. Future quantitative comparison of these and possibly additional types of CSF pulsations with the proposed approach may help clarify the conditions that affect CSF flow dynamics.
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Affiliation(s)
- Yicun Wang
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States.
| | - Peter van Gelderen
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Jacco A de Zwart
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Pinar S Özbay
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Hendrik Mandelkow
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Dante Picchioni
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Jeff H Duyn
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
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Rodriguez GG, Yu Z, O′Donnell LF, Calderon L, Cloos MA, Madelin G. Repeatability of simultaneous 3D 1H MRF/ 23Na MRI in brain at 7 T. Sci Rep 2022; 12:14156. [PMID: 35986071 PMCID: PMC9391473 DOI: 10.1038/s41598-022-18388-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/10/2022] [Indexed: 11/21/2022] Open
Abstract
Proton MRI can provide detailed morphological images, but it reveals little information about cell homeostasis. On the other hand, sodium MRI can provide metabolic information but cannot resolve fine structures. The complementary nature of proton and sodium MRI raises the prospect of their combined use in a single experiment. In this work, we assessed the repeatability of normalized proton density (PD), T1, T2, and normalized sodium density-weighted quantification measured with simultaneous 3D 1H MRF/23Na MRI in the brain at 7 T, from ten healthy volunteers who were scanned three times each. The coefficients of variation (CV) and the intra-class correlation (ICC) were calculated for the mean and standard deviation (SD) of these 4 parameters in grey matter, white matter, and cerebrospinal fluid. As result, the CVs were lower than 3.3% for the mean values and lower than 6.9% for the SD values. The ICCs were higher than 0.61 in all 24 measurements. We conclude that the measurements of normalized PD, T1, T2, and normalized sodium density-weighted from simultaneous 3D 1H MRF/23Na MRI in the brain at 7 T showed high repeatability. We estimate that changes > 6.6% (> 2 CVs) in mean values of both 1H and 23Na metrics could be detectable with this method.
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Affiliation(s)
- Gonzalo G. Rodriguez
- grid.137628.90000 0004 1936 8753Center for Biomedical Imaging, Department of Radiology, New York University Grossman School Medicine, 660 1st Avenue, 4th floor, New York, NY 10016 USA
| | - Zidan Yu
- grid.137628.90000 0004 1936 8753Center for Biomedical Imaging, Department of Radiology, New York University Grossman School Medicine, 660 1st Avenue, 4th floor, New York, NY 10016 USA ,grid.240324.30000 0001 2109 4251Vilcek Institute of Graduate Biomedical Sciences, NYU Langone Health, New York, NY 10016 USA
| | - Lauren F. O′Donnell
- grid.137628.90000 0004 1936 8753Center for Biomedical Imaging, Department of Radiology, New York University Grossman School Medicine, 660 1st Avenue, 4th floor, New York, NY 10016 USA
| | - Liz Calderon
- grid.137628.90000 0004 1936 8753Center for Biomedical Imaging, Department of Radiology, New York University Grossman School Medicine, 660 1st Avenue, 4th floor, New York, NY 10016 USA
| | - Martijn A. Cloos
- grid.1003.20000 0000 9320 7537Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD Australia ,grid.1003.20000 0000 9320 7537ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, QLD Australia
| | - Guillaume Madelin
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School Medicine, 660 1st Avenue, 4th floor, New York, NY, 10016, USA. .,Vilcek Institute of Graduate Biomedical Sciences, NYU Langone Health, New York, NY, 10016, USA.
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Sridi S, Nuñez-Garcia M, Sermesant M, Maillot A, Hamrani DE, Magat J, Naulin J, Laurent F, Montaudon M, Jaïs P, Stuber M, Cochet H, Bustin A. Improved myocardial scar visualization with fast free-breathing motion-compensated black-blood T 1-rho-prepared late gadolinium enhancement MRI. Diagn Interv Imaging 2022; 103:607-617. [PMID: 35961843 DOI: 10.1016/j.diii.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/12/2022] [Accepted: 07/19/2022] [Indexed: 11/19/2022]
Abstract
PURPOSE Clinical guidelines recommend the use of bright-blood late gadolinium enhancement (BR-LGE) for the detection and quantification of regional myocardial fibrosis and scar. This technique, however, may suffer from poor contrast at the blood-scar interface, particularly in patients with subendocardial myocardial infarction. The purpose of this study was to assess the clinical performance of a two-dimensional black-blood LGE (BL-LGE) sequence, which combines free-breathing T1-rho-prepared single-shot acquisitions with an advanced non-rigid motion-compensated patch-based reconstruction. MATERIALS AND METHODS Extended phase graph simulations and phantom experiments were performed to investigate the performance of the motion-correction algorithm and to assess the black-blood properties of the proposed sequence. Fifty-one patients (37 men, 14 women; mean age, 55 ± 15 [SD] years; age range: 19-81 years) with known or suspected cardiac disease prospectively underwent free-breathing T1-rho-prepared BL-LGE imaging with inline non-rigid motion-compensated patch-based reconstruction at 1.5T. Conventional breath-held BR-LGE images were acquired for comparison purposes. Acquisition times were recorded. Two readers graded the image quality and relative contrasts were calculated. Presence, location, and extent of LGE were evaluated. RESULTS BL-LGE images were acquired with full ventricular coverage in 115 ± 25 (SD) sec (range: 64-160 sec). Image quality was significantly higher on free-breathing BL-LGE imaging than on its breath-held BR-LGE counterpart (3.6 ± 0.7 [SD] [range: 2-4] vs. 3.9 ± 0.2 [SD] [range: 3-4]) (P <0.01) and was graded as diagnostic for 44/51 (86%) patients. The mean scar-to-myocardium and scar-to-blood relative contrasts were significantly higher on BL-LGE images (P < 0.01 for both). The extent of LGE was larger on BL-LGE (median, 5 segments [IQR: 2, 7 segments] vs. median, 4 segments [IQR: 1, 6 segments]) (P < 0.01), the method being particularly sensitive in segments with LGE involving the subendocardium or papillary muscles. In eight patients (16%), BL-LGE could ascertain or rule out a diagnosis otherwise inconclusive on BR-LGE. CONCLUSION Free-breathing T1-rho-prepared BL-LGE imaging with inline motion compensated reconstruction offers a promising diagnostic technology for the non-invasive assessment of myocardial injuries.
