1
|
Ponticorvo S, Canna A, Moeller S, Akcakaya M, Metzger GJ, Filip P, Eberly LE, Michaeli S, Mangia S. Reducing thermal noise in high-resolution quantitative magnetic resonance imaging rotating frame relaxation mapping of the human brain at 3 T. NMR IN BIOMEDICINE 2024:e5228. [PMID: 39169274 DOI: 10.1002/nbm.5228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 06/25/2024] [Accepted: 07/13/2024] [Indexed: 08/23/2024]
Abstract
Quantitative maps of rotating frame relaxation (RFR) time constants are sensitive and useful magnetic resonance imaging tools with which to evaluate tissue integrity in vivo. However, to date, only moderate image resolutions of 1.6 x 1.6 x 3.6 mm3 have been used for whole-brain coverage RFR mapping in humans at 3 T. For more precise morphometrical examinations, higher spatial resolutions are desirable. Towards achieving the long-term goal of increasing the spatial resolution of RFR mapping without increasing scan times, we explore the use of the recently introduced Transform domain NOise Reduction with DIstribution Corrected principal component analysis (T-NORDIC) algorithm for thermal noise reduction. RFR acquisitions at 3 T were obtained from eight healthy participants (seven males and one female) aged 52 ± 20 years, including adiabatic T1ρ, T2ρ, and nonadiabatic Relaxation Along a Fictitious Field (RAFF) in the rotating frame of rank n = 4 (RAFF4) with both 1.6 x 1.6 x 3.6 mm3 and 1.25 x 1.25 x 2 mm3 image resolutions. We compared RFR values and their confidence intervals (CIs) obtained from fitting the denoised versus nondenoised images, at both voxel and regional levels separately for each resolution and RFR metric. The comparison of metrics obtained from denoised versus nondenoised images was performed with a two-sample paired t-test and statistical significance was set at p less than 0.05 after Bonferroni correction for multiple comparisons. The use of T-NORDIC on the RFR images prior to the fitting procedure decreases the uncertainty of parameter estimation (lower CIs) at both spatial resolutions. The effect was particularly prominent at high-spatial resolution for RAFF4. Moreover, T-NORDIC did not degrade map quality, and it had minimal impact on the RFR values. Denoising RFR images with T-NORDIC improves parameter estimation while preserving the image quality and accuracy of all RFR maps, ultimately enabling high-resolution RFR mapping in scan times that are suitable for clinical settings.
Collapse
Affiliation(s)
- Sara Ponticorvo
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Antonietta Canna
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
- Montreal Neurological Institute and Hospital, the Neuro, McGill University, Montréal, Quebec, Canada
| | - Steen Moeller
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Mehmet Akcakaya
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
- Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA
| | - Gregory J Metzger
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Pavel Filip
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
- Department of Neurology, First Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic
| | - Lynn E Eberly
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Shalom Michaeli
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Silvia Mangia
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| |
Collapse
|
2
|
Radunsky D, Solomon C, Stern N, Blumenfeld-Katzir T, Filo S, Mezer A, Karsa A, Shmueli K, Soustelle L, Duhamel G, Girard OM, Kepler G, Shrot S, Hoffmann C, Ben-Eliezer N. A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla. PLoS One 2024; 19:e0297244. [PMID: 38820354 PMCID: PMC11142522 DOI: 10.1371/journal.pone.0297244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 01/01/2024] [Indexed: 06/02/2024] Open
Abstract
Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres' values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism.
Collapse
Affiliation(s)
- Dvir Radunsky
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Chen Solomon
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Neta Stern
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | | | - Shir Filo
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Aviv Mezer
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Anita Karsa
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | | | | | | | - Gal Kepler
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- School of Neurobiology, Biochemistry and Biophysics, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Shai Shrot
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Chen Hoffmann
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States of America
| |
Collapse
|
3
|
Sherman SE, Zammit AS, Heo WS, Rosen MS, Cima MJ. Single-sided magnetic resonance-based sensor for point-of-care evaluation of muscle. Nat Commun 2024; 15:440. [PMID: 38199994 PMCID: PMC10782019 DOI: 10.1038/s41467-023-44561-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
Magnetic resonance imaging is a widespread clinical tool for the detection of soft tissue morphology and pathology. However, the clinical deployment of magnetic resonance imaging scanners is ultimately limited by size, cost, and space constraints. Here, we discuss the design and performance of a low-field single-sided magnetic resonance sensor intended for point-of-care evaluation of skeletal muscle in vivo. The 11 kg sensor has a penetration depth of >8 mm, which allows for an accurate analysis of muscle tissue and can avoid signal from more proximal layers, including subcutaneous adipose tissue. Low operational power and shielding requirements are achieved through the design of a permanent magnet array and surface transceiver coil. The sensor can acquire high signal-to-noise measurements in minutes, making it practical as a point-of-care tool for many quantitative diagnostic measurements, including T2 relaxometry. In this work, we present the in vitro and human in vivo performance of the device for muscle tissue evaluation.
Collapse
Affiliation(s)
- Sydney E Sherman
- Harvard-MIT Program in Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alexa S Zammit
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Won-Seok Heo
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Matthew S Rosen
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Department of Physics, Harvard University, Cambridge, MA, 02138, USA
| | - Michael J Cima
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| |
Collapse
|
4
|
Cima M, Sherman S, Zammit A, Heo WS, Rosen M. Single-sided magnetic resonance-based sensor for point-of-care evaluation of muscle. RESEARCH SQUARE 2023:rs.3.rs-3335248. [PMID: 37790511 PMCID: PMC10543496 DOI: 10.21203/rs.3.rs-3335248/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Magnetic resonance (MR) imaging is a powerful clinical tool for the detection of soft tissue morphology and pathology, which often provides actionable diagnostic information to clinicians. Its clinical use is largely limited due to size, cost, time, and space constraints. Here, we discuss the design and performance of a low-field single-sided MR sensor intended for point-of-care (POC) evaluation of skeletal muscle in vivo. The 11kg sensor has a penetration depth of > 8 mm, which allows for an accurate analysis of muscle tissue and can avoid signal from more proximal layers, including subcutaneous adipose tissue. Low operational power and minimal shielding requirements are achieved through the design of a permanent magnet array and surface transceiver coil. We present the in vitro and human in vivo performance of the device for muscle tissue evaluation. The sensor can acquire high signal-to-noise (SNR > 150) measurements in minutes, making it practical as a POC tool for many quantitative diagnostic measurements, including T2 relaxometry.
Collapse
Affiliation(s)
| | | | | | | | - Matthew Rosen
- Massachusetts General Hospital and Harvard Medical School
| |
Collapse
|