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Affiliation(s)
- Soumaya Sridi
- Department of Cardiovascular Imaging, Groupe Hospitalier Sud, CHU Bordeaux, 33000, Pessac, France.
| | - Marta Nuñez-Garcia
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM U1045, 33600, Pessac, France
| | - Maxime Sermesant
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM U1045, 33600, Pessac, France; INRIA, Université Côte d'Azur, Sophia Antipolis, 06902, Valbonne, France
| | - Aurélien Maillot
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM U1045, 33600, Pessac, France
| | - Dounia El Hamrani
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM U1045, 33600, Pessac, France
| | - Julie Magat
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM U1045, 33600, Pessac, France
| | - Jérôme Naulin
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM U1045, 33600, Pessac, France
| | - François Laurent
- Department of Cardiovascular Imaging, Groupe Hospitalier Sud, CHU Bordeaux, 33000, Pessac, France
| | - Michel Montaudon
- Department of Cardiovascular Imaging, Groupe Hospitalier Sud, CHU Bordeaux, 33000, Pessac, France
| | - Pierre Jaïs
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM U1045, 33600, Pessac, France; Department of Cardiac Electrophysiologhy, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, 33600, Pessac, France
| | - Matthias Stuber
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM U1045, 33600, Pessac, France; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland; Center for Biomedical Imaging (CIBM), 1015, Lausanne, Switzerland
| | - Hubert Cochet
- Department of Cardiovascular Imaging, Groupe Hospitalier Sud, CHU Bordeaux, 33000, Pessac, France; IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM U1045, 33600, Pessac, France
| | - Aurélien Bustin
- Department of Cardiovascular Imaging, Groupe Hospitalier Sud, CHU Bordeaux, 33000, Pessac, France; IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux, INSERM U1045, 33600, Pessac, France; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland
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77
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Piredda GF, Hilbert T, Ravano V, Canales-Rodríguez EJ, Pizzolato M, Meuli R, Thiran JP, Richiardi J, Kober T. Data-driven myelin water imaging based on T 1 and T 2 relaxometry. NMR IN BIOMEDICINE 2022; 35:e4668. [PMID: 34936147 DOI: 10.1002/nbm.4668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 11/16/2021] [Accepted: 11/30/2021] [Indexed: 06/14/2023]
Abstract
Long acquisition times preclude the application of multiecho spin echo (MESE) sequences for myelin water fraction (MWF) mapping in daily clinical practice. In search of alternative methods, previous studies of interest explored the biophysical modeling of MWF from measurements of different tissue properties that can be obtained in scan times shorter than those required for the MESE. In this work, a novel data-driven estimation of MWF maps from fast relaxometry measurements is proposed and investigated. T1 and T2 relaxometry maps were acquired in a cohort of 20 healthy subjects along with a conventional MESE sequence. Whole-brain quantitative mapping was achieved with a fast protocol in 6 min 24 s. Reference MWF maps were derived from the MESE sequence (TA = 11 min 17 s) and their data-driven estimation from relaxometry measurements was investigated using three different modeling strategies: two general linear models (GLMs) with linear and quadratic regressors, respectively; a random forest regression model; and two deep neural network architectures, a U-Net and a conditional generative adversarial network (cGAN). Models were validated using a 10-fold crossvalidation. The resulting maps were visually and quantitatively compared by computing the root mean squared error (RMSE) between the estimated and reference MWF maps, the intraclass correlation coefficients (ICCs) between corresponding MWF values in different brain regions, and by performing Bland-Altman analysis. Qualitatively, the estimated maps appear to generally provide a similar, yet more blurred MWF contrast in comparison with the reference, with the cGAN model best capturing MWF variabilities in small structures. By estimating the average adjusted coefficient of determination of the GLM with quadratic regressors, we showed that 87% of the variability in the MWF values can be explained by relaxation times alone. Further quantitative analysis showed an average RMSE smaller than 0.1% for all methods. The ICC was greater than 0.81 for all methods, and the bias smaller than 2.19%. It was concluded that this work confirms the notion that relaxometry parameters contain a large part of the information on myelin water and that MWF maps can be generated from T1 /T2 data with minimal error. Among the investigated modeling approaches, the cGAN provided maps with the best trade-off between accuracy and blurriness. Fast relaxometry, like the 6 min 24 s whole-brain protocol used in this work in conjunction with machine learning, may thus have the potential to replace time-consuming MESE acquisitions.
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Affiliation(s)
- Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Veronica Ravano
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Marco Pizzolato
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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78
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Sveinsson B, Rowe OE, Stockmann JP, Park DJ, Lally PJ, Rosen MS, Barry RL, Eichler F, Rosen BR, Sadjadi R. Feasibility of simultaneous high-resolution anatomical and quantitative magnetic resonance imaging of sciatic nerves in patients with Charcot-Marie-Tooth type 1A (CMT1A) at 7T. Muscle Nerve 2022; 66:206-211. [PMID: 35621349 PMCID: PMC9308706 DOI: 10.1002/mus.27647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 11/07/2022]
Abstract
INTRODUCTION/AIMS Magnetic resonance imaging (MRI) of peripheral nerves can provide image-based anatomical information and quantitative measurement. The aim of this pilot study was to investigate the feasibility of high-resolution anatomical and quantitative MRI assessment of sciatic nerve fascicles in patients with Charcot-Marie-Tooth (CMT) 1A using 7T field strength. METHODS Six patients with CMT1A underwent imaging on a high-gradient 7T MRI scanner using a 28-channel knee coil. Two high-resolution axial images were simultaneously acquired using a quantitative double-echo in steady-state (DESS) sequence. By comparing the two DESS echoes, T2 and apparent diffusion coefficient (ADC) maps were calculated. The cross-sectional areas and mean T2 and ADC were measured in individual fascicles of the tibial and fibular (peroneal) portions of the sciatic nerve at its bifurcation and 10 mm distally. Disease severity was measured using Charcot-Marie-Tooth Examination Score (CMTES) version 2 and compared to imaging findings. RESULTS We demonstrated the feasibility of 7T MRI of the proximal sciatic nerve in patients with CMT1A. Using the higher field, it was possible to measure individual bundles in the tibial and fibular divisions of the sciatic nerve. There was no apparent correlation between diffusion measures and disease severity in this small cohort. DISCUSSION This pilot study indicated that high-resolution MRI that allows for combined anatomical and quantitative imaging in one scan is feasible at 7T field strengths and can be used to investigate the microstructure of individual nerve fascicles.
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Affiliation(s)
- Bragi Sveinsson
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Olivia E Rowe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Jason P Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Daniel J Park
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Peter J Lally
- Department of Brain Sciences, Imperial College London, London, UK
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Physics, Harvard University, Cambridge, Massachusetts, USA
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Cambridge, Massachusetts, USA
| | - Florian Eichler
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Reza Sadjadi
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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79
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A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Sci Rep 2022; 12:8682. [PMID: 35606398 PMCID: PMC9127105 DOI: 10.1038/s41598-022-10335-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 04/05/2022] [Indexed: 11/28/2022] Open
Abstract
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced image processing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth. In this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates clinical T2-weighted fast spin echo sequences of the fetal brain. This unique tool is based on a general, flexible and realistic setup that includes stochastic fetal movements, thus providing images of the fetal brain throughout maturation comparable to clinical acquisitions. We demonstrate its value to evaluate the robustness and optimize the accuracy of an algorithm for super-resolution fetal brain magnetic resonance imaging from simulated motion-corrupted 2D low-resolution series compared to a synthetic high-resolution reference volume. We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation.
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80
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Mehdizadeh N, Wilman AH. Myelin water fraction mapping from multiple echo spin echoes and an independent B 1 + map. Magn Reson Med 2022; 88:1380-1390. [PMID: 35576121 PMCID: PMC9321077 DOI: 10.1002/mrm.29286] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 03/23/2022] [Accepted: 04/13/2022] [Indexed: 11/11/2022]
Abstract
Purpose Myelin water fraction (MWF) is often obtained from a multiple echo spin echo (MESE) sequence using multi‐component T2 fitting with non‐negative least squares. This process fits many unknowns including B1+ to produce a T2 spectrum for each voxel. Presented is an alternative using a rapid B1+ mapping sequence to supply B1+ for the MWF fitting procedure. Methods Effects of B1+ errors on MWF calculations were modeled for 2D and 3D MESE using Bloch and extended phase graph simulations, respectively. Variations in SNR and relative refocusing widths were tested. Human brain experiments at 3 T used 2D MESE and an independent B1+ map. MWF maps were produced with the standard approach and with the use of the independent B1+ map. Differences in B1+ and mean MWF in specific brain regions were compared. Results For 2D MESE, MWF with the standard method was strongly affected by B1+ misestimations arising from limited SNR and response asymmetry around 180°, which decreased with increasing relative refocusing width. Using an independent B1+ map increased mean MWF and decreased coefficient of variation. Notable differences in vivo in 2D MESE were in areas of high B1+ such as thalamus and splenium where mean MWF increased by 88% and 31%, respectively (P < 0.001). Simulations also demonstrated the advantages of this approach for 3D MESE when SNR is <500. Conclusion For 2D MESE, because of increased complexity of decay curves and limited SNR, supplying B1+ improves MWF results in peripheral and central brain regions where flip angles differ substantially from 180°.
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Affiliation(s)
- Nima Mehdizadeh
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
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81
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Brackenier Y, Cordero‐Grande L, Tomi‐Tricot R, Wilkinson T, Bridgen P, Price A, Malik SJ, De Vita E, Hajnal JV. Data‐driven motion‐corrected brain
MRI
incorporating pose‐dependent
B
0
fields. Magn Reson Med 2022; 88:817-831. [PMID: 35526212 PMCID: PMC9324873 DOI: 10.1002/mrm.29255] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 02/15/2022] [Accepted: 03/11/2022] [Indexed: 11/18/2022]
Abstract
Purpose To develop a fully data‐driven retrospective intrascan motion‐correction framework for volumetric brain MRI at ultrahigh field (7 Tesla) that includes modeling of pose‐dependent changes in polarizing magnetic (B0) fields. Theory and Methods Tissue susceptibility induces spatially varying B0 distributions in the head, which change with pose. A physics‐inspired B0 model has been deployed to model the B0 variations in the head and was validated in vivo. This model is integrated into a forward parallel imaging model for imaging in the presence of motion. Our proposal minimizes the number of added parameters, enabling the developed framework to estimate dynamic B0 variations from appropriately acquired data without requiring navigators. The effect on data‐driven motion correction is validated in simulations and in vivo. Results The applicability of the physics‐inspired B0 model was confirmed in vivo. Simulations show the need to include the pose‐dependent B0 fields in the reconstruction to improve motion‐correction performance and the feasibility of estimating B0 evolution from the acquired data. The proposed motion and B0 correction showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo. Conclusion We have developed a motion‐correction framework that accounts for and estimates pose‐dependent B0 fields. The method improves current state‐of‐the‐art data‐driven motion‐correction techniques when B0 dependencies cannot be neglected. The use of a compact physics‐inspired B0 model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data‐driven approach.
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Affiliation(s)
- Yannick Brackenier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Lucilio Cordero‐Grande
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación Universidad Politécnica de Madrid and CIBER‐BNN Madrid Spain
| | - Raphael Tomi‐Tricot
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- MR Research Collaborations Siemens Healthcare Limited Frimley United Kingdom
| | - Thomas Wilkinson
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Philippa Bridgen
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Anthony Price
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Shaihan J. Malik
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
| | - Joseph V. Hajnal
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences King's College London, St. Thomas' Hospital London United Kingdom
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82
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Sharafi A, Zibetti MVW, Chang G, Cloos MA, Regatte RR. Simultaneous bilateral T 1 , T 2 , and T 1ρ relaxation mapping of the hip joint with magnetic resonance fingerprinting. NMR IN BIOMEDICINE 2022; 35:e4651. [PMID: 34825750 PMCID: PMC9233946 DOI: 10.1002/nbm.4651] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 10/22/2021] [Accepted: 10/26/2021] [Indexed: 06/13/2023]
Abstract
Quantitative MRI can detect early biochemical changes in cartilage, but its bilateral use in clinical routines is challenging. The aim of this prospective study was to demonstrate the feasibility of magnetic resonance fingerprinting for bilateral simultaneous T1 , T2 , and T1ρ mapping of the hip joint. The study population consisted of six healthy volunteers with no known trauma or pain in the hip. Monoexponential T1 , T2 , and T1ρ relaxation components were assessed in femoral lateral, superolateral, and superomedial, and inferior, as well as acetabular, superolateral, and superomedial subregions in left and right hip cartilage. Aligned ranked nonparametric factorial analysis was used to assess the side's impact on the subregions. Kruskal-Wallis and Wilcoxon tests were used to compare subregions, and coefficient of variation to assess repeatability. Global averages of T1 (676.0 ± 45.4 and 687.6 ± 44.5 ms), T2 (22.5 ± 2.6 and 22.1 ± 2.5 ms), and T1ρ (38.2 ± 5.5 and 38.2 ± 5.5 ms) were measured in the left and right hip, and articular cartilage, respectively. The Kruskal-Wallis test showed a significant difference between different subregions' relaxation times regardless of the hip side (p < 0.001 for T1 , p = 0.012 for T2 , and p < 0.001 for T1ρ ). The Wilcoxon test showed that T1 of femoral layers was significantly (p < 0.003) higher than that for acetabular cartilage. The experiments showed excellent repeatability with CVrms of 1%, 2%, and 4% for T1 , T2 , and T1ρ, respectively. It was concluded that bilateral T1 , T2 , and T1ρ relaxation times, as well as B1+ maps, can be acquired simultaneously from hip joints using the proposed MRF sequence.
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Affiliation(s)
- Azadeh Sharafi
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Marcelo V. W. Zibetti
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Gregory Chang
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Martijn A. Cloos
- Center of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ravinder R. Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
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83
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Yu Z, Hodono S, Dergachyova O, Hilbert T, Wang B, Zhang B, Brown R, Sodickson DK, Madelin G, Cloos MA. Simultaneous 3D acquisition of 1 H MRF and 23 Na MRI. Magn Reson Med 2022; 87:2299-2312. [PMID: 34971454 PMCID: PMC8847332 DOI: 10.1002/mrm.29135] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 11/23/2021] [Accepted: 12/10/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE To develop a 3D MR technique to simultaneously acquire proton multiparametric maps (T1 , T2 , and proton density) and sodium density weighted images over the whole brain. METHODS We implemented a 3D stack-of-stars MR pulse sequence which consists of interleaved proton (1 H) and sodium (23 Na) excitations, tailored slice encoding gradients that can encode the same slice for both nuclei, and simultaneous readout with different radial trajectories (1 H, full-radial; 23 Na, center-out radial). The receive chain of our 7T scanner was modified to enable simultaneous acquisition of 1 H and 23 Na signal. A heuristically optimized flip angle train was implemented for proton MR fingerprinting (MRF). The SNR and the accuracy of proton T1 and T2 were evaluated in phantoms. Finally, in vivo application of the method was demonstrated in five healthy subjects. RESULTS The SNR for the simultaneous measurement was almost identical to that for the single-nucleus measurements (<2% change). The proton T1 and T2 maps remained similar to the results from a reference 2D MRF technique (normalized RMS error in T1 ≈ 4.2% and T2 ≈ 11.3%). Measurements in healthy subjects corroborated these results and demonstrated the feasibility of our method for in vivo application. The in vivo T1 values measured using our method were lower than the results measured by other conventional techniques. CONCLUSIONS With the 3D simultaneous implementation, we were able to acquire sodium and proton density weighted images in addition to proton T1 , T2 , and B1+ from 1 H MRF that covers the whole brain volume within 21 min.
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Affiliation(s)
- Zidan Yu
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,The Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Shota Hodono
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,The Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA,The Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD, Australia
| | - Olga Dergachyova
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bili Wang
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Bei Zhang
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ryan Brown
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,The Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Daniel K. Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,The Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Guillaume Madelin
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,The Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Martijn A. Cloos
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,The Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA,The Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD, Australia
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84
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Hennig J, Barghoorn A, Zhang S, Zaitsev M. Single shot spiral
TSE
with annulated segmentation. Magn Reson Med 2022; 88:651-662. [DOI: 10.1002/mrm.29224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/11/2022] [Accepted: 02/20/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Juergen Hennig
- University Medical Center FREIBURG, Dept.of Radiology, Medical Physics Freiburg Germany
- Center for Basics in NeuroModulation (NeuroModulBasics) Faculty of Medicine, University of Freiburg Freiburg Germany
| | - Antonia Barghoorn
- University Medical Center FREIBURG, Dept.of Radiology, Medical Physics Freiburg Germany
| | - Shuoyue Zhang
- University Medical Center FREIBURG, Dept.of Radiology, Medical Physics Freiburg Germany
| | - Maxim Zaitsev
- University Medical Center FREIBURG, Dept.of Radiology, Medical Physics Freiburg Germany
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85
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Feng L, Ma D, Liu F. Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends. NMR IN BIOMEDICINE 2022; 35:e4416. [PMID: 33063400 PMCID: PMC8046845 DOI: 10.1002/nbm.4416] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 08/25/2020] [Accepted: 09/09/2020] [Indexed: 05/08/2023]
Abstract
Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T1 ), the spin-spin relaxation time (T2 ), and the spin-lattice relaxation in the rotating frame (T1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T1 -, T2 -, or T1ρ -weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.
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Affiliation(s)
- Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard University, Boston, Massachusetts
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86
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Tendler BC, Hanayik T, Ansorge O, Bangerter-Christensen S, Berns GS, Bertelsen MF, Bryant KL, Foxley S, van den Heuvel MP, Howard AFD, Huszar IN, Khrapitchev AA, Leonte A, Manger PR, Menke RAL, Mollink J, Mortimer D, Pallebage-Gamarallage M, Roumazeilles L, Sallet J, Scholtens LH, Scott C, Smart A, Turner MR, Wang C, Jbabdi S, Mars RB, Miller KL. The Digital Brain Bank, an open access platform for post-mortem imaging datasets. eLife 2022; 11:e73153. [PMID: 35297760 PMCID: PMC9042233 DOI: 10.7554/elife.73153] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Post-mortem magnetic resonance imaging (MRI) provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes-Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; and Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank data release includes 21 distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen nonhuman primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our lab's investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides cross-scale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.
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Affiliation(s)
- Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Taylor Hanayik
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Olaf Ansorge
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Sarah Bangerter-Christensen
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | | | - Mads F Bertelsen
- Centre for Zoo and Wild Animal Health, Copenhagen ZooFrederiksbergDenmark
| | - Katherine L Bryant
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Sean Foxley
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Department of Radiology, University of ChicagoChicagoUnited States
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Department of Child Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Amy FD Howard
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Alexandre A Khrapitchev
- Medical Research Council Oxford Institute for Radiation Oncology, University of OxfordOxfordUnited Kingdom
| | - Anna Leonte
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Paul R Manger
- School of Anatomical Sciences, Faculty of Health Sciences, University of the WitwatersrandJohannesburgSouth Africa
| | - Ricarda AL Menke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Jeroen Mollink
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Duncan Mortimer
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Menuka Pallebage-Gamarallage
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- Stem Cell and Brain Research Institute, Université Lyon 1, INSERMBronFrance
| | - Lianne H Scholtens
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Connor Scott
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Adele Smart
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Martin R Turner
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Donders Institute for Brain, Cognition and Behaviour, Radboud University NijmegenNijmegenNetherlands
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
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87
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Fujita S, Cencini M, Buonincontri G, Takei N, Schulte RF, Fukunaga I, Uchida W, Hagiwara A, Kamagata K, Hagiwara Y, Matsuyama Y, Abe O, Tosetti M, Aoki S. Simultaneous relaxometry and morphometry of human brain structures with 3D magnetic resonance fingerprinting: a multicenter, multiplatform, multifield-strength study. Cereb Cortex 2022; 33:729-739. [PMID: 35271703 PMCID: PMC9890456 DOI: 10.1093/cercor/bhac096] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/10/2022] [Accepted: 02/12/2022] [Indexed: 02/04/2023] Open
Abstract
Relaxation times and morphological information are fundamental magnetic resonance imaging-derived metrics of the human brain that reflect the status of the underlying tissue. Magnetic resonance fingerprinting (MRF) enables simultaneous acquisition of T1 and T2 maps inherently aligned to the anatomy, allowing whole-brain relaxometry and morphometry in a single scan. In this study, we revealed the feasibility of 3D MRF for simultaneous brain structure-wise morphometry and relaxometry. Comprehensive test-retest scan analyses using five 1.5-T and three 3.0-T systems from a single vendor including different scanner types across 3 institutions demonstrated that 3D MRF-derived morphological information and relaxation times are highly repeatable at both 1.5 T and 3.0 T. Regional cortical thickness and subcortical volume values showed high agreement and low bias across different field strengths. The ability to acquire a set of regional T1, T2, thickness, and volume measurements of neuroanatomical structures with high repeatability and reproducibility facilitates the ability of longitudinal multicenter imaging studies to quantitatively monitor changes associated with underlying pathologies, disease progression, and treatments.
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Affiliation(s)
- Shohei Fujita
- Corresponding author: Department of Radiology, Juntendo University School of Medicine, 12-1 Hongo, Bunkyo, Tokyo 113-8421, Japan.
| | - Matteo Cencini
- Imago7 Foundation, Pisa, Italy,IRCCS Stella Maris, Pisa, Italy
| | | | | | | | - Issei Fukunaga
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University, Tokyo, Japan
| | | | - Koji Kamagata
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Yasuhiro Hagiwara
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Yutaka Matsuyama
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Michela Tosetti
- Imago7 Foundation, Pisa, Italy,IRCCS Stella Maris, Pisa, Italy
| | - Shigeki Aoki
- Department of Radiology, Juntendo University, Tokyo, Japan
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88
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Ismail TF, Strugnell W, Coletti C, Božić-Iven M, Weingärtner S, Hammernik K, Correia T, Küstner T. Cardiac MR: From Theory to Practice. Front Cardiovasc Med 2022; 9:826283. [PMID: 35310962 PMCID: PMC8927633 DOI: 10.3389/fcvm.2022.826283] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/17/2022] [Indexed: 01/10/2023] Open
Abstract
Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.
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Affiliation(s)
- Tevfik F. Ismail
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Cardiology Department, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Wendy Strugnell
- Queensland X-Ray, Mater Hospital Brisbane, Brisbane, QLD, Australia
| | - Chiara Coletti
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
| | - Maša Božić-Iven
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
- Computer Assisted Clinical Medicine, Heidelberg University, Mannheim, Germany
| | | | - Kerstin Hammernik
- Lab for AI in Medicine, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, United Kingdom
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Centre of Marine Sciences, Faro, Portugal
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
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89
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Cao X, Liao C, Iyer SS, Wang Z, Zhou Z, Dai E, Liberman G, Dong Z, Gong T, He H, Zhong J, Bilgic B, Setsompop K. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. Magn Reson Med 2022; 88:133-150. [PMID: 35199877 DOI: 10.1002/mrm.29194] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/16/2021] [Accepted: 01/21/2022] [Indexed: 11/07/2022]
Abstract
PURPOSE To improve image quality and accelerate the acquisition of 3D MR fingerprinting (MRF). METHODS Building on the multi-axis spiral-projection MRF technique, a subspace reconstruction with locally low-rank constraint and a modified spiral-projection spatiotemporal encoding scheme called tiny golden-angle shuffling were implemented for rapid whole-brain high-resolution quantitative mapping. Reconstruction parameters such as the locally low-rank regularization parameter and the subspace rank were tuned using retrospective in vivo data and simulated examinations. B0 inhomogeneity correction using multifrequency interpolation was incorporated into the subspace reconstruction to further improve the image quality by mitigating blurring caused by off-resonance effect. RESULTS The proposed MRF acquisition and reconstruction framework yields high-quality 1-mm isotropic whole-brain quantitative maps in 2 min at better quality compared with 6-min acquisitions of prior approaches. The proposed method was validated to not induce bias in T1 and T2 mapping. High-quality whole-brain MRF data were also obtained at 0.66-mm isotropic resolution in 4 min using the proposed technique, where the increased resolution was shown to improve visualization of subtle brain structures. CONCLUSIONS The proposed tiny golden-angle shuffling, MRF with optimized spiral-projection trajectory and subspace reconstruction enables high-resolution quantitative mapping in ultrafast acquisition time.
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Affiliation(s)
- Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Siddharth Srinivasan Iyer
- Department of Radiology, Stanford University, Stanford, California, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Zhixing Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Zihan Zhou
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Erpeng Dai
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Gilad Liberman
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Ting Gong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Cambridge, Massachusetts, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
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90
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3D Echo Planar Time-resolved Imaging (3D-EPTI) for ultrafast multi-parametric quantitative MRI. Neuroimage 2022; 250:118963. [PMID: 35122969 PMCID: PMC8920906 DOI: 10.1016/j.neuroimage.2022.118963] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 12/09/2021] [Accepted: 02/01/2022] [Indexed: 12/11/2022] Open
Abstract
Multi-parametric quantitative MRI has shown great potential to improve the sensitivity and specificity of clinical diagnosis and to enhance our understanding of complex brain processes, but suffers from long scan time especially at high spatial resolution. To address this longstanding challenge, we introduce a novel approach termed 3D Echo Planar Time-resolved Imaging (3D-EPTI), which significantly increases the acceleration capacity of MRI sampling, and provides high acquisition efficiency for multi-parametric MRI. This is achieved by exploiting the spatiotemporal correlation of MRI data at multiple timescales through new encoding strategies within and between efficient continuous readouts. Specifically, an optimized spatiotemporal CAIPI encoding within the readouts combined with a radial-block sampling strategy across the readouts enables an acceleration rate of 800 fold in the k-t space. A subspace reconstruction was employed to resolve thousands of high-quality multi-contrast images. We have demonstrated the ability of 3D-EPTI to provide robust and repeatable whole-brain simultaneous T1, T2, T2*, PD and B1+ mapping at high isotropic resolution within minutes (e.g., 1-mm isotropic resolution in 3 minutes), and to enable submillimeter multi-parametric imaging to study detailed brain structures.
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91
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Plaehn NMJ, Mayer S, Jakob PM, Gutjahr FT. T 1-independent exchange rate quantification using saturation- or phase sensitive-water exchange spectroscopy. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 335:107141. [PMID: 35051740 DOI: 10.1016/j.jmr.2021.107141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 12/17/2021] [Accepted: 12/23/2021] [Indexed: 06/14/2023]
Abstract
PURPOSE Water Exchange Spectroscopy (WEX) is a direct measurement of the exchange rate ksw of labile protons from a solute to water in which the exchange time is varied. However the useful information can be masked by the T1-decay of the solvent pool. We propose Saturation-WEX and Phase Sensitive WEX (PS-WEX) as an extension upon the WEX approach to reduce T1-masking. Additionally PS-WEX takes advantage of the phase information contained in the WEX signal to improve the dynamic range. METHODS By introducing an additional RF-pulse and fixing the exchange time delay the T1-dependence of the signal is reduced. By exploiting the phase sensitivity of the WEX pathway the dynamic range can be increased. This approach is validated using simulations as well as phantom measurements. RESULTS The improved dynamic range is demonstrated in measurements. The fixed exchange time reduces the influence of the T1-decay on the signal curve leading to improved fit quality. CONCLUSION Sat-WEX and PS-WEX are an extension to the well established WEX approach with a less complex fit equation and in the case of PS-WEX improved dynamic range, allowing more accurate quantification.
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Affiliation(s)
- N M J Plaehn
- Universität Würzburg, Lehrstuhl für Experimentelle Physik 5, Am Hubland, 97074 Würzburg, Germany
| | - S Mayer
- Universität Würzburg, Lehrstuhl für Experimentelle Physik 5, Am Hubland, 97074 Würzburg, Germany; Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Correnstrasse 3, 06466 Gatersleben, Germany
| | - P M Jakob
- Universität Würzburg, Lehrstuhl für Experimentelle Physik 5, Am Hubland, 97074 Würzburg, Germany
| | - F T Gutjahr
- Universität Würzburg, Lehrstuhl für Experimentelle Physik 5, Am Hubland, 97074 Würzburg, Germany.
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92
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Lima da Cruz GJ, Velasco C, Lavin B, Jaubert O, Botnar RM, Prieto C. Myocardial T1, T2, T2*, and fat fraction quantification via low-rank motion-corrected cardiac MR fingerprinting. Magn Reson Med 2022; 87:2757-2774. [PMID: 35081260 PMCID: PMC9306903 DOI: 10.1002/mrm.29171] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 12/06/2021] [Accepted: 01/05/2022] [Indexed: 12/11/2022]
Abstract
Purpose Develop a novel 2D cardiac MR fingerprinting (MRF) approach to enable simultaneous T1, T2, T2*, and fat fraction (FF) myocardial tissue characterization in a single breath‐hold scan. Methods Simultaneous, co‐registered, multi‐parametric mapping of T1, T2, and FF has been recently achieved with cardiac MRF. Here, we further incorporate T2* quantification within this approach, enabling simultaneous T1, T2, T2*, and FF myocardial tissue characterization in a single breath‐hold scan. T2* quantification is achieved with an eight‐echo readout that requires a long cardiac acquisition window. A novel low‐rank motion‐corrected (LRMC) reconstruction is exploited to correct for cardiac motion within the long acquisition window. The proposed T1/T2/T2*/FF cardiac MRF was evaluated in phantom and in 10 healthy subjects in comparison to conventional mapping techniques. Results The proposed approach achieved high quality parametric mapping of T1, T2, T2*, and FF with corresponding normalized RMS error (RMSE) T1 = 5.9%, T2 = 9.6% (T2 values <100 ms), T2* = 3.3% (T2* values <100 ms), and FF = 0.8% observed in phantom scans. In vivo, the proposed approach produced higher left‐ventricular myocardial T1 values than MOLLI (1148 vs 1056 ms), lower T2 values than T2‐GraSE (42.8 vs 50.6 ms), lower T2* values than eight‐echo gradient echo (GRE) (35.0 vs 39.4 ms), and higher FF values than six‐echo GRE (0.8 vs 0.3 %) reference techniques. The proposed approach achieved considerable reduction in motion artifacts compared to cardiac MRF without motion correction, improved spatial uniformity, and statistically higher apparent precision relative to conventional mapping for all parameters. Conclusion The proposed cardiac MRF approach enables simultaneous, co‐registered mapping of T1, T2, T2*, and FF in a single breath‐hold for comprehensive myocardial tissue characterization, achieving higher apparent precision than conventional methods.
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Affiliation(s)
- Gastao José Lima da Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Begoña Lavin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Biochemistry and Molecular Biology, School of Chemistry, Complutense University, Madrid, Spain
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Rene Michael Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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93
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Comparison of T2 Quantification Strategies in the Abdominal-Pelvic Region for Clinical Use. Invest Radiol 2022; 57:412-421. [PMID: 34999669 DOI: 10.1097/rli.0000000000000852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES The aim of the study was to compare different magnetic resonance imaging (MRI) acquisition strategies appropriate for T2 quantification in the abdominal-pelvic area. The different techniques targeted in the study were chosen according to 2 main considerations: performing T2 measurement in an acceptable time for clinical use and preventing/correcting respiratory motion. MATERIALS AND METHODS Acquisitions were performed at 3 T. To select sequences for in vivo measurements, a phantom experiment was conducted, for which the T2 values obtained with the different techniques of interest were compared with the criterion standard (single-echo SE sequence, multiple acquisitions with varying echo time). Repeatability and temporal reproducibility studies for the different techniques were also conducted on the phantom. Finally, an in vivo study was conducted on 12 volunteers to compare the techniques that offer acceptable acquisition time for clinical use and either address or correct respiratory motion. RESULTS For the phantom study, the DESS and T2-preparation techniques presented the lowest precision (ρ2 = 0.9504 and ρ2 = 0.9849 respectively), and showed a poor repeatability/reproducibility compared with the other techniques. The strategy relying on SE-EPI showed the best precision and accuracy (ρ2 = 0.9994 and Cb = 0.9995). GRAPPATINI exhibited a very good precision (ρ2 = 0.9984). For the technique relying on radial TSE, the precision was not as good as GRAPPATINI (ρ2 = 0.9872). The in vivo study demonstrated good respiratory motion management for all of the selected techniques. It also showed that T2 estimate ranges were different from one method to another. For GRAPPATINI and radial TSE techniques, there were significant differences between all the different types of organs of interest. CONCLUSIONS To perform T2 measurement in the abdominal-pelvic region, one should favor a technique with acceptable acquisition time for clinical use, with proper respiratory motion management, with good repeatability, reproducibility, and precision. In this study, the techniques relying respectively on SE-EPI, radial TSE, and GRAPPATINI appeared as good candidates.
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94
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Tendler BC, Qi F, Foxley S, Pallebage-Gamarallage M, Menke RAL, Ansorge O, Hurley SA, Miller KL. A method to remove the influence of fixative concentration on postmortem T 2 maps using a kinetic tensor model. Hum Brain Mapp 2021; 42:5956-5972. [PMID: 34541735 PMCID: PMC8596944 DOI: 10.1002/hbm.25661] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 08/06/2021] [Accepted: 08/30/2021] [Indexed: 12/20/2022] Open
Abstract
Formalin fixation has been shown to substantially reduce T2 estimates, primarily driven by the presence of fixative in tissue. Prior to scanning, post‐mortem samples are often placed into a fluid that has more favourable imaging properties. This study investigates whether there is evidence for a change in T2 in regions close to the tissue surface due to fixative outflux into this surrounding fluid. Furthermore, we investigate whether a simulated spatial map of fixative concentration can be used as a confound regressor to reduce T2 inhomogeneity. To achieve this, T2 maps and diffusion tensor estimates were obtained in 14 whole, formalin‐fixed post‐mortem brains placed in Fluorinert approximately 48 hr prior to scanning. Seven brains were fixed with 10% formalin and seven brains were fixed with 10% neutral buffered formalin (NBF). Fixative outflux was modelled using a proposed kinetic tensor (KT) model, which incorporates voxelwise diffusion tensor estimates to account for diffusion anisotropy and tissue‐specific diffusion coefficients. Brains fixed with 10% NBF revealed a spatial T2 pattern consistent with modelled fixative outflux. Confound regression of fixative concentration reduced T2 inhomogeneity across both white and grey matter, with the greatest reduction attributed to the KT model versus simpler models of fixative outflux. No such effect was observed in brains fixed with 10% formalin. Correlations between the transverse relaxation rate R2 and ferritin/myelin proteolipid protein (PLP) histology lead to an increased similarity for the relationship between R2 and PLP for the two fixative types after KT correction.
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Affiliation(s)
- Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford
| | - Feng Qi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford
| | - Sean Foxley
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford.,Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | | | - Ricarda A L Menke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford
| | - Olaf Ansorge
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Samuel A Hurley
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford.,Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford
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95
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Shao X, Guo F, Shou Q, Wang K, Jann K, Yan L, Toga AW, Zhang P, Wang DJJ. Laminar perfusion imaging with zoomed arterial spin labeling at 7 Tesla. Neuroimage 2021; 245:118724. [PMID: 34780918 PMCID: PMC8727512 DOI: 10.1016/j.neuroimage.2021.118724] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/23/2021] [Accepted: 11/11/2021] [Indexed: 10/19/2022] Open
Abstract
Laminar fMRI based on BOLD and CBV contrast at ultrahigh magnetic fields has been applied for studying the dynamics of mesoscopic brain networks. However, the quantitative interpretations of BOLD/CBV fMRI results are confounded by different baseline physiology across cortical layers. Here we introduce a novel 3D zoomed pseudo-continuous arterial spin labeling (pCASL) technique at 7T that offers the capability for quantitative measurements of laminar cerebral blood flow (CBF) both at rest and during task activation with high spatial specificity and sensitivity. We found arterial transit time in superficial layers is ∼100 ms shorter than in middle/deep layers revealing the time course of labeled blood flowing from pial arteries to downstream microvasculature. Resting state CBF peaked in the middle layers which is highly consistent with microvascular density measured from human cortex specimens. Finger tapping induced a robust two-peak laminar profile of CBF increases in the superficial (somatosensory and premotor input) and deep (spinal output) layers of M1, while finger brushing task induced a weaker CBF increase in superficial layers (somatosensory input). This observation is highly consistent with reported laminar profiles of CBV activation on M1. We further demonstrated that visuospatial attention induced a predominant CBF increase in deep layers and a smaller CBF increase on top of the lower baseline CBF in superficial layers of V1 (feedback cortical input), while stimulus driven activity peaked in the middle layers (feedforward thalamic input). With the capability for quantitative CBF measurements both at baseline and during task activation, high-resolution ASL perfusion fMRI at 7T provides an important tool for in vivo assessment of neurovascular function and metabolic activities of neural circuits across cortical layers.
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Affiliation(s)
- Xingfeng Shao
- Laboratory of FMRI Technology (LOFT), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA 90033, USA
| | - Fanhua Guo
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Qinyang Shou
- Laboratory of FMRI Technology (LOFT), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA 90033, USA
| | - Kai Wang
- Laboratory of FMRI Technology (LOFT), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA 90033, USA
| | - Kay Jann
- Laboratory of FMRI Technology (LOFT), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA 90033, USA; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Lirong Yan
- Laboratory of FMRI Technology (LOFT), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA 90033, USA; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Peng Zhang
- State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Danny J J Wang
- Laboratory of FMRI Technology (LOFT), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Ave, Los Angeles, CA 90033, USA; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
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96
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Ultra-long-TE arterial spin labeling reveals rapid and brain-wide blood-to-CSF water transport in humans. Neuroimage 2021; 245:118755. [PMID: 34826596 PMCID: PMC7612938 DOI: 10.1016/j.neuroimage.2021.118755] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 12/20/2022] Open
Abstract
The study of brain clearance mechanisms is an active area of research. While we know that the cerebrospinal fluid (CSF) plays a central role in one of the main existing clearance pathways, the exact processes for the secretion of CSF and the removal of waste products from tissue are under debate. CSF is thought to be created by the exchange of water and ions from the blood, which is believed to mainly occur in the choroid plexus. This exchange has not been thoroughly studied in vivo. We propose a modified arterial spin labeling (ASL) MRI sequence and image analysis to track blood water as it is transported to the CSF, and to characterize its exchange from blood to CSF. We acquired six pseudo-continuous ASL sequences with varying labeling duration (LD) and post-labeling delay (PLD) and a segmented 3D-GRASE readout with a long echo train (8 echo times (TE)) which allowed separation of the very long-T2 CSF signal. ASL signal was observed at long TEs (793 ms and higher), indicating presence of labeled water transported from blood to CSF. This signal appeared both in the CSF proximal to the choroid plexus and in the subarachnoid space surrounding the cortex. ASL signal was separated into its blood, gray matter and CSF components by fitting a triexponential function with T2s taken from literature. A two-compartment dynamic model was introduced to describe the exchange of water through time and TE. From this, a water exchange time from the blood to the CSF (Tbl->CSF) was mapped, with an order of magnitude of approximately 60 s.
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97
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Snyder J, McPhee KC, Wilman AH. T 2 quantification in brain using 3D fast spin-echo imaging with long echo trains. Magn Reson Med 2021; 87:2145-2160. [PMID: 34894641 PMCID: PMC9299830 DOI: 10.1002/mrm.29113] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 11/16/2021] [Accepted: 11/17/2021] [Indexed: 11/15/2022]
Abstract
Purpose Three‐dimensional fast spin‐echo (FSE) sequences commonly use very long echo trains (>64 echoes) and severely reduced refocusing angles. They are increasingly used in brain exams due to high, isotropic resolution and reasonable scan time when using long trains and short interecho spacing. In this study, T2 quantification in 3D FSE is investigated to achieve increased resolution when comparing with established 2D (proton‐density dual‐echo and multi‐echo spin‐echo) methods. Methods The FSE sequence design was explored to use long echo trains while minimizing T2 fitting error and maintaining typical proton density and T2‐weighted contrasts. Constant and variable flip angle trains were investigated using extended phase graph and Bloch equation simulations. Optimized parameters were analyzed in phantom experiments and validated in vivo in comparison to 2D methods for eight regions of interest in brain, including deep gray‐matter structures and white‐matter tracts. Results Phantom and healthy in vivo brain T2 measurements showed that optimized variable echo‐train 3D FSE performs similarly to previous 2D methods, while achieving three‐fold‐higher slice resolution, evident visually in the 3D T2 maps. Optimization resulted in better T2 fitting and compared well with standard multi‐echo spin echo (within the 8‐ms confidence limits defined based on Bland‐Altman analysis). Conclusion T2 mapping using 3D FSE with long echo trains and variable refocusing angles provides T2 accuracy in agreement with 2D methods with additional high‐resolution benefits, allowing isotropic views while avoiding incidental magnetization transfer effects. Consequently, optimized 3D sequences should be considered when choosing T2 mapping methods for high anatomic detail.
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Affiliation(s)
- Jeff Snyder
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | | | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
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98
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Multi-exponential MRI T2 maps: A tool to classify and characterize fruit tissues. Magn Reson Imaging 2021; 87:119-132. [PMID: 34871716 DOI: 10.1016/j.mri.2021.11.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/26/2021] [Accepted: 11/29/2021] [Indexed: 11/24/2022]
Abstract
The estimation of multi-exponential relaxation time T2 and their associated amplitudes A0 at the voxel level has been made possible by recent developments in the field of image processing. These data are of great interest for the characterization of biological tissues, such as fruit tissues. However, they represent a high number of information, not easily interpretable. Moreover, the non-uniformity of the MRI images, which mainly directly impacts A0, could induce interpretation errors. In this paper, we propose a post-processing scheme that clusters similar voxels according to the multi-exponential relaxation parameters in order to reduce the complexity of the information while avoiding the problems associated with intensity non-uniformity. We also suggest a data representation suitable for the visualization of the multi-T2 distribution within each tissue. We illustrate this work with results for different fruits, demonstrating the great potential of multi-T2 information to shed new light on fruit characterization.
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99
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Canales-Rodríguez EJ, Pizzolato M, Yu T, Piredda GF, Hilbert T, Radua J, Kober T, Thiran JP. Revisiting the T 2 spectrum imaging inverse problem: Bayesian regularized non-negative least squares. Neuroimage 2021; 244:118582. [PMID: 34536538 DOI: 10.1016/j.neuroimage.2021.118582] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 08/12/2021] [Accepted: 09/14/2021] [Indexed: 01/24/2023] Open
Abstract
Multi-echo T2 magnetic resonance images contain information about the distribution of T2 relaxation times of compartmentalized water, from which we can estimate relevant brain tissue properties such as the myelin water fraction (MWF). Regularized non-negative least squares (NNLS) is the tool of choice for estimating non-parametric T2 spectra. However, the estimation is ill-conditioned, sensitive to noise, and highly affected by the employed regularization weight. The purpose of this study is threefold: first, we want to underline that the apparently innocuous use of two alternative parameterizations for solving the inverse problem, which we called the standard and alternative regularization forms, leads to different solutions; second, to assess the performance of both parameterizations; and third, to propose a new Bayesian regularized NNLS method (BayesReg). The performance of BayesReg was compared with that of two conventional approaches (L-curve and Chi-square (X2) fitting) using both regularization forms. We generated a large dataset of synthetic data, acquired in vivo human brain data in healthy participants for conducting a scan-rescan analysis, and correlated the myelin content derived from histology with the MWF estimated from ex vivo data. Results from synthetic data indicate that BayesReg provides accurate MWF estimates, comparable to those from L-curve and X2, and with better overall stability across a wider signal-to-noise range. Notably, we obtained superior results by using the alternative regularization form. The correlations reported in this study are higher than those reported in previous studies employing the same ex vivo and histological data. In human brain data, the estimated maps from L-curve and BayesReg were more reproducible. However, the T2 spectra produced by BayesReg were less affected by over-smoothing than those from L-curve. These findings suggest that BayesReg is a good alternative for estimating T2 distributions and MWF maps.
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Affiliation(s)
- Erick Jorge Canales-Rodríguez
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland.
| | - Marco Pizzolato
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland
| | - Thomas Yu
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland; Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland
| | - Gian Franco Piredda
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tom Hilbert
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Joaquim Radua
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Barcelona, Spain; Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; Department of Clinical Neuroscience, Centre for Psychiatric Research and Education, Karolinska Institutet, Stockholm, Sweden
| | - Tobias Kober
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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100
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Slator PJ, Palombo M, Miller KL, Westin C, Laun F, Kim D, Haldar JP, Benjamini D, Lemberskiy G, de Almeida Martins JP, Hutter J. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magn Reson Med 2021; 86:2987-3011. [PMID: 34411331 PMCID: PMC8568657 DOI: 10.1002/mrm.28963] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022]
Abstract
Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.
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Affiliation(s)
- Paddy J. Slator
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Marco Palombo
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Carl‐Fredrik Westin
- Department of RadiologyBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Frederik Laun
- Institute of RadiologyUniversity Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Daeun Kim
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBethesdaMDUSA
- The Center for Neuroscience and Regenerative MedicineUniformed Service University of the Health SciencesBethesdaMDUSA
| | | | - Joao P. de Almeida Martins
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Department of Radiology and Nuclear MedicineSt. Olav’s University HospitalTrondheimNorway
| | - Jana Hutter
- Centre for Biomedical EngineeringSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
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